Andrew Thean
email: andy.thean@gmail.com

Jean-Marc Deltorn

Patrice Lopez

Laurent Romary
email: laurent.romary@inria.fr

Textual summarisation of flowcharts in patent drawings for CLEF-IP 2012

Keywords: H.3 [Information Storage and Retrieval]: H.3.1 Content Analysis and Indexing, H.3.3 Information Search and Retrieval, H.3.4 Systems and Software, H.3.7 Digital Libraries Measurement, Performance, Experimentation Patent, Prior Art Search, Image Processing, Flowchart

INTRODUCTION

Certain inventions are most effectively summarised in pictorial form and patent professionals sometimes rely heavily on patent drawings for performing prior art searches. Searching patent drawings is currently a labour-intensive, error-prone task which would be facilitated by automatic indexing methods. Generic methods for automatically indexing patent drawings have been reported [START_REF] Hanbury | Patent image retrieval: a survey[END_REF][START_REF] Vrochidis | Towards content-based patent image retrieval: A framework perspective[END_REF][START_REF] Codina | Integration of semantic, metadata and image search engines with a text search engine for patent retrieval[END_REF] but the heterogeneity of patent drawings means that it is difficult for a one-size-fits-all approach to reach high levels of performance on all image classes. Flowcharts represent a large and useful subclass of patent images for which specially-adapted methods will improve indexing performance. They have a special role in the patents domain since they offer a way to graphically represent a process (a process is one of four legally-recognised categories of patent claim alongside products, apparatus and use e.g. Rule 43(2) EPC ( 2000)) and often contain information-rich text that is prohibited from other types patent drawing (they are usually considered an exception to rules that generally prohibit text from patent drawings e.g. Rule 46(2)(j) of [START_REF] Epc | European Patent Convention[END_REF] and Rule 6.2 (b) of PCT (1970)). Flowcharts were one of the image categories targeted in the Patent Image Classification task of CLEF-IP 2011 (see [START_REF] Piroi | CLEF-IP 2011: Retrieval in the Intellectual Property Domain[END_REF] and references therein), but the summarisation of flowcharts in patent drawings has not been discussed previously in the literature.

Few studies have addressed the automatic analysis of flowcharts in general. [START_REF] Ito | Automatic input of flow chart in document images[END_REF] used corner detection and edge-based block shape classification to derive FORTRAN code directly from a flowchart bitmap (see also US5386508). In [START_REF] Abe | Discrimination of symbols, lines and characters in flowchart recognition[END_REF], a method for discriminating between the various components of a flowchart diagram (symbols, lines and characters) was discussed. [START_REF] Kasturi | A system for interpretation of line drawings[END_REF] proposed an automatic line drawing analysis system (including flowcharts) based on the reduction of the image into segments and closed loops. [START_REF] Yu | A system for recognizing a large class of engineering drawings[END_REF] described a general framework for the analysis of engineering drawings characterised by alternating instances of symbols and connections lines. The particular case of flowchart summarisation was also discussed in [START_REF] Futrelle | Efficient analysis of complex diagrams using constraint-based parsing[END_REF] in the context of automatic document analysis. More recently, [START_REF] Szwoch | Recognition, understanding and aestheticization of freehand drawing flowcharts[END_REF] applied template matching to the recognition and description of handdrawn flowchart components for the purpose of automatic diagram aestheticization. [START_REF] Vasudevan | Flowchart knowledge extraction on image processing[END_REF] described a prototype system of flowchart knowledge extraction from images based on chain code boundary descriptors and neural network classifier. Finally, in [START_REF] Lemaitre | Interest of syntactic knowledge for on-line flowchart recognition[END_REF] the characteristic causal relationship between flowchart components (described as line strokes) was exploited through a set of syntactic constraints that helped address the problems of flowchart diagram recognition as well as text-graphics segmentation.

The paper is set out as follows: in Section 2 the problem addressed in the CLEF-IP 2012 Flowchart Recognition task is defined in detail; in Section 3 a method of solving this problem is described; in Section 4 the performance of the method is reported and discussed.

Problem Statement

Input image data

A training set of 50 training images were released in March 1012, followed by a set of 100 test images three months later: results were submitted on July 10th 2012. The input image data consists of binary images of flowcharts taken from patent drawings. Each image contains a single figure and has been cropped from a patent drawing to exclude irrelevant information such as patent metadata and page numbers. As such the task does not include figure segmentation: in general a single patent drawing may contain multiple figures and a single flowchart may be spread over multiple drawings/figures. In principle, the appearance of any patent drawing is constrained by the rules applied at large patent offices (although not all patent drawings are compliant). Examples of the most important of these rules as applied by the EPO are as follows (Rule 46(2) of EPC (2000), Section A-X, 7.5.1 of GL ( 2012)):

• only black lines drawn with drafting instruments are allowed;

• the scale of the drawing should allow for two thirds reduction in size;

• the character height should be larger than 0.32 mm;

• reference signs (no-box nodes) should be clear and without brackets;

• no text should be included in drawings unless it is indispensable (this rule is intended to minimise the need for language translation in drawings, however text in flowcharts is usually considered indispensable);

• leading lines (wiggly edges) should be as short as possible and extend to the feature indicated.

Text output

The aim of the Flowchart Recognition task was to produce textual summaries in a predetermined format containing information about the flowchart structure in the following 3 categories.

• Metadata describing the number of nodes and edges in the flowchart and, optionally, the 'title' of the flowchart (usually the figure number).

• Node data describing the type of each node and, optionally, the text appearing in the node.

The following node types are possible: oval, rectangle, double-rectangle, parallelogram, diamond, circle, cylinder, no-box, unknown and point. All types of node correspond to boxes or image regions that may contain text apart from point nodes which are the junction points where two or more edges meet.

• Edge data describing node connectivity relationships including, optionally, any text attached to the edge. Edges are either undirected or directed (in the sense indicated by arrow symbols) and have the following possible types: plain, dotted, or wiggly. The type wiggly refers to elements referred to as leading lines in patent law: wiggly edges are not necessarily wiggly in shape, but serve to connect reference signs to nodes (e.g. see Section A-X, 7.5.1 of GL ( 2012)).

Preprocessing and de-noising

The input images may be scanned (and binarised) versions of hard copies filed with the patent application. Note that online filing of patent applications in electronic form now dominates submissions and leads to less noisy images, but scanned images dominate archived patent image databases. To remove some of the defects introduced by the digitisation process the following two-step method is applied. Firstly, a morphological opening with a kernel of 3×3 pixels removes isolated black dots. Secondly, a sequence of local median filters is applied in order to remove isolated line breaks and small 1-to-2-pixel gaps and to perform de-jagging of the lines. The choice of a median filter rather than a morphological closing, is motivated by the aim of bridging line breaks without artificially connecting text characters with the neighboring flowchart outline.

Text-graphic segmentation

For 70% of the training set it is possible to separate text from graphics (nodes and edges) by assuming that graphics are the single largest connected component. Based on this observation, text-graphic segmentation was performed by analysing the spatial extent of all connected components in the image. A mixture of three two-dimensional Gaussians was fitted to the height-vs-width distribution of the connected components using the Expectation Maximisation (EM) algorithm.

The cluster with the largest height and width was deemed to contain all graphics components.

Finding implicit-graphics pixels

Certain types of connectivity between black pixels in a flowchart are merely implied i.e. the connectivity of dotted/dashed line elements, the connectivity of edges and nodes that do not physically touch and the outlines of broken box nodes (for example diamonds that are broken to accommodate text). The chosen method defines implicit-graphics pixels as white pixels that should be treated as black pixels in order to physically establish the connectivity implied by the flowchart author. While some of the smaller line breaks are removed by the first preprocessing step (see Section 3.1), larger gaps can remain that cannot be resolved by simple morphological operations. Wedgeshaped masks, defined by an opening angle and a radius, are used to bridge such line breaks and connect dashed line segments. Firstly, a skeleton of the isolated graphic component is obtained and an estimate of the orientation in the neighborhood of each endpoint is derived. A wedge-shaped mask is placed at each endpoint and aligned with the local skeleton orientation. All intersecting pairs of masks are identified and two endpoints are reconnected by a straight line if their respective wedge masks intersect. This method is similar in nature to the dashed line detection algorithm presented in [START_REF] Kasturi | A system for interpretation of line drawings[END_REF].

Segmentation of closed regions

A connector is defined as a line in a flowchart that does not define a box node. Usually a connector pixel belongs to an edge, but it may also belong to a point node. The approach chosen for nodeconnector segmentation relies on a first step of box node classification. Closed regions in the graphics may correspond to either box nodes or loops (a loop is defined as a background region enclosed by edge lines). Node-connector segmentation is performed as follows. Firstly, loops are identified using the box classification technique described below. Secondly, the interior region of any remaining box node is dilated and used as a mask to separate nodes from connectors.

Box classification

Estimating the global orientation of the flowchart was necessary to allow accurate box-node classification and OCR processing. The aspect ratio of the box node candidates identified after closed-region segmentation was measured and the entire image was rotated clockwise by 90 degrees if the fraction of box node candidates for which the height exceeded the width was greater than a fixed threshold.

Classifying box node candidates involved reducing each closed shape to a shape primitive. This step exploits the observation that during the flowchart authoring process individual box nodes may be squeezed or stretched in the horizontal and vertical directions according to the amount of text they contain. Since node classification should be invariant to such transformations, each candidate was subjected to a squeezing operation in the horizontal and vertical direction by application of the operators S H and S V , where

S H = ∂ ∂x ( y B(x, y) dy) and S V = ∂ ∂y ( x B(x, y) dx),
and discarding the segments for which S V or S V are below a predefined threshold.

