rafmacalaba/datause-encoder-data huggingface dataset:
already on a three-digit ISIC level in Rajan and Zingales (1998), we simply use their external dependence figures. For the sectors that are not already on a three-digit ISIC level, we apply the same method as in Rajan and Zingales (1998) to financial data on US finns from Compustat to estimate external dependence figures. Table 1 lists the three-digit ISIC level external dependence figures across sectors in the United States during the 1980s. We use these external dependence figures to cor-uc a hi. exe.aAeed.c _.IgL.D)d_ yval ht+le vlu foei etJi **~AL~LU** U%,a **L 1L"&11 V"%ALI.JLJaL** ~ **L.'IU** **_I&%5~_ L.JUUIILIIJ V** **_&JLUIO.1,_ LUCL&** "An.vo Y"iu"% **j.ii 'jJLIV iJ 0~.VLWJLJ wL** among the top-50% of most financially dependent sectors, and value of zero otherwise. Similarly, we construct a low external dependence _(LowED 1 )_ dummy variable that takes value of one if sectorj is among the bottom-50% of most financially dependent sectors, and value of zero otherwise.

As measure of firm performance we use real growth in industry value added (annually compounded), the same measure as in Raian and Zingales (1998). The data on value added for each _4_-_ **-nn-,** **_r, Cnn-d-,S . a a1.*-A 4rn*1-o T-A-. o4'~_ Q*n+ .44+n- VAr-4-nlr** **_A_ nfnk-ean++n~.. iL1lLU~L JL'** L **Ill I.GJ** _,_ **L UJU LLLJ L** _WULU 1_ **_%ALU_ L I %J JLII L1I'JA1.LL%ULLLL LL LI LULILLOLVUj** A VCLU WW% **_U L "JJ_ "UJ o'.** **_jFUL_ LW&J5UI4** **_U)'_** the United National Statistical Division. rhe value added data are corrected for inflation using CPI data from the Intemational Financial Statistics ofthe International Monetary Fund. We calculate the real growth in value added figures for sectors on a three-digit ISIC level as well. We also calculate the industry's share in total value added of the country, a variable used by Rajan and Zingales (1998). Our measures of financial deDth (total credit to GDP. Drivate sector credit to GDP. and M2 to GDP)F.u a,-uu,d he **evel** of Cj-DP p er c apitLa are fro,,,1 heI JLLtLerr.atioral inLancialC Statistics VI she International Monetary Fund.

We use the Caprio and Klingebiel (2002) data set to time crisis and pre-crisis periods. Since it is difficult to identify the crisis period precisely, we use (t- 1, t+l) as the crisis period, where t is the first crisis year reported in Caprio and Klingebiel (2002). To ensure that the pre-crisis period is a 10
In this paper, we use a two-parameter logistical model to estimate IRT scores.<sup>16</sup> As the main advantage of this approach, compared to using raw scores or z-scores, <mark>is that it takes into account dif</mark> ferences in difficulty of the 36 test questions in the calculation of the cognitive score, it is not surprising that the correlation between Raven’s raw scores and IRT scores is quite large at 0.97.

The survey does not report expenditure data, but collects detailed information on durable goods and assets owned by the household, and on the characteristics of the dwelling in which the household lives. Following a well-established approach pioneered by Filmer and Pritchett (2001), we use this information to construct an index for the household’s contemporaneous wealth based on principal component analysis. The estimate of relative wealth using the PCA is based on the first principal component, which we use as a proxy for household’s wealth.<sup>17</sup> Finally, height and weight measurements were obtained from all household members aged five and above.

# **6. Empirical Analysis**

# **_6.1. Determinants of Parental Consanguinity_**

We first discuss determinants of (parental) consanguinity both because the question is of interest in itself, but also because the consanguinity estimates will serve as first-stage equations in estimates of the effect of consanguinity on child height and cognitive ability. Using a linear probability model, we regress three alternative indicators of parental consanguinity on a set of family and individual characteristics that are likely to affect parental consanguinity. The most important factor that increases the likelihood of (parental) consanguinity is grandfather’s ownership of land. This is consistent with the (mostly qualitative) literature on the topic, where illiquid wealth, and in particular landholding of the previous generation, is seen as an important determinant of consanguineous marriage. At the same time, contemporary household liquid wealth as captured by an asset index is negatively related to consanguineous marriage. Finally, maternal grandfather’s death before age 65 is linked to a statistically significant reduction in

> 16Three-parameter models are also used in the literature, but tend to require larger sample sizes than available in the LSS in order to converge.

