rafmacalaba/datause-encoder-data huggingface dataset:
**Table** 7: **Excess Sensitivity Tests and the Augmented Euler Equation for Consumption (GMM Estimates)**

||_CHL_|**GHA**|**IDN**|**KOR**|**MYS**|**MEX**|**TUR**|**ZWE**|
|---|---|---|---|---|---|---|---|---|
|A**In yt**|_0.550_|1.705|1.606|0.359|1.185|0.687|0.575|**1.088**|
||(3.222)|(1.056)|(1.113)|(2.089)|(2.593)|(3.127)|(1.445)|(3.392)|
|r,|0.076<br>(2.464)|0.349<br>(-0.828)|0.490<br>(0.955)|0.275<br>(1.412)|0.767<br>(1.056)|0.181<br>(2.688)|0.028<br>(0.233)|0.280<br>(-0.837)|
|BG test|0.653|0.041|0.870|0.424|0.042|0.041|0.634|0.491|
|NOBS|24|24|23|23|24|24|24|19|
|Part 11|||||||||

||**CHL**|**GHA**|**IDN**|**KOR**|**MYS**|**MEX**|**TUR**|**ZWE**|
|---|---|---|---|---|---|---|---|---|
|a|-0.81||-7.566|-2.673|6.683|-0.136|4.811||
||(-2.914)||(-1.454)|(-0.462)|(0.30)|(-0.016)|(-3.411)||
|al|-0.109||0.272|-1.813|-1.42|0.701|0.731||
||(-0.735)||(0.90)|(-0.733)|(-0.591)|(0.49)|(2.36)||
|J.L|0.771||0.841|0.540|0.570|0.560|1.471||
||(2.668)||(2.241)|(91.844)|(18.943)|(2.791)|(34.736)||
|CY|-0.001||1.066|6.278|1.055|0.449|0.477||
||(-0.013)||(1.111)|(0.163)|(1.255)|(1.728)|(0.472)||
|OR test|0.057||0.880|0.931|0.079|0.655|0.916||
|NOBS|24||23|23|24|24|24||

Notes:

1. t-statistics in parenthesis.

2. The instruments used are 3. BG denotes the marginal significance level for the Breusche-Godfrey test for serial correlation up to the second order.

50
illustration of our arguments with long running panel data from Kagera, Tanzania. Section 6 extends the simple, identical agents, model of the previous sections to the case where agents are heterogeneous. It is shown that heterogeneity complicates the picture but the basic economic forces identified in the previous analyses still hold sway. Section 7 concludes the paper.

# **2. Model and Equilibrium**

We consider an extension of the basic Todaro (1969) model by introducing two potential destinations for out-migration from the rural area – small town and city. Rural, small town and city are identified by subscripts _r_ , _s_ and _c_ , respectively. In each destination, the migrant faces a prospect of getting a high paying modern sector job with wage _w (ws, wc)_ , or ending up unemployed with a low paying informal sector income _wo (wos, woc)_ . The probability of securing a modern sector job is _e (es , ec)_ . There is a single rural sector income which is certain, and denoted _wr_ . Incomes in the model are exogenously given.

Total population is �<sup>�</sup> , and the populations of the three sectors are denoted _Nr, Ns_ and _Nc_ . Modern sector employment is denoted by _E (Es, Ec)_ and informal sector employment (or unemployment) is denoted _U (Us, Uc)_ . Throughout we treat the words “unemployment” and “informal employment” as synonymous. Thus Further, as in the classic Todaro (1969) formulation, the probability of getting a modern sector job in a destination is identified with the modern sector employment rate in that destination: The “unemployment rate” is then the fraction of the population in the informal sector.

We come now to the migration decisions and migration equilibrium in this model with a single origin and two destinations. The agent in the rural sector faces the choice of staying in the rural sector with income _wr_ , migrating to the small town for a prospect of _ws_ with probability _es_ and _wos_ with probability 1 _– es_ , or migrating to the city for a prospect of _wc_ with probability _ec_ , and _woc_ with probability 1 _– ec_ . In addition, we suppose that there is a “migration cost” for each destination, denoted ��∗ and ��∗. These can be transportation costs to, and settlement and job search costs in, each of the destinations.<sup>3</sup> We assume that ��∗ > ��∗.

