rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure A.2. Further Meta-analysis with Between-Year Imputations, Marginal Effects from Logit Regressions**

<!-- Start of picture text --> Imputation method is normal<br>Poverty line is consistent<br>Base year is NPS 2020/21<br>Treatment Arm 2<br>Treatment Arm 3<br>Quaterly WB CPI<br>Monthly WB CPI<br>Annual IMF CPI<br>-.5 0 .5 1 1.5<br><!-- End of picture text -->

**Note:** The figure displays the results from logit regressions. The dependent variable is a binary variable that indicates whether the predicted poverty rate is within the 95% confidence interval of the true poverty rate. The robust standard errors are clustered at the model level. The reference groups are empirical distribution of the error terms for the “imputation methods,” inconsistent poverty line for “the type of the poverty line,” NPS 2019/20 for the “base year,” TA 1 for the “type of the questionnaire”, annual WB disaggregated CPI for the “inflation source.” The consistent poverty line is defined as poverty line measured at the base year (i.e., the 2020/21 poverty line used for the imputations from 2020/21 to TAs and the 2019/20 poverty line used for the imputations from 2019/20 to TAs). The error bars are 95% confidence intervals.

69
mental health measure (the correlation is 0.58).

We have information on demographic characteristics such as age, gender, years of schooling and ethnicity of the survey respondents. 44 percent of the survey respondents were Serbs, 40 percent were Bosniaks and 14 percent were Croats. 52 percent of the individuals were female and 17 percent of the respondents had migrated to their current locality after the conflict began in 1991. We also extracted information on transfers received by households in the form of aid and other forms of government assistance. 28 percent of the respondents in the 2001 survey received some form of aid from the government.<sup>10</sup> As is the case in most LSMS surveys, we have detailed information on household consumption patterns, which can be used to construct an overall consumption figure, adjusted for regional price differences across municipalities in each year. We find that per capita consumption increased by only 3 percent in nominal terms between 2001 and 2004.

# **3.3 Measuring the Intensity of Conflict**

We measure conflict intensity at the municipality level, using data from the Sarajevo Research and Documentation Center (RDC), which publishes regular updates on the number of missing or killed people in each of the municipalities of BiH. The data is also known as “the Bosnian Book of Dead Project 1991-1995”, or the “Human Losses in Bosnia and Herzegovina Project” (see Swee, 2009). The reliability of the data has been discussed Ball et al. (2007), who conclude that the “database is a unique and valuable source and deserves a prominent place among sources on victimization of the 1992-95 war in Bosnia and Herzegovina” (p. 59). We used the RDC data on the number of people missing or killed, combined with population data from the 1991 census to construct a measure of conflict intensity in each municipality. This measure is the number of casualties (missing or killed) per 100 inhabitants. The mean of this variable is 0.021 and it varies considerably across provinces from close to 0 to more than 0.10.

The LSMS records information on the current municipality of residence, as well as municipality of residence before the war started. We can therefore match each individual to the level of conflict in their current municipality of residence, as well as that in their pre-war

> 10 We include amounts received as old age pension, invalid pension, survivors' pension, military pension, war disability benefit and funds from the Civil Victims of War.

13
# _B. Sources_

Banking data come from two major sources. BANKSCOPE, maintained by Bureau Van Dijk, contains financial and ownership information for about 4,000 major banks. BANKERS ALMANAC, published since 1847, also contains a wealth of data, including detailed ownership data.<sup>9</sup> To obtain ownership information of banks, we also use annual reports, 20-F filings for companies with American Depositary Receipts, proxy statements, and country-specific publications. Also, many individual banks and national institutions (e.g., Central Banks, regulatory authorities) maintain websites that we used to compile ownership data.

Since (1) non-financial institutions own bank shares, (2) BANKSCOPE and the BANKERS ALMANAC only have information on financial institutions, and (3) we seek to trace bank ownership through corporations back to individuals, we need information on the ownership of non-financial institutions. WORLDSCOPE contains ownership data of firms. Thus, we use WORLDSCOPE along with 20-F filings, company reports, and filings with national stock exchanges and securities regulations to identify the ultimate owners of corporations that own shares in banks. The ownership data are from 2001 except in a few cases, where we use 2000 data. Because ownership patterns are very stable, this should neither induce problems, nor bias the results.

