rafmacalaba/datause-encoder-data huggingface dataset:
(19 percent), Chasa (9 percent), and Pana (7.5 percent) form the sampled castes in Odisha, and they belong to General, OBC and SC castes respectively. In Tamil Nadu, Adi Dravidar (22 percent), Vanniyar (13 percent), and Vellalar, also known as Gounder (10 percent) comprise our sample, and belong to SC, MBC and BC groups respectively.<sup>17</sup>

# **4. Results**

Using these data, we examine the relationship between caste and female empowerment. We measure empowerment with variables that are consistent with the literature described earlier and include three categories of indicators:

- Measures of intra-household decision-making: Women were asked who was the primary decision-maker in the household on (i) purchases of household durables, (ii) children’s education/tuition, (iii) their own livelihood activity, and (iv) voting behavior. For each of these indicators, we examine two measures, whether a woman respondent provides any input into the decision, or whether she makes the decision entirely by herself.

- Measures of female mobility: Women were asked if they can go—without seeking permission—to the general store, health centre, bank, and to visit their friends, neighbors and relatives<sup>18</sup> .

- Labor force participation: We use a dummy variable that takes a value of 1 if a woman works for income (in cash or in kind) outside the home in at least one season, and 0 otherwise. We use information collected from the detailed livelihoods module in the household general questionnaire, which contains employment activities of all working adults in the household for both rainy and non-rainy seasons.<sup>19</sup>

One hypothesis, based on the literature described earlier, is that there is a negative relationship between these three types of variables and low caste status: SC and ST women should have greater mobility, autonomy and the highest levels of labor-force participation rates. We test the hypothesis using a linear probability model, using a set of dummy variables for caste with the poorest group in each population (SCs in Bihar and Tamil Nadu, and STs in Odisha) treated as the omitted category.

We first regress empowerment indicators on official caste categories and then on jati identifiers. We report three sets of regressions. First, to ensure consistency with the

> 17 In our sample the jati distribution for Tamil Nadu is fairly representative of the general population, in which Vanniyars make up roughly 20 percent, Gounders 8 percent, Thevar 10 percent, and Vellalars 7 percent.

> 18 For Tamil Nadu, we use available indicators of female mobility, including whether woman can go to the bank, the Taluk office (Block office) and the police station.

> 19 An adult is considered employed if (s)he is employed in any income-generating activity in either season.

11
Ittihad, a company with a large negative equity, increased significantly its market share between 1990 and 1993. This company mostly engages in automobile and group health insurance. two of the loss making lines. The increase in market share may be expalined by a policy of underpricing. But given the large negative equity and the low coverage of reserves, its customers either suffer from an illusion of insurance or they rely on the implicit backing of the state.

The compulsory personal lines of insurance, which include third party automobile and work accident, account for over 40% of total business (Table 3). Group health insurance represents about 20%, while transportation, fire and miscellaneous risks, which tend to be large industrial and commercial risks, account for about one third of total premiums. Life insurance is poorly developed and represents only 6% of the total. Most life insurance is linked to bank loans and, though not imposed by the state, is effectively compulsory for borrowers. As discussed further below, the existence of social security probably crowds out the purchase of life insurance.

Table 4 **Structure of Insurance Business in Selected Countries, 1992 (percent of total premiums)**

||Life|Nonlife|
|---|---|---|
|Africa|||
|South Africa|80.5|19.5|
|Zimbabwe|60.4|39.6|
|Kenya|20.8|79.2|
|Morocco|19.0|81.0|
|Tunisia|6.0|94.0|
|East Asia|||
|South Korea|79.7|20.3|
|Taiwan (China)|68.8|31.2|
|Singapore|62.2|37.8|
|Philippines|60.9|39.1|
|Thailand|55.0|45.0|
|Malaysia|44.7|55.3|
|Indonesia|30.0|70.0|
|China|30.0|70.0|
|Latin America|||
|Chile|60.0|40.0|
|Mexico|33.3|66.7|
|Colombia|22.2|77.8|
|Argentina|12.5|87.5|
|<br>Brazil|11.6|88.4|

Source: Sigma, Swiss Reinsurance Company, 3/94.

