rafmacalaba/datause-encoder-data huggingface dataset:
shrunk slightly, to 6.3 million lenders, and although it remains very large, it no longer stands out, because most of the other major microfinance institutions have continued to expand. This raises a very basic question, which we return to below, which is why, unlike other microfinance institutions, the VF essentially stagnated after 2004.

A second measure of the importance of the Village Fund is by looking at the proportion of households for whom at least one member borrowed from it. The monthly time series is shown in Figure 1, and uses information from the Thailand Socio-Economic Surveys of 2002 (when the VF began to operate), 2004, and 2006-2010; there were no SES surveys in 2003 or 2005. Participation in the VF peaked in 2004, at about one household in three, and stabilized at approximately 30 percent of households thereafter. Participation is substantially higher in rural areas (roughly 40 percent) than in urban areas (about 10 percent).

<!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>2002 2004 2006 2007 2008 2009 2010<br>Proportion of households borrowing from VF<br><!-- End of picture text -->

**Figure 1.  Proportion of Households Borrowing From Village Fund: Rural (top), National (dashed), and Urban (bottom).**

_Source:_ Thailand Socio-Economic Surveys of 2002, 2004, and 2006 through 2010. Note: The outlier in mid-2010 is based on an unusually small sample, and cannot be considered to be representative.

Some further information on the evolution of the VF is provided in Table 2. Households appear to be serial borrowers from the Village Fund, with most of them borrowing year after year, just one in ten saying that they do not wish to borrow again, and about 95% reporting that they repaid their most recent VF loan in full.

4
# **VI.** LABOR **MARKETS.** REGULATION **AND** PSD

In spite of a seemingly strangling regulatory environment for employment and relatively high levels of worker compensation, entrepreneurs operating in CMte d'Ivoire do not consider labor as one of their major constraints. The reason -- in addition to simply indicating that there are worse constraints -- appears to be that they are finding legal (or, at least, non-prosecutable) means to attain flexibility and reduce labor costs. Hence, labor does not appear to be a binding constraint on enterprises operating in the formal sector, because the division between formality and informality is blurred in yet another dimension.

Segments and Linkages In a sense, there are three urban labor markets: the government, the formal private sector, and the informal sector. However, the links between these markets are stronger than is commonly represented and the distinctions between them smaller. In recent years, informal employment has grown rapidly, while formal employment in the private sector has declined. While the informal sector is notoriously difficult to measure quantitatively, official statistics acknowledge urban informal sector employmen **t** o be more than three times that of formal sector employment.

Table 9: Employment Statistics and Estimates (in 000's and annual % change over five year intervals)

||**1980**|%change|**1985**|Z**change**|1990 est.|
|---|---|---|---|---|---|
|Agricultural<br>Sector<br>|3,125<br>|<br>2.1<br> <br>|3,460<br>|<br>2.5<br> <br>|3,900<br>|
|Modern Sector|470|<br>-3.0|405|<br>-1.0|385|
|Informal Sector|719|<br>5.0|910|<br>5.7|1,200|
|TotalLaborForce|4,314||<br>2.1|[4.m|2.8|6,335|

Source: Direction de L'Efnploi, Ministere du Travail Government has traditionally provided the principal source of formal sector employment for Ivoirian graduates with a higher education. It established a generous level of compensation that appears to have shaped general expectations of skilled workers about the standard of living they deserve.' In spite of two pay freezes in the 1980's, public sector salaries average 10.3 times the per capita GDP, even before adding in generous benefits and access to informal sources of income.5" I Several entrepreneurs criticized the educational system for excessively orienting students towards being becoming civil servants, rather than business people. As a result, until recently, business was regarded as an occupation second to government service or teaching.

**S1** This high average apparently masks substantial differences between positions. Notably, teachers have much higher than average salaries.

