Antonio Lemus
email: alemus@cmfchile.cl

Cristian Rojas
email: carojas@cmfchile.cl

Credit Unions in Chile: What is their Role? 

Keywords: credit unions, financial inclusion, credit JEL Classification: G21, G28, P13

This paper questions the role that credit unions play in the Chilean financial system, particularly if they allow financial inclusion. For this purpose, using unique statistical information existing at the CMF, the credit and savings behavior of credit unions' members is studied, with granularity at the individual level. The results indicate that credit unions effectively contribute to financial inclusion, providing financial services mostly to people with low incomes, elderly, women, and inhabitants of small communities, far from the large urban centers.

Introduction

Credit unions ("Cooperativas de Ahorro y Crédito" in Spanish) have small participation in the Chilean financial system, in terms of assets, liabilities, and equity (around 2%). Then, it is valid to ask why credit unions exist and what function they accomplish that do not carry out other financial institutions, banks in particular.

When discussing the role that credit unions have in the Chilean financial system, often is argued that they fulfill a "social role," understood as financial inclusion, assigning credit to individuals excluded by banks and other financial institutions. Such a hypothesis seems to lack an answer in Chile's economic literature. Then, this document goal is to contribute to responding, based on statistically robust economic evidence, to this question.

The economic literature that has studied the credit unions in Chile has mostly focused on providing historical background, describing their market, discussing the regulation, taken as given the "social role." Regarding this last point, the related literature (Téllez, 2007; Hernández, 2008; Matus et al., 2010; Potin, 2012; Minecon, 2014; BCCh, 2017) argues that credit unions give access to financial services to individuals who would be excluded otherwise, though not providing robust evidence. [START_REF] Armendáriz De Aghion | The Economics of Microfinance[END_REF] Credit unions are a specific type of cooperative that according to the General Law of Cooperatives of 2004 (LGC, 2004) are defined as: "cooperatives whose unique and exclusive purpose is to provide financial intermediation services for the benefit of their partners." [START_REF]Posibilidades de perfeccionamiento del marco regulatorio y supervisor de las cooperativas de ahorro y crédito en Chile[END_REF] In Chile, credit unions are governed by the Decree N ° 5 ("Decreto con Fuerza de Ley" in Spanish) of 2003, the articles 86 to 90 of the LGC (2004), the Chapter III.C.2 of the Central Bank of Chile (BCCh), and the Guideline 108 of the Financial Market Commission (CMF). Specifically, the Article 87 of the LGC (2004) establishes that meanwhile, all those credit unions whose equity exceed 400,000 UF ("Unidad de Fomento" in Spanish) are under the regulation of the CMF, those not complying with this criterion are responsibility of the Social Economy and Associativity Division ("División de Economía Social y Asociatividad" in Spanish, and "DAES" hereafter) of the Ministry of Economy. [START_REF] Barajas | Macroeconomic Fluctuations and Bank Behavior in Chile[END_REF] This paper focuses on the credit unions regulated by the CMF as they represent more than 90% of the total assets, liabilities, and equity of the sector and the data available in the CMF allow us to have information with a granularity at the credit unions' member, not previously used, allowing us to respond to the hypotheses motivating this paper. [START_REF] Armendáriz De Aghion | The Economics of Microfinance[END_REF] Appendix A details the concepts in favor of credit unions' "social role" in the related literature. [START_REF]Posibilidades de perfeccionamiento del marco regulatorio y supervisor de las cooperativas de ahorro y crédito en Chile[END_REF] The LGC of 2004 defines cooperatives as "associations that, in accordance with the principle of mutual aid, aim to improve the living conditions of their members, having fundamental characteristics as: (i) their members have equal rights and obligations; (ii) membership and retirement of members is voluntary; (iii) they must distribute the surplus corresponding to their transactions with their members; (iv) they must observe political and religious neutrality, develop cooperative education activities and seek to establish among them federative and inter-cooperative relations". [START_REF] Barajas | Macroeconomic Fluctuations and Bank Behavior in Chile[END_REF] The UF is a unit of account indexed according to the inflation.

Our main results indicate that compared to banks, credit unions offer credit to low-income individuals, elderly, women, and residents of regions other than Santiago (Chile's capital), particularly to small communities far from the country's main urban areas. Also, there is a notable heterogeneity among customers, depending on whether they engage with banks, only with credit unions, or simultaneously with both types of institutions.

The rest of this document is structured as follows. Section 2 reviews the related literature. Section 3 presents the historical, market, and regulatory background. Section 4 characterizes the credit unions under CMF supervision. Section 5 responds to the hypothesis motivating this paper, i.e., credit unions financial inclusion role. Finally, Section 6 concludes.

Related literature

Credit unions are not homogenous institutions, having great diversity among them that depends on the cultural, historical, and economic context (Mckillop and Wilson, 2011). Nevertheless, credit unions also follow certain universal principles, and their study cannot be separated from the social, economic, and institutional environment in which they operate (Ojong, 2014).

Meanwhile, credit unions in developed countries include members from various and heterogeneous economic segments (offering a wide range of financial services and having assets for several billion dollars), those in developing countries mainly concentrate on people excluded from the financial system (offering essential savings and credit services).

Although credit unions emerged as a need for credit access of the unbanked, authors such as Jones (2008), argue that the cooperative model is changing, going from being focused on low-income sectors to a broad spectrum of customers, competing directly with other financial institutions such as banks and insurance companies.

Regarding the financial inclusion role of credit unions, for the Caribbean economies, Lemus and Wong (2012) argue that the credit unions play a decisive role in their financial system. As, by 2010, Caribbean credit unions had assets of about 13% of the regional GDP, a membership rate of 40%, and an essential financial intermediation role, reaching low and middle-income sectors with difficulties accessing credit. In the same vein, in Latin America, Barham et al. (1996) for Guatemala and Rojas et al. (2018) for Costa Rica, find that credit unions help to close the gaps in traditional banking, increasing the efficiency of local markets, and serving individuals rejected by banks.

Regarding the credit unions' resilience to economic downturns and the consequences in credit risk, Smith and Woodbury (2010) conclude that compared to other financial institutions, credit unions are more resilient to economic cycle contractions, exhibiting lower delinquency as well. Furthermore, Roelants et al. (2012) suggest that credit unions respond better to adverse economic cycles as their members are firmly committed with the administration and success of these institutions. In this line, Armendáriz de Aghion and Morduch (2004) point out that credit unions members' have lower delinquency and a more significant commitment to pay their loans as their resources are involved in the credit union performance, and diligent payments assuring future financing. Lastly, McKillop and Wilson (2011) suggest that belonging to a similar social group increases the knowledge of credit behavior, allowing better identification of who could default.

The Chilean literature that has studied the credit unions is relatively scarce, probably due to the general view that banks determine credit. Nevertheless, we can mention contributions by Téllez (2007), Hernández (2008), Larraín and Larraín (2010), Potín (2012), Minecon (2014), and BCCh (2017).

Minecon (2014) reviews all type of cooperatives in Chile, describing the cooperative sector broadly, examining its origin and evolution, and presenting their main characteristics (including credit unions) at the sector and regional level, and their economic importance.

Regarding the credit unions, Téllez (2007) provides an overview of those supervised by the CMF, in terms of their market, leading figures, and financial indicators. After, Hernández (2008) makes a brief historical contextualization, giving an overview of Chile's credit unions, the regulatory framework in which they operate, and their impact on the society. Larraín and Larraín (2010) and BCCh (2017) focus on the regulatory issue. Meanwhile the first analyzes the regulatory differences between the credit unions and banks, finding legal differences diffuse, the second suggests that credit unions participation in the financial system can imply economic and social benefits if prudential regulation guides their development.

Closer to this paper, questioning the credit unions role on financial inclusion, Potin (2012) seeks to understand the role of credit unions' consumer loans, in particular, the specificity of the credit granted by credit unions and how it differs from the issued by other financial institutions.

Credit unions' background

Historical perspective

Since its inception, Chilean credit unions have experienced periods of marked boom and bust. The first credit unions in Chile have their origins in 1947 consequence of the active role of the Catholic Church and the initiative of small groups of individuals with common interests such as workers, merchants and public servants (Hernández, 2008).

While in the sixties credit unions experienced strong support by the State, in the seventies and eighties, they suffered a strong decapitalization as a result of successive and deep economic crises, and high unemployment that they affected their members (Potin, 2012).

During the nineties and the present century, credit unions once again have seen a boom, achieving business consolidation, covering large geographical areas, providing financial services to social sectors that private banks typically do not serve (Hernández, 2008).

Credit unions in the World and in Chile

Credit unions are financial institutions existent in almost every country in the world. According to data from the World Council of Credit Unions (WOCCU) in 2017, there were more than 89 thousand credit unions in the world, with 260 million members, representing 9.1% of the global workforce. In terms of assets and liabilities, credit unions total deposits and capital for US$ 1.7 trillion and total loans for US$ 1.5 trillion.

Credit unions level of development and market penetration (number of members over the total active population) varies in geographic terms, with the presence of credit unions (penetration) led by North America with 52%, the Caribbean with 30%, Latin America with 11%, and Africa with 9%. It is worth to note the case of North America, which includes the United States and Canada two developed and high-income countries according to the World Bank, indicates that the credit unions' phenomenon could not be specific of low and medium-income countries (where financial markets are less developed). The credit unions' phenomenon relates to the historical and idiosyncratic background of each country.

In Chile, and emerging and high-income country according to the IMF and the World Bank, the participation of credit unions reaches 10.9% of the total active population (Figure 1), similar participation to the average Latin America country.

Figure 1. Credit unions participation and income level (2017)

Source: Authors calculations based on World Bank and WOCCU. Note: Each point represents a country. CHL: Chile.

Who regulates the credit unions in Chile?

