rafmacalaba/datause-encoder-data huggingface dataset:
post supply of credit, effectively worsening the financial access of poorer borrowers.<sup>8</sup> Since, analogous to many households in developing countries, all households in the sample are producers as well as consumers (Banerjee and Duflo, 2007), this paper also relates to the literature on credit and investment constraints among low-income households and micro-entrepreneurs. Dupas and Robinson (2012) study the impact of access to savings accounts on microenterprise development, and provide evidence of significant barriers to saving that constrain productive investment. de Mel et al. (2008) provide unconditional cash transfers to micro-entrepreneurs in Sri Lanka and find similar evidence of significant investment constraints and high returns to capital. In an evaluation of a program that extended access to microfinance in India, Banerjee et al. (2010) find a positive but economically small impact on business creation. Karlan and Zinman (2010) study the effect of randomized access to credit on microenterprise investment in the Philippines and find no impact. Similarly, Kaboski and Townsend (2011) study a field experiment expanding access to microcredit in Thailand and find strong consumption effects but no impact on investment.

The present study differs from the literature on investment constraints in two important ways. First, in contrast to studies analyzing a credit expansion, I focus on a population of existing rather than new bank clients, who are excluded from bank credit due to high levels of pre-existing debt. Second, unlike programs expanding access to credit, debt relief (like a bankruptcy settlement) reduced debt on the books, but did not automatically provide beneficiaries with improved liquidity.

The remainder of the paper is structured as follows. Section two provides details on the debt relief program and eligibility criteria. Section three presents a simple model of the household’s investment decisio to motivate the empirical analysis. In section four, I outline the identification strategy. Section five describes the dataset and household survey. Section six presents the results and Section seven concludes.

> 8See also Djankov, McLiesh and Shleifer (2007) who show that the protection of creditor rights has important effects on _ex-ante_ incentives for the provision of private credit. Visaria (2009) and von Lilienfeld-Toal, Mookherjee and Visaria (2012) provide empirical evidence on the effect of strengthening creditor rights using the introduction of debt recovery tribunals in India.

5
# **3. Cross-country covariates of SME financing**

This section discusses macroeconomic and institutional factors that affect lending to SMEs using cross-country data from the _Financial Access_ database introduced above, and common indicators of growth and development. We use the standard cross-country regression framework, which has both advantages and disadvantages. This framework is useful for obtaining a global picture of SME lending volumes, and for informing the policy debate from a global perspective. However, it is not necessarily the best tool to derive country-specific policy implications, as it does not consider within-country variations in SME lending.

To what extent is cross-country variation in the volume of SME financing driven by differences in the definition? This is an important first question before starting more detailed analyses of the data, since SME definitions vary greatly across countries, as evidenced in Section 2.1 above. This variation in SME definitions might in fact cause differences in levels of SME financing across countries rather than indicators of economic growth and development.

Estimation results are presented in Table 4 and show no consistent and robust correlations between the levels of SME finance and definition criteria. We do not find statistically significant correlations between the value of SME financing and the maximum number of employees used as a criterion to define SME.<sup>10</sup> In a smaller sample the maximum sales volume criteria is positively correlated with the ratio of SME loans to GDP but not with the share of SME loans in total commercial bank loans. Moreover, the correlation between the ratio of SME loans to GDP and sales volume is not statistically significant once we control for income per capita.

These results provide a certain level of comfort and allow us to proceed in analyzing SME finance data using national definitions. Even though the definition introduces a substantial degree of heterogeneity in the data, it does not appear to influence the volume of SME financing reported by countries in a manner that would prevent cross-country comparison.

> 10 All regressions exclude countries where the higher cutoff for the definition of SMEs as defined by number of employees is 1,000 employees or more.  In our sample, only South Korea and China fall into this category.