The result of the squeezing operations is a shape primitive which characterises the basic shape family of each node type (see Figure 3). This shape simplification step is particularly suitable for the present definition of shape categories. In the present task, the class oval does not only encompass elliptical outlines (ovals in the simplest sense) but also half-circles joined by two horizontal lines and even rounded rectangles (see Figure 3). Similarly, the class diamond includes, in addition to the standard four-sided parallelograms, hexagons and octagons (each having two parallel sides of the same size and aligned with the horizontal or the vertical). Reducing each of these shapes to a single primitive (an elliptical shape in the case of ovals, a quadrilateral shape for diamonds) enables each family to be characterised. and the diamonds (c). After applying squeeze simplifications (SH or SV ) to the original box regions, the resulting primitive shapes are decomposed in subcomponents and re-arranged using shape-specific transforms (TCB1 R and TCB2 R for the cylinder bodies, TP R for the parallelograms and TD R for the diamonds) prior to measuring the associated geometric features (ellipticity (CB1R) and rectangularity (CB2R, PR and DR).

(a) (c) (b) S V S H S H CB1 P R R D R T CB2 R T T D R T CB2 R P R CB1 R
Multiple shape features were extracted from the box nodes: a first set of features, f 0 , was derived from the original box node, while a second set, f R , was derived from the horizontally and/or vertically reduced shape. Features in the f 0 set include area (A 0 ), ellipticity (E 0 ), solidity (S 0 : the ratio between the area within the bounding rectangle and the area within the box node perimeter) and the horizontal and vertical symmetries (SyH 0 and SyV 0 ). Reduced shape features include area (A R ), ellipticity (E R ), solidity (S R ) as well as symmetry measures aimed at characterising specific node shapes: diamond, parallelogram and cylinder bodies (see Figure 4). D R measures the rectangularity (here estimated by the solidity, as defined above) of a reduced shape after decomposing it into four quadrants and reassembling them as in figure 4(a). Diamonds are thus expected to be formed by complementary shapes and can be characterised by a value of D R close to 1 (indicative of a high degree of rectangularity of the recomposed shape). In the same way P R offers a measure of rectangularity of a recomposed shape obtained by cutting in two halves a horizontally-squeezed node box and repositioning its two portions (Figure 4(b)). Here again a high degree of rectangularity is expected from a horizontally-sheared rectangle (i.e. a parallelogram under the present task definitions). CB1 R and CB2 R measure the symmetries and complementaries expected from typical cylinder body shapes: CB1 R measures the ellipticity of a composite shape obtained by adjoining the body half and the horizontally reflected top half of a vertically squeezed node box (Figure 4(c)), whereas CB2 R measures the rectangularity of the two recombined halves. For an input node box region B(x, y) the set of features given in Tables 1 and 2 is generated.

Based on the features defined in Tables 1 and2, the box nodes are first classified into the following set of shape families, as defined in the CLEF-IP task: circle, oval, parallelogram, rectangle, diamond. On top of these required categories, we introduced the following additional classes: loop (to identify closed areas defined by edges and box outlines) and small (to flag regions having an area below a fixed threshold). The two other node classes that are not defined by a closed outline shape (i.e. point node and no box node) are discussed in Sections 3.6 and 3.8.

A simple hierarchical classification scheme is used to discard the small regions and separate asymmetrical shapes (loops) from the other classes. The rectangles are then identified. The remaining shapes (oval, circle, parallelogram, diamond, cylinder-body) are then classified using heuristic thresholds on feature values. Statistical pattern recognition techniques have not been applied due to time constraints and the lack of image-level annotations in the training set. Obviously, the application of such techniques is expected to significantly improve classification performance.

Composite shapes (double rectangles and cylinders) are identified in a second pass as a combination of primitive shapes (double rectangles being formed by a collection of three adjoining rectangles, and cylinders by an oval and a cylinder body).

Point node detection

Point nodes, defined as the junctions where edges meet, were identified as follows. Firstly, horizontal and vertical lines in the connector-only image were detected using a simplified version of the Hough transform by projecting pixel values onto the vertical and horizontal axes and identifying maxima. Secondly, point node candidates were identified as the image coordinates where horizontal and vertical lines crossed. Thirdly, a box centered on the point node candidates was used for validation: candidates were accepted if the box contained a branch-point and 3 or more lines crossed the box perimeter.

Edge classification

The main challenge in classifying edge types surrounds the classification of dashed and wiggly edges. Dashed edges are not common and were identified as those containing multiple implicitgraphics segments (see Section 3.3). Counterintuitively, wiggly edges ('leading lines') are often straight and indistinguishable from plain undirected edges in the absence of context. These edges are classified solely according to their relationship to no-box nodes. Since wigglies correspond to leading lines and reference signs are of type no-box, any edge connecting a box node to a no-box was a assumed to be a wiggly. Preliminary trials suggested that truly curved wiggly edges could be detected using features describing the orientation of their endpoints which are neither horizontal nor vertical, such features could be useful for improving no -box classification in future.

Edge directivity was classified using a simple set of features. Firstly, the two endpoints of each edge were identified and a fixed region around each endpoint was analysed. Next, the number of erosions required to remove all black pixels from the endpoint region was measured and a binary classification tree with heuristic thresholds was applied. Again, the use of statistical pattern recognition techniques was not applied due to time constraints and the lack of image-level annotations.

Feature

R Area o S H o S V E R Ellipticity o S H o S V S R Solidity o S H o S V D R Solidity o T D R o S H o S V P R Solidity o T P R o S H CB1 R Ellipticity o T CB1 R o S V CB2 R Solidity o T CB2 R o S V

Text object segmentation and classification

The text-graphic segmentation method (Section 3.2) results in a set of connected components representing characters that must be further grouped into text objects and classified into boxnode labels, edge labels, no-box nodes (reference signs) and metadata. Segmenting text objects involved the hierarchical, agglomerative clustering of characters according to the proximity of their centroids. Since characters are generally grouped horizontally, before clustering, vertical distances were weighted with a positive scaling constant which favoured horizontal associations. Any text within the closed region of a box node was classified as a node label in a straightforward manner. To classify edge labels and no-box nodes each text object was dilated and checked for overlap with known edges. If the overlapping edge connects to two nodes (box nodes or point nodes) the text object is classified as an edge label, whereas if the overlapping edge connects to only one node the text object is classified as a no-box node and the edge is classified as a wiggly.

The fonts used for metadata (figure titles) tend to be larger than the other types of text objects and titles usually appear at the image edges. After classification of text objects into box-node labels, edge labels and no-box nodes any remaining text is re-clustered according to centroid positions. The new text objects were scored proportionally to their total area and their distance from the image center: the object with the highest score was classified as metadata.

Text object recognition

Reliable OCR of text in the patent flowchart collection is challenging for multiple reasons.

• The quality of image digitisation can be very low. This is mainly due to the low quality of the original scanning and the original paper support:

• The quality of patent flowchart draftsmanship is variable. Note that rules and guidelines discussed in Section 2.1 do not exclude hand-drawn flowcharts;

• The segmentation of textual content is difficult (see Section 3.8);

• The text used in flowcharts is a particular form of technical language, different from the general domain language addressed by the state of the art OCR engines.

These different issues have been addressed using flowchart-specific text segmentation followed by state-of-the-art OCR software (ABBYY FineReader, version 10 and Tesseract 3.0) and customised post-correction techniques based on a Shannon's noisy-channel model approach and language models.

It has been shown that post-correction of the recognition errors based on specialised models can provide a significant reduction of the word error rate produced by general OCR engines. The proposed technique is based on a Shannon's noisy-channel model approach, more precisely a probabilist modeling of the degradation of the original string by the OCR process. This approach is used by spell-checker systems and auto-correction/auto-completion systems for information retrieval systems. The model can then be applied to a noisy string for correcting recognition errors, as shown in [START_REF] Kolak | OCR error correction using a noisy channel model[END_REF].

Character Level Model

Following a probabilistic framework, finding the correct string c given an original string o corresponds to finding,

argmax c P (c|o)
and, following Bayes' Rule, argmax c P (o|c)P (c) Following Shannon's noisy-channel model approach, P (c) is the source model estimating which string is likely to be sent, and P (o|c) is the channel model estimating how the signal is degraded. In our case, P (o|c) estimates the probability that the string o is recognised by the OCR when the original string present in the bitmap image is c. For the source and channel model, we introduce first an N-Gram model of characters denoted CM with N=6:

P (c) P (c|CM ) = m P (c m |c m-1 , ..., c m-5 )
For the channel/error model, we use a probabilistic framework for edit operations following a confusion model.

P (o 1..n |c 1..m ) = 1..m P e (c 1..m → o 1..n )
where P e corresponds to the probability that the OCR recognised the string o 1..n when interpreting the string c 1..m based on the four following edit operations:

P match (c i → o j |c i ∈ c 1..m ∨ o j ∈ o 1..n ) = P match (c i ) P substitution (c i → o j |c i ∈ c 1..m , o j ∈ o 1..n ) = P substitution (c i → o j ) P insertion (c i → o j |c i ∈ c 1..m , o j ∈ o 1..n ) = P insertion (c i → ε) P deletion (c i → o j |c i ∈ c 1..m , o j ∈ o 1..n ) = P deletion (ε → o j )
Having a probabilistic interpretation of the edit distance allows the probabilistic costs of the edit operations to be learned from a corpus of errors instead of setting costs manually/experimentally as is often the case with existing spell checkers based on noisy channel models. The probabilities P substitution (c i → o j ), P insertion (ε → o j ) and P deletion (c i → ε) are learned from a corpus of OCR errors aligned with the expected correction following the Levenshtein distance:

P match (c i ) = 1 - count(c i → {c j } j =i ) + count(c i → ε) count(c i ) P substitution (c i → o j ) = count(c i → o j ) count(c i ) P insertion (ε → o j ) = count(ε → o j ) count(c) P deletion (c i → ε) = count(c i → ε) count(c i )
where c corresponds to any characters of the alphabet of the source. A smoothing operation is then applied for estimating the edit operations unseen in the OCRerror training data. This was done by giving a very small additive probability to all character substitutions, insertions and deletions (Lidstone's additive smoothing).