By 2018, 45 credit unions were registered in Chile. Credit unions supervision by the DAES or the CMF depends on a threshold amount (400,000 UF) of equity established by the LGC of 2004. The number of credit unions not reaching the 400,000 UF limit of equity are 38 and supervised by the DAES. Meanwhile, the seven remaining with equity over 400,000 UF and regulated by the CMF. Currently, the credit unions regulated by the CMF are Coocretal, Coopeuch, Oriencoop, Capual, Detacoop, Ahorrocoop and Lautaro Rosas. Coopeuch is the leading credit union of the system, with assets, liabilities, and equity, equivalent to 82.4%, 82.0%, and 83.3%, respectively, by December 2017. It means that the "biggest" credit unions of the system are under the CMF supervision. Figure 2 shows the credit unions supervised by the CMF (dots in red) and those supervised by the DAES (dots in blue).

Requirements to credit unions and their financial services

Among the legal requirements that all Chilean cooperatives must meet are: having the facilities, human resources, technology, procedures, and controls necessary to adequately develop their functions and operations and have equity not less than 10% of their weighted risk assets. Additionally, credit unions must have a ratio of equity over total assets of at least 5% and are subject to the General Banks Act ("Ley General de Bancos" in Spanish, hereafter "LGB").

The financial services that credit unions provide are: (i)

Characterization of the credit unions supervised by the CMF

As already mentioned, this paper focuses on the credit unions under the supervision of the CMF, for two reasons. First, these credit unions concentrate about 90% of the loans, assets, debt, and equity of the sector, therefore being highly representative. Second, the CMF has unique data, not previously used in the literature, with a level of granularity at the member/individual, allowing the study of credit and savings with a socio-demographic view. Hereafter, when we refer to credit unions in Chile, we are talking about the seven credit unions supervised by the CMF.

The data we use in this paper is administrative, including detail at the financial institution (banks and credit unions) and individual level (bank customer and credit union member).

The administrative nature of the data we use makes it possible to have a detailed characterization of credit unions and their members. Meanwhile, at the financial institution level, the data corresponds to the yearly credit unions balance sheets for the period 2007-2018, the data at the member level corresponds to 2017. Using time-series at the credit union level allow us controlling by idiosyncratic characteristics affecting a specific credit union.

Table 1 presents preliminary data of credit unions members and banks customers. By 2018 credit unions members reached more than one million two hundred thousand people, meaning that every four banks customers (about five million) there was one credit union member. Credit unions in the financial sector are small both in terms of assets and liabilities, representing in each case less than 1% of the total banking system (Table 2). However, in terms of equity, credit unions have about 3% of the total banking sector. In pesos, in 2018, the credit unions totaled assets for $ 1,959 billion, liabilities for $ 1,423 billion, and equity for $ 535 billion. Table 3 shows the main financial products that credit unions' members use. On the assets side, savings and deposits are the preferred instruments by credit unions members. Indeed one out of four credit unions members has a savings account. On the liabilities side, consumer loans discounted in the payroll are the preferred type of financial service by credit unions members (about one out of five members has this type of financial instrument).5

Do credit unions play a role in financial inclusion?

Considering the limited participation that credit unions have in the total assets, liabilities, and equity of the Chilean financial system (Table 2), it is valid to ask what role credit unions fulfill that other financial institutions do not comply with, mainly banks. To answer this question, subsection 5.1 lists and highlights the main differences, between credit unions and banks, both from a legal and a market perspective. [START_REF] Cameron | Microeconometrics: methods and applications[END_REF] Then, building on the information available at the CMF, an analysis of distribution and density functions is carried out (subsection 5.2). Next, subsection 5.3 estimates models for discrete dependent variables (Logit/Probit type). These results allow answering if credit unions play a role in financial inclusion.

Ex-ante, if credit unions play a role in financial inclusion, we should find that they provide financial services to a social and economic population in disadvantage, as individuals with low income and living far from the main urban areas. Thus, we first compare the income distributions of credit unions members and banks customers, to then characterize them according to their age, gender, and region where they live. Finally, we use Logit and Probit models to answer which variables determine the probability that an individual has a debt with a credit union. Hereafter we understand "debt" as any credit supplied by either credit unions or banks, meaning consumer and commercial credits, and mortgages. In terms of products, they correspond to credit cards, consumer loans, credit account credit lines, mortgages in letters of credit, and mutual endorsable and non-endorsable mortgages.

Comparing credit unions and banks

This subsection highlights the differences between credit unions and banks, from a legal and a market perspective, characterizing the debt profile of credit unions members and banks customers. Consequently, Table 4 points out that meanwhile, credit unions are notfor-profit banks do, that the minimum capital required for both types of financial institutions differs significantly, and that credit unions and banks compete in the deposits and loans markets. Figure 3 presents the participation of the leading products used by credit unions and banks debtors. The most popular credit instrument among credit unions debtors is the "Consumer loans in installments with a payroll discount" (almost 67% of credit unions debtors use this instrument), followed by "Consumer loans in installments with direct payment" (31%). On the other hand, the most popular product among banks debtors is "credit cards" (almost 80% of the total bank debtors have debt in this instrument), followed by "Consumer loans in installments with direct payment" (41%), and "Current account credit lines" (30%). In Figure 4 we characterize credit unions and banks debtors according to their amount of debt as only banks' customers (category "only bank"), both credit unions members and banks customers (category "both"), and credit unions members (category "only credit union"), showing their percentiles distribution. 7 8 Figure 4 shows that the type of debtor who reaches a higher level of debt is who has debt at the same time with credit unions and banks. Also, we can infer that the debt by "only credit union" debtors is higher compared to those "only bank." The exception is those debtors concentrated on the 70th percentile or higher.

It seems that the lack of funds explains the difficulty of credit unions to provide a high amount of loans to multiple members, as credit unions loans are directly related to their deposits. In other words, the credit unions members' deposits directly relate to the loans these institutions provide.

Analysis by income, age, gender, and region

In this subsection, we compare credit unions members and banks customers, according to their income, age, gender, and region where they live. As in the previous subsection, beside the categories "only bank" and "only credit union," we include the third category composed by those individuals who simultaneously are credit unions members and banks customers.

If the credit unions have a role in financial inclusion, we expect to find that they have members with a lower income (for example young people unemployed or seniors retired), women (with a lower labor participation than men), and individuals living in isolated areas different to cities (having a greater difficulty accessing to financial services). 7 Appendix C provides the same information as Figure 4 in a table format. [START_REF] Hernández | La trayectoria de las cooperativas agropecuarias de ahorro y crédito en el MERCOSUR[END_REF] As of December 2017, total credit unions debtors reached 347 thousand people. Of these, 40% had debt only in credit unions, and the remaining 60% also had debt with a bank. Debtors only with banks reached 4.7 million people.

Only bank

Only credit union Both

Figure 5 characterizes "only bank", "both", and "only credit union" members/customers, according to their income, showing that for all percentiles, credit unions members observe lower income than the banks' customers and the individuals belonging to the category "both". [START_REF] Jones | From tackling poverty to achieving financial inclusion -The changing role of British credit unions in low income communities[END_REF] When comparing banks customers with individuals in the category "both" we find that while in the low-income percentiles, 60th percentile and lower, banks' customers observe lower-income, the opposite occurs for high-income, 60th percentile and above, with banks customers having a higher income. A plausible explanation is that high-income banks customers are more likely to be sophisticated financial clients, demanding diversified financial instruments, not provided by credit unions. 10 Additionally, when we observe the distribution of banks customers, credit unions members, and the individuals in both categories (Figure 5), we find that almost 50% of credit unions members have an income lower than the national median ($349 thousand), while for customers in the categories "both" and "only bank" this threshold is achieved at the percentile 20th and 30th, respectively. Furthermore, about 30% of credit unions members have a monthly income below the minimum wage ($276 thousand in 2017). This statistical evidence allows us to infer that: (i) credit unions provide financial services to individuals with lower income than banks customers (both categories, "only bank" and "both"); (ii) the percentage of only credit unions debtors' with an income lower than the national median is higher than for banks' customers (both categories, "only banks" and "both"); and (iii) about 30% of debtors having a debt only in credit unions have an income [START_REF] Jones | From tackling poverty to achieving financial inclusion -The changing role of British credit unions in low income communities[END_REF] Appendix D provides the same information as Figure 5 in a table format. 10 In fact, for example, credit unions do not provide current accounts.

Only bank

Only credit union Both level below the minimum wage. Overall, the level of income evidence establishes that credit unions focus more on low-income individuals, vis-à-vis banks, and consequently contributing to financial inclusion.

Regarding the age variable, Figure 6 shows that for all percentiles, credit unions members are older than banks customers. 11 In particular, Figure 6 shows that the debtors of credit unions, compared to the other two types of clients ("only bank" and "both"), show a high concentration among elderly individuals, in particular in the retired segment. Thus, credit unions would tend to provide financial services to retired and older people, not attractive as customers for banks, constituting a new argument in favor of the financial inclusion role that credit unions seem to fulfill.

Figure 6. Age percentiles (December 2017)

Source: Authors calculations based on CMF.

One of the objectives declared by credit unions is their intention to give access to people to financial products are not a business attractive for banks. In this sense, it is worth asking, what is the scope of credit unions and banks in Chile at the regional level? To do so, we consider the number of commercial offices (offices) as a proxy variable for the presence of credit unions and banks in each region of Chile. 12 Figure 7 presents the number of offices that credit unions, private banks, and the Chilean State bank, have in each region of Chile.

11 Appendix E provides the same information as Figure 6 in a table format. 12 Offices include: head offices, branches, auxiliary branches, and support offices. Our proxy variable "offices" notes that credit unions reach their most extensive presence in the Maule region with 22% (27 offices) of the total number of offices in the region (123 offices). It is followed by the regions of Aysén and O'Higgins with participations of 17% and 15%, respectively. In the Metropolitan Region (MR), the 38 credit unions' offices represent only 4% of the total (1,073 offices). Whereas private banks only have 49% of their offices outside the MR, the State bank has 69% and credit unions have 80%. These results indicate that credit unions have a significant presence in Chile's regions different from the MR.