> 17This asset-based measure aims at capturing the household’s long-run economic status. The Filmer and Pritchett (2011) results were validated using both household assets and consumption data for a set of low and middle income countries, including Pakistan, and it was concluded that PCA “provides plausible and defensible weights for an index of assets to serve as a proxy for wealth”. Following Filmer and Pritchett, many studies, especially in the fields of economics and public policy, have implemented and recommend the use of PCA for estimating wealth effects

12
rural sector. However, our final price indices also incorporate some adjustments aimed at constructing a consistent time series over the long period of analysis; these adjustments relate to the use of the Consumer Price Index for the Working Class for the period before August 1968 for which the CPIIW did not exist, a correction for the constant price of firewood for a part of the Labour Bureau’s CPIAL series, and the use of a reweighted chain price index in both rural and urban areas to better approximate the weight of food in the consumption basket of the poor.<sup>30</sup> Our final rural and urban price indices are averages of monthly indices corresponding to the exact survey period of each NSS round.

**_Poverty measures:_** We use three poverty measures: the headcount index (H), given by the percentage of the population living in households with a consumption per capita less than the poverty line; the  poverty gap index (PG), defined by the mean distance below the poverty line expressed as a proportion of that line, where the mean is formed over the entire population, counting the non-poor as having zero poverty gap; and the squared poverty gap index (SPG), defined similarly to PG except that it is the mean of the squared proportionate poverty gaps. Unlike PG, SPG is sensitive to distribution amongst the poor, in that it satisfies the transfer axiom for poverty measurement.<sup>31</sup> In particular, to economize space we report results on H, PG and SPG for the higher line, and only H for the lower line.

The overall level of poverty at date _t_ can be additively decomposed using population weights. We will be interested in the urban-rural decomposition of the aggregate measure for date _t_ : where _nit_ and<sup>_P_</sup> _it_<sup>are the population shares and poverty measures for sectors</sup> _i = u,r_ for urban and rural areas respectively. “Time” ( _t_ ) represents the ordering of the _T_ survey rounds in time, which can differ from real time given the uneven spacing.<sup>32</sup>

**_Demographic data:_** The population estimates are based on the Census population totals and assume constant growth rates for urban and rural populations between censuses. We use the NSSO’s urban-rural classification.<sup>33</sup> Over such a long period, some rural areas would have

> 30 See Datt and Ravallion (2011) for further details on these adjustments.

> 31 All three measures are members of the class of measures proposed by Foster, Greer and Thorbecke (1984).

> 32 Controlling for unequal time interval between survey rounds made little difference to the results.

> 33 The NSS follows the Census definition of urban areas which includes all places with a municipality, corporation, cantonment board or notified town area committee, and places that meet a number of criteria including a population

9
Moreover, the paper shows how intermediates provided by foreign investment can be accompanied by services that improve the productivity of the downstream sector.

There could be technology transfers from foreign firms in the upstream (Newman et al., 2016). With evidence from 17 transition market economies in Europe, Gorodnichenko et al. (2014) confirm that positive effect of backward linkages on the productivity of domestic firms while horizontal and forward linkages demonstrate no consistent effect. Newman et al. (2015) document the existence of FDI spillovers through forward linkages in Vietnam. Javorcik and Spatareanu (2011) provides additional evidence from Romania showing that FDI spillovers take place through backward linkages. In their meta-analysis, Havránek and Iršová (2011) argue that FDI spillovers take place mainly through backward linkages to suppliers.

There is also a very broad literature on creating tax and other incentives to attract FDI, which assesses the benefits or costs of FDI from the point of view of the investor (see for example MercerBlackman et.al., 2020). These studies implicitly take for granted that FDI is beneficial and desirable for the host countries.

# 2.2 GVC Literature

## **GVC and MNEs**

Much of the recent GVC literature has placed emphasis on the importance of GVC participation, which is a new way of considering the gains from trade at the value-added level instead of the traditional gross output level. The problem with this approach from our point of view is that it equates direct exposure to international markets as the criterion for success, ignoring the fact that indirect exposure can also improve success. In other words, a firm can receive inputs from local firms and sell to the domestic markets but may still benefit from advances and efficiencies occurring through those input-output links with other sectors. Wang et al. (2017) show a way to measure the contribution of services along the value chain and find that they frequently are part of complex value chains.