10
Table 8 Average Impact for Firms Receiving overdraft facility.

|**Industry**|**B**|**everages**||||**Food**||||**Garments**|||**Met**|**als and machi**|**nery**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Impact**<br>**Variable**|**Obs**<br>**.**<br>**ATT**|**SE**|**z**|**P>z**|**Obs.**<br>**ATT**|**SE**|**z**|**P>z**|**Obs.**<br>**ATT**|**SE**|**z**|**P>z**|**Obs.**<br>**ATT**|**SE**|**z**|**P>z**|
|Technical|134||||107||||243||||515||||
|efficiency|-0.02649|0.025574|-1.04|0.3|0.03408|0.034031|1|0.317|-0.0262|0.018725|-1.4|0.162|-0.00291|0.0122|-0.24|0.811|
|Dummy|134||||107||||243||||515||||
|exporter|-0.10264**|0.051905|-1.98|0.048|0.087419|0.066266|1.32|0.187|-0.03621|0.067501|-0.54|0.592|0.033311|0.047338|0.7|0.482|
|Dummy<br>production<br>workers trained|129<br>-0.02158|0.099918|-0.22|0.829|103<br>0.01824|0.127035|0.14|0.886|236<br>-0.03743|0.072809|-0.51|0.607|496<br>0.047712|0.04969|0.96|0.337|
|Dummy<br>Expenditures of|103||||86||||207||||449||||
|R&D|0.018612|0.089425|0.21|0.835|-0.07904|0.08403|-0.94|0.347|-0.02203|0.063987|-0.34|0.731|0.119596***|0.0475|2.52|0.012|
|Note: * significan<br>Dummy variables<br>Impact variables:<br>Matching variable<br>Weighting matrix<br>Number of match<br>Number of match|t at 10%; **significant at 5%<br>take on the value of 1 if the f<br>Technical efficiency variable<br>s: Age, Age squared, dumm<br>: inverse variante<br>es (m) = 4<br>es,robust std. err.(h)= 4|; and *** signif<br>irm has the stat<br>is continuous,<br>y variables for s|icant at 1%<br>ed characte<br>Exporter, P<br>ize (Large,|.<br>ristic and 0<br>roduction w<br>Medium an|otherwise.<br>orkers trained and Expend<br>d Small), dummy variable|itures of R&D<br>for high/ upp|and are not.<br>er-middle inc|ome country a|nd dummy variable for|domestic owners|.||||||

Source: Authors‘ estimates 23
<!-- Start of picture text --> Sales: Agricultural land Others: Agricultural land<br>0 10 20 30 0 10 20 30<br>Distance to the DLO in km Distance to the DLO in km<br>Sales: Residential land Others: Residential land<br>0 10 20 30 0 10 20 30<br>Distance to the DLO in km Distance to the DLO in km<br>2014 2015 2014 2015<br>3 1.5<br>2 1<br>1 .5<br>No. of transactions<br>0 0<br>3 1.5<br>2 1<br>1 .5<br>No. of transactions 0 0<br><!-- End of picture text -->

**Figure 3: Number of quarterly registered transactions with transfer of ownership rights by distance of cell to the DLO and year**

<!-- Start of picture text --> Sales: Agricultural land Others: Agricultural land<br>0 10 20 30 0 10 20 30<br>Distance to the DLO in km Distance to the DLO in km<br>Sales: Residential land Others: Residential land<br>0 10 20 30 0 10 20 30<br>Distance to the DLO in km Distance to the DLO in km<br>With SLM No SLM With SLM No SLM<br>1<br>4<br>.8<br>3<br>.6<br>2<br>.4<br>1 .2<br>No. of transactions<br>0 0<br>1<br>4<br>.8<br>3<br>.6<br>2 .4<br>1 .2<br>No. of transactions 0 0<br><!-- End of picture text -->

**Figure 4: Number of quarterly registered transactions with transfer of ownership rights by distance of cell to the DLO and sector land manager in 2015**

26
# **Livelihoods and the Allocation of Emergency Assistance after the Haiti Earthquake**

Damien Echevin<sup>1</sup>

**Keywords** : Livelihoods; Assistance programs; Earthquake; Natural Disaster; Haiti. **JEL Codes** : H53; I38; Q18

> 1 Université de Sherbrooke. Email: damien.echevin@usherbrooke.ca. I thankfully acknowledge Gary Mathieu from CNSA-Haiti for providing the data used in this paper. Francesca Lamanna, Ana Maria Oviedo and Peter Horjus also provided very useful comments and suggestions. Marc-André Morin provided valuable research assistance. All errors and opinions expressed in this paper remain mine.
**Table 4.5 Private Companies by Sector, 2000**

|**Sector**|**Firms**|**Share**|
|---|---|---|
|Trade and Services|18,000|57.1%|
|Manufacturing|6,617|21.0%|
|Construction|2,400|7.6%|
|Transport|800|2.5%|
|Mining|136|0.4%|
|Other|3,566|11.3%|
|**_Total_**|**_31,519_**|**_100%_**|