# _C. Control rights_

We classify a bank as having a controlling owner if the shareholder has direct and indirect voting rights that sum to 10 percent or more. If no shareholder holds 10 percent of the voting rights, we classify the bank as widely held. Since 10 percent voting rights is frequently sufficient to exert control, this cut-off is used extensively (e.g., LLS, 1999; LLSV, 2002). When we use a 20 percent criterion, however, we obtain the same conclusions as those discussed below.

> 9 Reed Business Information, which is part of Anglo-Dutch Reed Elsevier, maintains Bankers Almanac.

9
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# **Table 6. Means and Standard Deviations for Determinants of Rural Income Regression in Egypt**

|Variable||
|---|---|
|Amount of land owned by household<br>(feddans)(a)|0.43<br>(1.68)|
|Amount of land rented in by household<br>(feddans)(a)|0.19<br>(0.71)|
|Household size|6.70<br>(3.54)|
|Mean age of all household members|26.39<br>(11.74)|
|Mean age of all members squared|842.56<br>(928.38)|
|Number of household males over 15 years|1.87<br>(1.32)|
|Mean education of household males over<br>15 years|5.56<br>(4.72)|
|Mean education of household males<br>squared|54.58<br>(68.17)|
|Value of farm equipment owned(LE)(b)|527.66<br>(3738.95)|
|Value of enterprises(C)<br>owned (LE)(b)|1058.18<br>(6624.03)|

Notes: N = 1301 households. Standard deviations in parentheses. Governorate-level dummy variables are not reported.

(a) 1 feddan = 1.038 acres (b) In 1997, 1 Egyptian pound (LE) = US$0.295.

(c) Enterprises include shops, stores, pharmacies and other business activities.
Rao and Sanyal’s (2009) other contribution is to highlight the important role that _gram sabhas_ play shaping the discursive styles of the poor. Since they are embedded within a democratic system that is also subject to electoral accountability, they are a relatively safe space for open speech. Things that cannot be said in private discourse become possible in a _gram sabha_ , because political elites may face electoral costs by taking action against a citizen for something said in a _gram sabha_ . This allows lower castes to use the space to transgress social norms and make claims for dignity; marginal groups voice their concerns and previously “hidden transcripts” become public – forcing public discussion on sensitive social issues that people would rather avoid. While deliberation can thus become a tool for social inclusion, such dignity claims may simply reflect the low levels of literacy characteristic of rural India that result in low “oratory competence” (Sanyal et al. 2015). As literacy increases to reach Kerala’s level, _gram sabhas_ are likely to become spaces to have a dialogue on topics of public interest. Such publicly relevant discussions are most effective, of course, when citizens are well informed and can demand accountability from public officials. Limited information and media coverage, however, often leave citizens at a “disadvantage when negotiating with local governments” (Bhattacharjee and Chattopadhyay 2011, p. 46). Analyzing transcripts from _sabhas_ in West Bengal, Bhattacharjee and Chattopadhyay find that villagers do try to use information from media to negotiate with elected officials and inquire about entitlements; these requests, however, are easily ignored or dismissed by GP members, who can evade requests by claiming that the media is misleading or incorrect. The authors attribute this to the “thinness” of news coverage, which does little to empower citizens to confront officials. Despite this troubling picture, however, the authors acknowledge that the very act of demanding entitlements – even seemingly small and selfish claims for rice or pensions – reflects citizens’ “capacity to aspire” for a better life (Appadurai 2004).

