rafmacalaba/datause-encoder-data huggingface dataset:
The prevalence of stunting among all children in the EGSF survey (61.6%) is slightly higher than in the 1987 DHS survey and quite a bit higher than in the 1995 DHS survey (57.9% and 49.7% respectively). This higher prevalence reflects the EGSF restriction on rural areas. The prevalence of stunting in rural areas of Guatemala is 62.1 % and 56.6% respectively in the 1987 and 1995 DHS surveys.<sup>5</sup> More than half (50.4%) of the stunted children in the EGSF are extremely malnourished (as determined by height-for-age z-scores below -3.0).

# **4.2 Age Pattern of Height-for-Age**

Figure 1 shows the mean z-scores for height-for-age by age group and sex in the EGSF sample. The height-for-age pattern does not seem to vary significantly by sex, a usual finding in Latin America. Following the typical age-pattem observed in developing countries, the average z-score decreases up to age 24 months and then tends to level off (Martorell and Habicht 1986). The negative z-scores observed for children at birth indicate that the malnutrition process leading to deficits in height-for-age is likely to have begun prenatally, when inadequately nourished pregnant mothers failed to provide a satisfactory nutritional intake to their fetuses.

# **4.3 Ethnic, Education and Income Differentials in Height-for-Age**

Table **1** displays the mean height-for-age and the percentage of children stunted among children of different ethnic, education and income categories in the EGSF sample. The figures confirmn the existence of a very marked socioeconomic gradient in child health and nutrition, which was found in previous research in Guatemala (Pebley and Goldman 1995). Note that the prevalence of stunting of children of ladino mothers is less than _50%_ while it is almost 80% among children of indigenous mothers who do not speak Spanish. Differentials are also very important in terms of both mothers' and husbands' education. Note the very low prevalence of stunting (8%) among children whose fathers have postsecondary education. The mean z-score in height-for-age increases monotonically across the quartiles of the sample distribution of expenditure peir capita.

# **4.4 Geographical Variation of Stunting in Rural Guatemala**

Table 2 shows the mean z-score for height-for-age and the prevalence of stunting by age of the child at the time of interview for both sexes combined, for the complete sample and each of the surveyed

> for-height and height-for-age; and (2) studying weight-for-age does not add any additional information to that provided by studying the other two indicators, weight-for-height and height-for-age.

> 1995 DHS data also indicate that the prevalence of stunting in the rural conmnunities of the four departments surveyed by the EGSF is 63.1%.

5
# **Graph (18): Stability (One Step and N Down Chow Test) of Parsimonious Open Economy VECM:**

<!-- Start of picture text --> 2010 2010 2010 2010<br>2000 2000 2000 2000<br>1% 1% 1% 1%<br>1990 1990 1990 1990<br>1up Drgdp 1up CHOWs Ndn Drgdp Ndn CHOWs<br>1980 1980 1980 1980<br>1.0 0.5 0.0 1.0 0.5 1.0 0.5 1.0 0.5<br>2010 2010 2010 2010<br>2000 2000 2000 2000<br>1% 1% 1% 1%<br>1990 1990 1990 1990<br>1up Drm2 1up DDISC Ndn Drm2 Ndn DDISC<br>1980 1980 1980 1980<br>1.0 0.5 0.0 1.0 0.5 0.0 1.0 0.5 1.0 0.5 0.0<br><!-- End of picture text --> 80
unable to reject equality between the estimated average impacts and mean forecasts.

Disaggregating the data by survey round assists in understanding the predominantly null impacts on consumption and adult well-being. In the short run (while the program was ongoing), treated households experienced meaningful gains in housing quality and financial stability: they reported increased access to clean water and less reliance on loans. These findings indicate that program investments were able to achieve some of their immediate goals, specifically, in terms of improving the housing quality experienced by recipients, and preventing them from accumulating more debt (much of which is rental related debt in this population). In fact, on average the control group holds 4 to 5 months of rental debt, so potentially reducing this burden is a meaningful benefit for treated households.

