rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 11: Difference (gap) between regulatory capital over RWA and capital over total assets Panel A: Top 20% banks Panel B: Bottom 80% banks**

<!-- Start of picture text --> 11.7<br>10.9<br>10.4<br>10.1<br>9.6<br>9.2<br>8.7 8.7<br>8.2<br>7.6 7.6<br>7.3<br>7.0<br>6.7<br>5.9 5.7 6.3 6.0 6.1 6.2<br>5.3 5.1<br>4.0 4.6 4.3 4.4 4.8 4.6 4.6 4.3 4.3<br>3.1 3.2<br>2.8 2.5<br>1.9<br>EU CE & BC WB SC CA Russia Turkey EE RoW EU CE & BC WB SC CA Russia Turkey EE RoW<br>2007 2017 2007 2017<br>13 13<br>12 12<br>11 11<br>10 10<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>Capital Gap - Top 20% (percentage points)<br>1 Capital Gap - bottom 80% (percentage points) 1<br>0 0<br><!-- End of picture text --> Source: Own calculation using archived data from Bureau van Dijk’s Bankscope and BankFocus.

Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe.

Significant changes in risk-weights and declines in the stringency of what constitutes Tier 1 capital call into question how informative these indicators are for bank risk. To test the importance of these variables, we next examine how bank risk is related to the quality of bank capital and risk-weights using bank-level information collected through Bankscope and Bank Orbis. Our measure of bank risk is the z-score, which is calculated as the sum of average bank returns on assets (net income divided by total assets) and the bank equity to assets ratio, scaled by the standard deviation of return on assets over a four-year rolling window. A higher z-score indicates lower bank risk (Mare et al., 2017).

In the first analyses we examine the relationship between bank risk (z-score) and regulatory capital (RC/RWA) and simple leverage (Equity/TA). The sample includes only developing countries in the ECA region and excludes high-income countries. In the analyses we control for a number of banklevel variables. These controls are: bank size (log(TA)), which is the natural logarithm of total assets; bank liquidity, which is liquid assets divided by total assets (LiquidA/TA); bank profitability measured as return-on-assets (ROA); reliance on short-term funding measured as short-term funding divided by total assets (ShortFund/TA); and loan ratio which is net loans divided by total assets (Loans/TA). All capital ratios and controls are lagged by one year. We also include year and region fixed effects to control for macro and region-specific factors that may affect the relationship between capital and bank risk. The descriptive statistics of the variables used in the regression analyses are reported in Table 1.

# **Table 1: Descriptive statistics**

13
Pongou, Salomon, and Ezzati (2006) focus on the effects of the macroeconomic decline in Cameroon between 1991 and 1998. They show that this decline was associated with an increase in weight-for-age malnutrition, which increased by 9 percentage points for boys, and 3 percentage points for girls. Increases in malnutrition were larger among children of mothers with no education than those with primary schooling, and larger for mothers with primary schooling than those with secondary schooling; they were also larger in rural areas than in urban areas, and among households with few assets.

Finally, Baird, Friedman, and Schady (2007) use data from DHS covering 59 developing countries and approximately 1.7 million births. On the basis of these data they construct countryspecific infant mortality series, and merge these with data on per capita GDP. After flexibly removing country-specific trends in the infant mortality and GDP series, they show that income shocks have large negative effects on infant mortality: a one percent decrease in per capita GDP is associated with an increase in infant mortality of between 0.31 and 0.79 percent.