40
at about 15% of GDP. This is similar to the median levels in the MENA and SAR regions which have the largest sovereign exposures. Second, banking systems appear to have taken on larger balance sheet exposures to sovereign securities. Using a restricted sample of banks that report both sovereign securities and total assets data, the sovereign securities holdings for the median banking system has doubled since 2007, from 5.1% to 10.9% of total assets. For the banking systems with high exposures (75<sup>th</sup> percentile), the holdings represent 16.4% of banking assets. Those numbers are lower than the ones from Dell’Ariccia et al. (2018), and closer to the Gennaioli et al (2014), as our sample covers 1,839 banks from 138 EMDEs.<sup>7</sup> Several EMDEs face a situation of high government debt _and_ elevated bank exposures to the sovereign – this suggests that risks related to the sovereign – nexus may have increased (Figure 5, A and B, Figure 6A). However, the latter could happen because there has been a shift in portfolio composition shares away from high‐capital risk weight exposures (i.e., private borrowers) to capital charge exempt government. As a result, the buffers may appear healthier than they are.

- **_Government debt versus banks’ total government exposure to GDP._** Figure 5A shows the banks’ total claims on central, state, and local government and non‐financial public entities to GDP. This figure suggests that countries with high government debt and high banks’ exposure to the government may face elevate risk.

- **_Government debt versus banks’ government securities to total bank assets._** Figure 5B takes a different perspective on bank exposures: sovereign securities holdings relative to the size of the banks’ balance sheets. Sovereign holdings of 15% or more of the bank balance sheet is quite high and many countries are well above this.

- **_EMBI spreads versus financial institutions’ probability of default._** Figure 6A takes another approach to get a sense of the nexus. Countries with both a high median

> 7 Dell’Ariccia et al (2018) established that the median bank holdings of government bonds is 16.7% of bank total assets in EMDEs, for a sample of 126 banks, between 2005 and 2014. The sample includes only banks that continuously reported information between 1999 and 2014. Gennaioli et al (2014) estimated that median bank holdings of government bonds in 8.11% (8.79%) of total assets for a sample of 3,976 (2,058) banks in 157 (128) Non‐OECD countries (and also reported data for at least 2 consecutive years and other variables such as leverage, profitability, cash and short‐term securities, interbank balances), between 1998 and 2012.

21
|**Table 3: Area plan**|**ted wit**|**h summer c**|**rops and un**|**cultivated l**|**and, nationa**|**l and by VC**|**and confli**|**ct status**|||
|---|---|---|---|---|---|---|---|---|---|---|
|||**2019**|**2020**|**2021**|**2022**|**2023**|**2019–21**|**Difference t**|**o 2019–21**|**No. of**|
|||||||||**absolute**|**%**|**VCs**|
|**Panel A Summer c**|**rops**||||||||||
|Total<sup>a</sup>||1,694.3|1,729.6|1,632.4|1,615.7|1,716.9|1,685.4|31.5|1.87|10,556|
|**2023 conflict indic**|**ator**||||||||||
|Cropland damage|Yes|3,491.4|3,425.0|2,970.8|2,433.4|2,424.1|3,295.7|-871.6|-26.45|827|
|from imagery|No|1,541.5|1,585.5|1,518.7|1,546.2|1,656.8|1,548.6|108.3|6.99|9,729|
|Any conflict|Yes|3,262.1|3,233.6|2,793.2|2,307.7|2,322.0|3,096.3|-774.3|-25.01|1,131|
|public source|No|1,506.1|1,549.1|1,493.2|1,532.7|1,644.3|1,516.1|128.2|8.46|9,425|
|**2022 conflict indic**|**ator**||||||||||
|Cropland damage|Yes|2,973.1|2,941.7|2,625.1|1,966.3|2,112.0|2,846.6|-880.3|-30.92|650|
|from imagery|No|1,610.4|1,650.1|1,567.3|1,592.7|1,691.0|1,609.2|-16.5|-1.03|9,906|
|Any conflict|Yes|2,561.5|2,558.1|2,319.8|1,926.9|2,041.8|2,479.8|-552.9|-22.30|1,895|
|public source|No|1,504.5|1,548.3|1,482.0|1,547.7|1,645.9|1,511.6|36.0|2.38|8,661|
|**Panel B: Winter c**|**rops**||||||||||
|Total<sup>a</sup>||942.0|750.6|945.1|837.3|675.4|879.2|-203.8|-23.18|10,002|
|**2023 conflict indic**|**ator**||||||||||
|Cropland damage|Yes|2,271.3|2,074.4|2,493.3|2,090.4|1,251.7|2,279.7|-1,028.0|-45.09|865|
|from imagery|No|816.1|625.2|798.6|718.7|620.9|746.6|-125.7|-16.84|9,137|
|Any conflict|Yes|2,273.6|1,970.1|2,452.4|2,074.5|1,443.7|2,232.0|-788.3|-35.32|1,284|
|public source|No|745.9|570.9|723.2|655.1|562.3|680.0|-117.7|-17.31|8,718|
|**2022 conflict indic**|**ator**||||||||||
|Cropland damage|Yes|2,013.4|1,726.8|2,051.4|1,702.1|819.9|1,930.5|-228.4|-11.8|395|
|from imagery|No|897.9|710.4|899.7|801.8|669.5|836.0|-34.2|-4.1|9607|
|Any conflict|Yes|1,595.1|1,342.7|1,703.0|1,440.7|1,025.5|1,546.9|-106.2|-6.9|1786|
|public source|No|800.0|621.8|780.4|706.2|599.4|734.1|-27.9|-3.8|8216|

