rafmacalaba/datause-encoder-data huggingface dataset:
# APPENDIX:

POVERTY ALLEVIATION AND INTERHOUSEHOLD TRANSFERS: EVIDENCE FROM BRAC’S GRADUATION PROGRAM IN BANGLADESH

## **Selim Gulesci**

# **S1: Additional Tables**

Table S1.1: ATTRITION

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Treatment|-0.015|-0.014|-0.014|-0.013|-0.012|-0.011|
||(0.013)|(0.012)|(0.011)|(0.011)|(0.011)|(0.013)|
|Received any transfers|0.023|||||0.012|
||(0.014)|||||(0.018)|
|Treatment_×_Received any transfers|0.010|||||0.009|
||(0.022)|||||(0.030)|
|Value of transfers received||0.032***||||0.026**|
|||(0.009)||||(0.012)|
|Treatment_×_Value of transfers received||-0.006||||-0.007|
|||(0.014)||||(0.018)|
|Gave any transfers|||-0.045|||-0.311**|
||||(0.040)|||(0.116)|
|Treatment_×_Gave any transfers|||-0.024|||-0.036|
||||(0.050)|||(0.137)|
|Value of transfers given||||-0.266||1.497*|
|||||(0.346)||(0.749)|
|Treatment_×_Value of transfers given||||0.468||0.565|
|||||(0.461)||(0.995)|
|Received any transfer from an employer|||||-0.001|-0.003|
||||||(0.020)|(0.020)|
|Treatment_×_Received transfer from employer|||||-0.024<br>(0.025)|-0.027<br>(0.025)|
|Attrition in control|0.162|0.162|0.162|0.162|0.162|0.162|
|F-test (p-value)||||||0.842|
|Adjusted R-squared|0.004|0.005|0.003|0.003|0.003|0.007|
|Observations|7953|7953|7953|7953|7953|7953|

**Source:** Author’s analysis based on original survey data.

**Notes:** The dependent variable is a dummy variable equal to 1 if the respondent has not been surveyed at midline and/or endline survey waves. The sample includes all ultra-poor households. All specifications control for subdistrict fixed effects, a dummy variable (DV) equal to 1 if the control variable is missing and the interaction of this DV with the treatment dummy. Standard errors are clustered at the BRAC branch office level (unit of randomization). ‘Received any transfers’ is a DV equal to 1 if the respondent’s household received any transfers in cash or in kind during the 12 months preceding the survey. ‘Gave any transfers’ is a DV equal to 1 if the respondent’s household gave any transfers in cash or in kind during the past 12 months. ‘Value of transfers received (given)’ is the monetary value of transfers received (given) by the respondents’ household from (to) other households during the past 12 months; expressed in 1000s of Bangladeshi TAKAs, top 1% of outliers have been coded to missing. In 2007, 1USD=69BDT. ‘Received transfers from any employer’ is a dummy variable equal to 1 if either the female or the male respondent is working for an employer who lives in the same community and is also reported as a source of transfers for the respondent’s household. In column (6), F-test gives the p-value associated with the null hypothesis that all interaction terms are jointly equal to zero. *** (**) (*) indicates significance at the 1% (5%) (10%) level.

1
**Does It Matter Where You Grow Up? Childhood Exposure Effects in Latin America and the Caribbean**<sup>∗</sup>

Ercio Mu ̃noz<sup>†</sup>

**JEL-Codes** : D63, I24, J13, J62, R23, Z13.

**Keywords** : _Educational intergenerational mobility, Geography, Latin America, Neighborhood effects_ .

> ∗I thank Jonathan Conning, N ́uria Rodr ́ıguez-Planas, Wim Vijverberg, Roy van der Weide, Chico Ferreira, Lucila Berniell, Dolores de la Mata and several seminar/conference participants for helpful comments. Pablo Vargas and Marcelo Gomez provided outstanding research assistance. I greatly acknowledge the financial support of CAF -development Bank of Latin America, the Center for Latin American, Caribbean and Latino Studies at CUNY, the doctoral student research grant round 16 from CUNY, and the Mario Capelloni dissertation fellowship from CUNY. The usual disclaimers apply. First version: July 2021.