Baird, Friedman, and Schady (2007) present various extensions to this basic finding. First, they include mother fixed effects in their regressions and show that changes in infant mortality are not accounted for by the changing composition of women giving birth—the estimates that include fixed effects are very close in magnitude to those that do not. Second, they merge their data with data on weather shocks (droughts, floods) and conflicts, and show that the association between changes in per capita GDP and changes in infant mortality is unaffected by the inclusion of these additional controls. This is important as weather shocks and conflict could have independent effects on mortality, above and beyond their effects on aggregate incomes. Finally, Baird, Friedman, and Schady argue that there are important asymmetries in the relationship between GDP shocks and infant mortality. Figure 4, reproduced from their paper, presents the results of nonparametric regressions of the infant mortality rate on the logarithm of per capita GDP, separately for boys and girls, after country-specific cubic trends have been removed from both time series. The units on the vertical axis thus correspond to the number of infant deaths per 1,000 children born, while those on the horizontal axis correspond (approximately) to percentage changes in per capita GDP. The picture shows that positive income shocks tend to imply quantitatively small improvements in infant mortality, even when these shocks are large; moreover, these improvements in income affect the likelihood of survival 23
**Table 12. Employment outcomes disaggregated by trade (ITT), 2010, 2011 and 2012 cohorts**

|||Pooled 201<br>IPSW<br>ITT|0‐2012 Cohorts<br>Model<br>Effects<br>monthly||
|---|---|---|---|---|
||any nonfarm|trade‐|earnings||
||IGA|specific IGA|(NRs)|earnings > 3000|
||(1)|(2)|(3)|(4)|
|Full Sample (pooled across training types)|0.160***|0.187***|921.323***|0.140***|
||(0.024)|(0.025)|(159.517)|(0.022)|
|training: Farming|0.155*|‐0.059|1167.151|0.081|
|n=92|(0.081)|(0.104)|(1000.983)|(0.169)|
|training: Poultry Technician|0.226|0.342***|1139.704|0.189|
|n=41|(0.173)|(0.099)|(969.082)|(0.145)|
|training: Food prep/Hospitality|‐0.057|0.007|‐965.418|‐0.146|
|n=265|(0.096)|(0.064)|(1048.109)|(0.095)|
|training: Electrician & Electronics|0.187***|0.258***|1282.843***|0.160***|
|n=641|(0.044)|(0.058)|(359.255)|(0.054)|
|training: Handicraft & Incense stick making|0.107|0.207***|967.311*|0.129|
|n=235|(0.082)|(0.075)|(524.717)|(0.094)|
|training: Construction|0.067|0.100*|509.836|0.089**|
|n=1128|(0.054)|(0.058)|(322.866)|(0.038)|
|training: Beautician/Barber|0.247***|0.402***|1529.259***|0.241***|
|n=239|(0.094)|(0.089)|(533.151)|(0.078)|
|training: Weaving/Tailoring/Garment Making|0.249***|0.222***|1185.755***|0.196***|
|n=1461|(0.038)|(0.037)|(233.399)|(0.035)|
|Clustered standard errors(byevent)|Yes|Yes|Yes|Yes|

Notes: No poultry technician trainings were included in the 2011 sample. *,**, and *** denote significance at the 10% level, 5% level, and 1% level.

35
**Figure 1.** Fraction completing selected grades by gender and rural/urban status

<!-- Start of picture text --> A) Men, Urban B) Women, Urban<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>75 78 81 84 87 90 93 96 75 78 81 84 87 90 93 96<br>Year of Birth(19xx) Year of Birth (19xx)<br>grade 5 g rade 6 grade 8 grade 5 g rade 6 g rade 8<br>grade 9 g rade 11 grade 9 g rade 11<br>C) Men, Rural D) Women, Rural<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>75 78 81 84 87 90 93 96 75 78 81 84 87 90 93 96<br>Year of Birth(19xx) Year of Birth (19xx)<br>grade 5 g rade 6 grade 8 grade 5 g rade 6 g rade 8<br>grade 9 g rade 11 grade 9 g rade 11<br>Notes: The birth cohorts to the left of the vertical lines are not affected by the policy,<br>whereas the birth cohorts to the right are affected.<br>Fraction Completed Fraction Completed<br>Fraction Completed Fraction Completed<br><!-- End of picture text --> _Notes_ : The birth cohorts to the left of the vertical lines are not affected by the policy, whereas the birth cohorts to the right are affected.

_Source_ : Authors’ calculations based on 2003 and 2008 Turkish Demographic and Health Survey.