Note that transposition is not used in our channel model because this sort of character error is relevant for spell-checker applications, but extremely rare in the case of OCR. The original Levenshtein distance was therefore preferred over the Damerau-Levenshtein distance generally used for spell-checking.

Language Model

N-gram language models are used to capture constraints on word sequences. For estimating P(c) a word trigram language model LM was combined with the character model CM :

P (c) P (c|CM )P (c|LM ) = P ( c p |CM ) p P (c p |c p-1 , c p-2 )
where c p denotes the complete concatenated character sequence with boundary characters corresponding to the p words c p .

At this stage the main issue is the word segmentation of the corrected word sequence. The character model handles word splits naturally and merges by inserting, substituting or deleting character boundaries. Consequently, the application of the character model may result in different hypotheses having different numbers of words. The application of a language model on these different hypotheses will be biased by the fact that the probability of a longer word sequence is always penalised. For solving this problem, empty final words are introduced so that all hypotheses have the same word length (set to the maximum among the hypotheses). The transition cost to empty final words is computed dynamically as the average of the existing transitions.

Given a noisy word sequence recognised by the OCR, the final aim is to find, for a sequence of corrections of words:

argmax c P (o|c)P (c) = argmax c 0≤p≤pmax m P e (c 1..m,p → o n..q ) m P (c m,p |c m-1,p , ..., c m-5,p ) P (c p |c p-1 , c p-2 )
where p max is the maximum number of words in the segmentation introduced by the character model and n to q correspond to the character range of the corrupted character sequence corresponding to the word p in the correction.

The decoding of the intended sequence of words given the observed sequence of words recognised by the OCR is implemented using the Viterbi approximation.

Training

For the present experiments a set of approximatively 2000 corrections of error produced by ABBYY FineReader was used. A correction is simply the alignment of the erroneous strings and the corrected ones which allow the probabilistic costs of the edit operations introduced in the previous section to be learned. Since the corpus of errors was only produced for ABBYY FineReader, the resulting model has limited applicability to Tesseract.

For training the character and language model, a corpus of 10.000 patent descriptions and 2.000 scientific articles was used. In both cases, the resources were a mixture of English, German and French texts.

Results and discussion

Graph structure recognition

At the time of writing no official performance results are available for the algorithm test run submitted. Instead, the performance of the algorithm has been measured by manually comparing algorithm output with known ground-truth values. This method of evaluation, although timeconsuming, allows a detailed analysis of the modes of failure of the algorithm. For each class of objects in the flowchart, P r, the precision and, Re, the recall value of the classification scores over the whole test set were determined as P r = true positive true positive + f alse positive and Re = true positive true positive + f alse negative .

In addition, the balanced F-score, F , is calculated as F = 2. P r.Re P r + Re . Table 3 summarises the results of the evaluation for the various node and edge types. Note the following.

• The relatively high values of P r and Re for box nodes is achieved despite the use of a simplistic classification approach. This indicates that the box features defined in Section 3.5 are discriminative.

• The relatively low values of P r for directed edges and Re for undirected edges are probably caused by an overly simplistic approach to extracting directivity features and classifying them. A large fraction of directed edges were misclassified as undirected edges as shown by the relatively high values of P r and Re when directivity is ignored.

• Since the identification of wiggly edges and no-box nodes occurs in conjunction, the performance scores for these two entities are almost identical: P r wiggly =0.96 and Re wiggly =0.76.

• A recall score of 83% was measured for identifying the position of the title (prior to OCR).

• The relatively low value of P r point is partly explained by text touching characters which causes false positives.

In order to examine the patterns of algorithm failure, a single combined F-score, F N , is calculated for all nodes of each flowchart (excluding no-box nodes since they are labels that do not define the flowchart logic). Similarly, a single combined F-score, F E , is calculated for all edges of each flowchart (ignoring directivity). F denotes the average of the node or edge F-scores for a given flowchart.

The results of the combined F-score evaluation over the whole test set are summarised in Figure 5. Out of a total sample of 100 analysed flowcharts, 34 show a F-score of F N =F E =1.00. 50% of the diagrams have an average F-score superior to 0.90, while 75% show an average score of 0.78 or better. Only 10% of the test sample have an average F-score below 0.50. For the purpose of identifying the most significant sources of errors, the flowcharts associated with F <0.8 were we identified. Each of these flowcharts was assigned to one of three categories according to the main mode of failure: (i) initial text-graphics segmentation error, (ii) presence of text characters touching the flowchart graphics and (iii) discontinuities in the flowchart graphic structure (mainly due to broken lines).

Looking in turn at each of these classes, it was observed that poor segmentation of the main flowchart component at the early stage of processing was the main cause of very low F-scores. Six flowcharts have F <0.5: EP00000821886 (13), EP00000926609 (23), EP000010469970029 (40), EP000010702880135 (45), EP0000110761 (50) and EP00001113655 (51). The mode of failure for this group of patents is associated with the text-graphic segmentation module (Section 3.2), in particular the assumption that the graphic component of the flowchart can be isolated from text by selecting the connected components with the largest heights and widths. A failure at this stage prohibits further node classification and edge identification.

Characters touching graphics (mostly text intersecting node boundaries) is also an important source of errors. Six flowcharts fall into category: EP000006215470107 (2), EP000006479080138 (3), EP000006879010063 (4), EP00001063844 (44), EP00001217549 (57) and EP00001526500 (91). Although this type of error is mostly due to a combination of design choices and scanning noise, it can also stem from the preprocessing steps designed to remove small gaps in the image components (Sections 3.1 and 3.3). This reflects the difficulty in bridging line breaks without creating unwanted pixel junctions between otherwise disconnected entities. It is noted that the problem of separating touching text from graphics remains a challenging issue for general OCR processing engines [START_REF] Nagy | Twenty years of document image analysis in pami. Pattern Analysis and Machine Intelligence[END_REF]). It has been studied in the document image analysis literature in relation to the analysis of maps [START_REF] Fletcher | A robust algorithm for text string separation from mixed text/graphics images[END_REF][START_REF] Luo | Directional mathematical morphology approach for line thinning and extraction of character strings from maps and line drawings[END_REF]), form processing [START_REF] Yoo | Line removal and restoration of handwritten characters on the form documents[END_REF]) and technical drawing understanding [START_REF] Kasturi | A system for interpretation of line drawings[END_REF]). An improved text-graphics segmentation method would benefit from the inclusion of an additional stage of detection and separation of touching characters.

The failure to correctly bridge broken lines (Section 3.3) is the leading source of error for values of F >0.5. A total of 10 flowcharts in the range 0.3< F <0.75 show significant errors that can be attributed to discontinuous graphical components. Line breaks can affect the connecting lines between nodes creating false positives in the edge components, as is the case for: EP00000866609 (17), EP000000884919 (18), EP00000098456 (29) and EP000001513121 ( 89)). Line breaks can also prevent a proper identification of the nodes as for: EP000001104172 (49), EP000001197682 (55), EP000001201195 (56), EP000001223519 (59), EP0000012274360 (60) and EP000001521176 (90). Improving gap bridging schemes would improve performance.

Recognition of text objects

In order to evaluate the performance of the text object recognition module (Section 3.9), text labels were transcribed manually for approximately 20% of the test set (412 out of a total of 2023 text boxes). The sentence and word scores given below are based on this sample.

The following scores are presented in Table 4.

• A coverage score, which is the percentage of cases where the OCR returns a result.

• An accuracy score which is the percentage of correct results produced by the OCR given the total expected results. When the OCR does not produce a result, it is considered that the result is incorrect. An accuracy evaluation is given at both word level and sentence level, i.e. if the complete text associated with a graphical object is correctly recognised.

• A precision score which is the percentage of correct results produced by the OCR given the total result produced by the OCR, similarly at word and sentence level. As a comparison, the highest accuracy of current state-of-the-art OCR engines is considered to be around 98-99% correctly recognised words in optimal conditions. It is clear from the coverage evaluation that Tesseract filters out a large amount of results that it judges incorrect. Although the Tesseract confidence threshold affects its coverage, no exploration of this parameter was performed for lack of time. It can be observed that, although producing fewer results, the precision of Tesseract out-of-the-box remains significantly below that of ABBYY FineReader.

As the post-correction technique has been trained only on ABBYY FineReader errors, it is less effective at improving Tesseract results. The accuracy of ABBYY FineReader is improved by 9.4%, meaning a reduction of the word error rate of 26.2%. This reduction of the error rate is lower than the 60% reduction reported by [START_REF] Kolak | A generative probabilistic OCR model for NLP applications[END_REF] using a similar approach (but correcting an OCR engine with a significantly lower baseline performance). These results should be considered carefully because, due to time constraints, only a partial of the models was applied to the flowchart text problem:

• The texts considered for training the probabilistic costs of the edit operations, the character and language models were patent descriptions and scientific articles without text from flowcharts. Ideally, the text present on the flowchart of the training data (50 flowcharts) should have been combined with the other training resources.

• The errors used for training the costs of the edit operations were generated for ABBYY FineReader only. A fairer comparison would use Tesseract-generated errors for correcting Tesseract output.

• The ideal source for training the language model is the patent description of the patent where the flowchart figure appears. As the patent publication number was given with the flowchart images, the usage of the description text would have been possible, for instance by retrieving the ST.36 document via the 'Open Patent Service' of the EPO.

These issues suggest clear directions for future improvement. It should be possible to improve the post-correction model significantly by improving the way specialised training data is selected and exploited.