The relative underdevelopment of the insurance industry in Tunisia is shown by the low share of life business in total premiums (Table 4). This is much lower than the corresponding share in Morocco 5
# 3.3 Descriptive statistics

Table 3 provides further summary statistics on average spending observed across treatment arms. The total per capita annual spending<sup>13</sup> is around 65 million VND<sup>14</sup> (over <mark>$2,900 USD in</mark> 2016 PPP terms); total food spending constitutes a little over 43 percent of this amount. Of total food spending, FAFH accounts for around 25 percent of the expenditures (though, as we show in the main results in Section 4, this varies across treatment arms). The table also provides some preliminary results on reported FAFH expenditures. T2 has the highest FAFH expenditure, followed by T4, T3, and T1. We explore these results in more detail in the next section.

> 13 Development of the total spending aggregate followed the standard VHLSS methodology. Note that we do not include complete housing spending, as we could not impute rent values for households that did not rent. Thus, we do not include household rent for any of our sample.

14 The average total per capita expenditures, measured by the VHLSS in 2014 and adjusted for inflation and spatial price adjustments, was around 51.7 million VND in the Red River Delta urban region. This was around 1.3 million VND lower than the respondents in the 30-day one-line recall (summary statistics provided upon request); this control arm is the one that matches the prior VHLSS surveys. The 2016 exchange rate was 22360.37 VND = $1 USD.

14
taken to avoid undercounting household-based informal sector businesses. Attention must also be paid to fluctuations of informal sector movement over different areas or times of the day or week.<sup>6</sup>

# **2. Collecting Data on Informal Businesses: Existing Methods and Trade-offs**

Two main data collection methods are typically used for surveying informal businesses: i) household surveys, the prototypical examples being labor force surveys (LFS) or living standards surveys<sup>7</sup> ; and ii) establishment surveys or censuses (ILO, 2013). For surveys it is useful to consider each method as a multi-stage sampling procedure, where with some probability of selection, units are selected in a first stage, with the selected units denoted as PSUs, followed by secondary sampling units (SSUs), and so on.<sup>8</sup> Define all individual PSUs , where is the universe of PSUs. Likewise, SSUs are defined . Sampling units at each stage are well-defined, so and ii∈NN NN may exclude certain elements.<sup>9</sup> Each allows for the possibility of stratification. jj∈UU NN The household survey approach is implemented through standard household surveys, such UU The household survey approach is implemented through standard household surveys, such as Labor Force Surveys, and can capture informal (i.e., “off-the-books”) employment as well as employment in the informal sector (i.e., employment in legally informal activities). PSUs are enumeration areas defined to capture households. As a result, often excludes explicitly nonresidential areas, such as markets, industrial centers, or commercial zones. Call this set of PSUs NN L .<sup>10</sup> By design, these surveys are representative of L —that is the geographical areas delimited to capture households. Measures of informal businesses only require a few additional LL LL NN NN questions in these surveys, exploiting economies of scale in a cost-effective way.

However, such approaches are not necessarily well suited for representative estimates of informal businesses. The number of businesses and counts of individuals are not always mapped one-to-one; an informal business can be jointly operated across individuals, and one individual may operate several businesses. More fundamentally, L rarely accounts for the concentration LL NN

> 6 Mobility and timing issues are not unique to ACS but plague any attempt to sample informal businesses.

> 7 The most widely known of these is the World Bank’s Living Standards Measurement Survey (LSMS), which is run globally, in partnership with National Statistics Offices. 8 Note that this embeds a single-stage sampling approach, where only PSUs are selected.