In terms of gender, information from the CMF indicates that the percentage of women debtors in credit unions (59% women versus 41% men) exceeds the share they have in banks (49% of women versus 51% of men). In particular, in almost all the counties in Chile, the ratio of debt of women in credit unions to the total debt of women is higher than in the case of men (Figure 8). Particularly interesting could be this information to study the gender gap in the credit system, and how credit unions could help to reduce them. Next, considering the gender and regional dimension, we analyze the debt and savings at the regional/county level by gender that people have in credit unions. Figure 9 presents the results. Panel (a) defines the ratio between the debt in credit unions to total debt, and panel (b) expresses the ratio between the savings in credit unions to total savings. The points correspond to counties where there is a more significant impact of credit unions in terms of funding and savings. These outliers represent extreme cases of high debt/savings ratio. Statistically, defined as: "the value smaller than the lower quartile minus 1.5 times the interquartile range, or larger than the upper quartile plus 1.5 times the interquartile range".

Panel (a) of Figure 9 shows that credit unions have a high presence of debt in regions of the center-south of Chile. On the contrary, the debt in credit unions in the MR is significantly lower than the debt on all the other Chilean regions. The panel (a) also shows that in almost all regions, women have a higher percentage of debt with credit unions, compared to men. While the average debt of women in credit unions is around 4% in credit unions, in the case of men, this percentage reaches about 1.8%. Hence, we infer that women give much more intensive use of credit unions, compared to men. In the case of savings (panel (b) of Figure 9), we find a similar behavior to that of debt, but less accentuated. Savings in credit unions also are concentrated in the south-central regions of Chile, with women saving more in credit unions than men. In brief, the credit unions would fulfill financial inclusion by giving access to debt and savings to women in regions different than the MR.

To reinforce the previous point, Figure 10 presents debt information in credit unions with greater detail, by county and number of inhabitants, distinguishing between those counties in the MR (dots in red) and those in the other regions of Chile (dots in blue). It is easy to observe that the share of debt in credit unions concentrates in those counties whose number of inhabitants is less than 5 thousand and different than those in the MR. Such a phenomenon confirms the role of financial inclusion that credit unions play in small counties where commercial banks are not available. This subsection presents evidence indicating that people with lower incomes, older, preferably women, and living in small counties far from the main urban areas, are the ones who tend to participate more actively in credit unions. 13 In brief, we conclude that credit unions in Chile effectively contribute to financial inclusion.

What factors determine to belong to a credit union?

This subsection complements the previous one by estimating the probability of having debt only in a credit union, using a binary variable. The data used in the estimation correspond to the same previously used, meaning information of the total debtors in credit unions and/or banks by December 2017, implying a cross-section analysis. In our estimates, we follow the methodology in Cameron and Trivedi (2009). The estimates consider a basic binary choice model where the existence of a latent variable depends on observable variables and an error term (equation ( 1)).

𝑦 𝑖 * = 𝑥 𝑖 𝛽 + 𝜀 𝑖 (1)
Where it is only observed:

𝑦 𝑖 = { 1, 𝑖𝑓 𝑦 𝑖 * > 0; 0, 𝑖𝑓 𝑦 𝑖 * ≤ 0
Then in our case, the probability that an individual is a member in a credit union is:

𝐸[𝑦 𝑖 |𝑥 𝑖 ] = 𝑃𝑟[𝑦 𝑖 = 1|𝑥 𝑖 ] = 𝑃𝑟[𝑥 𝑖 𝛽 + 𝜀 𝑖 |𝑥 𝑖 ] = 𝐹(𝑥 𝑖 𝛽) (2)
Where F (•) is the cumulative probability function. The specification of the accumulated function is tested using Logit and Probit models. From here, the models estimated have the form:

𝑃𝑟(𝑦 = 1 | 𝑋) = Φ(𝛽 1 + 𝛽 2 𝐿𝑛(𝐼𝑛𝑐𝑜𝑚𝑒) + 𝛽 3 𝐴𝑔𝑒 + 𝛽 4 𝐺𝑒𝑛𝑑𝑒𝑟 + 𝛽 5 𝑅𝑒𝑔𝑖𝑜𝑛) (3)
The exogenous variables included are income, age, gender, and region. Other variables could explain the dependent variable, for example, the level of debt, however, its inclusion could lead to reverse causality, as being a member of credit unions could determine the level of debt. The estimated models seek to test the statistical significance of each variable in a joint model and observe whether if in a system of equations they are still significant when an individual belongs to a credit union or not. Table 8 presents the results. 13 The results presented in this subsection are robust to the exclusion of the biggest credit union in the system (Coopeuch) from the study sample. These results are available in Appendixes F (debt, income and age of credit unions members), G (credit unions and banks offices), H (debt in credit unions to total debt, by gender and county), I (debt and savings in credit unions), and J (debt in credit unions by county and number of inhabitants). Table 5 presents the explanatory variables (first column), the results of the Probit model (column 2) and the Logit model (column 3). The dependent variable takes the value 1 when the client has debt only in credit unions, while it takes the value 0 when it has debt in credit unions and banks or only in banks. The results indicate that while income has a negative coefficient, both age and gender have a positive effect on the dependent variable. In other words, the higher the income, the less likely that an individual has debt only in credit unions. On its hand, age and gender have a negative effect on the probability of being a debtor only in a credit union (results are robust to the exclusion of Coopeuch from the study sample, see Appendix K). These results are consistent with our findings in subsection 5.2. [START_REF] Mckillop | Credit Unions: A Theoretical and Empirical Overview[END_REF] Finally, we note that the variables denoting the regions all exhibit positive coefficients, meaning that if a person lives in a region other than the MR, it is more likely that it has a debt only with credit unions. In other words, if a client lives in the MR is less likely, indebted only with a credit union.

Conclusions

Considering the small participation that credit unions have in the Chilean financial system (about 2% of total assets, liabilities, and equity), this document questions which role credit unions play that other entities, mainly banks, do not accomplish. We question whether the credit unions have a role in financial inclusion.

Using unique statistical information, existing in the CMF, we study the credit unions loans and savings with granularity at their member/individual level. The results indicate that credit unions have a role on financial inclusion as they mostly provide financial services to people of lower-income, older, women, and small counties inhabitants, far from large cities, without access to financial services otherwise. Our results confirm that credit unions play a role on financial inclusion in Chile, as previously argued but not formalized in the related literature (Téllez, 2007, Hernández, 2008 The points correspond to counties where there is a more significant impact of credit unions in terms of funding and savings. These outliers represent extreme cases of high debt/savings ratio. Statistically, defined as: "the value smaller than the lower quartile minus 1.5 times the interquartile range, or larger than the upper quartile plus 1.5 times the interquartile range."

Figure 2 .

 2 Figure 2. Assets and equity (2017)

Figure 3 .

 3 Figure 3. Instruments used by credit unions and bank debtors (December 2017)

Figure 4 .

 4 Figure 4. Debt percentiles (December 2017)

Figure 5 .

 5 Figure 5. Income percentiles (December 2017)

Figure 7 .

 7 Figure 7. Number of commercial offices per region (December 2017)

Figure 8 .

 8 Figure 8. Debt in credit unions to total debt by gender and county (Percentage, December 2017)

Figure 9 .

 9 Figure 9. Debt and saving in credit unions (Percentage) (December 2017)

Figure 10 .

 10 Figure 10. Debt in credit unions (percentage) by county and inhabitants (number) (December 2017)

  elaboration based on CMF. Note: The offices consider: head offices, branches, auxiliary branches, and support offices.H. Debt stock in credit unions, other than Coopeuch, to total debt by county and gender (Percentage, 2017)Source: Authors elaboration based on CMF. Note: Each point represents a county. The axes present how much of the total debt is in credit unions debt by gender.

  Debt ratio in credit unions per region and county (b) Savings ratio in credit unions per region and county Source: Authors elaboration based on CMF. Note: The bars denote the regions of Chile.

  Receive deposits from their members partners and third parties; (ii) Issue bonds and other types of securities open to public; (iii) Contract loans with domestic or foreign financial institutions; (iv) Acquire, preserve and dispose bonds issued by the State; (v) Grant loans to their members and, in general, provide credit, with or without guarantee, adjustable or not adjustable; (vi) Discount their members, bills, promissory notes and other documents that represent payment obligations; (vii) Grant loans to its members, who are covered by mortgage collateral, and mutual endorsable mortgages; and (viii) Manage mutual endorsable mortgages.[START_REF] Barham | Credit constraints, credit unions, and small-scale producers in Guatemala[END_REF]

Table 1 . Number of credit unions members and banks customers

 1

		Credit unions Banks Ratio
	2015Q4	1,165,497	4,646,364 25.1%
	2016Q2	1,172,266	4,676,523 25.1%
	2016Q4	1,188,192	4,723,361 25.2%
	2017Q2	1,205,583	4,814,919 25.0%
	2017Q4	1,224,307	4,927,535 24.9%
	2018Q2	1,253,028	4,980,805 25.2%

Source: Authors calculations based on CMF. Note: If an individual is a member of more than one credit union, it has been considered only once. The same logic applies for banks regarding their clients.

Table 2 . Credit unions balance sheet variables (Percentage of banks' balance sheets)

 2

		Assets	Liabilities	Equity
	2007	0.98%	0.74%	3.57%
	2008	0.98%	0.76%	3.70%
	2009	1.19%	0.94%	3.99%
	2010	1.18%	0.93%	4.08%
	2011	1.10%	0.86%	3.87%
	2012	1.05%	0.84%	3.43%
	2013	0.98%	0.77%	3.27%
	2014	0.90%	0.69%	3.16%
	2015	0.86%	0.65%	3.21%
	2016	0.90%	0.69%	3.00%
	2017	0.88%	0.70%	2.75%
	2018	0.88%	0.70%	2.60%

Source: Authors calculations based on CMF. Note: Data to December of each year.

Table 3 . Main financial instruments of credit unions members (December 2017) Assets Number of members having the instrument % of the total members

 3

	Sight deposit

Source: Authors calculations based on CMF.