Yet both food security and self-reported health worsened: household hunger increased for several measures and more household members exhibited COVID-19 symptoms. We find several pieces of evidence that these negative effects follow from unanticipated redistribution of aid. In particular, we document a meaningful and statistically significant reduction in food aid that treatment households report receiving (from both formal and informal sources), roughly equivalent to 10% of monthly pre-pandemic income. There is also suggestive evidence that treated households received an inflow of additional household members (especially adolescents), further diluting program benefits in per capita terms.

Data from the second round of surveys (collected shortly after assistance had ended) indicates that treated households continued to report reduced housing expenditures as well as increased savings, but at the same time reported a significant decrease in subjective well-being. In the final survey round, one to two years after assistance had ended, the only significant treatment effect that survives a multiple testing adjustment is the substantial decrease in child socio-emotional wellbeing, noted above.

The effects of the refugee housing assistance program over time suggest a possible link between the negative impacts on child well-being and neighbor attitudes. While all other treatment effects 5
**Table 3: Demand for formal documentation at village level**

||**Total**<br>|**Region**<br>|<br>||**Pr**<br>|**ovince**<br>|||
|---|---|---|---|---|---|---|---|---|
|||**Rural**|**Urban**|**Lusaka**|**North**|**East**|**West**|**South**|
|**Willingness to pay (WTP)**|||||||||
|Mean WTP > K 100|0.709|0.649|0.813|0.869|0.565|0.813|0.450|0.354|
|Mean WTP > K 200|0.618|0.534|0.764|0.833|0.500|0.583|0.325|0.313|
|Mean WTP > K 300|0.525|0.425|0.698|0.750|0.413|0.375|0.275|0.229|
|Mean WTP > K 400|0.444|0.326|0.648|0.667|0.326|0.188|0.200|0.146|
|Mean WTP > K 500|0.370|0.243|0.588|0.595|0.261|0.125|0.150|0.125|
|Mean WTP > K 600|0.327|0.211|0.527|0.536|0.239|0.125|0.075|0.083|
|Mean WTP > K 700|0.295|0.179|0.495|0.500|0.196|0.042|0.075|0.083|
|**Title demand**|||||||||
|> 90% wanting title|0.234|0.150|0.379|0.500|0.152|0.000|0.125|0.063|
|> 70% wanting title|0.370|0.288|0.511|0.631|0.304|0.208|0.200|0.125|
|<br>> 60% wanting title|0.442|0.367|0.571|0.690|0.413|0.354|0.250|0.125|
|< 40% wanting title|0.325|0.419|0.165|0.107|0.261|0.250|0.675|0.646|
|Number of villages|495|313|182|84|46|48|40|48|

_Notes_ : Own computation from 2017/18 Quarterly Labor Force Survey conducted by Zambia’s Central Statistical Office. Sampling weights adjusted with non-response probabilities are used to estimate the mean values.

23
Beyond the immediately reserved period, the literature identified three channels through which reservations could affect long-term outcomes. First, they might prompt those who previously had not participated in political process to change their behavior permanently. Evidence for persistent effects via greater participation along these lines is available from rural West Bengal (Beaman _et al._ 2010) and South India (Besley _et al._ 2005) as well as urban Mumbai (Bhavnani 2009). This is plausible as previously ignorant voters may require time to learn about how to access and use information to most effectively hold leaders accountable. Second, they may trigger a process of learning and revision of prejudices as in cases where exposure to female leaders led to and revision of stereotypes regarding females‟ leadership qualities (Beaman _et al._ 2009). A third option less documented in the literature is that, if it increases voice or shifts the composition of public goods in a direction that benefits certain groups, reservation may lead to increased contributions to public goods. These will be relevant because, even if key infrastructure such as roads or schools has been established (e.g. through central funds), its lifespan and effectiveness will be significantly enhanced by efforts towards local maintenance or monitoring to prevent service providers (e.g. school teachers) from shirking. The underlying mechanisms have been studied in a range of contexts (Bagnoli and Lipman 1989, Messer and Zarghamee 2007),though few studies explore the willingness to contribute to better service quality in India (Chandrashekhar 2008).