> 9 An example of this is where excludes certain inaccessible areas (like a government or military installations). 10 Note the use of subscripts ‘LFS’ and ‘ES’ to denote, by shorthand, the Labor Force Survey and Establishment Survey methods. This notation is dropped from later discussions of the proposed method (Adaptive Cluster Sampling) NN for simplicity.

5
We also depict the kernel density distribution of average monthly nightlight cells in the Dominican Republic for 2013 in Figure 1. As can be seen, essentially a third of the pixels are unlit over the year. Of those that experienced a non-zero value, the average value is around 9.5, with s standard deviation of 11.5. Nevertheless, there is a small proportion of cells that is characterized by values much larger of that. For the sample used in the regression analysis, we assumed that any pixel that over our sample period had only zero values had no economic activity and hence was dropped from the analysis. This left us with a total of 38,535 of 59,392 cells covering the Dominican Republic.

A discussion is warranted as to the legitimacy of DMSP nightlight imagery as a proxy for economic activity. In this regard, there are two aspects to consider: (i) weaknesses of the data in capturing what they are explicitly supposed to capture, namely brightness due to artificial lighting at night, and (ii) weaknesses in nightlight data per se to capture economic activity. With regard to (i), there are a number of papers that examined how nightlights are related to measures of GDP, see, for instance, Henderson et al (2012) and Chen and Nordhaus (2011). In general, these studies suggest that at the national level nightlights can act as reasonable proxies of GDP. At a more localized level, Doll et al (2006), Ghosh et al (2010), and Mellander (2015) found, for countries in the EU, for China, India, and `Mexico, and for Sweden, respectively, moderate correlation between nightlight intensity and localized economic activity measures.

In terms of (ii) there are a number of features of the data that are relevant. Firstly, while the processed data provide intensity measures at roughly the 1km level (near the equator), the actual swaths of the data are around 3km and thus processed cells are locally not independent of each other, see Yi et al (2014). Perhaps more importantly, given the normalization of the data, there is likely to be considerable top-coding, so that very bright 7
Policy Research Working Paper 10010

# **Abstract**

As income increases, people become more mobile and spend more on carbon-intensive transport goods and services. This paper estimates income elasticities of transport consumption using household survey data for 18 countries, which are then used to simulate transport carbon footprint and carbon inequality by 2035. It first shows that in low- and middle-income countries (i) many households mostly walk and do not use transport services, (ii) income elasticity of private transport expenditure is high, and (iii) many households do not own a car. Both results suggest a future steep growth of emissions as incomes expand. Using estimates of income elasticities of vehicle ownership and vehicle use, the paper shows that carbon footprint will increase on average by 52 percent for these countries as incomes reach their 2035 levels. Finally, it decomposes carbon dioxide emissions along the within-country income distribution. Car ownership and carbon dioxide emissions are highly concentrated at the top. By 2035, carbon inequality will increase in some countries but decrease in others. Such results can be used for modeling future distributional implications of climate and energy policies.

This paper is a product of the Infrastructure Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at mlebrand@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> Development Economics Research Group, the World Bank<br>Services7 Trade RestrictionsAti Database A Home<br>pF osm! Main policy measures All policy measures<br>Make Summary of key restrictons County: 11010 of 104 |PIEY<br>selection<br>Lending by banks Allowed. There are no restrictions,<br>except that 33 percent of the<br>employees be nationals.<br>Acceptance of deposits by banks | Not allowed. There are no restrictions,<br>except that 33 percent of the<br>employees be nationals.<br>Automobile Insurance Not allowed. There are no restrictions,<br>except that 33 percent of the<br>employees be nationals.<br>Life Insurance Not allowed. There are no restrictions,<br>except that 33 percent of the<br>employees be nationals.<br>Reinsurance Not allowed. There are no restrictions,<br>except that 33 percent of the<br>employees be nationals.<br><!-- End of picture text -->
the mobilization of foreign funding from both parent banks and wholesale markets abroad during the credit boom period. At the same time, DEPOSITS also respond positively to a PRIVATE CREDIT shock (row 3, column 4) but the magnitude is smaller than for foreign liabilities, arguably because it is more difficult to significantly raise deposits in the short term. Together, these findings imply that foreign sources of funding can be useful to temporarily fill domestic funding gaps. We also confirm that a PRIVATE CREDIT shock boosts GDP, which is expected since private credit typically expands consumption and investment. This corroborates with results of the finance and growth literature (e.g. Levine, 1997). Finally, all variables have a positive and significant response to their own shocks (the charts on the diagonal of Figure 2), but this is simply a mechanical result.