Table 4 . Main differences between credit unions and banks

 4

		Credit Unions	Banks
	Law	LGC (2004)	LGB (2019)
	Business purpose	Non-profit	For-profit
	Owners	Members	Shareholders
	Participation	One member one vote	Proportional to the number of
			shares
	Profits	Distributed according to the	Distributed according to the
		participation quota of each	number of shares
		member
	Loans	Only to members	Restriction to owners
	Minimum capital	3,000 UF	800,000 UF
	required to operate
	Allowed financial	Deposits and loans	Deposits, loans, derivatives, etc.
	operations

Source: Authors elaboration based on CMF.

Table 5 . Probability of having debt only in credit unions and member characteristics

 5 The number of observations corresponds to clients either with debt with banks, credit unions, or in both. The binary dependent variable takes the value one when the debtor is only in credit unions, and zero otherwise. The MR is the comparison region. Robust standard errors in parentheses. *, **, ***, indicate statistical significance at 10%, 5%, and 1%, respectively.

	Variables	Probit	Logit
	Ln(Income)	-0.0278*** -0.0424***
		(0.0006)	(0.0012)
	Age	0.0106*** 0.0234***
		(0.0000)	(0.0002)
	Gender (Male=0, Female=1) 0.2240*** 0.4970***
		(0.0025)	(0.0052)
	Region
	Tarapacá	0.919*** 2.141***
		(0.0087)	(0.0179)
	Antofagasta	0.877*** 2.056***
		(0.0065)	(0.0138)
	Atacama	1.135*** 2.569***
		(0.0083)	(0.0163)
	Coquimbo	0.882*** 2.066***
		(0.0062)	(0.0131)
	Valparaíso	0.996*** 2.288***
		(0.0041)	(0.0089)
	O'Higgins	0.923*** 2.152***
		(0.0056)	(0.0119)
	Maule	1.466*** 3.183***
		(0.0042)	(0.0085)
	Biobío	1.060*** 2.423***
		(0.0039)	(0.0084)
	Araucanía	1.052*** 2.405***
		(0.0054)	(0.0111)
	Los Lagos	1.065*** 2.435***
		(0.0056)	(0.0115)
	Aysén	0.987*** 2.271***
		(0.0131)	(0.0266)
	Magallanes	1.017*** 2.334***
		(0.0102)	(0.0205)
	Los Ríos	0.518*** 1.273***
		(0.0088)	(0.0201)
	Arica y Parinacota	0.788*** 1.869***
		(0.0099)	(0.0210)
	Constant	-2.812*** -5.928***
		(0.0114)	(0.0227)
	Observations	4,706,372 4,706,372
	R-squared	0.1568	0.1586
	Source: Authors calculations based on CMF.
	Note:

Debt, income, and age, of credit unions members other than Coopeuch (December 2017) Debt (in pesos) Debt percentiles

					C. Debt (in pesos)
					(December 2017)
		Percentile			Only bank	Both	Only credit union
			10		12,986	1,190,871	254,676
	Percentile	20 30 Only bank	Both	103,094 267,001 Only credit union	2,324,047 3,513,397	517,266 892,955
	10 20 30 40	40 12,986 50 103,094 60 267,001 614,973	77,838 392,564 907,916 1,684,692	614,973 149,596 1,320,629 284,776 2,680,833 437,937 614,053	4,887,271 6,566,956 8,808,363	1,375,977 2,019,383 2,756,873
	50	70 1,320,629	2,797,236	5,120,645 871,595	12,186,712	3,632,424
	60	80 2,680,833	4,353,463	11,036,706 1,150,820	18,328,999	5,149,116
	70	90 5,120,645	6,670,661	35,631,787 1,612,370	33,407,582	8,288,431
	Source: Authors calculations based on CMF. 80 11,036,706 10,956,403 2,409,383
	90	35,631,787 24,574,247	4,054,987
	D. Income (monthly in pesos) (December 2017) Income (monthly in pesos) Income percentiles
	Percentile 10 20	Percentile Only bank 10 214,834 273,921	Both 261,497 333,918	Only Only credit union bank 214,834 142,160 206,853	Both 261,497	Only credit union 174,363
	30	20 346,322	406,356	273,921 215,725	333,918	223,520
	40	30 437,617	480,596	346,322 242,420	406,356	260,894
	50	40 544,400	559,966	437,617 283,550	480,596	307,182
	60	50 682,744	650,226	544,400 333,163	559,966	357,678
	70 80 90	60 874,844 70 1,204,444 1,915,842	763,323 925,337 1,236,089	682,744 411,170 874,844 533,552 734,333	650,226 763,323	421,564 507,226
			80		, Matus et al., 2010, Potin, 2012, Minecon, 2014, 1,204,444 925,337 625,421
	90 Source: Authors calculations based on CMF. 1,915,842 BCCh, 2017). Age (years)	1,236,089 Age percentiles 818,676
	Possible areas of further research are: (i) analysis of the delinquency, i.e., are credit unions
	members effectively better payers than banks customers, given the same amount and type E. Age (years) Percentile Only bank Only credit Both union of loan?; (ii) why exists a significant group of individuals having debt with both credit (December 2017) 10 28 32 33 unions and banks? is it because the current asymmetry of information allows them having a higher total debt?; (iii) is it true that credit unions provide loans to individuals who cannot access to banks' credit?. Percentile Only Both Only credit union 20 32 36 41 bank 10 28 32 30 36 39 48 29 20 32 36 34 40 40 43 53
	50	45	30	47	58	36	39	38
	60	49	40	51	62	40	43	43
	70	54	50	55	67	45	47	48
	80	59	60	59	72	49	51	53
	70 80 90 Source: Authors calculations based on CMF. 90 66 65 78	54 59 66	55 59 65	57 63 71

Source: Authors calculations based on CMF.

F. Note: "Only credit union" denotes credit unions' member other than Coopeuch.

Probability of having debt only in credit unions and members characteristics (Data by 2017)

  The number of observations corresponds to clients either with debt with banks, credit unions, or in both. The binary dependent variable takes the value one when the debtor is only in credit unions other than Coopeuch, and zero otherwise. The MR is the comparison region. Robust standard errors in parentheses.

	K. Variables								Probit	Logit
	debt	.15	Ln(Income) Age							-0.0889*** -0.1860*** (0.0008) (0.0018) 0.0235*** 0.0599***
	/ Total		.1	(0.0002) Gender (Male=0, Female=1) -0.0157*** 0.0092 (0.0004)
	debt												(0.0048)	(0.0115)
	union Credit	.05	Region Tarapacá Antofagasta							0.624*** 1.815*** (0.0188) (0.0493) 0.411*** 1.202***
			0										(0.0175)	(0.0484)
			Tarapacá	Antofagasta Atacama Atacama Coquimbo Valparaiso O'Higgins Maule Coquimbo Valparaíso	Biobío Araucanía	Los Lagos	Aysén Magallanes	Metropolitana	Los Ríos	Arica y Parinacota	Tarapacá Antofagasta Atacama Coquimbo Valparaiso O'Higgins Maule Biobío Araucanía Los Lagos Aysén Magallanes Metropolitana Los 0.414*** 1.221*** (0.0253) (0.0699) 0.290*** 0.908*** (0.0181) (0.0516) 0.652*** 1.825*** Ríos Arica y Parinacota (0.0090) (0.0239)
				O'Higgins	Men						Women 0.831*** 2.308***
													(0.0106)	(0.0272)
				Maule									1.712*** 4.261***
													(0.0067)	(0.0166)
				Biobío									0.995*** 2.651***
	savings	.0015	Araucanía								(0.0074) 0.632*** 1.796*** (0.0193) (0.0126) (0.0334)
	/ Total	.001	Los Lagos								0.585*** 1.679*** (0.0139) (0.0375)
	savings union	.0005	Aysén Magallanes							0.305*** 0.833*** (0.0457) (0.1330) 0.344*** 1.032*** (0.0310) (0.0872)
	Credit	0	Los Ríos Arica y Parinacota					0.254*** 0.756*** (0.0221) (0.0630) 0.496*** 1.454***
				Tarapacá Constant Antofagasta Atacama Coquimbo	Valparaíso O'Higgins Maule	Biobío Araucanía	Los Lagos	Metropolitana	Los Ríos	Arica y Parinacota	Tarapacá (0.0226) Antofagasta Atacama Coquimbo -3.024*** -7.184*** Valparaíso O'Higgins Maule Biobío Araucanía (0.0613) (0.0180) (0.0428)	Los Lagos	Metropolitana	Los Ríos	Parinacota Arica y
				Men Observations R-squared					4,706,372 4,706,372 Women 0.2538 0.2574
	Source: Authors elaboration based on CMF.
	Note:

*, **, ***, indicate statistical significance at 10%, 5%, and 1%, respectively.

Further detail of the financial operations allowed to the credit unions supervised by the CMF, is available in Appendix B.

It is worth to note that credit unions do not provide current accounts or cash management.

An aggregate view of assets, liabilities, and equity, tends to bias the analysis. Consequently, we have decided to focus on specific products where credit unions and banks compete.

These models do not provide probabilities directly through their parameters; the technique that does this is partial effects.

anonymous referees for their comments and suggestions, to Marcelo Chávez for access to statistical information, and seminar participants at the Universidad de Chile Business School and the Financial Market Commission (or "Comisión para el Mercado Financiero" (CMF) in Spanish) Research Department.

Appendix

A. Related literature and the "social role"

Author

The concepts about the credit unions' "social role" Téllez (2007) "The target market of credit unions, focus on medium and low socioeconomic segments of people and small businesses (microcredits), being the main product offered consumer loans." Hernández (2008) "At the local level, credit unions play a fundamental role in the economic revitalization of the territories where they are inserted, allowing access to social groups postponed by the rest of the financial system, supporting the entrepreneurship of micro and small economic units." Matus et al. ( 2010) "Credit unions have their focus in low-income households, where the average amounts of credit granted tend to be lower than in other institutions of the system." "Despite focusing on low-income debtors, credit unions members belong, mainly, to public sector companies, where debt payments are through payroll discounts, helping to mitigate credit risk." Potin (2012) "Credit unions still are considered as oriented to the most vulnerable segments of the population within the consumer credit industry"…"However, when credit unions began they had a clear social connotation as banks were the main financial institutions providing them only to individuals with a certain level of economic resources, with that the focus of credit unions were low-income people. Currently, the situation is different, at least in formal terms the credit granted by credit unions does not differ from consumer loans from other financial institutions." Minecon (2014) "Credit unions generate opportunities for people who cannot easily access to the financial system, as the requirements to access, risk policies defined, among others, leave many individuals and firms out of the financial market." BCCh (2017) "In the case of the cooperatives whose purpose includes financial intermediation activities, their contribution to financial inclusion is added, by granting services to people who do not necessarily have access to credit."