Source: GSO, 2001 27. Within the manufacturing sector, firms are further highly concentrated in the food and beverage processing subsector—which accounts for 45 percent of all private manufacturers. Considering that four-fifths of the country still lives in the countryside and is closely tied to agriculture, the food and beverage processing industry holds special significance with regard to its potential to stimulate economic activity in the countryside. These firms have the potential to pass on important information on markets and technologies to those in the countryside from whom they source their inputs. Food processing firms tend to be quite small with labor forces of 10-15 workers, though in 2000 there were 36 firms in the subsector with 500 or more workers. Garments and leather products producers are also important manufacturing subsectors as a result of their high per firm employment of 220 and 624 workers, respectively.

**Table 4.6 Private Companies by Manufacturing Subsector, 2000**

|**Subsector**|**Firms**|
|---|---|
|Food and Beverages|2,952|
|Textiles|172|
|Garments|317|
|Leather Products|109|
|Wood Products|491|
|Paper Products|210|
|Chemical Products|188|
|Plastic Products|256|
|Non-metallic Products|696|
|Metallic Products other than Machinery|290|
|Other|936|

Source: GSO, 2001 28. About one third of the manufacturing companies in the survey sell their products directly in foreign markets and generate an average 70% of their revenues from foreign customers. Exports are mainly concentrated on Asian and European markets. As seen in earlier research, companies with 100 or more workers were particularly likely to be heavily oriented towards exports. Fifty-seven percent of such larger manufacturers named a foreign customer as their most important customer.<sup>22</sup> Compared with firms in

> 22 This number is actually lower than the number found in research by MPDF in 1999.  The difference, however, is most likely the result of the replacement of the highly export-oriented Dong Nai province with rural Long An province in the South—rather than representing a slump in export orientation.  In fact, macro statistics indicate that the private sector’s role in export has risen in the last couple years.

14
# Table 19. (cont.)

||**_Prob[Activity--_**|**_jI _**X,_dummy'kl_|Marginal||Prob(Activi%=j|**I****_X, dummy=k<_**|**Marginal**|
|---|---|---|---|---|---|---|---|
|**Variable**|**k=1**<br>**Survey 98**|**k=0**<br>**Survey 94**|**Effect**|**Variable**|**k=1**<br>**Survey 98**|**k=O**<br>**Survey 94**|**Effect**<br>**E**|
|**_j_**_= Student_||||_= Housework_||||
|98 Survey|0.06|0.09|-0.031|98 Survey|0.08|0.04|0.034|
|Women|0.07|0.06|0.003|Women|0.17|0.01|0.160|
|Age|||-0.005|Age|||0.001|
|South-East Area|0.06|0.06|0.000|South-East Area|0.06|0.07|-0.009|
|Center-South Area|0.05|0.07|-0.014|Center-South Area|0.05|0.07|-0.021|
|North-East Area|0.03|0.07|-0.040|North-East Area|0.03|0.07|-0.040|
|North-West Area|0.04|0.07|-0.029|North-West Area|0.06|0.07|-0.005|
|Center-North Area|0.04|0.07|-0.033|Center-North Area|0.05|0.07|-0.025|
|Pacific Area|0.05|0.07|-0.014|Pacific Area|0.05|0.07|-0.023|
|92 Cohort||0.07||92 Cohort||0.07||
|93 Cohort||0.06||93 Cohort||0.07||
|94 Cohort|0.10|||94 Cohort|0.04|||
|96 Cohort|0.07|||96 Cohort|0.05|||
|97 Cohort|0.10|||97 Cohort|0.03|||
|Lower-secondary HH|0.08|0.06|0.022|Lower-secondary HH|0.06|0.07|-0.011|
|Upper-secondary HH|0.12|0.06|0.063|Upper-secondary HH|0.07|0.07|0.003|
|University - +HR|0.13|0.06|0.063|University - + HH|0.05|0.07|-0.020|
|Do not know HH|0.04|0.06|-0.028|Do not know HH|0.11|0.07|0.041|
|GDP per capita|||-0.016|GDP per capita|||0.014|

Base categories for independent variables: 94 Survey, men, Center area **(D.F.** and Mexico), 91 cobort, none or primary school education of household head (HH). Units for GDP thousands of 1998 pesos.