_Source:_ Computations based on data from crop maps for winter and summer crop area and Sentinel imagery in 2019–23. a. The reported absolute and percentage differences are between 2023 and the pre-conflict (2019–21) average, and the figures between 2022 and 2019–21 are -69.7 ha and -4.14% for summer crops and -41.90 and -4.77% for winter crops. VC = village council.

20
30 the usual misreporting and measurement issues in an income survey like the PNAD). It is therefore possible to simulate the impact of these counterfactual changes in wages on the distribution of household incomes, and on the inequality and poverty levels associated with it. These results are reported on Table 9, and in Figures 12-17, for the same inequality indices used so far and for the three standard FGT poverty measures. We adopt a relative poverty line of R$87.55 in 2004 prices, which corresponds to 50% of the 1988 median household per capita income.

Before discussing these results, it is important to recognize that their limitations are even greater than those for the wage distribution decompositions analyzed so far. In addition to the same caveat about path dependence, now the absence of general equilibrium effects extends to any indirect impacts of trade (or any other changes) on family composition, or on the occupational decisions of household members other than spouses. There are also important changes in other, unrelated policy parameters, such as the real value of pension payments and other transfers, which are consigned to the residual – which is therefore larger than in the decomposition described in Table 8.

As in the hourly wage distribution, trade-mandated changes in industry wage premia and industry-specific skill premia have very limited effects (although they are somewhat more inequality increasing, suggesting that the workers hardest-hit by wage declines in contracting industries belonged to poorer households: Figures 12 and 13). The biggest impact, as before, comes in the transition from the second to the third counterfactual (Figure 14). The changes in occupations across the distribution which occur in response to changes in the occupation-specific terms in the multinomial logit model are vastly poverty- and inequality-reducing. They contribute a decline of six points in the headcount index, actually overshooting the observed decline. Inequality measures move very close to the observed 1995 values, and the counterfactual growth incidence curve corresponding to these “full trade effects” lies quite close to the observed GIC (19951988).
significant first mover advantage and thus dissuade potential imitators. As a result, they might have a greater incentive to do research if they know their research will be protected legally.

Following the now familiar method, our country level variable is Intellectual Property Rights, IntProp, which is a measure of the protection of intellectual property in a country. This variable is estimated by the World Economic Forum (2002) and was used previously by Claessens and Laeven (2003).

The industry variable measured from U.S. data, R&D, is a measure of dependence on research and development and equals the industry-level median of the ratio of research and development expenses to sales for Compustat firms in the same industry over the period 199099.<sup>39</sup> The numerator and denominator are summed over all years for each firm before dividing.