> 3 In practice, transport costs are often only a fraction of the annual income gain that could be obtained from moving. Among migrants for Kagera, in northern Tanzania, Christiaensen, De Weerdt and Kanbur (2017) show for example

4
# **1. Introduction**

The impact of bank competition on financial markets and firms is an important topic for policymakers and researchers alike.<sup>1</sup> Interest on this subject intensified during the recent global financial crisis, as many questioned whether high competition was partly to blame.<sup>2</sup> At the same time, the downfall of some institutions as a result of the crisis and the emergency measures taken by some governments to deal with this episode - such as mergers, bailouts, recapitalizations, and extension of guarantees- have led to concerns about the future of bank competition and its potential implication for access to bank finance.<sup>3</sup> How bank competition affects firms’ access to finance is in itself a much debated question in the economic literature and in policy circles and, as we discuss below, the theoretical predictions and the empirical evidence on this subject are mixed. Combining multi-year firmlevel surveys with panel country-level data on bank competition for 53 countries, this paper offers new evidence on the link between competition and firms’ access to finance. In particular, our paper evaluates whether competition improves access to finance and analyzes the extent to which different features of the environment in which banks operate affect the link between competition and access.

Theory provides ambiguous predictions regarding the impact of competition on access to finance. The conventional _market power hypothesis_ argues that competition in the banking system reduces the cost of finance and increases the availability of credit. On the other hand, the _information hypothesis_ argues that, in the presence of information asymmetries and agency costs, competition can reduce access by making it more difficult for banks to internalize the benefits of investing in building lending relationships, in particular, with opaque clients (Petersen and Rajan, 1995; Marquez, 2002; Hausewald and Marquez, 2006).

> 1 _The Economist_ magazine hosted a virtual debate on this topic on June 1st, 2011. See http://www.economist.com/debate/days/view/706

> 2 For example, Dell’Ariccia, Igan, and Laeven (2008) document that the rapid growth of credit in U.S. mortgage markets in the run up to the crisis was accompanied by a reduction in lending standards (lower loan application denial rates), which they argue was in part explained by the entry of new and large lending institutions.

> 3 See OECD (2009, 2010).

2
Exploiting the availability of a propensity-for-violence measure estimated by Dinarte (2018), we analyze if the ASP in this particular context generates a differential impact according to participant violence levels at baseline. As we explained before, this IVV measure does not capture effective violence, but a propensity. In addition, it is more comprehensive than misbehavior reports from schools because the IVV includes vulnerability to violence in different children’s domains such as school, home, and communities.

To study these heterogeneous treatment effects, we include in equation (1) an interaction between _Tij_ and an indicator _IV V_ _~~h~~ ighij_ ( _t−_ 1). This dummy classifies students whose percentile in the IVV distribution before the intervention was greater than the median at the group (C and T) and stratum level. Specifically, we estimate: where _θ_ 2 indicates the marginal impact of the intervention between treated students with high and low levels of propensity for violence. The rest of the variables are defined as in specification (1), and _Xij_ ( _t−_ 1) includes the dummy _IV V_ _~~h~~ ighij_ ( _t−_ 1).

# **_C. Spillovers on Nonenrolled Children_**

Besides the ASP’s direct effects, spillovers from treated students on their nontreated classmates can occur via at least two channels: (1) better school climate: if treated children are less disruptive during classes and/or behave better, this can reduce violence within schools and improve the learning process for all; (2) John Henry effect: the interaction between treated and nontreated students can allow the latter to imitate or learn some skills from the former. If any of these situations occurs, estimations from the specification (1) may be lower bounds of the ASP’s total impact due to the presence of spillovers from the program.

To calculate spillover effects, we have to use the sample of nontreated students. However, a plausible concern in this estimation is that students assigned to C group can be more motivated than their nonenrolled peers, proxied by their registration decision, and may have better socioemotional skills or behavior at school. Thus, by measuring spillovers on the C group we may be underestimating them, since the motivation effect seems to go in the same direction as the spillovers. Therefore, to capture the total indirect effect while accounting for a nonobservable motivation effect, we use only the sample of nonenrolled children.

Are the enrolled and nonenrolled samples similar on observables? Using administrative data on academic performance and misbehavior at school, we compare the characteristics of these two groups and find the groups are similar regarding academic performance and behavior at school 18
overdrafts.<sup>17</sup> The average size of our firms is about 100 employees, and it varies from one to over 1,700, with the median firm size of 25 employees. Thus, most of the firms in our sample are small and medium-sized enterprises. The median firm age is 12 years, while the average is almost 18. Firm age varies from one year old startups to close to 200 years old firms. In our sample, 62 percent of firms are in manufacturing (the rest are in services, retail or construction), 23 percent are exporters (classified as such if they export at least 10 percent of their total output), about 10 percent are foreign-owned, and about 5 percent of firms are considered government owned.

The Lerner index has an average of 0.25, a median of 0.23 and a standard deviation of about 0.07. The range is between 0.07, which indicates very low markups and, hence, high competition and 0.43, which implies very high markups and, therefore, low competition. Average concentration is high, with the top three banks comprising close to 60 percent of total bank assets. The lowest share of assets held by the top three banks is about 28 percent, while the highest is over 98 percent. The Herfindahl index varies between 0.05 and 0.74.