Low literacy also results in emotions playing an important role in deliberative discourse (Sanyal 2015). They can play a constructive role as a form of “rude accountability” (Hossain 2009) 13
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- <u>Panel C: Regulatory and supervisory capacity for microfinance (0 4)</u>

|||<br>Version 1(Ful samp|<br>Depvar: Portfolio at<br>les)|Risk(>30days)<br>Version 2|<br> (DroppingRural b|<br>ank samples)|
|---|---|---|---|---|---|---|
||OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Regulatory and supervisory capacity for microfinance (0-4)|-0.0041|-0.0077***|-0.0077**|-0.0032|-0.0069***|-0.0080**|
||[0.445]|[0.001]|[0.042]|[0.542]|[0.002]|[0.028]|
|NBFI dummy * Regulatory and supervisory capacity for microfinance (0-4)|0.0067|0.0065*|0.0063|0.0079|0.0058*|0.0055|
||[0.276]|[0.055]|[0.261]|[0.210]|[0.066]|[0.358]|
|Combined group dummy * Regulatory and supervisory capacity for microfinance (0-4)|-0.0101|0.0021|0.0021|0.0097|0.0074*|0.0070*|
||[0.219]|[0.566]|[0.625]|[0.192]|[0.072]|[0.077]|
|NBFI dummy|-0.0049|-0.0142*|-0.0116|-0.0067|-0.0134*|-0.0124|
||[0.793]|[0.082]|[0.446]|[0.726]|[0.075]|[0.415]|
|Combined group dummy|0.0435**|0.0048|0.0081|-0.0019|-0.015|-0.0093|
||[0.038]|[0.580]|[0.427]|[0.939]|[0.117]|[0.371]|
|Observations|627|627|598|572|572|546|
|Number of countries|39|39|39|39|39|39|
|R-squared|0.076|0.209|0.165|0.071|0.247|0.172|
|Adjusted R-squared|0.0475|0.184|0.138|0.0388|0.221|0.142|
|_Microscope index+ interaction term (P-value in brackets)_|||||||
|NBFI dummy|0.00264|-0.00123|-0.00142|0.00465|-0.00109|-0.00257|
||[0.576]|[0.668]|[0.783]|[0.319]|[0.682]|[0.618]|
|Combined group dummy|-0.0141*|-0.00561*|-0.00569|0.00646|0.000553|-0.000987|
||[0.050]|[0.085]|[0.172]|[0.248]|[0.884]|[0.746]|

51
study by Bryan et al. (2021) shows that psychometric variables can be combined with machine learning techniques to predict which firms have the highest potential returns to larger loans (although when comparing impacts it should be noted these are an order of magnitude smaller than the loans in our study), and that these are not the same as the firms with the highest returns to smaller loans. Other studies observe that entrepreneurs with high levels of intelligence and motivation, and with particular character traits, benefit disproportionately from interventions for MSMEs (Fafchamps et al., 2014; Koudstaal et al., 2016).

We also provide one of the first RCTs to estimate the impact of a large loan product of this magnitude on microenterprises, particularly women-owned microenterprises. Smaller loans have shown disappointing results. For example, Tarozzi et al. (2015) find that access to standard microfinance in Ethiopia increases female borrowing substantially (despite no specific guidelines to target women for the loans) but largely leads to null results on consumption and business outcomes. Further studies find that cash grants can have an impact for male entrepreneurs but not female entrepreneurs; at least without additional intervention such as training (Berge et al., 2015), or unless delivered in kind, due to pressure faced by women to redistribute liquid funds (Fafchamps et al., 2014).

# **2 Context and intervention**

Enterprises in Ethiopia rank among the most credit-constrained in the world, with 40% rating access to finance as the biggest constraint they face (Mthimkhulu and Aziakpono, 2015). Banks and MFIs granting larger loans have stringent collateral requirements, typically in the form of housing or a salaried guarantor. The Women Entrepreneurship Development Project (WEDP) is a national program of the Government of Ethiopia, with financial and technical assistance from the World Bank. The project targets growth-oriented women-owned enterprises with interventions including information and training, and access to collateralized 5
15 In contrast, if the election falls at the end of the year, pre-electoral spending will be in heavily inflated nominal dollars and correspondingly exaggerated.<sup>8</sup> Bulgaria and Nicaragua experienced hyperinflation in 1997 and 1989, respectively, and illustrate this problem. In both countries, reported nominal expenditures as a fraction of GDP during these years experienced a massive reduction of 26 percentage points (Bulgaria) and 14 percentage points (Nicaragua). These drops exceed any plausible fiscal adjustment that these governments might have undertaken to stamp out hyperinflation, and they are orders of magnitude greater than the mean annual change in expenditures of 0.2 percentage points. To eliminate this potential for noise, our base specification excludes expenditure observations where GDP inflation is two standard deviations above the sample mean.<sup>9</sup> Our results are, however, robust to restoring these observations.

