rafmacalaba/datause-encoder-data huggingface dataset:
than half the average loan size received by men. In rural areas, loans have been mainly used for agriculture production (including livestock), while in urban areas they have been mainly used for enterprise development. In both rural and urban areas, some loans have been used for consumption purposes, particularly for households with low income potential. One study that gathered detailed consumption expenditure data finds that borrowers reported improvements in their diet.

There are very few studies of the rates of return to investments, but the ones available tend to find returns to enterprise development to be between 8 and 30 percent. One difficulty in accurately assessing rates of return is that families often have multiple sources of income, with family members working elsewhere providing substantial support, some of which is funneled into family businesses. Also, only a few studies examine the impact of microfinance programs on employment. A study of the Kashf microcredit program interviewed 129 female respondents and finds that the program led to more family members participating in family enterprise activities. Also, women borrowers reported higher levels of confidence, self-esteem, and measures of empowerment and demonstrated increased awareness of social issues.

In summary, microfinance programs in Pakistan mainly have had male borrowers unless they have been explicitly targeted at women, and they tend to have benefited the near-poor and middle-income groups rather than the poorest, unless they explicitly targeted the poorest. The rate of return studies suggest that there were relatively high rates of returns to the loans, although the returns are somewhat difficult to measure given multiple household income sources. There is suggestive evidence that the programs have had some nonmonetary benefits, such as an increase in women‟s confidence and empowerment and improvements in the household‟s diet.

# **4.1.4  Microcredit in India**

Banerjee, Duflo, Glennerster and Kinnan (2009) evaluate the effects of placing a microfinance institution in a new market using a large-scale randomized experimental design. In 2005, 52 of 104 neighborhoods of Hyderabad (a large city in India) were randomly selected for opening a microfinance lending branch, called Spandana, with the remaining serving as controls. The loans were group liability loans and were targeted at groups of women (women formed their own groups). The eligibility criteria favored women who owned their homes and who had longer residence in the community, and the program did not serve the “poorest of the poor.” There was no training component.

33
7 On theoretical grounds, however, it is plausible<sup>to expect intertempor</sup> relationships between food aid and food production. The most<sup>probable relationship</sup> that food production will be affected by aid in previous periods.<sup>One might also expe</sup> that past levels of production would help predict current food aid. This mutua. dependence of production and aid on the lagged values of own<sup>and other variables giv</sup> the following vector auto-regression: where p denotes food production and A denotes food aid. 'he a (a') and<sup>the</sup><sup>_6_(8') a</sup> the coefficients of the linear projections of At (pt) onto a constant<sup>and past valu</sup> of At and pt, and the lag length m and k are sufficiently large<sup>to ensurt.</sup> that ut and are white noise error terms. While it is not essential that the<sup>lag length for A a</sup> _P_ be equal, we follow typical practice by assuming that they<sup>are identical.</sup> To estimate the dynamic relationship described in equation (1)<sup>and obta</sup> consistent estimates of the a's and a's, there must be enough<sup>observations on p and</sup> Each country sample does not have the requiFite number of observations.<sup>Panel data th</sup> combine large numbers of crosr; sectional units (countries), but only a few years observation in each unit may yield enough observations.<sup>To estimate the system</sup> equations, we would therefore typically pool data from different<sup>countries, at t</sup> expense of imposing the constraint that the underlying<sup>structure is the same for ea</sup> country.

In an attempt to relax somewhat the 'coimomon' structure assumption, it
Table J.2. Descriptive Statistics – Pre-migration Characteristics: Refugees with Formal Jobs