While this paper studies the impact on productivity of the sector from the foreign financing and its links, it takes a slightly different approach to that adopted by Cadestin et.al. (2018) and Meng et. al. (forthcoming, 2020). In those approaches, the inter-country input-output tables for OECD and
29

# **Table 3. Long and Short-Term Determinants of the Real Exchange Rate in the World**

_Estimators: Pooled mean group, Mean group, and Dynamic fixed effects (all controlling for country and time effects)_ _Dynamic specification: ARDL(1,1,1,0,0,1,0,1) Sample: annual data, 1980-2004_

||**Pooled**<br>**Gr**<br>|**Mean**<br>**oup**<br>|**Mean**<br>|**Group**<br>|**Hausm**<br>|**an Tests**<br>|**Dynam**<br>**Eff**<br>|**ic Fixed**<br>**ects**<br>|
|---|---|---|---|---|---|---|---|---|
|**Variables**|Coef.|St.Er.|Coef.|St.Er.|h-test|p-val|Coef.|St.Er.|
|**Long-Run Coefficient**|||||||||
|Terms of Trade, logs|0.208|0.031|0.688|1.038|0.740|0.390|0.078|0.055|
|Productivity, logs|0.518|0.017|0.315|0.113|3.300|0.070|0.560|0.040|
|Trade Openness|-0.558|0.031|-0.337|0.193|1.340|0.250|-0.454|0.047|
|Government Consumption/GDP, logs|2.625|0.258|-23.496|23.438|1.240|0.270|0.227|0.399|
|Net Foreign Income/GDP|0.004|0.002|0.071|0.072|0.870|0.350|0.013|0.003|
|Foreign Aid Net of Int'l Reserves/GDP|0.002|0.001|-0.007|0.005|3.410|0.060|0.002|0.002|
|Taxes onNon-traded Goods,logs|2.031|0.403|8.550|7.317|0.800|0.370|1.102|0.730|
|**Error Correction Coefficients**|||||||||
|Phi|-0.20|0.02|-0.70|0.04|||-0.25|0.01|
|**Short-Run Coefficients**|||||||||
|D(Terms of Trade, logs)|0.082|0.037|-0.028|0.035|||0.009|0.022|
|D(Productivity, logs)|0.429|0.029|0.195|0.043|||0.392|0.020|
|D(Net Foreign Income/GDP)|0.004|0.002|0.002|0.003|||0.001|0.001|
|D(Taxes on Non-traded Goods, logs)|0.447|0.470|-0.486|0.489|||1.093|0.317|
|Intercept|0.021|0.029|0.014|0.099|||||
|No. of countries|8|3|8|3|||8|3|
|No. Observations|18|75|18|75|||18|75|
_Ingo Borchert and Mattia Di Ubaldo_ Figure 2: Average Value of Services Trade Outside and Within Services PTAs <!-- Start of picture text --> GBRFRA DEU<br>LUX IRL AUT BEL NLD ESP ITA<br>GRCDNK POLSWE USA<br>HRV HUNROM CZ EPRTFIN<br>CYP SVNBGR SVK CHE GBR DEU JPN USA<br>BMU MLT ISL ESTESTCYPLVALVAPAN LTULTUSVNBGRL UX HRV SVKMAR UKRHUNNZLNZLROMPHLPHLEGYCZECHLCHLSGP S HKGHKGISRIRLGPRTMYSMYS P FINCOLARE GRC NGATHATHADNKZAFVENARGAUTIRNNORNORPOLBELSWESAUCHETURIDNIDNNLDKORKORMEXAUSAUSESPRUSINDINDCANCANBRAITA FRA CHNCHNJPN<br>FJI MLTMKD ISL PAN CRIUZBURYAZETUN VNMVNMUKRKAZ<br>SWZMDA PNG BRN JOR MAR<br>MHL SYC LBR MNGBHSMKDALBGEOBRNBIH TKMJOR KENTUN LKACUBLBYECUAGO QATKWTIRQDZA MEX<br>COL<br>PER<br>CRI<br>TZA<br>20 25 30<br>Log (GDP)<br>Outside PTAs Within PTAs<br>15<br>10<br>5<br>Average Share<br>0<br>-5<br><!-- End of picture text --> Source: Authors’ elaboration using the World Bank DTA 2.0 Database and ITPD-E data. Note: dashed lines denote the log value of average trade within (red) and outside of (black) services PTAs.

have greatly increased the number of partners with which they have signed a services PTA, there was no change at all over the 2000-16 period for Brazil and South Africa (Figure 3), whose connectivity remains at very low levels.<sup>1</sup> When countries sign or join plurilateral agreements, their connectivity displays a discrete jump, as is apparent from Figure 3 when China signed a PTA with ASEAN in 2007, as did Australia—jointly with New Zealand—shortly afterwards in 2010. Overall, Since 2000, economies in Europe, Asia-Pacific and in North America have been most active in concluding services PTAs (Appendix Figure 9). Singapore concluded 19 services PTAs in that period, followed by Chile which signed 18 agreements.

Latin America and the Caribbean is a region with economies at very different stages of

> 1A similar figure showing the increasing connectedness of Chile, the US, Japan and India is included in the Appendix as Figure 10.