44
Results are presented in Table 6. As in other papers in the literature (e.g., Bento and Restuccia, 2018), GDP per capita shows up always with a positive and significant coefficient. Weighting the estimated _φ_ s by the average share of the sector from 1970 to 2007, we obtain a mean GDP elasticity of the average number of employees (denoted by _φ_ ) equal to 0.217 and 0.208 in the OLS and IV cases, respectively.<sup>21</sup> Again, we prefer the IV estimate. We are, however, interested in the _Amt_ elasticity of firm size. From equation (13) that takes into account the induced effect of _Amt_ on the different inputs and equation (28), we can obtain _ψ_ as _φ_ divided by 1 _− α − γ_ , which implies _ψ_ = 1 _._ 390.

Estimates for grid capacity in Table 6 are significant for agriculture and services with OLS but become insignificant with IV, which uses a much lower number of observations. Nonetheless, as above, we choose the IV estimates for the electricity-infrastructure elasticity of operating fixed costs, that is, ( _θa, θm, θs_ ) equal (0.023, _−_ 0.103, 0.081). Observe that this means that the provision of electricity helps to reduce fixed costs in manufacturing but increases them in agriculture and services.<sup>22</sup> During the simulations, we scale up grid capacity by a factor of 5.05 to generate an average GDP share of electricity infrastructure in Brazil from 1971 to 2007 of 1.81 percent;<sup>23</sup> this is calculated from estimates in Calderon and Serven (2017) and Frischtak (2017). These two sources also provide an average investment share in infrastructure as a fraction of GDP of 0.0382.<sup>24</sup> This last number can be used to calibrate the mismanagement parameter _ξ_ as follows. According to FIESP (2010) the average annual cost of corruption in Brazil can be between 1.38 and 2.3 percent of the country’s total GDP. Taking a conservative intermediate value of 1.45 percent, we estimate _ξ_ to be equal to 1 _−_ 0 _._ 0145 _/_ 0 _._ 0382 = 0 _._ 6204.

Some of the parameters are normalized. In particular, _ei_ = 1 for all _i_ , _Am_ 1970 = 1, and

> 21Bento and Restuccia (2018) find a larger elasticity of 0.3 in a wide cross-section of countries. They do not though disentangle the effects of electricity infrastructure.

> 22As will become clear, assuming that electricity infrastructure does not affect fixed costs would not have a significant impact on our main results.

> 23The rescaling of the electricity index should not have any impact on the results. Notice, for example, that the estimated elasticities are not affected, as the scaling factor would show up in the log regression in the intercept.

> 24Infrastructure include land transport, electricity, telecommunications, and water and sanitation.

38
Despite their importance in these businesses, the value of women's entrepreneurial activities is not adequately reflected -- if at all -- in the national accounts. There have been very few attempts to assess their contribution to the economy or to analyze the relative performance of men and women in the informal economy. This is particularly true in Peru and other Latin American countries where such work has gone largely unnoticed, with the possible exception of domestic work.

Moreover, official Latin America data'° do not give accurate information about women's economic activities. Most of the empirical economic research on family businesses has focused on agricultural activities or on the activities of self-employed urban men. Agricultural research tends to ignore informal nonfarm economic activities in rural areas. And most of the research on the selfemployed analyzes individuals rather than the enterprise, ignoring the contributions to income of capital, nonlabor inputs, and the labor of women and children. These last are typically excluded because most surveys report them as unpaid family workers, while men are usually reported as paid family workers (Chiswick 1983).11 This analysis focuses on the family business rather than the self-employed individual, thereby incorporating enterprise characteristics -- capital,<sup>2</sup> location, nonlabor inputs -- and the labor of all family workers.<sup>1</sup>

**10** See Recchine de Lates and Wainerman 1986; Bunster and Chaney 1985; Babb 1984.

11 Often it is not clear whether these self-employed earnings refer to the value of gross sales or to the value of net production, that is, sales less cost of materials and other inputs.