||Formal Job|Informal Job|Mean Difference<br>(p-value)|
|---|---|---|---|
||(1)|(2)|(3)|
|_A. Demographics_||||
|Female [=1]|0.25|0.364|0.013|
||(0.435)|(0.481)||
|Age (years)|35.169|33.905|0.190|
||(8.848)|(10.251)||
|Number of Venezuelan children|1.129|1.482|0.014|
||(1.196)|(1.532)||
|Years of education before migration|14.395|13.637|0.003|
||(2.230)|(2.759)||
|Migrated for health reasons|0.089|0.116|0.368|
||(0.285)|(0.320)||
|Time in Colombia (months)|26.098|27.566|0.122|
||(10.006)|(9.911)||
|Friends or family in Colombia before migration|0.726|0.704|0.621|
||(0.448)|(0.457)||
|_B. Employment in Venezuela_||||
|Ever worked in Venezuela [=1]|0.984|0.986|0.838|
||(0.126)|(0.117)||
|Employed at private frm in Venezuela [=1]|0.629|0.587|0.376|
||(0.485)|(0.493)||
|Employed with Government in Venezuela [=1]|0.185|0.159|0.455|
||(0.390)|(0.366)||
|Self-employed or employee in Venezuela [=1]|0.169|0.212|0.267|
||(0.377)|(0.409)||
|Had a written contract in Venezuela [=1]|0.653|0.543|0.020|
||(0.478)|(0.498)||
|Knew of job opportunity before migrating [=1]|0.274|0.327|0.240|
||(0.448)|(0.469)||
|Gap between last job and migration (months)|1.032|0.982|0.899|
||(4.371)|(4.139)||
|_C. Housing Characteristics in Venezuela_||||
|Had smartphone in Venezuela [=1]|0.782|0.661|0.007|
||(0.414)|(0.474)||
|Had a dwelling in Venezuela [=1]|0.774|0.865|0.007|
||(0.420)|(0.342)||
|Had electricity in Venezuela [=1]|0.992|0.995|0.702|
||(0.090)|(0.073)||
|Had running water in Venezuela [=1]|0.952|0.885|0.024|
||(0.215)|(0.319)||
|Had sewage in Venezuela [=1]|0.927|0.947|0.376|
||(0.260)|(0.225)||
|Lived in Venezuela with parents or siblings [=1]|0.403|0.422|0.686|
||(0.493)|(0.494)||
|Lived in Venezuela with partner or spouse [=1]|0.613|0.59|0.632|
||(0.489)|(0.492)||
|Observations|124|940|1,064|

_Notes:_ The table depicts the difference between Venezuelan refugees with PEP by having a formal job. The variable Formal Job is a indicator [=1] if the respondent has a written or verbal contract and saves in a pension fund.

90
# **2. Main trends**

In this paper, we use a newly-created data base of inequality statistics for 26 transition economies. It has three important characteristics: (i) it is the largest data base because it includes detailed inequality data for more than 200 country/years covering the 16 year period (1990-2005), (ii) it is overwhelmingly calculated from micro (householdlevel) survey data, and (iii) we are therefore able to go, in the empirical analysis (Sections 3 and 4), beyond the use of the synthetic inequality statistics (such as the Gini coefficient) and to use decile shares. The advantage of the last point is that it allows us to have many more observations, and more importantly, presents a much more nuanced and accurate picture of the entire distribution than a single number can, be it a Gini, Theil or any other synthetic inequality indicator.

Figure 1 summarizes the evolution of inequality in transition countries over the period 1990-2005. The top line shows the share of the top decile in total income, the bottom line the share of the lowest decile. The biggest distributional changes occurred between 1990 and 1995, and since then the distributions have, on average, been stable. What happened between 1990 and 1995 was that the share of the top decile increased from about 20 percent of total income to about 25 percent (and has been remarkably stable since) while the share of the bottom decile dropped from about 4.5 percent of total income to 3 percent. The top and the bottom deciles registered the biggest swings: positive by about a quarter of its previous share for the top decile and negative by almost a third for the bottom decile. In contrast, the shares of the middle deciles did not change much: the share of the fifth decile dropped from about 8.6 percent to 8.1 percent; the share of the sixth from 9.6 to 9.3 etc. This is consistent with other evidence which shows that the biggest difference (in cross-country studies) between the relatively unequal and relatively equal countries resides in their top and bottom decile shares whereas the middle classes’ income shares are relatively stable (Milanovic, 2008; Palma, 2006). One can thus expect that a temporal change in inequality as here would involve most important swings for the two extreme deciles.

6
|**Table 3. Revenues And Costs, Mum**<br>(Million Rs.)|**bai Suburban Railways**<br>**2005-06**|
|---|---|
|Passenger Revenues|9936.7|
|Operational Expenses|8949.6|
|Depreciation|701.7|
|Interest onCapital|409.2|
|TotalCost (Million Rs.)|10060.5|

Source: S.K. Singh, Executive Director, Economics & Statistics, Ministry of Railways, India (personal communication).