Source: Authors elaboration.

J. Debt in credit unions other than Coopeuch (percentage) by county and number of inhabitants (December 2017)

Source: Authors elaboration based on CMF.


Margaux Escoer

Emmanuel Hache

Valérie Mignon
email: valerie.mignon@parisnanterre.fr

Anthony Paris

Determinants of solar photovoltaic deployment in the electricity mix: Do oil prices really matter? *

Keywords: Q4, Q42, C23, C24 Solar photovoltaic, Renewables deployment, Oil prices, Panel smooth transition regression

This paper investigates the determinants of solar photovoltaic (PV) deployment in the electricity mix for a panel of OECD and BRICS countries from 1997 to 2016 by paying particular attention to the impact of oil market conditions. Relying on a nonlinear, regime-switching specication, we show that rising oil prices stimulate PV deployment only if their growth rate is important, above 6.7%. Although we nd that various other determinants matterwith the inuence of some of them depending on the situation on the oil marketpublic policies play a crucial role. In particular, our ndings show that feed-in-taris should be encouraged to ensure a continuous ght against climate change, whatever the dynamics followed by oil prices.

Introduction

While the ght against climate change represents the most important driver of international energy and environmental public policies, the objective of the Paris agreement seeking to limit the increase of global temperature below 2°C in 2100 seems to be hardly achievable with current trends (UNFCCC, 2019). Despite their crucial role in achieving this objective, energy sector industries indeed encounter diculties in ensuring the energy transition as their greenhouse gases (GhG) emissions have followed a 5.77% upward trend between 1990 and 2016 in OECD countries. Global energy-related CO 2 emissions rose by 1.7% in 2018, with China, India, and the US accounting for 85% of new emissions (IEA, 2019). The electricity and heat production sector produces around half of the CO 2 emissions, highlighting the relevance of focusing on the transformation of the electricity sector. In response to growing energy needs and, in particular, electricity needsglobal electricity demand increased by 4% in 2018natural gas and renewables' consumption respectively rose by 4.6% and 4% in 2018 with renewables representing almost 45% of global energy generation growth (IEA, 2019). However, if the share of electricity generation from coal has registered a downward trend since 2007, this source of energy remains predominant in the world electricity mix. 1 Electricity generation from renewables grew by 7% in 2018, while electricity generation from nuclear increased by 3.3%, with China accounting for half of the new nuclear power plants. Regarding fossil-based energy sources, coal still stands as a major energy with a share representing 27% of total new electricity generation, as well as natural gas amounting at around half of total additional generationan increase mainly due to the US power sector transformation and, more especially, the switch from coal to gas in many power plants. However, renewables, primarily wind turbines and solar photovoltaic (PV), tend to be more and more attractive.

Although investments in renewable energy (RE) play a central role in the energy transition, they have decreased from $323 in 2015 to $274 billion in 2016 (IRENA, 2019).

2 This decline is partly due to the falling costs of technology (-$30 billion in 2016), as well as investment decisions induced by policy changes. This decreasing trend continued in 2017 for solar PV costs. As an example, utility-scale PV projects were down by 15% in 2017. However, despite the drop in investments, RE capacities have not decreased. On the contrary, solar PV capacities have increased by around 30% (IRENA statistics). According to BP Statistical Review 2019, this cost fall allows RE to reach 4% of the world energy consumption in 2018 against 3.6% in 2017, with an increase of 8.5% in OECD countries between 2017 and 2018. 3 However, in the current energy transition context and given the cost reduction trends in renewable technologies, this dynamic could have been more pronounced. This evolution 1 In 2018, according to IEA, the global electricity generation mix is as follows: coal (38%), gas (23%), hydro and others (19%), nuclear (10%), solar and wind power (7%), and oil (3%).

2 See Figure 1 in Appendix A.

3 Excluding hydroelectric sources.

1 calls for an in-depth analysis to identify the main determinants of RE deployment to provide policymakers with real insights into designing their emissions target policies. This is the aim of the present paper.

In the economic literature, The role of political factors in RE deployment has been the subject of various studies. [START_REF] Bird | Policies and market factors driving wind power development in the United States[END_REF], [START_REF] Menz | The eectiveness of dierent policy regimes for promoting wind power: Experiences from the states[END_REF], and [START_REF] Yin | Do state renewable portfolio standards promote in-state renewable generation?[END_REF] show that the Renewable Portfolio Standard 9 (RPS) mechanism in the US plays a major role in promoting RE deployment, while Delmas and Montes-Sancho ( 2011) demonstrate its negative eect on the development of RE capacities. However, when splitting utility ownership types between private-owned and publicly-owned electric utilities, the latter authors nd a positive impact: investor-owned utilities are more responsive to RPS than publicly-owned electric utilities. [START_REF] Carley | State renewable energy electricity policies: An empirical evaluation of eectiveness[END_REF] nds no evidence that RPS fullls its objective of increasing the percentage of RE sources in the electricity mix.

Other standalone countries' analyses raise the importance of political motivation:

van Rooijen and van [START_REF] Van Rooijen | Green electricity policies in the Netherlands: an analysis of policy decisions[END_REF] for the Netherlands, [START_REF] Wang | Renewable electricity in Sweden: an analysis of policy and regulations[END_REF] for Sweden, and [START_REF] Wüstenhagen | Green energy market development in Germany: eective public policy and emerging customer demand[END_REF] for Germany. 10 Gan et al. (2007) compare incentive policies and instruments for those dierent countries. Through a panel data analysis, [START_REF] Polzin | Public policy inuence on renewable energy investments A panel data study across OECD countries[END_REF] study the eectiveness of public policies for the deployment of RE for institutional investors located in OECD countries. For mature technologies, RPS and tradable permit systems appear to be more eective than feed-in-taris (FITs).

However, FITs prove to be more eective than subsidies when considering mature technologies. Considering also panel data for 26 European Union countries, Jenner et al. ( 2013) assess the eectiveness of FIT policies in stimulating solar PV and 9 RPS is a regulation ensuring that a certain proportion of electricity production comes from RE sources.

10 See also [START_REF] Shrimali | The impact of state policy on deployment and cost of solar photovoltaic technology in the U.S.: A sector-specic empirical analysis[END_REF] for a study of the impact of 12-state level policies in 16 US states over 1998-2009 on the cost and development of solar PV technologies. The eectiveness of policy incentives to increase solar PV capacity has also been examined by [START_REF] Crago | Are policy incentives for solar power eective? Evidence from residential installations in the Northeast[END_REF] in the Northeast. onshore wind power development. They show that while such policies signicantly encourage solar PV, the interaction of policy design, electricity price, and electricity production cost has more impact on RE deployment than the policy adoption alone. [START_REF] Romano | Renewable investments: The impact of green policies in developing and developed countries[END_REF] investigate the role of policy instruments for developed and developing countries. For developing economies, scal policies are signicant, while it is not the case for regulatory and public investments. For developed countries, scal policies and public investments appear to play a key role in promoting RE sources. When it comes to RE policy instruments, FITs emerge as the most eective tool in encouraging the deployment of RE at a lower price and a lower risk compared to other supporting mechanisms such as tradable green certicates.

11 [START_REF] Couture | An analysis of feed-in tari remuneration models: Implications for renewable energy investment[END_REF], [START_REF] Fouquet | European renewable energy policy at crossroads Focus on electricity support mechanisms[END_REF], Kilinc-Ata (2016), [START_REF] Menanteau | Prices versus quantities: choosing policies for promoting the development of renewable energy[END_REF], [START_REF] Nicolini | Are renewable energy subsidies eective? Evidence from Europe[END_REF], [START_REF] Rickerson | If the Shoe FITs: Using Feed-in Taris to Meet U.S. Renewable Electricity Targets[END_REF], and [START_REF] Zhao | Do renewable electricity policies promote renewable electricity generation? Evidence from panel data[END_REF] also conclude that FITs are eective RE instruments. [START_REF] Dijkgraaf | On the eectiveness of feedin taris in the development of solar photovoltaics[END_REF] nd a positive impact on RE as well, but specify that this eect is generally underestimated in the literature. However, del Río and Bleda (2012) suggest that FITs can be more eective when included in a mix of green policies. [START_REF] Cadoret | The political drivers of renewable energies policies[END_REF] show for a panel of European countries that the manufacturing industry lobbying hampers the deployment of RE.

Traditionally, positive oil price shocks negatively impact economic growth by increasing ination and unemployment, 12 oil being viewed as a hard-to-substitute input, especially for the transport sector. However, another perspective is to consider and analyze oil shocks in terms of investment opportunities. Indeed, high oil prices can stimulate investments in RE sources as these technologies become more protable.

Concerning this last point, some studies have examined the interactions between oil, natural gas, coal and electricity prices, and the deployment of RE sources. However, there is no consensus in the literature regarding the impact of the oil price level on GhG emissions. van Ruijven and van Vuuren (2009) conclude that the eect of high hydrocarbon prices on RE sources depends on the implementation (or not) of climate policies. Specically, (i) with climate policies, high oil prices lead to more commitments towards RE sources, while (ii) without climate policies, they give rise to a shift from natural gas to coal for electricity production. 13 [START_REF] Vielle | On the climate change eects of high oil prices[END_REF] argue that the impact of high oil prices would be lower than expected due to fuels' substitution eects, and because GhG emissions' reductions obtained via high oil prices would be unequally distributed across regions and sectors. [START_REF] Khan | 2014 oil plunge: Causes and impacts on renewable energy[END_REF] suggest that the 2014 oil plunge from 114 dollars per barrel in June 2014 to 28 dollars per barrel in February 2016 has exerted no signicant eect on the deployment of RE. [START_REF] Chang | Threshold eect of the economic growth rate on the renewable energy development from a change in energy price: Evidence from OECD countries[END_REF] focus on the link between economic growth, hydrocarbon 11 Concerning the lower risk, see, e.g., [START_REF] Bürer | Which renewable energy policy is a venture capitalist's best friend? Empirical evidence from a survey of international cleantech investors[END_REF], [START_REF] Butler | Comparison of feed-in tari, quota and auction mechanisms to support wind power development[END_REF], and [START_REF] Mitchell | Eectiveness through risk reduction: a comparison of the renewable obligation in England and Wales and the feed-in system in Germany[END_REF].