In Table 10 column (iii), we report regression estimates. The interaction variable is positive and significant suggesting there is more entry in R&D intensive industries in countries that protect intellectual property better. We find similar results when using a more general index of property rights from the Economic Freedom Index constructed by the Heritage Foundation (not shown).

# _3.6.3 Access to Finance_

Let us turn next to access to finance. Evans and Jovanovic (1989) show theoretically that wealth constraints affect entrepreneurship, e.g., liquidity constraints hinder people from starting businesses. This suggests that entry rates should be lower in countries with less developed financial systems.<sup>40</sup> In fact, Rajan and Zingales (2003a) suggest the absence of regulations protecting investors could be a very effective barrier to new firm creation.

As our country measure, we use FinDev, which proxies for the depth of financial markets, and indirectly captures the regulations that promote financial development. We use the sum of two measures: First, as a measure of banking development we include the ratio of

> 39 We measure R&D using Compustat item 46, and sales using Compustat item 12.

> 40 Rajan and Zingales (1998) find that there are more new establishments in industrial sectors with greater external financing needs in more developed financial systems.

28
Table E5: Share of Significant Rainfall Coefficients in Tanzania

||CHIRPS|CPC|MERRA-2|ARC2|ERA5|TAMSAT|
|---|---|---|---|---|---|---|
|Mean Daily Rainfall|0.33|0.50|0.50|0.33|0.42|0.25|
|Median Daily Rainfall|0.25|0.08|0.33|0.25|0.42|0.25|
|Variance of Daily Rainfall|0.25|0.58|0.58|0.08|0.25|0.58|
|Skew of Daily Rainfall|0.25|0.50|0.08|0.42|0.17|0.25|
|Total Rainfall|0.33|0.50|0.50|0.33|0.42|0.25|
|Deviation in Total Rainfall|0.33|0.33|0.33|0.17|0.58|0.17|
|Z-Score of Total Rainfall|0.25|0.50|0.25|0.17|0.33|0.17|
|Rainy Days|0.58|0.58|0.75|0.50|0.58|0.42|
|Deviation in Rainy Days|0.50|0.42|0.92|0.33|0.33|0.33|
|No Rain Days|0.42|0.75|0.58|0.50|0.67|0.33|
|Deviation in No Rain Days|0.50|0.42|0.92|0.33|0.33|0.33|
|% Rainy Days|0.58|0.58|0.75|0.50|0.58|0.42|
|Deviation in % Rainy Days|0.50|0.42|0.92|0.33|0.33|0.33|
|Longest Dry Spell|0.08|0.58|0.17|0.25|0.25|0.42|
|Observations|168|168|168|168|168|168|

_Note:_ The table presents the share of statistically significant rainfall coefficients in Tanzania.

Table E6: Share of Significant Rainfall Coefficients in Uganda

||CHIRPS|CPC|MERRA-2|ARC2|ERA5|TAMSAT|
|---|---|---|---|---|---|---|
|Mean Daily Rainfall|0.33|0.75|0.58|0.58|0.58|0.42|
|Median Daily Rainfall|0.33|0.08|0.50|0.33|0.50|0.50|
|Variance of Daily Rainfall|0.42|0.75|0.67|0.50|0.67|0.33|
|Skew of Daily Rainfall|0.33|0.42|0.42|0.17|0.42|0.50|
|Total Rainfall|0.33|0.75|0.67|0.67|0.58|0.50|
|Deviation in Total Rainfall|0.33|0.58|0.50|0.58|0.58|0.58|
|Z-Score of Total Rainfall|0.25|0.42|0.33|0.58|0.75|0.58|
|Rainy Days|0.25|0.58|0.42|0.50|0.67|0.67|
|Deviation in Rainy Days|0.17|0.33|0.50|0.58|0.50|0.67|
|No Rain Days|0.25|0.58|0.42|0.42|0.50|0.67|
|Deviation in No Rain Days|0.17|0.42|0.50|0.58|0.58|0.67|
|% Rainy Days|0.25|0.58|0.42|0.42|0.67|0.67|
|Deviation in % Rainy Days|0.17|0.33|0.50|0.58|0.58|0.67|
|Longest Dry Spell|0.50|0.33|0.50|0.42|0.58|0.33|
|Observations|168|168|168|168|168|168|

_Note:_ The table presents the share of statistically significant rainfall coefficients in Uganda.