# **3. Regression Model**

Our goal is to evaluate the impact of bank competition on firms’ access to finance. To do that, we estimate the following simple model: where _Access_ is the indicator variable for whether firm i in country c at time t has a bank loan, line of credit, or overdraft; _Competition_ refers to either the Lerner index or to two measures of concentration: the share of assets held by the top three banks and the Herfindahl index. F and X represent firm-level (e.g., size, manufacturing, exporter, etc.) and country-level (e.g., inflation and financial development as proxied by private credit to GDP) control variables, respectively, described in the data section. We capture unobservable differences between countries by including country fixed effects (represented in equation 1 by ac) and we cluster errors at the

> 17 While in principle it would be interesting to distinguish between firms that have access to each of these types of products, unfortunately, the design of the Enterprise Surveys does not allow for this possibility.

8
**<u>Table 15: Regressions of log cell consumption per capita</u>**

||**Once married**|**Remarried**<br>**widows**|**Remarried**<br>**divorcees**|**Widows**|**Divorcees**|
|---|---|---|---|---|---|
|Age|0.010|0.033<sup>+</sup>|0.020|-0.008|-0.007|
||(0.010)|(0.021)|(0.019)|(0.015)|(0.020)|
|Age squared|-0.000|-0.0002|-0.000|0.0001|-0.00003|
||(0.000)|(0.0002)|(0.000)|(0.0001)|(0.0002)|
|Age at first marriage|0.010<sup>**</sup>|0.002|0.008|-0.002|0.011|
||(0.004)|(0.013)|(0.014)|(0.007)|(0.009)|
|ln household size|-0.315<sup>***</sup>|-0.035|-0.331<sup>***</sup>|-0.213**|-0.233**|
||(0.059)|(0.147)|(0.094)|(0.087)|(0.111)|
|ln cell size|-0.193<sup>***</sup>|-0.096|-0.084|-0.049|-0.337**|
||(0.053)|(0.136)|(0.172)|(0.085)|(0.138)|
|Share of kids in cell|<br>-0.442<sup>***</sup>|<br>-0.658***|<br>-0.749<sup>**</sup>|<br>-0.461**|<br>-0.757***|
||(0.101)|(0.223)|(0.353)|(0.187)|(0.302)|
|Belongs to head’s cell|0.088|0.020|-0.126|-0.010|0.194|
||(0.085)|(0.170)|(0.165)|(0.10)|(0.153)|
|Cell head|-0.002|0.180|0.014|0.031|0.101|
||(0.052)|(0.135)|(0.209)|(0.102)|(0.151)|
|French school|0.308<sup>***</sup>|0.475***|0.442<sup>***</sup>|0.460***|0.525***|
||(0.055)|(0.149)|(0.128)|(0.111)|(0.132)|
|Koranic school|0.006|0.469***|0.222<sup>+</sup>|0.249***|0.071|
||(0.066)|(0.177)|(0.136)|(0.089)|(0.191)|
|Fostered before age 15|<br>0.030|<br>0.176|<br>-0.043|<br>-0.045|<br>-0.153|
||(0.053)|(0.145)|(0.117)|(0.125)|(0.134)|
|Has son 18 or older|<br>-0.044|<br>-0.079|<br>0.065|<br>0.070|<br>0.251**|
||(0.038)|(0.112)|(0.113)|(0.092)|(0.126)|
|Husband informal|0.042|0.187*|0.269<sup>**</sup>|-0.079|-0.088|
||(0.057)|(0.109)|(0.121)|(0.126)|(0.207)|
|Husband formal/public|0.209<sup>***</sup>|0.721***|0.355<sup>**</sup>|0.105|0.30|
||(0.060)|(0.177)|(0.178)|(0.138)|(0.228)|
|Husband other|-0.071|0.399**|0.269|-0.002|0.022|
||(0.102)|(0.192)|(0.228)|(0.143)|(0.280)|
|Polygamous marriage|-0.026|-0.090|-0.013|-0.123|0.110|
||(0.049)|(0.146)|(0.140)|(0.082)|(0.115)|
|Urban residence|0.582<sup>***</sup>|0.358**|0.223<sup>+</sup>|0.628***|0.362**|
||(0.074)|(0.148)|(0.141)|(0.129)|(0.152)|
|Constant|12.687<sup>***</sup>|10.831***|12.579<sup>***</sup>|12.635***|12.963***|
||(0.229)|(0.148)|(0.535)|(0.468)|(0.474)|
|R-squared|0.37|0.46|0.35|0.28|0.48|
|Observations|2,082|146|241|394|160|

Note: Robust standard errors are given in parentheses, clustered at the sampling unit level. “Husband” is the current one for once-married women, and ex-husband for all other groups. Ditto for polygamous marriage. Husband in agriculture is the left out category.<sup>+</sup> _p_ < 0.12,<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01 Source: Authors’ estimations using PSF1.