> †Email: emunozsaavedra@worldbank.org.
2

# **1) Introduction.**

Measured by the Gini coefficient for the distribution of household income per capita, inequality in Brazil rose from 0.574 in 1981 to 0.625 in 1989. After that five-point (or 9%) increase during the 1980s, Brazil’s inequality was the second highest in the world, narrowly behind Sierra Leone’s Gini of 0.629. From its peak of 0.625 in 1989, Brazil’s Gini fell by six points, or roughly ten percent, to 0.564 in 2004. These are not insubstantial changes. According to World Bank (2005), the 2004 number would place Brazil as the tenth most unequal country in the world, behind Bolivia, Botswana, the Central African Republic, Guatemala, Haiti, Lesotho, Namibia, South Africa, and Zimbabwe.<sup>1</sup> While Brazil’s inequality level continues to be very high by international standards, its inequality has been less stable than is sometimes argued.<sup>2</sup> Large changes had also occurred in earlier periods, with the Gini coefficient for the distribution of (adjusted) household per capita incomes as measured by the decennial Censuses, rising from around 0.500 in 1960 to 0.565 in 1970 (see Bonelli and Sedlacek, 1989). Inequality rose further between 1970 and 1976, reached a peak on that year, and then fell from 1977 to 1981. See Bonelli and Sedlacek (1989), Hoffman (1989) and Ramos (1993).

In this paper, we describe the evolution of inequality and poverty in Brazil from 1981 to 2004, drawing on one the longest time-series of broadly comparable annual household surveys available anywhere in the developing world. Using standard decomposition techniques, we also seek to identify candidate determinants for both the levels and the changes that we observe in inequality and, to a lesser extent, poverty. The paper is mostly descriptive, and the purpose of the decomposition analyses is simply to generate plausible hypotheses for the causal processes behind Brazil’s distributional dynamics over the last two and a half decades. Sensible explanations must be

> 1 While useful as motivation, international rankings of inequality are fraught with severe comparability problems. Some of the measures included refer to distributions of consumption expenditures, while others refer to income. Even across the same welfare concept, survey questionnaires and collection methods differ substantially across countries. Data for different years are included in the comparisons, and the coverage of countries has varied sharply over time. Data for Sierra Leone, for instance, is not included in the World Bank (2005) tables. A country’s position in these rankings is therefore subject to considerable error and uncertainty, and should be taken as very roughly indicative only.

> 2 See, for instance, Barros, Henriques and Mendonça (2000)’s well-known paper on “The Unacceptable Stability: Inequality and Poverty in Brazil” (our translation).
- (1) Do conservancies significantly increase household welfare?

- (2) How does the change in a household’s welfare resulting from a conservancy vary by household socio-economic characteristics?

- (3) Does participation in the conservancy increase household welfare relative to those who choose not to participate?

A key concern with the 2002 SEHS is that it collected household information only after the implementation of CBNRM programs. Thus, we do not have the baseline data that is particularly useful for conducting program evaluation. Because of this and other information limitations related to data inconsistencies, we try to evaluate the impact of conservancies by using different methods to cross-check robustness of results.

We measure household welfare using four different indicators: household income and expenditure, and per capita income and expenditure. It is often argued that income measures are subject to larger measurement errors and more volatile, in particular, in countries where agricultural and informal sectors constitute the major part of the economy (Deaton, 1997). In contrast, household expenditure yields a more accurate measure of living standards. In the following program evaluation, we employ both measures.

# **_Evaluation of conservancy impact_**

Method (1): Simple comparison without controls This method essentially involves comparing the mean income/expenditure between two groups of households: those living within conservancies (in our case, established conservancies within the region) and those residing outside conservancies (the newly established ones). The differences in the mean income (or expenditure) between the two groups of households are expected to capture the impact of the conservancies. A significant t-test suggests that the existence of conservancies increases household welfare.

It should be noted that the simple-comparison-without-controls method is only valid when the conservancy is randomly assigned among different localities, i.e. localities with and without conservancy programs should have similar observable and unobservable characteristics. In reality, such random experiment design is often not possible simply because public programs are often intended to improve the welfare of targeted groups. Also it is often impossible to randomly assign programs across localities because of institutional or political constraints. Another problem is that households may selectively migrate to areas where conservancies are more successful, in which case, this method will lead to a biased estimate of the conservancy effect. Given the data available and limited evidence of selective migration, we assume that the selective migration is less of a concern.