# **III. Travel Patterns in Mumbai**

In Mumbai, as in other developing country cities, the journey to work constitutes the largest fraction of household trips in terms of distance traveled, accounting for approximately two-thirds of miles traveled (Takeuchi, Cropper and Bento, 2007). Table 4 describes the modal shares of workers in Mumbai, based on data in Baker et al. (2005). These data come from a random sample of 5,000 households in Greater Mumbai who were surveyed in the winter of 2003-2004. Commuting patterns are based on the usual commutes of the first and second principal earners in the household.<sup>10</sup> Table 4 describes the ‘main mode’ used on a typical commute trip. The main mode is defined to be the mode that takes the longest time, with the exception of “on foot” and “bicycle,” which are counted as the main mode only if they are the only mode used on the trip. Table 4 indicates that 45% of commuters walk to work, 22% rely on rail or rail+bus as their main mode, while 22% ride a bus to work—either as a main mode (14%) or to connect with rail.<sup>11</sup> The modal shares for private vehicles are much smaller—approximately 3% each for bicycle and car and 8.4% for two-wheelers.<sup>12</sup> Of commuters who take motorized transport to work, 70% take either rail or bus or both.

The respective modal shares are somewhat different for the poorest income group in the survey, defined as households with a monthly income below Rs. 5,000: 63% of the workers in these households walk to work, 6% ride a bicycle, 15% take the train (or train+bus) and 16% ride the bus (either alone or in conjunction with the train). However, of those workers who use motorized transport, 84% take either rail or bus or both.

As household income goes up, the modal shares of bus and motorcycle increase for short to medium commutes, while the share of trips made on foot declines. (See

> 10 For details on the sampling protocols and questionnaire used in the survey, see Baker et al. (2005).

> 11 In Table 6, these shares, based on travel diaries are, respectively, 46% walking, 21% rail and 15% bus. 12 The shares based on the travel diaries are 3.5% for bicycle, 3.2% for own car and 8.6% for own twowheeler.

7
# 2. Literature

Many analyses of middle class values remain descriptive in nature. The Pew Research Center (PRC), among others, recently carried out two insightful studies analyzing the attitudes of the middle class. PRC (2009) defines the middle class in absolute monetary terms, and explores the hypothesis that the values of the middle classes in emerging countries tends to reflect similar opinions to those of industrialized ones on democracy and social issues, once a certain level of wealth is reached. Compared with the lower classes, the report shows that the middle class assigns a higher importance to democratic institutions and individual liberties, considers religion less central to their lives, and holds more liberal social values. The report fails however in looking at “middle class particularisms,” as it does not report if the observed differences are statistically significant, and does not consider whether middle class values differ with the one of the upper classes. In PRC (2008), a public opinion survey was conducted among a nationally representative sample of US adults, to collect information on which social group people perceive themselves to be a part of (lower, middle or upper class) and their views on different values and perceptions about the future. The results show that 53% of adults in the US state that they are middle class, but roughly 80% indicate that it has become harder to sustain their current lifestyle, which suggests that, while wealthier than the poor, the middle class remains vulnerable to shocks.

The OECD’s Latin America Economic Outlook (2011) makes a relevant effort to analyze the attitudes of the Latin American middle class towards democracy, fiscal policy, and redistribution. It shows that the Latin American middle class supports democracy – yet remains dissatisfied with how democracy actually functions. Regarding taxation and public services, it finds that the Latin American middle class is more likely than other groups to consider that citizens should pay their taxes, and less likely to consider that taxes are too high or justify tax evasion. The middle class is also less satisfied with the provision of public services, compared to the more affluent. The analysis, however, is based on households’ self-perceived position in the income distribution, which can differ substantially from their actual position (see below).

Using data from the World Values Survey for 80 countries, Amoranto, Chun and Deolalikar (2010) examine how perceived class status relates to values that may be associated with higher economic growth and greater accountability in public services. They find that people who perceive themselves as middle class are found to have values that are more likely to contribute to economic growth than the lower class for six indices, but generally have less liberal values and attitudes than the upper class 4
**Figure 3 Value of Collateral as % of Loan Value**

<!-- Start of picture text --> 120<br>100<br>100 91<br>76<br>80 70<br>60<br>40<br>20<br>0<br>Micro Small Medium Large<br><!-- End of picture text -->