43
output and technological development. The analysis also controls for time‐varying factors that are specific to states to capture state‐level policies. To ensure that later‐life health outcomes are measured in the same location of conception and birth where exposure occurred, the sample is restricted to individuals who have never migrated from their place of birth. In this way, our empirical strategy controls for a wide number of potential confounders in an effort to identify causal effects. To test for external validity, similar analyses are conducted in geographies outside India ‐ Vietnam and 33 countries in Africa.

Our results find that nitrogen exposure experienced by infants can have durable, long‐term impacts that stretch well into adulthood. In India, women exposed to nitrogen pollution in their earliest years of life are shorter on average in adulthood than women of similar circumstances who were not exposed to such pollution. Early‐life exposure to nitrogen pollution also lowers later‐life labor productivity and depresses adult wages, decreasing overall welfare. This finding is robust to several sensitivity and falsification tests. Analyses across different geographic settings in Vietnam and Africa that measure the impact of nitrogen pollution during early‐life provide further supporting evidence for the results found in India. Taken together, this paper provides new evidence that early‐life exposure to nitrogen pollution has enduring and irreversible costs on human capital, with decreases in height observed across different life‐stages: in adulthood (India), in childhood (Vietnam), and in infancy (Africa).

The rest of the paper is organized as follows. In Section 2 we describe the health and water quality data, the construction of the main variables used in the analysis as well as the procedure we use to match the health data to upstream pollution. Section 3 outlines our empirical strategy and Section 4 discusses the results. Robustness checks are provided in Section 5. Section 6 investigates the external validity of the results reported for India in other geographic settings such as Vietnam and Africa. Section 7 discusses the plausible mechanisms linking nitrogen pollution to height impacts, and Section 8 concludes.

# 2. Data

In this section, we describe the data sources that were used in the empirical analysis, and the construction of the main variables in the analysis.

# 2.1 Health Data

The data on our outcomes of interest come from the fourth round of the National Family and Health Surveys (NFHS) conducted in India. The NFHS is the Demographic and Health Survey (DHS) equivalent in India. The survey was conducted between January 2015 – December 2016 and covered all areas of the country. In a departure from the previous DHS surveys, the sample for this survey was designed to be representative at the district level. Close to 600,000 households were interviewed which included 0.7 million eligible women in the age group 15‐49.

4
INTRooucTnoN uLXXvU

* Where total fertility h.- shown unusually slow decline, it is assumed to take 20 rather ta 15 _yea_ for total fertility to fall one point to replacement.

* Where, toward the end of a fertility transition, total fertility is still recording rapid declines even though it is close to or even below replacement level (2.25 to 1.75), it is assumed to fall further for one period, generally going below replacement, to stay constant in the next period, and then to return to replacement.

* Where the transition has been completed and total fertility is below replacement, it is assumed to stay at the current level for two quinquennia, and then to return gradually to replacement, along a linear path, by 2030.

In applying late-transition procedures, the level of total fertility that provides replacement is approximated by a quadratic formula based on female life expectancy (e(fn) in the replacement year: TFRmm. I = 6.702 - .1l07 e;r) + .0006592 **e(f)**<sup>**2**</sup> **.** The replacement year is first approximated in order to determine life expectancy and then calculated given this formula and whichever pattern of fertility change above is appropriate.

For all stages of fertility tiansition, the age pattern of fertility is determined in the same fashion, depending on the level u*' total fertility (Figure AS). Two basic schedules of age-specific fertility are defined: for total (ertility levels greater than six and for total fertility levels of three and lower. In between, age pattemns are obtained by interpolation between the two schedules. At a total fertility level of six, a greater proportion of births are at younger and older ages of the reproductive span, and the mean age of childbearing is 28.9 years. At a total fertility level of three, births are concentrated in a narrower band of ages, and the mean age of childbearing is 28.0. The only period