Figure 1 :

 1 Figure1: Possible membership states of image pixels in a flowchart. Numerical labels refer to the section headings below which describe how the various membership values are assigned. Note that joint membership states are, in principle, possible but are not allowed in the approach chosen.

Figure 1

 1 Figure1shows the possible membership states of image pixels in a flowchart. The method steps of the algorithm implemented are shown in Figure2.

Figure 2 :Figure 3 :

 23 Figure 2: Method steps of the present flowchart summarisation process.The present processing architec-ture can be separated in three main components: (I) a preprocessing and segmentation stage (see sections 3.1 to 3.4), (II) a node and edge classification stage (sections 3.5 to 3.7), and (III) a text extraction and recognition stage (sections 3.8 and 3.9).In addition to the processing steps, the rounded vertical boxes represent the data structures aggregating the output of the connector identification (box "C"), the edge classification (box "E", which also contains the edge-node relationships) and the node classification (box "C"). Dashed lines represent a delayed transfer of data: Either the passage of the graphic component of the flowchart for the detection of the lines connections between nodes, or of the text layer used to extract individual text regions prior to OCR.

Figure 4 :

 4 Figure 4: Derivation of the symmetric features for the cylinder bodies (a), the parallelograms (b)

Figure 5 :

 5 Figure 5: The distribution of F-scores for individual flowcharts FE denotes the F-scores for all edges (omitting directionality), while FN corresponds to the F-score of all nodes (except no-boxes) in each flowchart. Those flowcharts with low F-scores have been marked with numbers and symbols that indicate the mode of failure: Diamonds indicate poor text-graphic segmentation, dotted circles denote the problems caused by characters touching graphics, open squares relate to a substantial fraction of broken lines. All other flowcharts are represented by small disks. The larger black disk indicates a group of 34 flowcharts at FN =FE=1.00 . The grey dashed quarter circles mark the regions of the plot containing respectively 90%, 75% and 50% of the data points.

Table 1 :

 1 Features and operators for the original

		Feature Operator
		A
		Operator
	A 0	Area
	E 0	Ellipticity
	S 0	Solidity
	SyH 0	Horizontal symmetry
	SyV 0	Vertical symmetry
	box regions

Table 2 :

 2 Features and operators for the shape primitives

Table 3 :

 3 Precision and Recall scores for nodes and edges (node classes cylinder, parallelogram and unknown are not included since there are too few examples). The combined scores free all box nodes (excluding point and no-box nodes) and all edges (ignoring directivity) are also given.

	Class	Precision	Recall
	All box nodes	0.91	0.83
	rectangle	0.99	0.90
	diamond	1.00	0.87
	oval	0.97	0.81
	circle	1.00	0.79
	double rectangle	1.00	0.72
	point	0.53	0.80
	no-box	0.94	0.77
	All edges (ignoring directivity)	0.90	0.89
	DE	0.97	0.47
	UE	0.31	0.81

Table 4 :

 4 Result

	Technique	ABBYY	ABBYY & post-correct.	Tesseract	Tesseract & post-correct.
	Coverage (sentence)	99.76	99.76	61.84	61.84
	Coverage (word)	96.81	96.70	77.84	78.25
	Accuracy (sentence)	72.27	79.06	22.98	23.88
	Accuracy (word)	76.13	82.43	44.43	46.36
	Precision (sentence)	72.53	79.52	37.16	39.22
	Precision (word)	78.63	84.13	54.03	57.08

of OCR of text object with ABBYY FineReader and Tesseract with and without postcorrection techniques.


Keywords: Economic development < Economics, Agricultural development < Economics, Access child schooling, market access, fair trade. JEL numbers: O19, O22, D64

We outline a methodology which aims to give an answer to the widespread demand of impact methodologies by regulators or by funding agencies which need to evaluate the current and past performance of development projects and may lack of time series evidence. We devise a retrospective panel data approach to evaluate the dynamics of the effects of fair-trade affiliation on the schooling decisions of a sample of Thai organic rice producers across the past twenty years. We find that the probability of school enrolment in families with more than two children is significantly affected by FT affiliation years. We try to ascertain whether our finding is robust to endogeneity of producers' choices of local cooperative affiliation and adoption of organic techniques. The significant difference between pre-and post-FT affiliation performance documents that fair-trade participation generates a significant break in the schooling decisions of affiliated households.

Introduction

The aim of this paper is to extend the child schooling literature by examining the impact of affiliation to fair trade and its (income and non-income) effects on child schooling for a sample of Thai rice producers. It is widely acknowledged in the literature that income levels affect child education and that income variation changes the ability of households to afford the opportunity costs of education [START_REF] Behrman | Household Income and Child Schooling in Vietnam[END_REF]. The effect of parental income on child education, however, crucially depends on parental preferences and on the bargaining rules within the household.

On the one hand, [START_REF] Basu | The Economics of Child Labor[END_REF] assume that parents dislike sending their children to work and do so only out of necessity. On these premises, they develop the so-called "luxury axiom" stating that children only work when parental income is below a given threshold related to subsistence levels. On the other hand, the parental agency literature argues that parents do not fully internalize the present and future well-being of their children. When this approach is taken to the extreme, parents draw advantages from their children's work, and the child labour supply is mainly driven by labour demand on the market.

Theoretical and empirical findings show that the prevalence of one or the other model also depends on the evolution of bargaining power between the parents within the family and, in some cases, also on the bargaining power of working children (Moehling, 2005;[START_REF] Basu | The collective model of the household and an unexpected implication for child labor: Hypothesis and an empirical test[END_REF].

The subfield in the parental income-child labour and schooling literature which is most relevant to this work is the one that examines the dynamics of child labour and schooling when parental income is affected by price shocks of internationally traded goods.

In this regard, Edmonds and Pavcnik (2005a) show that the negative association between market openness and child labour depends mainly on the positive link between trade and income. In another empirical study on Vietnamese rice producers, Edmonds and Pavcnik (2005b) document that an increase in export prices significantly reduces child labour. All the above-considered FT features suggest that an empirical analysis on the effects of FT affiliation on child labour can make an original contribution to the literature by complementing the results obtained to date. The relevance of empirical analyses of this kind may also be clarified by looking at their potential effects on the logical framework of FT projects (see Appendix 1).

A second contribution of the paper is that it proposes a simple and effective retrospective panel data approach. The latter helps with investigation of research topics when the collection of information on the same sample of individuals over many years is too costly or, if not begun in advance, makes an ex-post impact analysis impossible. In this regard, we devise a very simple approach which enables the collection of panel data retrospectively without requiring unreasonable memory efforts by respondents (see section 4). Differently from [START_REF] Mcintosh | Microfinance and Home Improvement: Using Retrospective Panel Data to MeasureProgram Effects on Discrete Events[END_REF], who examine house restructuring events, we build our retrospective panel data on simple questions about children's age and schooling years. This approach allows us to reconstruct the pattern of household schooling decisions over a long time interval. The retrospective panel approach has the advantage of measuring the dynamics of child schooling across years, something which is not possible with the two most typical data structures to be found in the literature (cross-sections or short panels with observations repeated twice in time).

Given these potential advantages we are obviously aware of limitations in our analysis of the impact of fair trade on child education. Firstly, it is impossible to disentangle the effects of the different facets of the "bundle" of fair-trade initiatives. Nevertheless, examining whether the package works as a whole is, we believe, worthwhile.

Secondly, the focus of the analysis on a mechanism already at work prevents us from solving the endogeneity problem by implementing randomized control trials. Whilst the latter have documented the effect of conditional cash programs on child education (see for instance Schultz, 1994;De Janvry, et al. 2004), we cannot use them if we want to shed light on the effect of unconditional income changes on parental income. 3 In this regard, the fair-trade experiment described is an interesting benchmark against which to measure the non-"artificial" reaction of child education to the income variability generated by affiliation/non affiliation with the project. Thirdly, our retrospective panel approach allows accurate reconstruction of only child-schooling choices, and not also the interaction between school and (domestic or market) work, about which we have only current information. 4 Summing up, with the application of retrospective data and several controls for endogeneity (e.g. comparison of pre and post affiliation child schooling trends) and in spite of the above mentioned limitations, our paper outlines an original methodology which may give an answer to the widespread demand of impact methodologies required by regulators or by funding agencies which need to evaluate the current and past performance of ongoing development projects.

The specific hypothesis tested in the paper is that FT affiliation has a positive effect on child schooling in large families (families with more than two children).

As well known, for a given budget constraint, the higher the number of children, the lower the investment in available per child education [START_REF] Becker | Child Endowments and the Quantity and Quality of Children[END_REF]. On this basis, a causal relationship between the number of children and the probability of school attendance may arise in the case of an exogenous increase in family offspring. This theoretical point has been confirmed by recent empirical studies [START_REF] Booth | Birth Order Matters: The Effect of Family Size and Birth Order on Educational Attainment[END_REF][START_REF] Iacovou | Family Composition and Children's Educational Outcomes[END_REF] showing that education is negatively correlated with family size and birth order. The argument is also supported by the findings of Hanushek (2002), [START_REF] Steelman | Acquiring capital for college: The constraints of family configuration[END_REF] and [START_REF] Yilmazer | Saving for Children's College Education: An Empirical Analysis of the Tradeoff Between the Quality and Quantity of Children[END_REF], the last two works showing that large families have fewer financial resources for school fees. 5 This paper seeks to verify the impact of FT in situations in which the quality (of education) 6 /quantity trade-off is expected to matter (among agricultural producers in LDCs close to the poverty line). The trade-off may be eased by policies which ease FT certification and affiliation of producer cooperatives to FT importers.

The paper deals with these issues and is divided into eight sections (including the introduction and conclusions). The second section briefly explains FT characteristics and the The fourth section describes the survey design and the memorable event methodology used to transform cross-sectional into panel data. The fifth and sixth sections present and discuss the descriptive and econometric findings. The seventh section focuses on the endogeneity problem and discusses how we dealt with it.