Impulse-response functions are useful to study the short-term response of each variable to a shock in another variable. To examine the long-term cumulative impact of a shock we compute variance decompositions. Table 7, Panel A displays the variance decomposition for our baseline model. Each cell in the table shows what percent of variation in the row variable is explained by the column variable after the 10 quarters, i.e. 2.5 years. Note that by construction shocks to a variable of its own lag explains most of its variance (i.e. the diagonal of Table 7, Panel A contains the largest values).

Row 4 of Table 7, Panel A reports that a FOREIGN LIABILITIES shock explains a large part of the variance in PRIVATE CREDIT (9.7%). DEPOSITS shocks explain an even larger portion of the variance (14.2%), but this is expected, given that typically DEPOSITS account for the largest share of total bank liabilities in all countries, including countries that rely more on foreign funding. The fact that FOREIGN LIABILITIES explain a large part of the variance of PRIVATE CREDIT, almost equal to that of DEPOSITS, despite representing typically a much smaller share of total bank liabilities, reveals again the downside risks associated with cross-border borrowing. By contrast, shocks to GDP do not seem to contribute to the variance in PRIVATE CREDIT (2.4%). This could be due to longer response lags or noise in the quarterly GDP data in some countries in the sample.

20
three months. Columns 4 to 6 report the local average treatment effects (LATE) using the random encouragement assignment to instrument for the take-up of reference letters.<sup>18</sup> The coefficient estimates in Table 5 are very much consistent with the main findings from the audit study shown in Table 1. In the pooled sample (Panel A), coefficients on – both the number of applications submitted and on employment outcomes are sizable – LATE estimates range between 20% and 30% of the control mean but not statistically significant. Panel B and C confirm there is important treatment effect heterogeneity by gender: after three months, women in the treatment group submit more applications and are significantly more likely to receive interviews and find employment.

Employment effects are large in magnitude: 5.7 percentage points for ITT estimates (col. 3) and 11.7 p.p. for LATE estimates (col. 6), effectively doubling employment rates for the group of compliers. Coefficients for men are close to zero and insignificant. We can reject that employment effects for women and men are equal at the 10 percent level.

While the magnitudes of the effects are very high, the estimates are relatively imprecise. This warrants some caution in the interpretation of these results. However, it is remarkable that treatment effects estimated from separate experiments, and on different outcomes, point in the same direction. Overall, the combined evidence from the Experiments 1 and 2 show that reference letters can improve employment outcomes. In partic– ular, reducing information asymmetries through reference letters or other interventions – may improve equity by leveling the playing field for women in labor markets.

> 18We do not report results after five weeks as they are generally small and insignificant. This is because the follow-up period is too short for effects to manifest. It takes on average about 3 weeks to obtain reference letters, with males and females equally likely to report having attempted to get a letter. Qualitative evidence also suggests that participants waited for the letter templates to be completed before applying for certain jobs. Finally, a non-negligible number of participants report that it takes them longer than weeks to obtain a reference letter.