We use the election dummy variable ( _election_ ) from Brender and Drazen to capture election timing. Since we rely on party variables from the DPI, however, we also check the consistency of their dummy with election dates reported in the DPI. In those few cases where there were inconsistencies, 8 Assume real spending for the year is given by _B_ . Total nominal spending for the year is given by ∑121 e<sup>itB</sup> where _p_ is the pt share of spending in month _t_ and _i_ is the monthly inflation rate. Assume nominal national income is similarly given by ∑121 e<sup>itY</sup> ~~,~~ where _Y_ is real national income for the year and _q_ is the share of national income created in month _t_ . Political qt

> <sup>BB</sup> that the relationship between observed nominal spending in the two months is given by e<sup>i1</sup> < e<sup>i12</sup> :  though real p1 p12 spending is the same in both months, nominal spending in the last month of the year is e<sup>i11</sup> times greater than in the first month.  When inflation is high (say, 50 percent per year), observed spending in month 12 is 36 percent larger than in month 1.  When inflation is low (say, 10 percent per year), the effect is small (end of year politically-motivated spending inflates that spending by nine percent relative to beginning of the year spending).

9 Another, minor, adjustment is the correction of Spain’s expenditure in the last year of the IFS series: spending was erroneously recorded in terms of its absolute currency amount rather than its percentage of GDP.
Table 9 Impact ofPolicy, Institutions, Globalization, and Other Determinants on the Poor (the Late 1990s-2000s)

|Dependent Variable: PeriodAverage|Growth Rate of|Per Capita Inc|ome ofthe Quint|ile (real terms)||||||
|---|---|---|---|---|---|---|---|---|---|
||Polic|yand Instituti|ons||Globalization||Ot|her Determinan|ts|
||G/GDP|Inflation|AInst|AT/GDP|FDI/GDP|Remittance|Health/G|ATOT|A Agri-FoodEx|
||(1)<br>Quintile 1|(2)<br>Quintile 1|(3)<br>Quintile 1|(4)<br>Quintile 1|(5)<br>Quintile 1|(6)<br>Quintile 1|(7)<br>Quintile 1|(8)<br>Quintile 1|(9)<br>Quintile 1|
|PeriodAverage Growth Rate of|0.9664***|1.1056***|1.1630***|1.3090***|1.2936***|1.2004***|1.1002***|1.1314***|1.2863***|
|Per Capita Income #|(4.47)|(9.89)|(13.69)|(7.90)|(5.05)|(7.57)|(6.79)|(9.00)|(7.50)|
|Policy/ Institutions / Globalization /|0.0008|0.0154|-0.1897|-0.0474|-0.0004|0.0003|0.0005|0.0311|0.0182|
|Other Determinants|(1.33)|(0.42)|(1.59)|(0.40)|(0.28)|(0.63)|(1.22)|(0.39)|(0.40)|
|CrisesDummy|-0.0050|-0.0006|-0.0027|-0.0040|-0.0018|-0.0024|-0.0039|0.0007|-0.0068|
||(0.86)|(0.01)|(0.63)|(0.72)|(0.39)|(0.50)|(0.70)|(0.13)|(1.23)|
|No. of Observations|156|147|126|136|156|151|156|126|128|
|R-squared|0.33|0.38|0.45|0.34|0.29|0.33|0.32|0.32|0.35|

Notes: i) Absolute value of t-statistics calculated with White-corrected standard errors is in parentheses. i) Absolute value of t-statistics calculated with White-corrected standard errors is in parentheses. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. ii) Panel two-stage least squares methods is used for these analyses.

iii) # denotes variable is instrumented with the initial level and period average growth rate of the previous spell.