What is FT ?

According to IFAT, the main umbrella organization gathering most fair-trade producers and importers, "Fair Trade is a strategy for poverty alleviation and sustainable development. Its purpose is to create opportunities for producers who have been economically disadvantaged or marginalized by the conventional trading system" (EFTA, 2009). Beyond official declarations, fair trade may therefore be conceived as an economic initiative promoted by organizations of importers, distributors and retailers from Europe and the US which aim to promote capacity-building, market inclusion, and the improved well-being of marginalized producers. The first and basic FT impact is its "antitrust" effect achieved with the simple diversification of sale channels offered to producers.

Fair-trade criteria 7 potentially include: i) an anti-cyclical mark-up on producers' prices incorporating a price floor as an insurance mechanism against price falls below a decent standard of living threshold; ii) anticipated financing schemes reducing the likelihood of credit rationing; iii) export services and access to foreign markets; iv) direct investment in local public goods (health and education) through the premium provided to the local producers' associations.

Given these characteristics, FT has the potential to address market failures such as credit rationing, underinvestment in local public goods (health, education and professional training), monopsony of local intermediaries and/or moneylenders [START_REF] Becchetti | Globalisation and the death of distance in social preferences and inequity aversion: empirical evidence from a pilot study on fair trade consumers[END_REF]. 8 On the consumer side, it has also been demonstrated that FT satisfies consumer willingness to pay for social and environmental intangibles incorporated in the final product, generating contagion effects on profit-maximising competitors [START_REF] Becchetti | On ethical product differentiation[END_REF]). 9 What the above mentioned debate indicates is that impact studies on FT are of extreme interest, and they are so for three main reasons.

Firstly, the phenomenon is growing more rapidly than the capacity of economists to analyse it. Between 2006 and 2007, total FT sales registered a 127 percent increase by volume and 72 percent increase by estimated retail value. Growth in Europe has averaged 50 percent per year in the past six years, and FT has gained significant shares in some market segments (47 percent of bananas in Switzerland and 20 percent of UK bananas since the decision taken by some of the main UK distributors to import only these products).

A second reason for the importance of empirical investigations of this phenomenon is that they dispel the doubts of FT consumers about what really lies behind the products that they buy. In essence, the social and environment-friendly characteristics of the products are not those of experience goods (that is, the information gap on their socially responsible characteristics cannot be bridged by repeated consumption). Hence, rigorous empirical work is required to bridge informational asymmetries between buyers and sellers and to evaluate whether or not FT promises are fulfilled. 10 A third argument is that the results of FT impact analyses may be very useful for the critical evaluation of ongoing FT projects and possible revision of FT criteria.

Within this literature the specific goal of our study is to analyse the effects of FT affiliation on child schooling via creation of economic opportunities for poor producers. To be stressed in this regard is that fair trade does not explicitly ban child labour, so that its impact on it may only be indirect. This is clearly documented in the IFAT charter by criterion vii) on Working conditions, which states that "The participation of children, if any, does not adversely affect their well-being, security, educational requirements and need for play and conforms to the UN Convention on the Rights of the Child as well as the law and norms in the local context". However, FT should act indirectly on child schooling by creating the conditions for capacity-building and higher producer productivity and household income. 11 Moreover, as said, FT may help in addressing market failures like credit rationing by providing members with various advantages, such as interest-free credit

The FT Project in Thailand

GreenNet is the main fair-trade producer and exporter of organic rice in Thailand. It was founded in 1993 and received fair trade certification from the Fair Trade Labeling Organization in 2002. Farmers affiliated to GreenNet produce organic long grain red, white and brown Jasmine rice.

GreenNet provides advance payments for producer groups to stock their paddy. It receives export orders for the year and accordingly instructs producer groups on the quantity to be delivered.

Producer groups then deliver the milled rice to GreenNet, which exports and/or sells it locally once packaged.

Organic farmers receive two main benefits from GreenNet: i) a fair-trade premium to be used for social needs and capacity-building activities (scholarships, emergency funds, credit facilities, training, etc.) in accordance with the FLO laws; ii) individually, an extra yearly fair-trade bonus as a mark-up on the sale price.

GreenNet is a second-level cooperative. The second level is generally necessary to coordinate production among local cooperatives, implement research and promote organic farming, as well as to provide export services on a wider scale. All members of first-level associations are also members of GreenNet. Our research on FT impact looked at two different (GreenNet-

The Bak Reua Farmer Organization (BRFO)

Created in April 1976, the BRFO aims to: i) support members in growing rice without using chemical inputs and establish rice farmlands appropriate to local ecology; ii) strengthen farmer organization so that it can manage and control rice quality throughout the chain; iii) encourage learning among farmers so that they can manage rice mills as sustainable rural enterprises.

BRFO started pesticide-free rice farming in 1996 in accordance with the following certification standards: i) ACT Organic Standards according to IFOAM Basic Standards (IFOAM programme); ii) EU Regulation 2092/91; iii) BioSwiss organic standards.

Since 2002, BRFO has received the FLO's certification as part of the GreenNet Cooperative.

The fair-trade premium is divided into several funds to which farmer members can apply to support: i) green manure seed; ii) farmer training; iii) member welfare, e.g. education of their children, iv) natural disaster relief.

The Nature Care Society (NCS)

The objectives and goals of the Nature Care Society (NCS) are: i) encourage members to grow rice without using chemical inputs; ii) solve farmers' problems of unfair price and trading in paddy; iii) expand milling capacity in order to exploit economies of scale; iv) strengthen farmer organizations; v) provide instruction on running a community business.

NCS began organic rice farming in 1992, while a group of farmers first received organic certification in 1996. The standards fulfilled by such a certification were: i) ACT Organic Standards according to IFOAM Basic Standards (IFOAM programme); ii) EU Regulation 2092/91; iii) BioSwiss organic standards. The fair-trade premium is allocated as follows: i) 50% to the mill for improvement of its management; ii) 25% to extension works; iii) 25% to the Organic Fair-Trade Fund, which provides credits to members willing to convert to sustainable production as well as other community benefits.

The dataset and the retrospective panel approach

On August 2008, 360 farmers were surveyed in the Kud Chun and Bak Reua districts. For each district, an equal number of respondents were randomly chosen between two extended lists of affiliated and non-affiliated farmers in order to create a treatment group (members of the two GreenNet organisations, BRFO and NCS, affiliated to FT) and a control group (non-members of GreenNet). A random selection from the list of all members in the two areas was drawn for the former group, whereas, for the latter, a random sample of farmers living close (within 10 km) to organic farmers was generated. Non-responses were extremely low (around 2 percent among treatment and 5 percent among control sample producers). The descriptive statistics will highlight that the treatment and control samples were not significantly different in terms of sociodemographic features (see Table A1 in the Appendix available online). 13 As to the kind of data collected, the questionnaire contained 75 questions designed to measure qualitative and quantitative well-being. More specifically, in addition to the classic socioeconomic variables, it collected information on income and wealth according to their various measures (i.e. land size, housing, sanitation and durables, etc.), savings and productivity, child schooling and farmer education, work activity and working conditions, price and trading information, human and social capital indicators, self-esteem and happiness. 14 As already mentioned in the introduction, we used the retrospective approach to reconstruct the pattern of household schooling decisions over time with very simple questions. Retrospective data are used in the literature when the costs of collecting data across time are too high, or when the researchers need to evaluate an economic phenomenon for which this information is not available. Among various examples see [START_REF] Peters | Retrospective Versus Panel Data in Analyzing Lifecycle Events[END_REF][START_REF] Mcintosh | Microfinance and Home Improvement: Using Retrospective Panel Data to MeasureProgram Effects on Discrete Events[END_REF] and [START_REF] Becchetti | Fall and recovery. Disruption and catching up effects after tsunami on a sample of MFI borrowers[END_REF]. 15 The reliability of this approach depends crucially on the identification of memorable events.

In specifying what can be considered as memorable events, McIntosh et al. ( 2010) include major diseases, deaths, school enrollments, and major asset purchases, while they regard changes in profits and revenues among the events most difficult to remember with precision. More in general, the three above-mentioned papers consider the following to be facts which do not require strong memory efforts: divorces and remarriages [START_REF] Peters | Retrospective Versus Panel Data in Analyzing Lifecycle Events[END_REF], house restructuring decisions [START_REF] Mcintosh | Microfinance and Home Improvement: Using Retrospective Panel Data to MeasureProgram Effects on Discrete Events[END_REF], and schooling years and age of children [START_REF] Becchetti | Fall and recovery. Disruption and catching up effects after tsunami on a sample of MFI borrowers[END_REF].

Interesting evidence on the reliability of this approach is provided by [START_REF] Peters | Retrospective Versus Panel Data in Analyzing Lifecycle Events[END_REF], who draws on both panel and retrospective data to demonstrate the accuracy of retrospective information in his specific case, given that both sources of data yielded substantially the same results when hazard rates of divorce and remarriage were estimated.

In our case, respondents were asked about their family size, the age of their offspring, the schooling years of each family member, and the age at which they had started school (usually 5 or 6). A final question was whether and when school had been suspended and restarted by some of the respondent's children. Overall, we maintain that this information is highly memorable, considering that (besides age) parents must be informed and aware of this basic information about their children's education. Further details on the retrospective approach are given in section 7.

Descriptive findings

A first point to be mentioned is that cooperative membership was more common than fair trade affiliation, since 84% and 77% of farmers from Kud Chun and Bak Reua, respectively, were cooperative members. In other words, while all GreeNet affiliated farmers were by definition By taking into account this feature, and by controlling separately for both cooperative and FT membership, it was possible to isolate the specific effect of FT and/or organic certification from a generic cooperative effect when conducting the econometric analysis (see econometric results in Tables 1 and2).