**_Fiure_** _A6 Assumed Total Fernlity Trend, Less Developed Countres, and U.N. and Previous World Bank Trends_

_Figure_ **_A7_** _Assumed Total Fernlity Trend, More Developed Counnes, and U.N. and Previous World Bank Trends_ <!-- Start of picture text --> 20*<br>--<br>60 1<br>. 3 ,,,<br>32-2 33.. F MD 5 D Dtt ?DliPO ^ g % tXD 0 ?iF8D tD$t 3tt9<br>20- g- HAE<br>2~~~~~~~~~~'<br>26~~~~~~~~~~~~~<br>iUj ~ aa//<br>i 4 11 A~-N XNf/<br>23-). ~~~~~~~~~~~~~~~~~~~~<br><!-- End of picture text -->
variable. Another extension was made to analyze the impact of export promotion of industries using the interaction of export dummy, trade protection and labor market flexibility.<sup>14</sup>

# **4.3 Some Simple Data Analysis**

In Figure 1, we plot the overall GDP growth rate, growth rate in manufacturing value added and the growth rate in value added in services (all obtained from WDI) over time for the period 1980-2004. After the 1991 reforms, only in 5 of the 13 years (only in 1993, 1994, 1995, 1996 and 2000) do we find growth rates in manufactures to be higher than in services. There is a clear acceleration in growth in the case of manufactures from 1991 to 1994, which is followed by a sharp deceleration. We see much lower volatility in the growth rate of service value added. In Figure 2, we clearly see that value added in services as a proportion of GDP has clearly been gradually increasing over time for the period 1980-2004 from roughly 37 percent to about 51 percent. In the case of manufactures, the proportion has remained in the narrow range of 15 percent to 18 percent. This clearly shows that the growth is service driven, and that manufacturing has not taken off in a big way. Figure 3 clearly indicates that most of the trade is in goods, though both trade overall and services trade as a proportion of GDP have been rising.

In Figure 4, we plot the simple yearly averages of our policy and institutional variables from our dataset. While trade liberalization clearly starts in 1991, delicensing started in 1985. We see that output share of delicensed industries increased from 0 before 1985 to roughly 40 percent in 1985, which next rose to roughly 85 percent in 1991 and finally to about 92 percent in 2000. It is also interesting to see that starting from 1981 until about 1991, there was about a fifty percent increase in average state per capita development expenditure.<sup>15</sup> While this itself may have been driven by economic growth in the 1980s, it is also quite plausible that economic growth itself was partly driven by per capita development expenditure growth. This may look like a virtuous circle, especially if we fail to take into account the macroeconomic crisis of the late 1980s and the early 1990s. Our labor market flexibility variable, namely FLEX, remains unchanged in this 1980-2000 period and there we do not plot it. It only varies across states and not over time.

> 14 We also included the export dummy by itself, as well as its interaction with trade protection in our analysis as an alternative specification. However, these variables did not turn out significant and the effect of their exclusion on other coefficients was very small. For this reason, they are not presented in the paper.

> 15 The logarithm of per capita development expenditure increased by 0.49 points, which can be seen on the right axis in Figure 4.

16
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formula already appears suitable for estimation. However, some problems arise since it is well known that most macroeconomic time series contain unit roots and that the regression of one non-stationary series on another is likely to yield spurious results. As reported in the Appendix, the data for Guatemala is no exception. The estimation bias can be removed by transforming the time series to stationarity. This can be done by first differencing. In any case, this will create its own problems, notably because of the risk of losing valuable information on the long-run relationships of the variables.