133
# **3.2 The Size Distribution of Firms**

Besides affecting the sectoral allocation of production and the aggregate gaps in productivity, frictions that misallocate resources will manifest also in the shape of the firm size distribution. Thus, it is informative to confront the measurement of misallocation that we perform below with some information about the shape of the firm size distribution in the countries that we cover in this study.

Table 1 presents some descriptive statistics. The table illustrates that, Kenyan firms, on average, are much larger (in terms of the number of workers) than firms in the other countries. While the average number of workers is approximately 145 in Kenya and 67 in Cˆote d’Ivoire, it is only 30 in Ethiopia and 29 in Ghana. The distribution of firm size in all countries is skewed to the left with the median firm in Kenya employing 34 workers while the corresponding figures in Cˆote d’Ivoire, Ethiopia and Ghana are only 9, 8 and 12 workers, respectively. Figure 2 clearly shows that the size distribution of firms in Kenya looks different from the distribution in the other countries.<sup>3</sup> Table 1: Size Distribution of Firms

||**Cote d’Ivoire**|**Ethi**|**opia**|**Gh**|**ana**|**Kenya**|
|---|---|---|---|---|---|---|
||Census (2012)|Census|(2011)|Censu|s (2003)|Census (2010)|
|**Size**|**N**|**N**|**N(wt)**|**N**|**N (wt)**|**N**|
|_<_ 5|469|1,618|17,779|464|13,027|171|
|5–9|210|1,657|22,813|423|7,044|255|
|10–19|184|1,540|10,621|1,683|1,706|325|
|20–49|161|495|810|486|499|410|
|50–99|81|214|219|110|122|295|
|_>_ 99|123|302|302|138|146|602|
|Total|1,228|5,826|52,544|3,302|22,544|2,058|
|Mean|67|30|9|29|8|145|
|Median|9|8|6|12|4|34|
|S.D.|390|154|52|118|48|404|

Note: ‘wt’ denotes for the wighted statistics. For the manufacturing censuses in Ethiopia and Ghana, we use the sampling weights constructed by the respective national statistical offices. Each census covers all formal manufacturing firms in the respective countries.

<!-- Start of picture text --> Cummulative Distribution Function<br>0 2 4 6 8 10<br>Log of Number of Workers<br>CIV ETH<br>GHA KEN<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> Figure 2: Commutative Density Function (Log Number of Workers) Notice, too, that re-weighting observations for small firms according to the weights provided by the

> 3Since there is no alternative data source, we are not able to verify if this is an the quality of Kenyan data. Nevertheless, the size distribution of firms in Kenya looks similar to Uruguay and the Rep ́ublica Bolivariana de Venezuela (Busso et al., 2013).