By providing evidence on some of the trade-offs involved, the focus on long-term effects of reservation at the individual level that we are able to take can help clarify some of the issues in the literature. It allows us, in particular, to test whether a possible short-term reduction in quality of public good provision induced by reservation may be offset by the fact that this intervention exposes individuals, especially females, to new ideas and enhances their participation in local political decisions with beneficial effects in the long term.

# **2.3 Econometric approach**

To explore the issues discussed above, we use data from a nationally-representative panel survey of 233 villages in rural India conducted in 2008 by the National Council of Applied Economics (NCAER). In addition to its national coverage, this dataset, which is based on a long-running panel, has a number of desirable features. First, with information on the last three village-level elections, it provides information data on the reservation status of all panchayats elected after the provisions of the 73<sup>rd</sup> amendment were adopted in a state.<sup>8</sup> This allows us to test for long-term impact of reservations in a setting of decentralized decision-making. Table 1, which provides a breakdown of reservation by region, illustrates that out of the

> 8 While this includes three elections for most of the states, Bihar, Orissa, Punjab, Tamil Nadu and West Bengal held the earliest elections in 1992, before the amendment was adopted.

9
by smallholders using low-tech methods of production. The domestic sector produces an import competing good with capital intensive methods. The elasticity of substitution between imports and the domestic good is assumed to be 0.5 for the intermediate good and 2.0 for the final good.

Table 2.2 Calibrated Parameters for the Two-Sector Model

||αl|αk|A|βM|βD|B|
|---|---|---|---|---|---|---|
|Export|0.80|0.20|1.98||||
|Domestic|0.40|0.60|1.98||||
|Final||||0.38|0.62|1.89|
|Intermediate||||0.69|0.31|1.92|

Source: Author’s calculations As far as the functional distribution of income is concerned, it is assumed that the rural household owns 70 percent of labor and 10 percent of capital. Therefore the urban household owns 30 percent of labor and 90 percent of capital. Furthermore, when we introduce the government, we assume that the rural population gets 60 percent of transfers from the public budget. The urban households receive 40 percent. In this simple framework, the role of the government is solely to redistribute its revenue from taxes to households. Finally, we make the assumption that rural households receive 20 percent of the trade balance and the urban folks 80 percent. In the base case, the balance of trade is equal to zero.

# _Lorenz Parameter Estimation_

When household data are available, either in the form of unit records or aggregated in income or expenditure classes, one can use regression analysis to fit the data to a model such as the General Quadratic model. We follow the procedure described in Datt(1992, 1998). This calls for regressing **[L(1-L)]** on **(p**<sup>**2**</sup> **-L)** , **L(p-1)** and **(p-L)** without an intercept, and dropping the last observation since the chosen functional form forces the curve to go through (1, 1). Here **p** stands for the abscissa (horizontal coordinate) while **L** stands for the ordinate of the Lorenz curve estimated from the initial 11
host economy also implies that some less productive firms exit, which generates an efficient reallocation of factors that increases aggregate productivity.

# **6. References**

Ahmad, N. 2008. “A Proposed Framework for Business Demography Statistics.” In Measuring Entrepreneurship, edited by Emilio Congregado, 113–74. Boston, MA: Springer.

Alfaro, L., A. Chanda, S. Kalemli-Ozcan, and S. Sayek. 2006. “How Does Foreign Direct Investment Promote Economic Growth? Exploring the Effects of Financial Markets on Linkages.” NBER Working Paper 12522, National Bureau of Economic Research, Cambridge.

Alfaro, L., and M. X. Chen. Forthcoming. “Selection and Market Reallocation: Productivity Gains from Multinational Production.” American Economic Journal: Economic Policy.

Alfaro, L., and A. Rodriguez-Clare. 2004. “Multinationals and Linkages: Evidence from Latin America.” Economia 4: 113–70.