In a previous paper, Becchetti, Conzo and Gianfreda (2008) documented that, in the same sample on which we perform our analysis, fair-trade-affiliated subjects had a significantly higher per capita income than the control sample. From a descriptive point of view, household income from agriculture was on average 60,942 THB (Thai Baht) for affiliated against 41,646 for nonaffiliated producers (the average number of household members being around 3.8 for both subsamples) (Table A1 in the online Appendix) and the difference was significant at 95 percent level in both parametric and nonparametric tests. It remained significant as well when the authors considered the same variable adjusted for the market value of self consumption (the latter being significantly larger for affiliated producers) and total income (including other productive activities).

From an econometric point of view, Becchetti et al. (2008) showed that any additional FT affiliation year raises per capita income from agriculture by an amount within the 600-1,200 THB range. The result remained significant after various robustness checks (propensity score matching, IV estimates with instruments satisfying exclusion restrictions, estimates on the treatment sample only). Unfortunately, here we cannot directly use this evidence on productivity gains of affiliated versus non-affiliated farmers since we do not have time series, but only evidence related to the year of the survey. However, this observed income effect is at the basis of our analysis, the purpose of which is to check whether the creation of higher economic value (reasonably assumed to work proportionally to affiliation years based on the above mentioned cross-sectional evidence) induces farmers to modify their schooling decisions. the probability of school attendance is positively correlated with birth order. From a descriptive point of view, fair-trade affiliation seems to matter for children of lower birth order. The probability of school attendance for the fourth child is 80 against 65 percent in affiliated and non-affiliated farmer households respectively. The same numbers are 63 and 31 percent when the sixth child is considered.

Figures 1a and 1b

The findings are similar when the probability of school attendance (irrespective of the age order) in smaller and larger families is considered. Such probability is roughly the same for affiliated and non-affiliated single-child families, while a gap progressively widens as the number of children grows and is largest (61 against 47 percent in families with six children).

Econometric findings

In the econometric analysis we check whether our descriptive findings on the effects of FT affiliation on child education are significant and robust when controlling for concurring determinants of child schooling.

Based on descriptive evidence showing that FT affiliation makes a difference when households have more children, and on the wider evidence on the quality of education/quantity of children trade-off reported in the introduction, we use the number of FT affiliation years for families with more than two children as a measure of the affiliation effect.

The following selected specification is estimated with a panel probit estimate with random effects and Maldonado and Gonzalez-Vega, 2008).

We estimate the model in the overall sample (Table 1, column 1) and in the subsample which includes only families with more than two children (Table 1, column 2). Although we attempt to simulate the counterfactual situation as rigorously as possible by enhancing similarities between the two groups, a typical criticism of results such as ours is that they are driven by heterogeneity between treatment and control sample producers. In order to reduce this problem further, we reestimate all our specifications in the subsample containing GreeNet-affiliated producers only (Table 1, columns 3 and 4).

The regression findings show that the significance of affiliation years for families with more than two children is confirmed as positive and significant in both the overall and the FT-affiliated only estimates, with or without restriction of the sample to families with more than two children. The marginal impact states that an additional affiliation year increases by 2.7 percent the probability of schooling in large families in the first specification (Table 1, column 1) and up to 7 percent in the estimate in large families of affiliated farmers only (Table 1, column 4). An important aspect of this finding is that it is not just the treatment per se, but also the graduation of the treatment (exposure to affiliation), which has significant effects on our dependent variable.

Among other variables, to be noted is that the pre-affiliation trend (TrendfutureFT) is positive but of far lower magnitude than the FT effect in three out of four estimates (the null of equality of coefficients is rejected in all estimates). This is an important indirect check of the validity of the assumption of homogeneity between the treatment and control samples. In the presence of selection bias and ex ante superior skills of the affiliated producers, we should observe a continuity between pre-affiliation and post-affiliation trend effects on our performance variable (the probability of sending children to school). This is not the case; indeed, the significant difference in coefficients indicates a break, and not a continuity, around the year of entry into FT.

Consider as well that we also control for years of job experience in agriculture, of adoption of organic farming and affiliation to the cooperative (which includes the period before the cooperative enters fair trade).

Among other regressors, note that the negative shock variable is negative and significant when the sample is not restricted to large families, which supports the hypothesis that memorable events generating negative shocks on income matter. The significance and sign of the other regressors are consistent with the usual findings in the literature: children's age is negative and significant; females are relatively less likely than males to go to school; parent education is positive and parent age negative; the number of children in the family has a negative and significant effect.

Note also that years of organic adoption and of cooperative affiliation are positive and weakly significant in the overall sample estimates. The estimates just discussed suffer from potential endogeneity problems. Consider that each producer, by choosing to enter, remain with, or exit from the cooperative at a given moment in time implicitly took three important choices: affiliation to the GreeNet cooperative, adoption of organic farming, and participation in fair trade. 20 We therefore have three potential selection biases in affiliation, because unobservable factors related to the producer's innate ability and activism could affect both one of the three choices and the inherited pre-schooling talents of the children.

Moreover, we also have the traditional endogeneity problem related to the quantity/quality trade-off described in the schooling literature. Note that, in principle, we are interested only in the differential effect generated by affiliation on quality for a given level of quantity. Hence, if we assume that the two endogeneity problems are independent from each other, we can focus on the first one (selection bias). Since this assumption may be restrictive, however, we adopt a set of strategies with which to deal with both biases at a time.

More specifically, we devise the following three checks. First, we estimate the model in the treatment group only (Table 1, columns 2 and 4). 21 Second, we look at discontinuity between pre and post affiliation trends of affiliated farmers in child schooling (see the effect of this variable in Table 1). Third, as described in this section, we devise a way to tackle the endogeneity problem by using three stage least squares to estimate a system into which we introduce three selection equations for each of the three choices (organic adoption, fair trade, and cooperative affiliation), together with the performance equation in which an household schooling index (HSI) is the dependent variable and the regressors are those in (1).

The HSI variable is a time-varying household schooling index for each producer built on retrospective data and represented by the number of children attending school over the total number of children in the schooling age cohort in a given year. More formally, the household schooling index (HSI) ratio is given by the following expression: where the HSI jt index is the number of children of the j-th producer in a chosen school age cohort,e.g. age range between 6(5) 22 and 18(17), if we are interested in elementary, middle and high school, and between 13 and 18 if we are only interested in high school, etc. -who actually went to school in a given year t (TOTSCH ijt ), divided by the number of children of the j-th producer who are in the related school age cohort in the same period (TOTPOT ijt ). 23 In other words, the HSI jt index is a ratio of effective to potential household schooling. Using this indicator we estimate with 3 stage least squares (3SLS) a system (whose specification is described in Table 2) comprising three selection equations for cooperative and FT affiliation and organic choice.

Results from the 3SLS approach confirms the significance of FT affiliation years (Table 2 and Tables A2-A5 in the Appendix available online). Other effects in the first equation confirm the importance of negative shocks and are in the expected direction. An interesting finding from the selection equations is that negative and positive shocks on income are positively correlated with the three endogenous choices. This is as expected since cooperative affiliation, FT affiliation and organic adoption (required by the FT importers) reduce farmers' risk. 24 Moreover, schooling years positively and significantly affected all the three choices.

Further interpretation of our findings based on qualitative data

In order to provide a richer interpretation of our results we add to quantitative results information from a small qualitative survey used for a group interview with project coordinators and local government members. Here below is the synthesis of their answers which provides us additional insights to interpret our findings. Families who do not send their children to primary/elementary school (now 6 years) are rare, it is also rather rare not to continue until the 3rd year of high school (about age 15-16). At this point leaving school is much more common and would appear to relate to identified factors among which the number of total children. The costs of sending children to senior high school are significantly higher than junior high school or elementary school. As well, youth of 15/ 16 can work legally and find employment in many areas. Then, again, when looking at post-secondary school and university, costs increase significantly, and work opportunities are even greater for those 18 and older. Other factors leading to school abandonment might be earlier marriage and pregnancy, becoming a monk (although the temples often are alternative points of education) and attending military service (for men 18 years or older).

Social norms and schooling habits

Parent attitudes

The interview confirmed that parents care a lot about children higher education reinforcing our assumption that the benevolent household model is a better framework than the bargaining model to explain household schooling choices.

The group agreed that Most Thais are very generous to their children and willing to invest a lot in them. For many farmers, farming is considered a low status job and they would like their children to do better, being teachers, doctors, engineers, etc, almost all of which require 12 yrs or more of education. Hence, it is normal to take loans to finance child education and it is sometimes necessary to sell land to send one's child to university. Of course there are budget limits, so for private schools and university both public and private, this may pose a challenge to the family to support these costs for their children. In larger families, it is not uncommon to invest more in a child with high potential. Birth order does not necessarily matter in this respect. There are cases where elder children leave school early to help earning money and taking care of house and other Then a second or third child who is a good student can be supported. One of the incentive for child education is the expectation and tradition that a successful child will give money back to his or her parents and support his or her siblings particularly in such a case.

Difference of FT

Our additional qualitative information from project managers shows that farmer groups have a consciousness about expenses and how to reduce them and also have a fair and dependable market.

Hence more farmers in these groups are likely to reduce their expenses as well as at least make some earnings from their agricultural activities, which is not at all guaranteed for normal conventional production. Many farmers may invest a lot in inputs and labor and then get low prices when they sell or have poor production, both of which can lead to losses and increased debt. With organic and self-sufficiency methods, there is less cash investment for production and more product (food and other) that are consumed and used by the family. This puts such farmers at less risk, combined with the FT price floor characteristic which reduces price volatility.