Source: Authors! compilation
The summary statistics of all relevant variables in our sample are reported in Table 1. The first two moments and order statistics of each variable are displayed.<sup>4</sup> As shown, the data is made up of two sampling years: 2011/2012 and 2014/2015. Considerable variations exist among the variables which we shall exploit for identifying variation. Our main outcomes of interest are binary, denoted “Business Enterprise” and “Poor”. Business Enterprise is based on whether households are engaged in any form of non-agricultural business enterprise within the past 12 months. In our sample, about 22% of households are engaged in some form of business enterprises. Summaries about various aspects/dimensions of business enterprises are displayed in the bottom panel. Poor is an indication of whether a household is classified as either poor or not poor. Poverty classification of households uses Niger’s 2011 National Absolute Poverty Line NAPL of 189233 CFA. The sample suggest overall absolute poverty rate of about 40.2%.

**<u>Table 1: Data and Summaries, 2011/12-2014/15</u>**

||OBS (N)|Mean|SD|Min|Max|
|---|---|---|---|---|---|
|Business Enterprise|7,590|0.220|0.414|0|1|
|Mobile use and activity|2,788|0.223|0.417|0|1|
|**1**[Sex = female]|7,072|0.229|0.458|0|1|
|**1**[Married = Yes]|7,072|0.629|0.483|0|1|
|Age (years)|7,072|35.30|20.57|18|95|
|Educational level+|1,466|3.281|1.752|1|7|
|1 [Education = notNil]|4,399|0.999|0.037|0|1|
|Poor++|6866|0.402|0.459|0|1|
|Year|7,781|-|-|2011|2014|
|Transform Agr. Product|7,590|0.160|0.366|0|1|
|Make clothing|7,590|0.040|0.197|0|1|
|Own construction|7,590|0.039|0.195|0|1|
|Any profession for own|7,589|0.039|0.195|0|1|
|Own business service|7,589|0.047|0.212|0|1|
|Own commercial transport|7,589|0.024|0.154|0|1|
|Own restaurant /bar|7,589|0.015|0.123|0|1|
|Own small activity|7,589|0.160|0.366|0|1|

_Notes_ : Table reports summary statistics of sample variables including: household’s ownership of business enterprise; mobile use and activity in the sampling year; gender of respondents and other demographic information. **+** For education: 1=None, 2=primary, 3=secondary first cycle-general, 4=secondary first cycletechnical & professional, 5=secondary second cycle-general, 6=secondary second cycle-technical & professional, and 7=superior levels. **++** While our sample suggests about 40.2% overall absolute poverty rate, rates reported <u>elsewhere can be around 48%.</u>

> 4 Data is harmonized and matched across two survey years to obtain the panel. While the the main data frame houses many variables’ information; we restrict attention to those relevant to our empirical analysis.

‐ 8 ‐
# _Unemployment_

How do layoffs and involuntary quits of adults affect the time use of children? Existing empirical evidence suggests that the inability of poor households to insure themselves against income fluctuations can result in increased child labor. One source of income fluctuation can be unexpected changes in the employment status of household members. Using panel data from Brazil, Durryea (1999) finds that grade children's attainment suffers when the father loses his job. Jacoby and Skoufias (1997) also find transitory and idiosyncratic shocks to household income result in changes in child time use in rural India.'<sup>8</sup> The unexpected layoff of a working parent may release the time of children from housework and allow them to pursue education-a substitution effect. At the same time, such a layoff may also reduce household income thereby reducing the household's demand for children's education and increasing their market labor-an income effect. The net effect of unemployment shocks on child labor and schooling would be ambiguous and may differ by boys and girls. This is because the housework time of the mother and young girls tends to be more substitutable.

How can one conduct this test in a consistent and clean manner? Ideally, what one needs is an indicator for whether a member of the household was laid off from his/her job, or whether they experienced a wage cut. However, even quite sophisticated labor surveys do not make a distinction between voluntary and involuntary job losses. The distinction is extremely important for our purposes because the former will be endogenous and the latter exogenous. In the absence of this distinction in the Peru survey, I calculate unemployment shocks at the cluster level-I calculate separate unemployment rates for men and women. Cluster unemployment rates for women are defined as the proportion of women in cluster not employed divided by total number of prime-age women in cluster. 19,<sup>2 0</sup>

> 18 Also see Grootaert and Patrinos (1999).

> 19 Prime age: between 18 and 60 years.

11