Almus, M. 2002. “What Characterize a fast-growing Firm?” Applied Economics, 34(12) pp. 14971508.

Barba Navaretti, G., and A. Venables. 2004. Multinational Firms in the World Economy. Princeton, NJ: Princeton University Press.

Birch, D.G., 1987. Job creation in America: How our smallest companies put the most people to work. Free Press. New York.

Blalock, G., and P. J. Gertler. 2008. “Welfare Gains from Foreign Direct Investment through Technology Transfer to Local Suppliers.” Journal of International Economics 74 (2): 402–21.

Blalock, G., and P. J. Gertler. 2009. “How Firm Capabilities Affect Who Benefits from Foreign Technology.” Journal of Development Economics 90 (2): 192–99.

Coad, A., S. Daunfeldt, W. Hölzl, D. Johansson, and P. Nightingale. 2014. “High-Growth Firms: Introduction to the Special Section.” Industrial and Corporate Change 23 (1): 91–112.

- 23 -
for each combustion power plant, we compute the count of the population 65 or older residing within 50 km by multiplying the all-age population counts, described in the data section, by the fraction of the population 65 or older. Information on the fraction comes from World Bank (2023a) and is available at the country year level. Accordingly, this calculation assumes that the age distribution is uniform within countries.

In the fourth step, we derive counts of premature deaths at the plant-month-year level by multiplying the variables described in steps one to three. For ease of presentation, we aggregate this count of premature deaths to the LAC by year level.

Last, to account for uncertainty in our estimate of excess air pollution, in the first step, we draw our coefficient from a normal distribution with a mean equal to the estimated coefficient (1.55) and a standard deviation equal to the standard error (0.31). We then repeat 1,000 times steps one to four, taking a new draw each time.<sup>11</sup> Figure 6 plots the evolution of drought-induced premature deaths in LAC. The spread of the box plot displayed each year results from the uncertainty in our estimation of excess PM2 _._ 5 concentrations and the observed FHD. The figure reveals the scale of the cost for LAC, with median premature deaths per year ranging from 3,681 to over 10,610. In Figure A2 in the appendix, we show that using the CRFs from Liu et al. (2019) leads to median premature deaths per year that are in the order of 503 to 4,987. The CRFs from Liu et al. (2019) have the advantage of being derived for Chile and Mexico but have the disadvantage of not being causal CRFs.

Another important consideration is that our counts of premature deaths could underestimate the actual number of deaths if groups with lower socioeconomic status are disproportionately exposed and vulnerable to PM2 _._ 5. There are several reasons why exposure might be higher among individuals with lower socioeconomic status, including a greater likelihood of residing near sources of PM2 _._ 5 and engaging in outdoor occupations. Moreover, the adverse impact of PM2 _._ 5 on health is exacerbated among the disadvantaged due to their comparatively lower baseline health status and more limited access to medical services.

11Specifically, we compute the number of premature deaths per plant-month-year for each draw by: 0.69 (CRF) _×_ 30 (days) _×_ FHD _×_ draw of FHD coefficient from _∼N_ (1 _._ 55 _,_ 0 _._ 31) _×_ exposed population 65 or older.

19
single peak in 2019 when the amount that matures within 12 months reaches $40bn. According to current market expectations, these peaks will occur after the Federal Reserve has raised interest rates.

12. **By 2020, all regions will have experienced peaks in which more than 10% of their currently outstanding stocks will mature within 12 months (Figures 13 and 14).** China’s peak should occur in 2017 in when almost 20% of its currently outstanding bonds will mature ($333 billion). A significant portion of these bonds however are denominated in renminbi which ameliorates currency risks. South Asia peaks in 2019 with 20% of its current stock ($81 billion). Eastern Europe peaks at almost 15% in 2018 (current stock: $239 billion). East Asia (ex-China) peaks at 12% in 2019 (current stock: $174 billion). Africa, Middle East, and Latin America peak at 15%, 15% and 10% in 2020, respectively (current stocks; $64 billion, $42 billion, and $751 billion).