Conclusions

Poverty can be usefully conceived as a set of exclusions (from credit, product markets, insurance and education) which prevent individuals from fully exploiting their talents, limiting their productive contribution to society. In this paper we have shown how exclusions can interact with each other to generate virtuous or vicious circles. More specifically, by performing an impact study on the effects of affiliation to fair trade for a cooperative of organic farmers, we have documented that the improved access to foreign markets ensuing from the creation of an ad hoc trade channel (together with a package of initiatives promoting the inclusion of affiliated farmers) has positive and significant effects on access to education of the children when producers have large families. Our findings document that years of FT affiliation significantly ease the well-known quantity/quality trade-off which implies the lower probability of school enrolment for children in larger families. From a methodological point of view, we have obtained these results by developing a retrospective panel data approach based on memorable events and controlling for selection bias and endogeneity with various techniques (analysis of preformation trends, restriction of the estimate to the treatment sample only, adoption of 3SLS estimates). We are aware that there is no perfect solution to the endogeneity problems in our as in many other studies, but we tried to do our best to solve them given the possibilities provided by our data.

Our findings are consistent with FT criteria and the prediction of the luxury axiom. A plausible interpretation of them consistent with FT criteria and characteristics is that FT affiliation increases producers' revenues by easing access to foreign markets and financing for technical innovation. This enables producer families to rise above income subsistence levels and induces them to send more children to school when families are large.

Notes

1. Both the price mark-up and cooperative premium are monetary transfers from the importers. The first goes to producers, and the second to the producers' organization for investment in collective goods.

2. This last point is important because income volatility forces households to adopt risk-coping strategies which may require the interruption of a child's education in the presence of negative shocks [START_REF] Maldonado | Impact of Microfinance on Schooling: Evidence from Poor Rural Households in Bolivia[END_REF][START_REF] Kanbur | The Evolution of Thinking about Poverty[END_REF][START_REF] Kanbur | The Evolution of Thinking about Poverty[END_REF].

3. Empirical papers using approaches akin to ours look at income changes generated by commodity price changes (Edmonds and Pavcnik 2005;Kruger, 2007) or shocks on production [START_REF] Beegle | Child Labour and Agricultural Shocks[END_REF].

4. We nevertheless consider that our focus on FT affiliation and child schooling is appropriate to the study of child well-being. Whilst schooling obviously gives children opportunities to increase their future well-being, it is not so evident that child labour (especially if domestic) has a clear-cut negative effect on it, given the non-negative association found with children's health (O'Donnell et 5. For a more general survey on related issues in the child labour literature see, among others, [START_REF] Deb | Determinants of Child Labor and School Attendance: The Role of Household Unobservables[END_REF] and [START_REF] Bhalotra | Child Farm Labor: The Wealth Paradox[END_REF].

6. It is clear that here the quality/quantity trade-off is considered in its simplest form, that is, the tradeoff between the number of children and the probability that they are enrolled at school. 7. See the IFAT (International Fair Trade Association) charter (EFTA, 2009).

8. For a theoretical evaluation of the effects of FT from the perspective of trade theories see [START_REF] Maseland | How Fair is Fair Trade?[END_REF] and Reinsteing and Song (2008). Other relevant papers dealing with various aspects of the impact of FT are those by [START_REF] Leclair | Fighting the tide: Alternative trade organizations in the era of global free trade[END_REF], [START_REF] Moore | The Fair Trade Movement: parameters, issues and future research[END_REF], [START_REF] Hayes | Strategic management implication of the ethical consumer[END_REF] and [START_REF] Redfern | Creating market opportunities for small enterprises: experiences of the fair trade movement[END_REF]. 9. Nestlè introduced a fair trade product in its product range in October 2005; Co-op UK has launched its own fair-trade product line, while Starbucks has rapidly become the main buyer of FT coffee in recent years. The partial (or planned) adoption of FT practices has been made by Tesco and Sainsbury, and it has been announced by Mars. For a discussion on competition between fair-tradededicated retailers and supermarkets see also [START_REF] Kohler | The Economics of Fair Trade: For Whose Benefit? An Investigation into the Limits of Fair Trade as a Development Tool and the Risk of Clean-Washing[END_REF] 10. At present, the FT impact study literature mainly consists of some well-structured case studies [START_REF] Bacon | Confronting the Coffee Crisis: Can Fair Trade, Organic, and Specialty CoffeesReduce Small-Scale Farmer Vulnerability in Northern Nicaragua?[END_REF][START_REF] Pariente | The impact of fair trade on a coffee cooperative in Costa Rica. A producers behaviour approach[END_REF][START_REF] Castro | Impact assessment of Oxfam's fair trade activities. The case of Productores de miel Flor de Campanilla[END_REF][START_REF] Nelson | Social Impact of Ethical and Conventional Cocoa Trading on Forest-Dependent People in Ecuador[END_REF][START_REF] Ronchi | Fairtrade" and Market Failures in Agricultural Commodity Markets[END_REF] and a few econometric impact analyses. For a survey of these and other impact analyses on FT see [START_REF] Ruben | The impact of fair trade[END_REF]. with child labour at article 32. There is no explicit prohibition of child labour in this article, and no direct encouragement of child schooling. Note, however, that article 32 of the UN convention on child labour stipulates that "it must not interfere with child's education". Finally, the requirement of transparency on child labour from organizations working with fair trade is another point which suggests that child labour is not banned.

12. See, among others, [START_REF] Ranjan | Credit Constraints and the Phenomenon of Child Labor[END_REF] and [START_REF] Cigno | Child Labor Handbook[END_REF].

13. Besides attention to the sample design, we addressed selection bias by: i) comparing preformation and postformation trends, ii) estimating our model on the restricted sample of affiliated producers only; iii) running a 3-stage least squares with selection equations for the three choices (see sections 6 and 7).

14. The questionnaire is omitted for reasons of space and available upon request.

15.

Other examples of the use of retrospective data are provided by i) [START_REF] Garces | Longer-Term Effects of Head Start[END_REF], who use PSID data, with the addition of retrospective questions on early childhood education, in order to assess the impact of a public preschool program for disadvantaged children; ii) [START_REF] Smith | The Impact of Childhood Health on Adult Labor Market Outcomes[END_REF], who examines impacts of childhood health on socioeconomic status outcomes observed during adulthood relying on retrospective self-evaluations of the general state of one's health and iii) [START_REF] Ilahi | The implications of child labor for adult wages, income and poverty: Retrospective evidence from Brazil[END_REF] who, using unique retrospective data from Brazil, explore the relationship between child labor and future adult earnings and poverty status.

16. Surveyed events with a positive impact on respondents' income are: i) increase in the paddy rice market price, ii) a positive shock on production, iii) monetary or non-monetary gifts from farmers' sons and daughters, v) positive wage shocks in the second activity, vi) lottery wins and vii) granting of awards. Surveyed events with a negative impact on respondents' income are: i) disease, ii) car accidents, iii) fire, iv) car breaking, v) increase in the input market price, vi) the death of animals used as capital investment (such as water buffalos), vii) slow development of the soil. 17. As expected, the probability of leaving school grows with child age. Hence, the older the child, the more likely it becomes that she/he will show an education gap. This result has been confirmed by Maldonado (2005) and Islam and Choe ( 2009), who find that children of primary-school age have a higher enrolment rate than their older siblings, the latter being more likely to drop out from school and go to work. For further comments on these findings see section 8.

18. We expected girls' education to be less valued than that of boys. In this regard, [START_REF] Edmonds | Child labor[END_REF] shows with data from UNICEF's Multiple Indicator Cluster Surveys that there is a sizeable increase in participation rates in market and domestic work for males at age 12, while girls experience discrete jumps at age 8, 10, and 12. The increase at age 8 for girls appears to be most marked in domestic work, whereas most of the increase at age 10 and 12 for girls is in market work.

19. In order to check whether our findings are robust to the introduction of time invariant family traits we re-estimate our two specifications in the treatment sample only with logit fixed effects The fixedeffect estimate findings confirm the significance of FT affiliation years and the fact that their impact is significantly stronger than that of pre-affiliation years. Results are available on request.

20. Even if affiliation to fair trade is chosen by the GreeNet cooperative in 2002 and therefore automatically by all members at that date, late entries and stay decisions are also implicitly individual member's choices with regard to FT.

21. Consider that, for a spurious result between affiliation years and child education driven by heterogeneity between young and old affiliated, we should have that old affiliated are more likely to send their children to school. We controlled for this, however, and found that the problem did not apply because there was no significant difference between the preformation trends of young and old affiliated.

22. Entry age is generally 5 or 6 based on the respondent's declaration.

23. The total number of children for each farmer (n j ) is indexed to account for heterogeneity in household size.

24. See in particular the FT price floor insurance mechanism mentioned in section 2 which can be verified by inspecting the dynamics of FT coffee and cocoa prices between 1980 and 2000. The contribution of our methodology to FT with a logical framework analysis

If we look at the intermediate, medium and long term goals of FT with the logical framework approach we find that the first output (fifth row in the Table ) coming from application of FT criteria (long run relationship of FT importers with FT producers, fair trade price and cooperative premium, prefinancing opportunities for affiliated farmers, technical assistance and export services) is assumed to lead to intermediate results such as positive effects in terms of net sales, product and market channel diversification, child schooling, productivity and local wellbeing indicators (fourth row). Such intermediate results are assumed to be crucial to achieve medium term outcomes (capacity building, increased export capacity, increased bargaining power along the value chain, reduced vulnerability) (third row) which ultimately allow to achieve long term goals (create opportunities for producers who have been economically disadvantaged or marginalized by the conventional trading system and increase their wellbeing and quality of life) (second row).