**Table 1 Data Details**

|Size and A<br>|ge Distrib|ution ‐|All Firms|<br>|
|---|---|---|---|---|
|Employ\Age|1‐5|6‐15|≥16|Total|
|Micro|0.30|0.42|0.28|206|
|Small|0.18|0.48|0.33|374|
|Medium|0.09|0.38|0.53|223|
|Large|0.05|0.38|0.57|111|
|Total|155|395|364|914|

|Size and Ag<br>|e Distrib<br>|ution ‐ P<br>|anel Onl<br>|y<br>|
|---|---|---|---|---|
|Employ\Age|1‐5|6‐15|≥16|Total|
|Micro|0.18|0.42|0.40|60|
|Small|0.15|0.48|0.37|158|
|Medium|0.09|0.36|0.55|139|
|Large|0.03|0.29|0.68|62|
|Total|50|169|200|419|

13
**Figure 1. Regional Orientation and** RCA **Indices for Products Accounting for 92 Percent of the**<sup>**1988-94 Change in Mercosur's Intra-Trade.**</sup>

<!-- Start of picture text --> (2.89)<br>a) 1.00 --- - - - - - - - - - - - _ - - - - - - - _<br>CD<br>a) Note: The width of each bar in this chart is proportional to the share<br>of the product in the 1988-94 increase in Mercosur's intra-trade.<br>X 0.75 _<br>cCo_<br>-o~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~c<br>0) CU<br>o Products oco ~~~~~2 CU a. 0i C C<br>0.25CU coU C,,2n<br>O O ~~~WC 0 ~~~~~~~~~~ aa<br>0) -~~~~ *~~~~~~i 0 CU Cco 2a 0a a CU C~~~~~~~~~ : (D* CC<br>3.00~6. CU_~<br>0 ~ ~ ~ ~~~U~~<br>4.00-<br>C<br>0)<br>CD<br>CD<br>0<br>1. 0 - - - - - - - - - - - - - - - - - - - - - - - - - -<br>C ~ ~ ~ ~ ~ ~ ~~~~Pout ~ ~ eie tteToDgtLvlo h ICRvso<br><!-- End of picture text -->
6 Table 1.2. Institutional and Noninsitutional Sources of Credit (percentage of tota faim credit)

|Year|institutional Sources|Noninstitutional Sources|
|---|---|---|
|1973|11|89|
|1985|27|73|
|1990|38|62|

Sbuuce: Rural Credit Survey 1973, 1985; Agrculturn Census, 1990.

Table 1.3. Itrnational Comparison of the Share of Infoml Rural Credit

||Informal credit share<br>(percentage)|Reporting year|
|---|---|---|
|Philippines|71|1978|
|India|70|1972|
|Bangladesh|63|1974|
|Pakistan'|73|1985|
|Maaysia|62|1986|
|Thailand|52|1985|
|Indonesia|52|1985|
|South Korea<br>..................................................|_50_<br>...........................................................................|1981|

Pakistan data are from the 1985 rual credit survey. The Agricultural Census 1990 reports infonral credit at 62 per cent Saurc: P.B. Ghate, AsianDevelowmentReview (1988) Vol. 6: 64-85.
**Figure 7 - Impact of part time work on returns to education**

<!-- Start of picture text --> 20%<br>15%<br>10%<br>5%<br>0%<br>age 25 age 35 age 45 age 25 age 35 age 45 age 25 age 35 age 45<br>-5%<br>Primary Secondary Tertiary<br>-10%<br>-15%<br>Part-time work (Base scenario) No part-time work<br><!-- End of picture text --> Source: Authors’ calculation based on Encuesta de Hogares 1995-2000 _<u>Tuition</u>_ <u>. If adult learners have to pay for primary and secondary education, the return is</u> diminished by three percent points for primary and secondary education (for a 35 year old). This is a reduction of around 67 percent for primary education and 31 percent for secondary education. This scenario can also be interpreted as the impact of private cost of education other than tuition that is not included in the calculations, such as school supplies and transportation. For tertiary education, the base scenario assumes full tuition. In the case of students attending public tertiary institutions, where tuition fee amount to only 20 percent of the full costs, the return to increases by 11 percentage point compared to the base case scenario of full tuition costs. This represents an increase of 68 percent. The robust return equally prevails for an adult student. A 45 year-old would receive a return of more than 20 percent.<sup>11</sup> _<u>Unemployment</u>_ <u>. Unemployment affects the return through two channels: (i) the expected</u> income increases for workers prior to entering education and after the completion of a higher level of education. Therefore, there is a level effect; and (ii) the return changes according to the relative difference in unemployment between the previous level of 11 Tuition fees affect returns to tertiary education more in Colombia than in OECD countries. In the OECD, the average return decrease by approximately 2 percentage point, see Annex 4: International Comparison.