45
# **1. Introduction**

Even before the earthquake of January 2010, Haiti was the poorest country in the Western Hemisphere, with 78 percent of its citizens living on less than US$2 a day. The returns to schooling in the country are substantial, which suggests that education can offer Haitians the hope of moving out of poverty, but school is out of reach for many Haitian children<sup>1</sup> . Three characteristics distinguish Haiti’s education system from others in the region: the low attendance rates for primary school, the fact that many primary school students are over-age for their grades, and the minor role played by the public sector. Only 83 percent of those ages 6-14 were attending school in 2005, with much lower attendance rates for the poor. While the mandated age for entering grade 1 is 6, the actual mean age is nearly 10, and students in grade 6 are on average almost 16, (i.e. five years older than expected). Unlike in other countries in the region, schools in Haiti are operated chiefly by non-public organizations.<sup>2</sup> Foremost among these are religious groups, which account for 47 percent of primary schools. An additional 28 percent of schools are classified as “secular independent” (a category which includes those that are for profit) and a smaller number of schools are operated by community groups and other non-governmental organizations. To shed light on Haiti’s unusual education system, we examine the historical evolution of school attainment, the correlates of school attendance, the determinants of school fees, and the market structure of schooling.

Since the 1930s, the relative share of public schools in Haiti has declined steadily over time, falling to just 8 percent of all primary schools in 2003. At the same time, the expansion of the non-public sector has increased education access. The primary completion rate grew from 35 percent for Haitians born 1950-1954 to 88 percent for those born a generation later (1980-1984). Despite this progress, as of the most recent (2005) survey data, severe gaps between rich and poor remained. Primary attendance and completion rates for those in the wealthiest quintile are comparable to those of middle income countries in the region, while the rates for the poorest quintile are similar to those of the poorest countries in Africa. Another key feature of the Haitian education system is that many students are over the prescribed age for their grades. Multivariate analysis presented in this paper indicates that parents’ education, household composition, and

> 1 See World Bank (2006) for estimates of returns to schooling in Haiti based on 2001 survey data. Overall, the estimates for the marginal return from an additional year of education are 15.6-19.7 percent for wage earnings and 14.0-17.2 percent for total earnings (including wages and self-employment earnings).

> 2 In Haiti, all non-state providers of education are collectively referred to as the “non-public” sector. In this paper, the terms “private” and “non-public” are used interchangeably.

2
# **1. Introduction**

Influenced by the work of Hernando de Soto (1989), and the World Bank/IFC’s _Doing Business_ project, efforts to make it easier for individuals to register a business have been the most common form of regulatory reform over the past decade. Since 2004, 75 percent of the countries included in the Doing Business survey have adopted at least one reform making it easier to register a business (IFC, 2009). These is some evidence that, starting from a point of burdensome entry, big reductions in barriers to entry are associated with increases in firm registrations. Djankov et al. (2002) show that countries with more burdensome entry regulations have larger informal sectors, while Klapper et al. (2006) find that costly entry regulations are associated with the creation of fewer limited liability companies. Reforms which dramatically reduced the time taken to start a business led to increases in formalization in Mexico (Bruhn, 2011; Bruhn 2012; Kaplan et al, 2011), Peru (Mullainathan and Schnabl, 2010), and Portugal (Branstetter et al, 2010).

However, recent research has suggested there is little benefit to reform if the reduction in the barriers to entry are not large (Klapper and Love, 2011), and despite these reforms, the majority of firms in many developing countries remain informal. That is, although some informal firms register when a reform makes it easier to do so, this number is small compared to the overall stock of informal firms. While there is evidence that distance and information serve as barriers to some firms registering who would benefit from doing so (McKenzie and Sakho, 2010), several recent experiments which have provided firms with information on how to register, and which have lowered the cost of registering, have found very few informal firms formalize as a result (de Mel et al, 2012 in Sri Lanka; Jaramillo, 2009 in Peru; Andrade et al, 2012 in Brazil).<sup>1</sup> One explanation appears to be that very few informal firms seem to benefit from formalizing. This raises the question of whether government efforts to extend simplification efforts to firms outside the main cities will lead to increased formalization rates, given the likelihood that such firms may be even less likely than firms in large cities to benefit from many of the supposed benefits of formalizing like increased customer bases, access to government programs and contracts, and available of lower cost financing sources. Furthermore, we know of no evidence to show whether governments gain more from such a process in taxes than they spend implementing these reforms.

> 1 Similar results have also been found by Giacomo de Giorgi and Aminur Rahman in an ongoing information experiment in Bangladesh (personal correspondence with authors).
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