Our impact study based on retrospective data mainly concerns columns 2 and 3 of the logical framework (objectively verifiable indicators and means of verification) as it aims to implement the capacity of verifying assumptions on the links between different rows in order to evaluate the effectiveness in terms of achievements of the FT approach. Standard monitoring from FT importers and collection of qualitative and descriptive evidence on the field fails to verify rigorously assumptions on the links between application of FT criteria intermediate results and medium and long term goals. In order to do so what is needed is the identification of treatment and control groups, and an econometric analysis of the effects of FT affiliation on measurable indicators which verifies whether effects are robust to controls and, possibly, potential biases.

F

  them. The third section provides a short history of the cooperative investigated.

  financial schemes, a countercyclical mark-up on producers' prices which incorporates an insurance mechanism, and product risk diversification which lowers the producers' vulnerability to shocks. As we synthetically document in the introduction the theoretical and empirical child-labour literature emphasises the importance of access to credit markets and the containment of shocks in determining household decisions on children's time allocation. The imperfections of both formal and informal credit and insurance markets represent, particularly in developing countries, a major cause of the suboptimal allocation of household resources to human capital investment.

  -level organizations in Yasothorn province: the Bak Reua Farmer Organization (BRFO) and the Nature Care Society (NCS).

  of GreenNet Cooperative, NCS has received the FLO's certification since 2002.

  of non-GreeNet affiliated members belonged to cooperatives as well.

  Figures 1a and 1b document the relationship between the likelihood of school enrolment and

  ) and the non-significant effect on education (see Edmonds 2005's comments on UNICEF's Multiple Indicator Cluster Surveys data).

  11. The FT approach to child labour is clearly non-prohibitionist. This is shown by a second point on the issue Working conditions in the 10 IFAT criteria, which states that "Fair Trade means a safe and healthy working environment for producers. The participation of children (if any) does not adversely affect their well-being, security, educational requirements and need for play and conforms to the UN Convention on the Rights of the Child as well as the law and norms in the local context. The IFAT Charter makes reference to the UN Convention on the Rights of the Child, which explicitly deals

  Figure 1aSchooling probability and birth order.

Figure

  Figure 1bSchooling probability and number of children in the family.

F

  NChild jt is the number of children in the family j at time t, Area is a dummy taking the value of one if the producer is located in Kud Chun, and zero otherwise, ParentEducation is father's schooling years, Controlcoop is a dummy for control producers which takes value one if they belong to a cooperative different from GreeNet, AgricYears is the respondent's job seniority (number of years worked in agriculture), ParentBirthYear is the producer's year of birth, TrendfutureFT is a (pre-affiliation) trend variable measuring the number of years in the sample of the child's family before entry into FT, FTyearbigfam, is the number of FT affiliation years for families with more than two children, PosShock (NegShock) is a dummy taking value one in the year in which the respondent declares to have experienced a memorable event with

	otherwise. Among regressors,
								F o r
														P
																		e
																						e r
																								R
																								e
																								v i e
																								w
																								O n l
	School	ijt	=	0 α α +	1	NChild	jt	+	α	2	Area	j	+	α	3	ParentEducation	j	+	α	4	Controlcoop y	j	+
			+	α	5	AgricYears	jt	+	α	6	ParentBirthYear	j	+	α	7	TrendfutureFT	jt	+
			+	α	8	FTyearbigfam	jt	+	α	9	PosShock	jt	+	α	10	NegShock	jt	+	α	11	ijt Childage	+	(1)
			+	α	12	Male	j	+	α	13	CoopYear	s	jt	+	α	14	OrganicYears	jt	+	∑	β	l	l DYear	j υ ε + +	ijt
																								l
																								14

with υ j being a normally distributed random family effect. The dependent variable (School ijt ) is a dummy taking the value of one if the i-th child of the j-th family went to school in year t and zero positive (negative) effects 16 on his income, Childage is child's age,

17

Male is a dummy for male children,

18

CoopYears, and OrganicYears are respectively years of affiliation in the cooperative and of adoption of organic farming techniques, DYear are time dummies (1989 is the omitted benchmark). Among socio-demographic variables we introduced those for which the theoretical and empirical literature on child schooling has extensively demonstrated their relevance and significance for child schooling decisions (see among others,

Edmonds, 2007, Islam and Choe 2009

Table 1 . The effect of fair-trade affiliation on schooling decisions: random effect probit regression

 1 Null hypothesis: equality of coefficients of FTyearsbigfam and TrendfutureFT. Variable legend: The dependent variable (School ijt ) is a dummy taking value one if the i-th children of the j-th family went to school in year t and zero otherwise. Among regressors, NChild jt is the number of children in the family j at time t, Area is a dummy taking value one if the producer is located in Kud Chun and zero otherwise, ParentEducation is father's schooling years, Controlcoop is a dummy for control producers which takes value one if they belong to a cooperative different from GreeNet, AgricYears is the respondent's job seniority (number of years worked in agriculture), ParentBirthYear is the producer's year of birth, TrendfutureFT is a (pre-affiliation) trend variable measuring the number of years in the sample of the child's family before entry into FT, FTyearbigfam, is the number of FT affiliation years for families with more than two children, PosShock (NegShock) is a dummy taking value one in the year in which the respondent declares having experienced a memorable event with positive (negative) effects on his income (see footnote 16 for the event list), Childage is child's age, Male is a dummy for male children, CoopYears, and OrganicYears are respectively years of affiliation with the cooperative and of adoption of organic farming techniques, DYear are time dummies (1989 is the omitted benchmark).

		All sample	GreeNet producers only
	(1)		(2)	(3)	(4) ¥
			¥
	NChild	-1.15***	-2.39***	-1.03***	-1.47***
	(0.23) [-0.019]	(0.25) [-0.060]	(0.32) [-0.018]	(0.33) [0.028]
	Area	-4.87***	-0.19	-5.13***	-2.50***
	(0.44)[-0.144]	(0.55)	(0.73) [-0.164]	(0.91) [-0.070]
	ParentEducation	0.32***	0.61***	0.30***	0.46*
	(0.07)[0.005]	(0.17) [0.012]	(0.09) [0.005]	(0.24)
	Controlcoop	1.73***	0.89
	(0.47)[0.029]	(1.02)
	AgricYears	0.001	0.01	-0.39	0.05
		(0.02)	(0.03)	(0.02)	(0.05)
	ParentBirthYear	0.12***	0.05	0.08*	0.16**
	(0.03)[0.003]	(0.05)	(0.05)	(0.08) [0.005]
	TrendfutureFT	0.21***	0.29***	0.14**	0.30
	(0.03)[0.005]	(0.10) [0.005]	(0.05)	(0.24)
	FTyearbigfam	1.38***	1.73***	1.16***	2.57**
	(0.18)[0.027]	(0.50) [0.032]	(0.28) [0.024]	(1.10) [-0.070]
	PosShock	-0.14	-3.04	-0.71	-2.18
		(0.92)	(2.29)	(1.08)	(1.78)
	NegShock	-2.70***	-1.96	-2.54***	-2.22
	(0.53)[-0.070]	(1.32) [-0.039]	(0.68) [-0.070]	(1.90)
	Childage	-1.55***	-1.81***	-1.14***	-1.73***
	(0.1)[-0.024]	(0.12) [0.033]	(0.13)	(0.17) [0.032]
	Male	-1.44***	-0.98*	-1.54***	-1.51*
	(0.35) [0.027]	(0.54)	(0.50) [0.029]	(0.87)
	CoopYears	0.25**	0.12	0.13	0.24
	(0.13)[0.006]	(0.22)	(0.16)	(0.39)
	OrganicYears	0.41**	-0.52	0.40*	-0.64
	(0.16) [-0.010]	(0.39)	(0.21)	(0.51)
	Constant	-206.4***	-70.50	-143.6	-291.7*
		(69.91)	(100.73)	(97.08)	(152.6)
	Dummy year	Yes	Yes	Yes	Yes
	Observations	5652	2820	3870	1798
	Number of id	717	385	489	244
	Wald χ 2 (22)	425.65	430.35	231.3	244.12
	Log Likelihood	-875.24	-641.68	-562.14	-242.23
	Prob > χ 2	(0.00)	(0.00)	(0.00)	(0.00)
	Wald χ 2 (1)(p-	(0.00)	(0.00)	(0.00)	(0.00)
	value) ±
	Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Marginal effect of the regressor on the probability of child schooling
	in square brackets.
	¥ Only families with more than two children.The estimated specification is equation (1) at page 14.

±

Table 2 . 3-stage least squares regression

 2 Null hypothesis: equality of coefficients of FTyearsbigfam and TrendfutureFT.

	The system estimated is
	HSI	jt	=	1 α	NChild	jt	+	α	2	AgricYears	jt	+	α	3	ParentBirthYear	j	+	α	4	TrendfutureFT	jt	+
			α +	5	FTyearsbigfam	jt	+	α	6	PosShock	jt	+	α	7	NegShock	jt	+	α	8	Male	j	+	α	9	Childage	jt	+
			10 α +	ParentEducation	j	+	11 α	FTyears	jt	+	12 α	OrganicYears	jt	+	ε	jt
	FTyears	jt	= 1 β	NegShock	jt	+		β	2	PosShock	jt	+	β	3	DistFromCoop	j	+
			+	β	4	LandSize	j		+	β	5	Area	j	+	β	6	ParentEducation	j	+	η	jt
	CoopYears	jt	=	γ	1	DistFromCoop	j	+	γ	2	Area	j	+	γ	3	LandSize	j	+	γ	4	ParentEducation	j	+
			+	γ			5	PosShock	jt	+	γ		6	NegShock	jt	+	ν	jt
	OrganicYears	jt	=	δ	1	P osShock	jt	+	δ	2	NegShock	jt	+	δ	3	DistFromCoop	j	+	δ	4	Area	j	+
			δ +	5	Landsize	j		+	δ	6		ParentEducation	j	+	υ	jt
	For variable legend see section 6 and Table 1 legend.

±

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