17
cipals, SAEB similarly provides data on qualifications, experience, and participation in training, but adds how principals were selected and the provision of textbooks. Exploring this information allows us to investigate some mechanisms explaining the _RBF_ effects on learning outcomes.

As our empirical strategy compares schools that are geographically close during the critical periods of the _RBF_ reform, we restrict the working sample to municipalities at the border of _Ceará_ and adjacent states: _Piauí_ , _Rio Grande do Norte_ , _Paraíba_ and _Pernambuco_ . However, the narrower the definition of ‘’border", the smaller is the sample power. Or alternatively, a wide definition of ‘’border" may not effectively control for unobservable factors explaining the differences in learning performance between schools. Thus, we define border as the first _three_ municipalities on each side of the _Ceará_ border. By using this definition, approximately one-third of the municipalities in these five states become part of the working sample (see Figure 1 for an illustration of all municipalities in the sample). Table 1 presents descriptive statistics using this definition.

An immediate evidence from Table 1 is that, during the baseline (2005 _−_ 2007), there is no systematic difference between _Ceará_ and its adjacent states. This is valid for 5<sup>_th_</sup> grade or 9<sup>_th_</sup> grade, test scores, and student characteristics. Other evidence is that for _RBF_ periods from 2009 to 2017, test scores in _Ceará_ steadily increase relative to adjacent states, especially in “ _RBF_ ( _re_ ) + _TA_ ” periods. Considering students in Panel A, around 50% are boys, the students are around 11 years old and, on average, there are 5 people per household. When the re-formulated RBF started (2013), the difference in student performance in test scores between _Ceará_ and adjacent states is 7 _._ 6 points in Portuguese and 9 _._ 2 points in Mathematics comparing 2013 to 2011.

We complement the empirical analysis by merging additional data-sets. In terms of including school _−_ level controls, a useful source of information is the _Censo Escolar_ , which provides comprehensive information on school infrastructure and unique ids that allow us to merge it with the SAEB data. For municipal-level controls, we include data on GDP _per capita_ and population size 13
More broadly, a wide range of empirical literature identifies the importance of financial inclusion in promoting economic growth and reducing income disparities. Demirgüç-Kunt et.al (2013) observed a strong positive correlation between the use of formal financial services and the level of GDP per capita in a country. A similar conclusion was drawn by King et al. (1993) who studied the sources of growth of 80 countries between 1960 and 1989, which illustrated that advancement in bank assets (measured by the ratio of liquid liabilities to GDP) was positively associated with economic growth, physical capital accumulation, and economic efficiency, even when controlled for country and policy characteristics. This relationship was further evidenced by the study of financial inclusion and economic growth in a research paper covering 55 Organization of Islamic Cooperation (OIC) countries by Dai-Won et al. (2018). A study of 37 developing Asian economies by Park and Mercado (2015) showed that financial inclusion contributed significantly to reducing poverty and lowering income inequality in those countries. Financial inclusion is therefore recognized as a key enabler for 7 of the 17 United Nation’s Sustainable Development Goals and for meeting the World Bank’s twin goals of reducing extreme poverty and boosting shared prosperity.

Access to a basic account at a formal financial institution where money can be stored safely is a primary metric of financial inclusion. Access to stored funds can be used as a bulwark against sudden economic shocks among the poor, as seen in the study of financial diaries of 250 poor households by Collins et al. (2009). Other studies have shown that access to a formal account by underserved individuals helped them to meet unexpected expenses such as health needs (see Dupas and Robinson (2013)).

Yet financial exclusion is still widespread around the world. World Bank estimates show that 1.7 billion adults worldwide still have no access to formal financial services – meaning that nearly 38 percent of the world’s population is still financially excluded (Demirgüç-Kunt et all., 2017). Within the South Asian Association for Regional Cooperation (SAARC) region, Islam et al. (2017) observed that “although much progress had been achieved, huge work remains to be done to foster financial inclusion for inclusive growth”.

3