12 See [START_REF] Blanchard | The macroeconomic eects of oil shocks: Why are the 2000s so dierent from the 1970s ?[END_REF] and [START_REF] Hamilton | What is an oil shock?[END_REF]. 13 More precisely, on the one hand, high prices of oil and natural gas increase the use of coal; on the other hand, the cost dierence between fossil-based energy and non-carbon energy options decreases.

prices, and RE sources by relying on a Panel Threshold Regression model from 1997 to 2006 for OECD countries. They show that high-growth countries tend to react to a positive shock on oil prices by developing RE, while low-growth countries do not respond to high oil prices by changing their energy mix towards RE (mainly because of scal considerations). Another study conducted by [START_REF] Shah | How do oil prices, macroeconomic factors and policies aect the market for renewable energy?[END_REF] targeting Norway, the UK, and the US and based on the use of Granger causality tests, suggests that movements in oil prices, GDP, and interest rates each contribute to the dynamics of the RE sector. Interestingly, the authors show that oil prices explain 22% of the total variance of RE investments for the US. [START_REF] Awerbuch | Exploiting the oilgdp eect to support renewables deployment[END_REF] argue that RE investments can partly oset GDP losses implied by raising prices 14 through the GDP-oil eect. [START_REF] Rout | Impact assessment of the increase in fossil fuel prices on the global energy system, with and without CO2 concentration stabilization[END_REF] simulate fossil fuel price hikes scenarios from 2000 to 2030, and nd that growing prices reduce CO 2 emissions by 23%.

Aguirre and Ibikunle (2014), [START_REF] Wüstenhagen | Social acceptance of renewable energy innovation: An introduction to the concept[END_REF], and [START_REF] Sadorsky | Renewable energy consumption and income in emerging economies[END_REF] focus on environmental commitments. [START_REF] Sadorsky | Renewable energy consumption and income in emerging economies[END_REF] nds that CO 2 emissions drive RE consumption. [START_REF] Dalby | Green investment under policy uncertainty and bayesian learning[END_REF] examine green investments under policy uncertainty, and show that investments in RE are high when policy risk is weak and investors prefer a low FIT with a long-expected lifespan. [START_REF] Chassot | When energy policy meets free-market capitalists: The moderating inuence of worldviews on risk perception and renewable energy investment decisions[END_REF] also conclude that policy uncertainty negatively impacts RE investment decisions. Furthermore, [START_REF] Lüthi | The price of policy risk Empirical insights from choice experiments with european photovoltaic project developers[END_REF] nd that investors choose to invest in the country with the most favorable risk-return prole, and [START_REF] Mitchell | Eectiveness through risk reduction: a comparison of the renewable obligation in England and Wales and the feed-in system in Germany[END_REF] conclude that risks are lower when (i) regulators have a greater autonomy from elected politicians, and (ii) policy-making processes are more rigid. Among other potential drivers of RE deployment, [START_REF] Masini | Investment decisions in the renewable energy sector: An analysis of non-nancial drivers, technological forecasting and social change[END_REF] investigate non-nancial determinants and point out the role of institutional and behavioral factors for green investments. They show that investors seem to have very little faith in dedicated policy measures supporting RE technologies.

3

Data and methodology

The PSTR model

To analyze interactions between the determinants of the deployment of RE and the oil price dynamics, we rely on the PSTR methodology proposed by [START_REF] González | Panel smooth transition regression models[END_REF]. This specication allows these determinants to vary over time depending on the evolution of the price of oil, the change in the coecients' value being smooth betweenat leasttwo regimes.

Specically, let ∆SC i,t be the variation of the share of solar PV capacities in the total electricity capacities in country i at time t. The PSTR with two regimes can 14 A 10% increase in RE can oset up to $53 billion of GDP losses in the US and the European Union (EU), which corresponds to one-fth of the RE investments' needs according to the European Renewable Energy Council. be expressed as:

∆SC i,t = β 1 X i,t + β 2 X i,t × F (∆OP t ; γ, c) + i,t (1)
with i = 1, . . . , N , N being the number of countries, and t = 1, . . . , T . F is a transition function, bounded between 0 and 1, and is given by:

F (∆OP t ; γ, c) = 1 + exp -γ m l=1 (∆OP t -c l ) -1 (2)
where ∆OP t denotes oil price growth used as the transition variable,15 γ is the slope parameter describing the transition speed between the various regimes, and c l = c 1 , . . . , c m denotes the threshold parameters with c 1 ≤ c 2 ≤ . . . ≤ c m . As mentioned by [START_REF] González | Panel smooth transition regression models[END_REF], it is usually sucient to consider a maximum value of 2 for m as it allows to capture commonly encountered types of nonlinearities. 16With this model, the eects of the determinants of RE deployment included in X i,t can vary depending on the change in the price of oil, and are bounded between β 1 in the rst regime, i.e., F (.) = 0, and β 1 + β 2 in the second one, i.e., F (.) = 1.

Following [START_REF] González | Panel smooth transition regression models[END_REF], we apply the PSTR methodology using a threestep strategy: (i) specication, (ii) estimation, and (iii) evaluation. First, we test the null hypothesis of linearity against the PSTR alternative to check the presence of nonlinearity linked to oil price growth. 17 We employ the bootstrapped version of the Lagrange-multiplier (LM) test with the residual-based wild bootstrap (WB) and the wild cluster bootstrap (WCB) to handle heteroskedasticity and cluster-dependency issues. 18 Second, we estimate the model using the nonlinear least squares (NLS) es- timator on demeaned data. Finally, we evaluate the validity of our estimated model by applying the WB and WCB versions of (i) the time-varying specication test aiming at checking the eciency of our PSTR specication against a time-varying parameter PSTR, and (ii) the no-remaining nonlinearity test aiming at testing a one-transition function PSTR against a two-transition function PSTR (see [START_REF] González | Panel smooth transition regression models[END_REF].

Explanatory and dependent variables

The explanatory variables included in X i,t in Equation ( 1) are chosen according to the literature previously reviewed in Section 2, selected by accounting for the importance of their eects and depending on data availability issues. Specically, the determinants can be categorized into three groups, as detailed below.

First, we consider two economic determinants, namely economic growth and foreign electricity trade. Regarding economic growth, we use the growth rate of GDP per capitain constant US dollarsto eliminate the eect of population growth. 19 As recalled by [START_REF] Marques | Motivations driving renewable energy in European countries: A panel data approach[END_REF] and [START_REF] Cadoret | The political drivers of renewable energies policies[END_REF], the contemporaneous impact of GDP growth is hard to anticipate as high economic growth could stimulate RE consumption and production through an income eect, but could also dampen them due to the intermittency problem and on the spot availability. On the contrary, past economic growth should have a positive eect on RE production through higher resources that can be mobilized for RE deployment. Furthermore, by focusing our analysis on RE deployment in terms of variation in production capacities, we expect that the eect of GDP growth could be delayed. We also use foreign electricity tradei.e., exports minus importsas a measure of energy security. We expect a negative eect of international trade as electricity net importers could have more incentive to develop new electricity capacitiesespecially RE sourcesthan exporters.

Second, we consider various energy and environmental factors : (i) environmental commitments measured by CO 2 emissions, (ii) fossil-based energy endowments, (iii) nuclear based-energy capacities, (iv) energy prices, and (v) solar PV potential.

Following [START_REF] Marques | Motivations driving renewable energy in European countries: A panel data approach[END_REF], Marques and Fuinhas (2011), and Romano and Scandurra (2016a), we use CO 2 emissionsin variationper capita and kilowatt- hour (kWh) considered as a proxy of environmental commitments in the electricity market. The reduction of CO 2 emissions is at the very heart of energy transition policies and represents a means of measuring country eorts in terms of environmental commitments. We expect that the higher the variation in CO 2 emissions, the lesser the environmental commitment of the country at time t and so, the smaller the deployment of solar PV. We, therefore, anticipate a negative eect of CO 2 emissions' variations on solar PV deployment. We also use non-renewable energy endowments variables, such as the growth of oil production and coal production, as well as nuclearbased electric capacities. A negative eect is expected for these three variables, as an upward trend in these energy sources could create a barrier to RE deployment. Indeed, countries with increasing oil, coal or nuclear-based electricity production could be less interested in investing in RE as (i) economies with oil reserves usually have fossil-based energy assign for electricity generation as coal or gas, and (ii) those with nuclear capacities own an electricity sector with low GhG emissions. Furthermore, we follow [START_REF] Marques | Motivations driving renewable energy in European countries: A panel data approach[END_REF], Marques and Fuinhas (2011), and van Ruijven and van Vuuren (2009) and include as determinants of solar PV deployment the changes in the prices of oil and gas. 20 These three variables allow us to account for substi- tution eects between dierent energy sources in electricity production. We expect that an increase in energy prices could lead to incentives to develop solar PV capacities through an improvement in their relative protability. The deployment of solar PV should also depend on annual sunshine hours and the geographical area, as in Marques et al. (2011). However, these variables being time-invariant and inappropriate in our retained specication, 21 we integrate solar PV potential of countries through the urbanization rate. Indeed, the higher the urbanization rate, the higher the number of buildings and thus, the higher the number of large roofs usable for solar-based electricity production. We, therefore, expect a positive eect of the urbanization rate on solar PV deployment.