7
# **The Economic Effects of Market Integration in the Western Balkans**<sup>∗</sup>

Maria Del Mar Gómez<sup>†</sup> Daria Taglioni<sup>‡</sup> Román D. Zárate<sup>§</sup> _Keywords_ : Quantitative Spatial Models, Western Balkans, European Union accession, Trade Facilitation, Regional Integration. _JEL Codes_ : D51, E61, F15, O18, O52, R40.

> ∗We thank Alberto Criscuolo and Satya Prasad Sahu for the insightful discussions on the trade facilitation program in the Western Balkans as well as participants at the Conference on "Regional Economic Integration in the Western Balkans" held in Vienna on October 25th, 2022. All remaining errors are ours.

> †World Bank, email: mgomezortiz@worldbank.org

> ‡World Bank, email: dtaglioni@worldbank.org

> §World Bank, email: rzaratevasquez@worldbank.org
**Figure 3: Informal employment rates estimated using LFS**

<!-- Start of picture text --> 0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>LFS informal (broad) Rosstat official<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> <u>Source: Authors’ calculation using LFS.</u> <u>Note: In the LFS, informal employment includes workers that are employed but not at an enterprise in their primary job, workers</u> employed without a written contract in their primary job, and self-employed workers without registration. This LFS-based measure is henceforth referred to as “LFS (broad)”. Rosstat’s official measure of informal employment is estimated using the LFS and includes workers that are employed but not at an enterprise in their primary or secondary job. All estimates are weighted.95% confidence intervals are shown using dotted lines.

**Figure 4: Informal employment rates estimated using LiTS**

<!-- Start of picture text --> 0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>LiTS informal Rosstat official<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> <u>Source: Authors’ calculation using LiTS.</u> <u>Note: In the LiTS, informal employment is defined as working without a written contract in the main job. Rosstat’s official measure</u> of informal employment is estimated using the LFS and includes workers that are employed but not at an enterprise in their primary or secondary job. All estimates are weighted. 95% confidence intervals are shown using dotted lines.

33 Informal Employment and Worker’s Wellbeing in Russia / June 2019
schooling decisions are endogenous and describe the conditions which place economies at risk of being negatively affected by automation if education quality is poor. Our empirical application follows with a description of the data, methods and results. A discussion of the findings concludes.

# **THEORY AND MODEL**

We develop a general equilibrium, overlapping-generations model with individuals with heterogeneous ability and endogenous schooling choices, drawing on Heckman, Lochner and Taber (1998a; 1998b; 1998c) and Ábrahám (2008). In the initial period, individuals choose their level of schooling which incurs both a direct cost and the opportunity cost of lost wages, but then they reap the rewards with higher earnings in the future. Production in our model arises from a low-productive informal sector that employs low-skill labor and from a high-productive formal sector that employs middle skill labor and high skill labor such that capital affects high-skill labor productivity more than middle-skill labor. The informal sector is a perfect substitute to the formal sector and represents the informal agriculture sector that is a substantial sector of employment in many developing countries. Agents are born with heterogeneous ability that determines their skill type; however, they can attend basic education for one period or higher education for two periods, to increase their ability and become higher skilled workers. Unlike typical technological change, automation reduces the contribution of middle skill labor in the formal sector. If production were fully automated, then middle-skill workers’ marginal product is zero; they have no function in the economy as many fear. If schooling is high enough quality, then newly born labor can shift from low to medium to high skill as needed. In this case, automation has a positive effect on steady state output and wages because there is no constraint to skilled labor supply. If schooling is not of sufficient quality, then lower ability individuals who attend school will not graduate with high enough ability to enter the next level of skill category. In this case, the supply of skilled labor can be constrained, and automation can be detrimental to both overall output and low skill wages.

We use this model to study how automation can affect steady state outcomes for countries at different stages of development, derived from differing levels of exogenous capital stock. We define developed countries in our model as having virtually no informal employment while 6
# **4.  Expansion of the Program: Targeting and Coverage**