**12** For related approaches see Blau (1985), Chiswick (1983), Strassmann (1987), Vijverberg (1988), and Teilhet-Waldorf and Waldorf (1983).

11
**Figure 4 Share of Households in the Largest Lineage Group (1995) and Probablity of Significant Land Reallcoation in the Village (1995-2003)**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Share of Households in the Largest Patrilineal Clan (1995)<br>.35<br>.3<br>.25<br>.2<br>.15<br>Probability of Significant Land Reallocation (1995-2003)<br><!-- End of picture text --> Source: 2004 Rural Household Social Network, Labor Allocation and Land Use Survey, RCRE, Ministry of Agriculture.

Note: the share of households in the largest family lineage group (patrilineal clan) is measured in 1995, which is the initial year of the analysis sample.

_Land Tenure Insecurity and Migration, Page 41_
22 The last equality indicates that the conversion factor must be selected appropriately and should depend on the prices and quantities of the agricultural commodities (Rao et al., 1991). Failure to take this into account introduces serious biases in the measurement. Recent advances by Sunmmers and Heston (1988) solve some aspects of this problem by using the purchasing power parity of currency'".

2.5.2.2. Comparability of prices and exchange rates Prices and exchange rates are not always easily comparable across countries. To recall, real output price is the ratio of nominal output price to other prices. At the micro level, we argued that there are several candidates as deflator, all depending on the farmer's behavior. At the aggregate level, the usual candidate is the consumer price index (CPI).

There are two sets of problems here. First, CPI, on which RER and real producer price are based, is not strictly comparable across countries as it is generally based on different baskets, different weights and different base periods. Second, misaligrnment is not taken into account in the nominal exchange that helps convert foreign currency into local currency (or vice versa). As the empirical studies reveal, few authors pay attention to this problem.

Similarly to the case of cross-border output comparison, some authors use a wheat equivalent measurement to solve the twin problems. Peterson (1969, p.l3-14) formulates his price as follows: _"P_ = wm/' } _w,_ where Pi is the average domestic price of the ith commodity during a given period; Pi is the average world market export price in US dollars of the ith commodity during a certain period; P., average world market export in US dollars of wheat during a certain period; **it,,,** proportion of the ith commodity in total output of each country in wheat equivalent units; and P, overall average wvheat equivalent price for each country during a certain period. To avoid the problem of exchange rate distortion as in the case of overvaluation, these average prices (expressed in domestic currency) are converted to price ratios by dividing by the weighted average of currency

> 1<sup>3</sup> "Purchasing Powver Currency (PPC) is understood as the number of units required to purchase the same amount of goods and sern-ices as say, one US dollar would buy in the United States." ( World Bank, 1993, p.2).
as an instrument clearly predicts secondary schooling, and moreover, secondary school completion. The estimates, however, drop to insignificance when estimating the probability of attaining more than 12 years of schooling: the KCPE score that induces a marginal student to attend and complete seondary school does not induce the student to attend college.

# **4.3 Estimation of outcomes**

## **4.3.1 Human capital**

I begin with analysis of the impact of schooling on human capital. The KLPS2 survey includes a commonly used test of cognitive ability—a subset of Raven’s Progressive Matrices—and an English-language vocabulary test based on the Mill Hill synonyms test. Adaptations of both measures have been used internationally for several decades, and each captures different aspects of intelligence.<sup>32</sup> I standardize both outcomes so that they are measured in terms of standard deviations in the KLPS2 population, and show both OLS and 2SLS results for a combined Z-score<sup>33</sup> and separately by test in Panel A of Table 3: completing secondary school improves performance on these tests by 0.6 standard deviations, with very similar estimates given 32Though standardized to have mean zero and standard deviation one in the population, in Table 1 these two cognitive measures have positive mean and standard deviations slightly less than one, because these summary statistics are only shown for the sample with a restricted range of first KCPE scores. The “Matrices” are often considered to measure something akin to “fluid” intelligence, while the vocabulary test measures something more related to what specialists in the field call “crystallized” intelligence (Cattell 1971). The relationship of the two measures appears similar here to in other settings: in these data, as elsewhere (Raven 1989), their correlation is near 0.5.

> 33The combined Z-score is equivalent to the “mean effect” of Kling, Liebman, and Katz (2007) when no data are unevenly missing and the estimation procedure is the same for both.

20