# <u><mark>Credit quality</mark></u>

13. **The credit quality of post-crisis external issuance has improved significantly (Figure 15).** Before the crisis, only 30-40% of issuance was investment grade. Since 2010, this fraction has steadily improved from around 50% to 70%. While this is a positive trend, it is important to keep in mind that ratings can be pro-cyclical.

# **IV.** **<mark>Data</mark>**

<mark>We now turn to the description of our two data sets that cover the universe of EMDE external bond issuance in the period 2000-14.</mark> Table 1 describes the variable definitions. <mark>Our data sets matches three types of data: i) highly granular bond data (i.e. industry or bond deal level), ii) high frequency financial global push factors, and iii) country pull factors. D</mark> ata on <mark>bonds a</mark> re derived from Dealogic <mark>which p</mark> rovides information on borrowers, bond yields and non-pricing terms at origination on the individual deal level, which typically comprises several tranches. Global push factors are from Bloomberg and country pull factors are sourced from the IMF’s World Economic Outlook.

# **A.** **<mark>Bond Deals</mark>**

# <mark>-</mark> <u><mark>Country industry panel dataset</mark></u>

<mark>First, to analyze the impact of global factors on the propensity to issue external bonds by EMDE entities, we compile a balanced panel data set of monthly total external bond issuance for each industry in 71 emerging and developing countries between 2000 and 2014. There are 7 industrial sectors, which translates to 497 individual country-industries for which we have monthly observations. Note that some of these country-industries have not issued externally at all in our sample. Hence the number of country-industry-month observations in the panel is about 84,000.</mark> <mark>Our dependent variable is a dummy which denotes for a particular country-industry whether its total volume issuance in a given month is above its historical average over the period 2000-07. In doing so, we essentially control for general issuance patterns for each country-industry and ameliorate bias due to absolute size effects.</mark>

# <u><mark>Bond tranche deal dataset</mark></u>

9
# **2. Data and empirical methodology**

# _2.1 Data sources_

# <u>World Bank Enterprise Surveys</u>

The primary data source used in our analysis is nationally representative firm-level data from the World Bank Enterprise Surveys. The Enterprise Surveys are establishment-level surveys based on face-to-face interviews with firms’ owners or top managers. The surveys are designed to provide a representative sample of firms in the nonagricultural, non-extractive formal private economy with five or more employees, and to ensure that the data are comparable across countries and over time. The surveys have high coverage in terms of topics including access to finance, corruption, infrastructure, crime, competition, labor, the business environment, and performance.

The surveys are based on a stratified random sampling; stratified by industry, firm size, and geographic region within a country. Firm size levels are 5-19 (small), 20-99 (medium), and 100+ (large) employees. For most economies, the sector breakdown is manufacturing, retail, and other services; however, for larger economies, certain manufacturing sub-sectors are selected as additional strata, based on their contribution to economic activity. The within-country regional stratification is based on which cities or regions collectively contain the majority of economic activity. Survey weights are calculated and can be applied in order to make inferences about the whole population of firms in a country.<sup>6</sup> As of February 2021, the surveys had been completed using a global, internationally comparable methodology in 146 countries—we are able to match the firm level data with data on control variables for 141 countries.<sup>7</sup> In some countries, several waves of data collection have taken place; we use the most recent round of data collected for each country. While the Enterprise Surveys focus primarily on firms in developing economies, data have also been collected for several high-income countries and, therefore, our sample spans all regions and income categories.

# <u>Women, Business and the Law index</u>

The Women, Business and the Law (WBL) index measures the legal inequalities that a woman faces as she navigates her working life, from the time she enters the workforce through to her retirement. There are 35 individual data points underlying the index, aggregated into the following eight indicators: The _Mobility_

> 6 Additional information on the Enterprise Survey methodology can be found at https://www.enterprisesurveys.org/en/methodology

> 7 The data we use in our analysis were published on February 16, 2021.

6