The analysis of HBS surveys shows that health insurance coverage in Turkey increased considerably between 2003 and 2008. Coverage expansion occurred both for formal sector insurance schemes as well as the Green Card program. The coverage of formal contributory insurance schemes ( _SSK, Emekli Sandığı_ and _Bağkur_ ) expanded from 40.6 million individuals (59% of the population) in 2003 to 50.1 million individuals (69% of the population) in 2008. The number of Green Card beneficiaries rose from 2.5 million in 2003 to 9.5 million in 2008 with peak registration at 10.2 million in 2006. Table 1 provides details on the numbers of individuals in the Green Card scheme as estimated from the HBS datasets. These household level findings are consistent with administrative data sources: According to the statistics by the Ministry of Health, as of September 2008, the total number of Green Card holders in Turkey was about 9.4 million people.<sup>6</sup> As a result of the expansion of health insurance schemes in Turkey, the total share of the population covered by one of the different health insurance schemes increased from 64 percent in 2003 to 87 percent in 2008. The poor, who had traditionally not been covered by formal sector health insurance mechanisms, benefited significantly from this expansion, largely through the expansion of the Green Card program. In 2003, only about a fourth of the population in the poorest decile had been covered by some kind of health insurance plan (of which about half had been covered through the Green Card), this share rose remarkably to 82 percent in 2008 (with over two-thirds of these having access to the Green Card scheme). Figure 1 maps the rapid and progressive expansion of the Green Card scheme for each year between 2003 and 2008.

While such rapid expansion of any program puts pressure on the administrative capacity at various government levels, the rapid roll-out of the Green Card program from 2003 to 2008 was accompanied by an improvement in overall targeting performance (Figure 2). In 2003, 55 percent of beneficiaries pertained to the bottom quintile (33 percent in the poorest decile, 22 percent in the second decile) and this ratio continuously improved to 64 percent in 2005, 68 percent in 2007 and finally 70 percent in 2008. As such, the targeting performance of the Green Card scheme in Turkey compared well internationally: 60 percent of Chile‘s SUF cash transfers, 39 percent of Brazil‘s _Bolsa Escola_ CCT program, 34 percent of Mexico‘s

> 6 <mark>The Ministry of Health reports that there have been cancellations of about 6 million Green Cards from 20032008, which may explain the drop in the total beneficiary numbers from 2006-2007 to 2008.</mark>

7
|**Imports**|0.46|0.63|0.17***|
|---|---|---|---|
||0.04|0.03|0.05|
|**Private investment**|0.34|0.43|0.09|
||0.06|0.07|0.10|
|**Public investment**|0.30|0.34|0.04|
||0.08|0.11|0.13|
|**Public expenditures**|0.19|-0.06|-0.25**|
||0.09|0.06|0.11|
|**Public revenues**|0.38|0.52|0.13|
||0.09|0.05|0.09|

Note: Standard errors below statistics. * p<0.10, ** p<0.05, *** p<0.01 Table **12** reports the correlations of key macroeconomic variables with the real interest rate. The results reveal no systematic differences in the patterns of cyclicality between countries with and without fiscal rule.

|**Table 12.** **Cyclicality of key m**|**acro aggregates wit**<br>**rule**<br>**no fiscal rule**|**h real int rate, by the p**<br>**with fiscal rule**|**resence of a fiscal**<br>**diff**|
|---|---|---|---|
|**GDP**|-0.08|-0.04|0.03|
||0.05|0.05|0.07|
|**GNI**|-0.01|-0.01|0.00|
||0.09|0.06|0.11|
|**Trade balance/GDP**|0.00|-0.04|-0.03|
||0.07|0.06|0.09|
|**Current account/GDP**|0.03|0.09|0.06|
||0.07|0.05|0.09|
|**Terms of trade**|-0.10|0.09|0.18|
||0.10|0.08|0.13|
|**Fiscal balance/GDP**|-0.11|0.04|0.15|
||0.07|0.06|0.11|
|**Inflation**|-0.81|-0.70|0.11|
||0.06|0.05|0.08|
|**Total investment**|0.01|0.01|0.00|
||0.09|0.06|0.10|
|**Employment**|-0.53|0.17|0.70|
||.|0.09|.|
|**Government consumption**|0.05|0.03|-0.02|
||0.07|0.05|0.09|
|**Private consumption**|0.05|-0.06|-0.11|
||0.09|0.06|0.11|
|**Exports**|-0.04|-0.09|-0.05|
||0.08|0.05|0.09|
|**Imports**|-0.02|-0.04|-0.02|

33
The following panels are included when the effect of the combined FPE and MTI reforms are studied using the national sample, in Appendix Tables D.4 and D.5.

# **Panel P – No Tigray**

Observations from the Tigray region are dropped. Tigray is the region for which the model estimates a positive return to the MTI reform.