Third, we include a political driver, namely the existence or not of FITsrelating to solar PVas in [START_REF] Romano | Renewable investments: The impact of green policies in developing and developed countries[END_REF] among others. A FIT is a contract allowing to x the price of electricity produced from RE sources during a specied period (usually between 15-25 years). This policy has been quite famous during the studied period due to its attractiveness for investors as it ensures a certain regularity of cash-ows. The related dummy variable takes the value 1 if the policy is at play in country i at time t, and 0 otherwise. 22 We expect the existence of FITs to stimulate the solar PV capacities deployment.

Turning to our dependent variable, we consider the variation in the share of solar PV capacity in the total electricity capacities. As mentioned by [START_REF] Bourcet | Empirical determinants of renewable energy deployment: A systematic literature review[END_REF], the installed capacities in RE reect the commitment of policymakers to engage the energy transition. Note that all explanatory variables are stationary and are expressed in growth rate terms, except FITs, the urbanization rate, the nuclear capacities share, and foreign electricity trade (see Table 3 in Appendix B for more 20 Note that oil price growth is used both as an explanatory variable and as a transition variable. For gas prices, we rely on the available prices for each region. See appendix B for more details.

21 Indeed, the rst step of the PSTR estimation consists of removing the individual-specic means to eliminate the individual eects [START_REF] González | Panel smooth transition regression models[END_REF]. 22 It is worth mentioning that due to the inclusion of emerging countries in our sample, we cannot account for FITs through the level of the tari or the duration of the contract, as in [START_REF] Dijkgraaf | On the eectiveness of feedin taris in the development of solar photovoltaics[END_REF]. We construct our dummy variable by using the information contained in the country reports of the International Energy Agency (IEA) and consider the FIT policy to be in force if the FIT is given for medium and large-scale projects (above 1 MW). If there is no change, the variable takes the value 1 the year corresponding to the starting date of the FIT until the policy ends for new projects. Concerning federal states (Australia, Canada, India, and the US) we proxy the country by the major state in terms of solar PV capacities (corresponding respectively to Queensland, Ontario, Karnataka, and California). Finally, we do not take into account feed-in-premium contracts as they correspond to the spot price plus a premium, meaning that such contracts are more risky than a xed price for a given period. Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Latvia, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, South Korea, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States.

BRICS

Brazil, Russia, India, China, South Africa.

details, including all data sources).

Time period and sample of countries

We rely on annual data for 39 economies (see

Empirical results

The results from the estimation of our PSTR specication are presented in Table 2.

We start by checking the existence of nonlinearity with oil price growth as the transition variable in Equation (1). As shown, the null hypothesis of linearity is rejected at the 5% signicance level when accounting for cross-sectional dependence in the residuals. This justies the use of a nonlinear, PSTR specication, indicating that the eect of some determinants of RE deployment depends on the behavior of oil prices. It is worth mentioning that our estimated model successfully passed all the misspecication tests. Indeed, in all cases, the null hypothesis of our PSTR specicationagainst either a time-varying parameter PSTR or a two-transition function PSTRis never rejected, whatever the bootstrapping methodology used.

Regarding the transition function, the threshold parameteri.e., the value of the annual growth rate of oil prices for which the transition function takes the value of 0.5is estimated at 6.7%. Hence, the rst regime corresponds to periods in which the price of oil decreases or exhibits a quite low growth rate, i.e., below 6.7%. The second regime refers to a high increase in oil prices, above 6.7%. These two regimes can be interpreted as reecting two main conditions on the oil market: (i) calm or normal periods, characterized by a decline or a quite stability in oil prices, and (ii) 23 Iceland has been removed from our panel due to its particular electricity mix based on hydroelectric (70%) and geothermal (30%) sources. Lithuania is also excluded from our analysis as it has joined OECD only since 2018. periods of pressures on oil prices, hereafter referenced as boom periods. The estimated model reported in Table 2 thus accounts for the fact that some explanatory variables have a dierent impact on RE deployment depending on the conditions on the oil market, i.e., normal or booming.

Let us now consider the two economic determinants, namely economic growth and foreign electricity trade, both variables being one-period lagged. These variables are signicant in both regimes, i.e., whatever the oil market conditions. In more detail, past economic growth exerts a negative eect on RE capacities. Here, we go further than [START_REF] Cadoret | The political drivers of renewable energies policies[END_REF] who highlight an adverse impact of contemporaneous economic growth on the share of RE in gross nal energy consumption. Indeed, while these authors nd that economic growth slows down RE deployment in terms of consumption, we show that this variable also negatively impacts the share of RE capacity in the energy mix. The direct negative eect on consumption would be due to the high elasticity of fossil-based energy consumption. However, this one is also transmitted in terms of RE deployment as the share of solar PV capacity in the electricity mix decreases by 2.9 percentage points for each percent of the increase in past economic growth. Based on the assumption that solar PV capacities do not decline, past economic growth could thus lead to additional deployment of fossilbased electricity capacities to address the supplementary energy needs. Turning to the past value of foreign electricity trade, it is associated with a decrease in solar PV capacities. More specically, an increase of one GWh in the past balance of electricity trade leads to a reduction of 8.9×10 -6 percentage point in the solar PV share. This result was obviously expected as the net importers of electricity have an additional incentive to deploy RE-based capacities to reduce their trade decit: this is the well-known double dividend of RE deployment. 24 This result could also be interpreted from a geopolitical point of view as foreign electricity trade could be seen as a proxy for energy insecurity in the electricity sector. The double aim to reduce foreign trade decit and energy dependency could lead to incentives in RE deployment in addition to GhG emissions' reduction.

Concerning the various energy and environmental explanatory variables included in our specication, their respective eectexcept for fossil-based energy endowments depends on the oil market conditions. First, environmental commitmentsmeasured by the past variation of CO 2 emissions per capita and per kWh in the electricity miximpact the solar PV deployment positively. Indeed, a 1% increase in CO 2 emissions variation leads to a decrease in the share of solar PV capacities in the electricity mix by 0.43 percentage point during normal periods on the oil market.

On the contrary, in boom times, the environmental commitment of OECD and BRICS countries appears to be non-signicant in stimulating solar PV deployment.

The higher the price pressures on the oil market, the higher the interest for countries to develop solar PV capacities to minimize the impact of oil prices on electricity 24 See, among others, [START_REF] Criqui | European climateenergy security nexus: A model based scenario analysis[END_REF]. prices, whatever their willingness to ght against climate change through reduction in CO 2 emissions. Second, fossil-based energy endowments, in terms of oil produc- tion growth, slow down the deployment of RE electricity capacities regardless of price conditions on the oil market. An increase of one million barrels per day in oil production is associated with a decrease of 0.037 percentage point in solar PV share growth. This result is in line with [START_REF] Papiez | Determinants of renewable energy development in the EU countries. A 20-year perspective[END_REF], highlighting higher RE deployment in countries that are not producers of their fossil sources. While we could expect that an increase in oil production could have a positive eect on solar PV deployment in times of booming oil prices by rising energy rms' prots and, in turn, generating a transfer of these nancial resources in renewable deployment, our results contradict this expectation. Indeed, the impact of oil endowments does not dier between the two regimes. Third, the growth rate of coal production 25 harms solar PV deployment only during boom periods in the oil market. During regimes of high growth in the price of oil, countries are more prone to produce coal-based electricity, providing coal producers with higher nancial resources. However, as a 1% increase in coal production leads to a decrease of 0.07 percentage point in solar PV deployment, the additional nancial gain seems not to be invested in deploying solar PV capacities. Fourth, a negative eect is found regarding the nuclear capacity share in the electricity mix during normal and boom periods. The presence of nuclear plants hampers the deployment of solar PV, albeit to a lesser extent, in times where oil price growth is higher than 6.7%. As expected, countries with low GhG emissions thanks to nuclear power plants tend to have less incentives to deploy solar PV. Fifth, Table 2 shows an interesting result concerning fossil energy prices. Gas price growth has an eect on solar PV deployment during normal periods, while oil prices are at play during boom periods. More specically, a 1% increase in gas price growth leads to 0.38 percentage point rise in PV deployment when oil price growth is lesser than 6.7%. On the contrary, in times of high oil price growth, a 1% increase in oil price growth raises by 0.73 percentage point the solar PV share. As expected, all these fossil energy sources could be seen as substitute energies compared to RE, and a rise in their prices thus allows solar PV relative protability to increase leading to its deployment. Sixth, our results conrm our expectation of a positive eect of the urbanization ratewhich accounts for the potential in solar PV installationon solar PV deployment. An increase in the urbanization rate has a higher eect during normal periods, indicating that pressures on oil prices tend to reduce the positive impact on the share of solar PV capacity.

Finally, the political driver proxied by the existence of FITs policy positively aects solar PV deployment regardless of price conditions on the oil market. Adopting this policy is associated with an additional increase in the share of solar PV capacity of 0.44 percentage point per year compared to countries without FITs. By setting the price of electricity during a xed period for RE sources, policymakers can have an impact on RE deployment. This result is in line with the existing studies presented in Section 2. Furthermore, there is no interaction eect with oil price growth, meaning that the FITs policy is always eective whatever the conditions on the oil market.

This result is obviously quite reassuring from a policy-maker point of view.

On the whole, our analysis emphasizes three kinds of determinants regarding the deployment of solar PV. The rst category concerns drivers which do not depend on oil market conditionsor to a small extent: (i) past economic growth, electricity independence, and fossil fuel endowments which inuence negatively RE deployment, and (ii) the urbanization rate, as well as FITs which exert a positive eect. Second, two variables play a role in solar PV deployment only when oil price growth is lower than 6.7%: past variation in GhG emissions and gas price growth, which have a negative and positive eect, respectively. Finally, the oil price variation aects positively solar PV capacities only if its annual growth exceeds 6.7%, putting forward the existence of an asymmetric and nonlinear eect of oil prices on RE deployment.

Conclusion and policy implications

This paper aims at identifying the determinants of solar PV capacities' deployment, and at investigating their dynamics depending on the conditions on the oil market.