7
Table 5. Marginal Probabilities for Likelihood of Employment using Predicted Values, Nepal DHS, 1996-2006

||_Linear For_|_est Instrument_|_Linear Forest an_|_d Other Instruments_|
|---|---|---|---|---|
||_Employed_|_Self-Employed_|_Employed_|_Self-Employed_|
|Interaction Terms (reference=conflict_1996)|||||
|Conflict_2001|0.098<sup>***</sup>|0.063<sup>***</sup>|0.130<sup>***</sup>|0.065<sup>***</sup>|
||(0.011)|(0.008)|(0.011)|(0.010)|
|Conflict_2006|0.095<sup>***</sup>|0.046<sup>***</sup>|0.056<sup>***</sup>|0.034<sup>**</sup>|
||(0.013)|(0.009)|(0.014)|(0.014)|
|Education (reference=no schooling)|||||
|Some or all primary school|-0.024<sup>***</sup>|0.025<sup>***</sup>|-0.023<sup>**</sup>|0.025<sup>***</sup>|
||(0.008)|(0.006)|(0.009)|(0.007)|
|Some secondary school|-0.054<sup>***</sup>|0.013|-0.053<sup>***</sup>|0.013|
||(0.013)|(0.011)|(0.014)|(0.010)|
|Completed secondary school+|-0.076<sup>***</sup>|-0.325<sup>***</sup>|-0.075<sup>***</sup>|-0.324<sup>***</sup>|
||(0.020)|(0.028)|(0.018)|(0.027)|
|Age (reference=age<=20)|||||
|20<age<=35|0.109<sup>***</sup>|0.004|0.109<sup>***</sup>|0.005|
||(0.007)|(0.007)|(0.009)|(0.007)|
|age>35|0.146<sup>***</sup>|0.038<sup>***</sup>|0.144<sup>***</sup>|0.038<sup>***</sup>|
||(0.007)|(0.007)|(0.008)|(0.007)|
|Socioeconomic status|YES|YES|YES|YES|
|Household composition|YES|YES|YES|YES|
|Year and sub-region dummies|YES|YES|YES|YES|
|Pseudo R<sup>2</sup>|0.194|0.207|0.194|0.206|
|N|25,724|21,807|25,724|21,807|

Notes: Weighted to national level with weights provided by the Nepal DHS in each year. Standard errors, in parentheses, are clustered by regionyear and bootstrapped to adjust for use of first-stage predicted values in the second-stage. The notation<sup>***</sup> is p<0.01,<sup>**</sup> is p<0.05,<sup>*</sup> is p<0.10. In each regression the conflict-year interactions are constructed as predicted values based on the linear forest instrument only (columns 1 and 2) and based on all the geographical variables in linear form (columns 3 and 4).

Source: Authors‟ calculations based on MHP/NE/MI (2007), MH/NE/ORC (2002), and Pradhan _et al._ (1997).

39
counterparts.

Figure 6. Service Access of Children and Adolescents <!-- Start of picture text --> (a) School Enrollment (b) Sisb ́en Enrollment<br>0.96 0.94 0.96 0.74 0.73<br>0.89<br>0.74 0.73<br>0.30<br>0.28<br>Colombian Venezuelan Colombian Venezuelan 5-10 yrs 11-17 yrs<br>5-10 yrs 11-17 yrs Colombian Venezuelan<br>pre-migration enrollment 2022 enrollment<br>(c) Health Insurance Enrollment (d) Regularization Program Take-up<br>0.92 0.89 0.70<br>0.64<br>0.58 0.57<br>0.39 0.39<br>Colombian Venezuelan Colombian Venezuelan<br>5-10 yrs 11-17 yrs<br>5-10 yrs 11-17 yrs<br>pre-migration insurance 2022 insurance<br>1 .8<br>.8<br>.6<br>.6<br>.4<br>.4<br>Share of children enrolled<br>Share of children enrolled .2<br>.2<br>0 0<br>1 .8<br>.8<br>.6<br>.6<br>.4<br>.4 Share enrolled<br>Share of children insured .2<br>.2<br>0 0<br><!-- End of picture text --> _Notes:_ Panel (a) depicts the likelihood of school enrollment for children in each nationality-age-group enrolled in school, Panel (b) depicts the likelihood of Sisb ́en enrollment in each nationality-age-group (the proxy means test used to target social programs in Colombia), Panel (c) depicts the likelihood of health insurance in each nationality-age-group, and Panel (d) depicts the take-up rates of the Colombian regularization program, the Estatuto Temporal de Pertenencia (ETPV), to which all the individuals in our sample were eligible.

To gain a deeper understanding of the reasons behind the low take-up rates of services, we identify significant disparities in the attributes of children and adolescents who utilize health and educational services and those who do not. Both Colombian and Venezuelan households with children enrolled in school are wealthier relative to households where children are not enrolled. Parents of enrolled children tend to have higher levels of educa31