# **Panel Q – Boothe and Walker (1997) MTI Definition**

In addition to the corroborated set of languages included in the paper’s joint definition, Boothe and Walker (1997) also find evidence that Somaligna was introduced in the Somali region for the first six grades in 1993, during the second round of translation introduced by the Council of Representatives.

Table D.2: Additional Language(s) in Boothe and Walker <u>(1997)</u> Definition

|||||**Fracti**<br>**MT Speakers**|**on of:**<br>**Region Speaking**|
|---|---|---|---|---|---|
|**Language**|**Year**|**Region**|**Grades**|**Living in Region**|**Language as MT**|
|Somaligna|1993|Somali|1-6|0.96|0.95|

_Source_ : Author’s summary based on information from Boothe and Walker (1997). _Note_ : MT = mother tongue.

A non-zero MTI measure for the Somali region is introduced in the calculation of new MTI and joint intensity measures.

# **Panel R – Zenebe Gebre (2014) MTI Definition**

In addition to the corroborated set of languages included in the paper’s joint definition, Zenebe Gebre (2014) also finds evidence of six additional languages being introduced prior to the 1995 fee removal. Three of these languages were smaller languages introduced in SNNPR in 1992 and 1993. The other three are found to be introduced in 1994, one is an expansion of Oromigna in the Amhara region, and the final two are new languages in new regions.

Table D.3: Additional Language(s) in Zenebe Gebre <u>(2014)</u> Definition

|||||**Fracti**<br>|**on of:**<br>|
|---|---|---|---|---|---|
|**Language**|**Year**|**Region**|**Grades**|**MT Speakers**<br>**Living in Region**|**Region Speaking**<br>**Language as MT**|
|Gamogna|1992|SNNPR|1-4|0.96|0.07|
|Gofgna|1992|SNNPR|1-4|1.00|0.02|
|Dawurogna|1993|SNNPR|1-4|0.88|0.03|
|Oromigna|1994|Amhara|1-8|0.02|0.03|
|Anyiwakgna|1994|Gambela|1-4|0.98|0.27|
|Hareriegna|1994|Harari|1-6|0.47|0.07|

_Source_ : Author’s summary based on information from Zenebe Gebre (2014). _Note_ : MT = mother tongue; SNNPR = Southern Nations, Nationalities, and Peoples’ Region.

A.46
to detect a 25 percent increase in profits. We see this more clearly in the next section, which shows confidence intervals for the treatment effects estimated from different training programs.

# **3) What does the experimental evidence of the impacts of business training show?**

Most studies of training show statistically significant impacts of training on business practices. Comparing these impacts across studies is complicated by different studies measuring different practices, and reporting treatment effects in different units. Studies which measure a range of different practices and then estimate the impact of training on the proportion of practices that firms use typically find effect sizes in the range of 0.02 to 0.07 (e.g. De Mel et al. (2014), Campos et al. (2017), Ubfal et al. (2019), McKenzie and Puerto (2020), Dalton et al. (2020)). That is, for every 20 practices that business training attempts to teach firms to do, on average firms invited to training only implement one additional practice. McKenzie and Woodruff (2017) discuss this as one way of reconciling the strong association between business practices and firm profits and sales with the lack of statistical significance of many of the early studies of training on these firm outcomes – business practices matter, but short training sessions often do not lead to big changes in these practices.

Of course, training may change practices that researchers are unable to measure, provide useful information about accessing finance, or change entrepreneurial attitudes. What matters is how these changes translate into impacts on profits and sales. Measuring the impacts of training on the sales and profits of small firms is not straightforward for several important reasons. The first is that not everyone who is invited to training, attends. For this reason, most studies report what is called the intent-to-treat (ITT) effect, which measures the impact of being invited to training, regardless of whether an owner attends all sessions. But this incomplete take-up of the training lowers statistical power, making it harder to detect training impacts. Second, small firms in developing countries die at an average rate of 8.2 percent per year, so that half of the firms operating at a given point in time will die within the next six years (McKenzie and Paffhausen, 2019). Some researchers code firms that die as having zero profits, while others report results conditional on survival, which can introduce the potential for bias if training changes how many or which firms survive.<sup>9</sup> Third, one consequence of poor business practices is that many small

> 9 This can be an even larger issue for programs focused on individuals trying to start businesses, especially youth programs, since few of those trained may be operating a firm, and training may affect which individuals start firms.

10