Estimating a PSTR model on a wide sample of countries, we show that the dynamics of oil prices aect various determinants of solar PV deployment. Interestingly, an increase in oil price growth above 6.7% stimulates solar PV capacities: rising oil prices reduce the relative costs between oil and renewables, making renewable investments relatively more aordable. We also nd that energy factor endowments are signicant drivers for solar PV development. Foreign electricity trade, oil production variation, and nuclear capacities negatively impact the development of solar PV capacities. CO 2 emissions play a negative role during normal conditions on the oil market, which may be the result of a lesser or insucient level of environmental commitments from economies during the studied period. However, policy support Note: Authors' calculations based on data extracted from IRENA.

4

  the deployment of RE is explained by dierent factors generally split into three classes: (i) economic drivers, (ii) energy and environmental determinants, and (iii) political factors. While these various factors are well documented concerning the deployment of wind turbines technologies, the literature is more scarce regarding the solar PV side. This is an important lack since solar PV constitutes one of the most attractive RE sources since around 2010, and remains the only renewable technology that has registered an investment increase in 2017(IRENA, 2019). From a macroeconomic perspective and to the best of our knowledge, only[START_REF] Chang | Threshold eect of the economic growth rate on the renewable energy development from a change in energy price: Evidence from OECD countries[END_REF] analyze interaction eects between determinants of RE production and economic growth. They show that the impact of energy prices on RE production depends on the GDP growth level. Only countries recording high economic growth levels respond to an increase in energy prices by deploying RE technologies. Macroeconomic variables and the oil price dynamics are yet interconnected, 5 and the conditions observed on the oil market could trigger or not a spillover eect on RE deployment. Specically, one may expect that the deployment of PV depends on oil price changes. Obviously, such deployment is more likely to be stimulated when oil prices riseincreasing the attractiveness of more aordable alternative energy sourcesthan when they decrease. Taking these interactions into account is the main contribution of this article since, to the best of our knowledge, no study has been implemented so far on this topical subject.To this end, we rely on the panel smooth transition regression (PSTR) framework introduced by[START_REF] González | Panel smooth transition regression models[END_REF], which allows the eects of the determinants of solar PV deployment to switch betweenat leasttwo states depending on oil price changes. The use of a non-linear model enables us to account for the leading role played by oil prices on energy markets, 6 and to assess to which extent their dynamics impact the deployment of solar PV depending on the level reached by the oil price growth rate. 7 In addition to analyze these interactions, our paper lls a gap in the literature as only a few studies exist on the drivers of solar PV deployment. As a further contribution, our variable of interest is the variation in the share of solar PV capacities in the total electricity mix, which is particularly relevant to analyze the role played by the deployment of solar PV in the energy transition. Indeed, growing 4 See references in Section 2.

(Figure 1 :

 1 Figure 1: Investments in renewable energy (in billion US dollars)

  solar PV capacities do not necessarily reect a process towards energy transition if fossil fuel capacities grow faster. Reasoning in terms of shares overcomes this issue as a positive share variation inevitably results in a higher growth speed of solar PV capacities compared to the total electricity mix, and thus illustrates substitution to-

	while energy security appears to be non-signicant. They also highlight that ensur-
	ing supply security does not encourage RE commitments.
	Other studies aim at identifying the determinants of deployment of RE by focusing
	wards solar PV-based electricity. Finally, whereas most of the existing studies deal on specic RE technologies. In particular, Romano and Scandurra (2016a) propose
	with quite reduced samples of countries, we consider a wide panel of 39 economies an analysis comparing dierent types of RE, namely hydroelectric, solar, wind, and
	from 1997 to 2016, including both OECD and BRICS countries. biomass. They highlight that the determinants vary according to whether one con-
	siders hydroelectric or non-hydroelectric technologies, i.e., solar, wind, and biomass
	Our results show that oil market conditions play a key role in explaining the dynam-technologies. CO 2 emissions positively explain the development of investments in
	ics of solar PV deployment since they aect their main determinants: environmental hydroelectric technologies, but their impact is found to be non-signicant for non-
	commitments, nuclear-based endowments, and the solar PV potential. Specically, hydroelectric technologies. Policies supporting RE investments are signicant for
	an increase in oil price growth above 6.7% has a positive eect on solar PV by reduc-both cases, but the eect is stronger for non-hydroelectric sources. They also nd
	ing the relative cost between oil and renewables, making this technology relatively that the availability of nuclear or geothermal energy in the electricity mix reduces the
	more aordable. We nd that fossil-based endowments in non-RE are signicant incentives to invest in RE. Regarding the nuclear question, Romano and Scandurra
	drivers for solar PV development by slowing down the incentive to increase RE ca-(2016b) show that the deployment of RE presents an inverse relationship with the
	pacities. Foreign electricity trade, oil production variation, and nuclear capacities share of nuclear power generation in the electricity mix for countries with nuclear
	negatively impact the deployment of solar PV capacities. A rise in CO 2 emissions activities. However, in countries with power generation based on fossil fuels, the
	plays a negative role during periods of low oil price growth, reecting a weak level deployment of RE depends on other dierent factors.
	of environmental commitment.
	The rest of the paper is organized as follows. Section 2 reviews the literature on
	the determinants of RE deployment. Section 3 describes the data and methodology.
	Section 4 presents our ndings, and Section 5 concludes the paper.
	2	Literature review
	Factors promoting the deployment of RE have been analyzed in many dierent ways. 8
	Eyraud et al. (2011, 2013) study the determinants of green energy deployment us-
	ing a panel xed-eect estimator and conclude that ve variables are statistically
	signicant: (i) GDP in constant dollars, (ii) the long-term real interest rate, (iii)
	the relative price of international crude oil, (iv) a feed-in-tari (FIT) dummy, and
	(v) the carbon pricing mechanism. Focusing on European countries, Marques et al.
	(2010) use a Fixed Eect Vector Decomposition (FEVD) model and show that both
	the lobbying eorts and activities from traditional key actors in fossil energy sectors
	(oil, coal and natural gas) and CO 2 emissions negatively impact RE technologies'
	development. Considering also the case of Europe, Papiez et al. (2018) conclude
	that countries with the lowest shares of RE in their energy mix show relatively high
	energy self-suciency, and countries which are not producers of their fossil sources
	are the ones where RE deployment is the highest. Aguirre and Ibikunle (2014) in-
	vestigate the drivers of the development of RE from 1990 to 2010 by using a FEVD
	model. They nd that environmental concerns matter in explaining RE deployment,
		8 See Bourcet (2020) for a recent literature review on the determinants of RE deployment.

Table 1 :

 1 Panel of countries OECD

  Table 1) from 1997 to 2016, focusing on OECD and BRICS countries. 23 Our choice of the starting date is guided by data availability considerations.

Table 2 :

 2 PSTR estimation results *, *) denotes signicance at the 1% (resp. 5% and 10%) level based on robust standard errors. WB (resp. WCB) Linearity test is the result of the test checking the null hypothesis of linearity against the PSTR model with residual-based wild (resp. wild clustered) bootstrap. The TVP parameters WB and WCB tests check the null hypothesis of our PSTR specication against the alternative hypothesis of time-varying PSTR. The RNL WB and WBC tests mention the results of tests checking the null hypothesis of our PSTR specication against the alternative hypothesis of PSTR with two transition functions.

		Regime 1	Regime 2
	Economic growth i , t-1	-2.900 * *	-2.900 * *
	Foreign elec. trade i , t-1	-8.922 × 10 -6 * * -8.922 × 10 -6 * *
	CO 2 growth i , t-1	-0.433 * *	-0.065
	Oil prod. growth i , t	-0.037 * *	-0.037 * *
	Coal prod. growth i , t	0.117	-0.076 * *
	Nuclear capacity share i , t	-9.731 * * *	-9.058 * * *
	Gas price growth i , t	0.383 * *	-0.050
	Oil price growth i , t	-0.096	0.734 * * *
	Urbanization rate i , t	5.119 * * *	4.663 * * *
	FIT i , t	0.443 * * *	0.443 * * *
	c	0.067 * * *
	γ	119.3 * *
	WB Linearity test	0.055
	WCB Linearity test	0.047
	TVP parameters WB test	0.999
	TVP parameters WCB test	0.999
	RNL WB test	0.942
	RNL WCB test	0.977
	Note: *** (resp.

*

For a survey, see, e.g.,[START_REF] Brown | Energy prices and aggregate economic activity: an interpretative survey[END_REF] and[START_REF] Hamilton | Oil and the macroeconomy[END_REF].

Note that the inuence of gas or coal prices as driving the switch from one regime to another has also been tested, but their impact was non-signicant.

Regarding the related literature,[START_REF] Reboredo | Is there dependence and systemic risk between oil and renewable energy stock prices?[END_REF] relies on copulas and shows that high oil prices encourage the development of the RE sector, and[START_REF] Shah | How do oil prices, macroeconomic factors and policies aect the market for renewable energy?[END_REF] nd that the link is particularly acute for the US with oil prices explaining 22% of the variance of investments in RE. See also the references in Section 2.

It should be noticed that we consider the price of oil in rst-logarithmic variation and not in level to address unit root issues.

Note that m = 1 implies the use of a logistic function, while m = 2 refers to a quadratic logistic function.

Note that we also tested for the presence of nonlinearity using the growth rate of GDP per capita as the transition variable, but the tests failed to reject the null hypothesis of linearity (as for the growth rate of gas and coal prices).

See González et al. (2017) for more details about these test statistics.

In other words, we do not consider GDP in level as it is usual in the literature (see, e.g.,Eyraud et al.,

2011, 2013), as this variable contains a unit root.

Note that we also included the growth rate of gas production in our specication, but this variable was found to be non-signicant whatever the considered regime.

tinuous ght against climate change. In other words, whereas tightened oil market conditions may temporary contribute to a reduction in CO 2 emissions, only struc- tural reforms based on public policies will help in durably achieving this objective.

participants to the 42nd International Association for Energy Economics (IAEE) Annual Conference in Montréal, and to the EconomiX and IFPEN seminars for helpful comments and suggestions.

This study received the nancial support of the French National Research Agency (ANR) through the GENERATE project.

B Appendix. Data description