23
To motivate our results, we first present the mean infant mortality rates in Table 5 for each mother or child characteristic we use in our analysis. The first row of the table shows that girls are almost ten percentage points less likely to die in the first year of life than boys—a wellknown finding in the demographic literature.<sup>19</sup> The other coefficients show that children born in rural areas are more likely to die than those born in urban areas; that the mortality of children born to mothers with less than primary schooling is more than twice as high as that of children born to mothers with completed primary schooling or more; that children born to young mothers (age 15-19) and older mothers (age 35-39) are more likely to die than those born to “prime-age” mothers (age 20-34); and that high-parity births (5<sup>th</sup> birth or higher) are also more likely to die than lower parity births.

We next analyze heterogeneity in the relationship between detrended per capita GDP and infant mortality along these observable dimensions of mothers and children. Our approach is straightforward. In each case, we generate an indicator for the characteristic in question—for example, an indicator for the birth of a girl—and then interact this indicator with the measure of log per capita GDP. Table 6 then reports the coefficients on the main effect for log per capita GDP and on the interaction between log per capita GDP and the given characteristic. We focus on the specifications that include country-specific cubic time trends, as these account for underlying time trends most flexibly.

Table 6 shows that the mortality of infants born to mothers with low education levels is significantly more sensitive to changes in economic conditions than that of children born to mothers with higher education levels. In part, this is likely the result of the higher mortality rates among women with lower education levels, although this cannot fully explain the differences in

> 19 For example, the World Health Organization (2006) estimates that the male-to-female ratio in neonatal mortality and in early neonatal mortality in developing countries is 1.3.

22
# **Table 1: 'Raw' estimates from conditional logit estimation**

||All<br>manufacturing|Food and<br>Beverages|Textiles and<br>apparel|Paper and<br>printing|Chemicals<br>and<br>Petroleum|Rubber and<br>plastics|Metal<br>products|Furniture|
|---|---|---|---|---|---|---|---|---|
|**Infrastructure**|||||||||
|Market Access|-0.0008|-0.0012|0.0016|-0.0002|-0.0417|0.0007|0.0021|-0.0011|
||[0.0003]**|[0.0007]*|[0.0012]|[0.0017]|[0.0193]**|[0.0019]|[0.0022]|[0.0007]|
|Electric grid|1.5043|2.3627|1.8944|12.7944|13.4268|15.5558|13.7753|0.6305|
||[0.2846]***|[0.7139]***|[1.0224]*|[386.6443]|[1059.9987]|[1197.6892]|[345.9622]|[0.3322]*|
|**Agglomeration**|||||||||
|Localization|-0.0007|0.0107|-0.0001|-0.0757|0.7126|0.1058|0.0043|0.0037|
||[0.0002]***|[0.0025]***|[0.0006]|[0.0235]***|[0.3422]**|[0.0178]***|[0.0024]*|[0.0009]***|
|Diversity index|2.5932|2.9509|2.4040|3.0168|0.4531|4.7781|3.4196|1.2262|
||[0.2546]***|[0.4087]***|[0.9135]***|[1.1395]***|[3.996]|[1.1240]***|[0.8113]***|[0.4811]**|
|**Human Capital**|||||||||
|Education|14.0667|5.5984|14.1216|26.4592|-14.6166|4.9415|13.0636|3.9464|
||[0.4061]***|[1.3373]***|[1.6408]***|[2.8674]***|[14.0647]|[2.1058]**|[2.6103]***|[1.9758]**|
|**Natural Geography**|||||||||
|Roughness|-0.0005|-0.0004|-0.0012|0.0002|0.0066|-0.0008|-0.0015|0.0002|
||[0.0002]***|[0.0003]|[0.0006]*|[0.0005]|[0.0026]**|[0.0009]|[0.0006]**|[0.0002]|

++++Other controls: yields of coffee, cotton and maize

|Observations<br>86562<br>28998<br>6642|8964|2322|4482|12150|21654|
|---|---|---|---|---|---|
|Standard errors in brackets||||||
|* significant at 10%; ** significant at 5%; *** significant at 1%||||||
|Estimates were bootstrapped 1000 times to produce confidence bands.||||||

17 -