One approach to dealing with this dilemma is to employ an error-correction model which combines long-run information with a short-run adjustment mechanism. This methodology has been used successfully in alternative growth studies. Examples of this are Nehru and Dareshwar (1994), Morales (1998), and Bassanini and Scarpetta (2001). The error-correction model can be estimated in different ways. Banerjee et al. (1993) show that the generalized one-step error-correction model is a transformation of an autoregressive distributed lag model. As such, it can be used to estimate relationships among non-stationary processes. Based on Hendry’s (1995) concept of general-to-specific modeling, the error-correction model of the human capital augmented production function for Guatemala can be specified as follows: For Guatemala, in line with much empirical cross-country research, the short-run effects of schooling on growth have been found insignificant and are as such excluded from the regressions. This suggests that only the _level_ of human capital has a long-run effect on economic _growth_ . As it stands, the equation can be estimated by ordinary least squares (OLS) or instrumental variables (IV) techniques, but the coefficients cannot be formed without knowledge of _α_ and _β_ . However, one can estimate the re-parameterized form: Estimates of the parameter γ 3 can now be used to calculate the required elasticities _α_ and _β_ . The loading coefficient _γ 3_ contains additional information because it can be interpreted as a measure of the speed of adjustment in which the system moves towards its equilibrium on the average. In addition, Banerjee et al. (1998) argue that in a single equation framework a significant coefficient serves as a test for cointegration. Notice that the technology parameter, _A_ , is allowed to change overtime as a function of different variables, _Z_ : (19) log _At_ = _f_ ( _Z t_ )

-30-
However, homogeneity varies by gender, with men’s groups less so than women’s, which may have implications for observed differences in loan default rates of male and female members. That is, observed loan recovery rates are higher for women than men presumably because women’s groups are relatively more homogeneous (or similar in observed characteristics) than men’s groups.

# **5.2** **<u>Testing the Relevance of Group Liability</u>**

Given the features of groups in the Grameen Bank, the most relevant issue is to test for the presence of ―group effects‖ in a parsimonious model that includes rate of missed payments of other group members as of the week prior to any observation (i.e., rate of missed payments by other members of the group lagged one week) and the number of group members, in addition to controlling for weeks of group duration and other initial conditions. If individual borrowing or repayment behavior is unrelated to that of other group members, then the measure of past missed payment of other members, as well as the group characteristics, should not affect either current loan disbursements to an individual or his or her loan repayment behavior.

Two outcomes are considered in this paper: (i) member-level loan disbursement and (ii) loan recovery rate. The rate of missing weekly payments does not indicate whether the payment missed was actually due or how much was due at the time payment was missed. However, the rate of loan recovery is measured as the percentage of the amount due that was paid on time. Higher rates of missed payments mean lower rates of loan recovery, while lower rates of missed payments likely mean higher rates of loan recoveries. Thus, the rates of loan recovery and missed payments are interrelated measures of loan delinquency.

19
<!-- Start of picture text --> P 1 ity<br>— =CpY(Ws +1) ¥<br>Ss<br>~> 1<br>us<br>1<br>pY<br>4 1+y 4 1+y<br>== 4 ( -) 4 Y (Ws +1) — 7 = () Y (Ws +)atTYY<br><!-- End of picture text --> s Zz s(¥%) = Uz(¥z) = Up(Y) = up $ s(¥%) = up ~~=—~~ s = YCpit7(Ws + 1) Z z(Vz) = up ~~a~~ 1(, ~~2~~ Zzp\1+v
Table 15

||Flow|of<br>Funds|of<br>Person<br>Sources|al<br>Sector<br>of<br>Funds|<br>(billion|yen)|
|---|---|---|---|---|---|---|
|||Loans|(PF1)|(GFI)|Trade<br>Credit||
|1961/65||4139|3492|646|2319||
|1966/70||11521|9923|1598|6547||
|1971/75||31843|26792|5051|8990||
|1976/80||48647|35530|12118|10196||
|_1981/85_||46629|33222|13405|11470||
|1986/90||114005|96498|17507|20464||
||||Uses|of<br>Funds|||
||Cash|Demand|Time<br>Save|Secur|Trust|Insur|
|1961/65|1000|2202|7586|2956|1103|1799|
|1966/70|2332|4474|19216|4203|2527|5695|
|1971/75|5162|11322|54646|10347|6310|12982|
|1976/80|4269|7297|94188|18779|10084|24575|
|1981/85|4680|7789|101154|30830|18691|45052|
|_1986/90_|11102|17790|132203|28417|24639|106963|

Note: Except for 1961/65, all figures are for fiscal years. Source: Bank of Japan, _Economlc Statlotics Annual,_ various **issueo.** 46