5
empowerment. One example is microcredit programs, which have been globally popular and disseminated worldwide since the 1990s.<sup>7</sup> This paper is based on a study of an antipoverty development intervention, _Jeevika_ (meaning livelihood or subsistence), targeted at women and structured to induce rapid economic and cultural change. We observed the intervention in action as it rolled out and examined the process of facilitated cultural change. We used a quasi-experimental methodology using matched pairs of “treatment” and “control” villages combined with qualitative data (personal interviews and focus groups with program participants, non-participants, women, and men) collected over a three year period.

The four villages (two treatment and two control) studied here are a subset of a sample of 400 villages (200 treatment and 200 non-treatment) that were the subject of a survey in 2011 (across 4,000 households). The survey asked retrospective questions on changes observed by respondents between 2006 and 2011. Quantitative analysis of that data using propensity score matching methods found that _Jeevika_ had a large and significant impact on women’s empowerment measured as physical mobility, participation in decision-making, political participation, and confidence in undertaking collective action (Datta 2015). Women in treatment households had improved physical mobility (going to grocery stores and health centers, visiting

> 7 The focus on women is guided by a complex set of assumptions and rationales: men are not as compliant clients as women with regard to the repayment of credit; women make more familyfriendly use of credit; poor women do not have access to credit and by giving them collateralfree loans they can start or boost their small-scale income-earning enterprises and, thereby, become more empowered.
37

- Shorrocks, A.F. (1980): "The Class of Additively Decomposable Inequality Measures", _Econometrica_ , **48** , pp.613-25.

- Shorrocks, A.F. (1982): “Inequality decomposition by Factor Components” _Econometrica,_ **50** (1), pp.193-211.

- Shorrocks, A.F. (1984): "Inequality Decomposition by Population Subgroup", _Econometrica_ , **52** , pp.1369-85.

- Tanner, C. (1987): “Malnutrition and the Development of Rural Households in the Agreste of Paraíba State, North-East Brazil”, _Journal of Development Studies_ , **23** , 2.

- Urani, A. (1993): "Inflação e Desemprego como Determinantes do Nível e da Distribuição da Renda do Trabalho no Brasil Metropolitano; 1982-1992", IPEA Série Seminários no. 04/93, Rio de Janeiro.

- World Bank (2005): _World Development Report 2006: Equity and Development_ (Washington, D.C: The World Bank and Oxford University Press)
for the health and nutrition welfare index. Indeed, they seem to have comparable power in targeting chronically malnourished children.

**Table 2. Correlations between HAZ-score and alternative measures of well-being**

||Predicted income|Asset index|
|---|---|---|
|Area under ROC curve|0.6087<br>**0.6521**|0.6002<br>**0.6415**|
|k li|0.2133|0.1879|
|Spearman ran correaton|**0.2606**|**0.2230**|

Source: Own computations using HLCS 2001 and DHS 2000 (2005 in bold) In a last analysis of correspondence between welfare indices, we use the methodology proposed by Sahn and Stifel (2003). In order to assess the explanatory power of the asset index and the predicted income in predicting well-being, separate models of health and nutritional status are estimated conditioned on (i) the log of predicted per capita household income, (ii) the log of household asset index, (iii) both the log of predicted per capital household income and the log of household asset index. The probit regression model is fitted using an indicator whose value is one when the child is malnourished (HAZ-score under -2) and zero otherwise. Once the models are run, we use them to predict child HAZscores and compare the rank correlations and ROC curves between the fitted nutritional outcomes and the actual measured outcomes.

**Table 3. Probit estimates**

|HAZ-score<br>Predicte<br>onl|d income<br>y (i)|Asset<br>only|index<br> (ii)<br>Predict|ed income & Asset in|dex (iii)|
|---|---|---|---|---|---|
|Elasticity<br>-0.3304|**-0.3778**|-0.2895|**-0.3424**<br>-0.2158|**-0.2177**<br>-0.1879|**-0.2416**|
|z-statistic<br>-8.38|**-6.37**|-8.96|**-7.53**<br>-5.03|**-3.50**<br>-5.01|**-4.61**|
|Pseudo R2<br>0.0334|**0.0539**|0.0327|**0.0552**|0.0367**0.0598**||

Source: Own computations using HLCS 2001 and DHS 2000 (2005 in bold) Table 3 shows that the pseudo R-square is approximately the same for the model with asset index and for the model with predicted income: it is slightly higher in 2005 for the former. Looking at the measures of correlations between actual and fitted values of the health and nutrition index in Table 4 shows that fitted values are better correlated to actual values when asset index is used as a regressor. Using both indices in a regression does not significantly improve the correlations. In conclusion, these findings suggest that analysts are not worse off, and may be better off, conditioning child nutrition models on the asset index rather than predicted income in their effort to predict nutritional outcomes and target programs.

10
|Change in local average Tier 1 ratio (% points)|0.002<br>(0.001)|-0.001<br>(0.003)|
|---|---|---|
|Change in local average Tier 1 ratio (% points) x MENA|0.031*|0.090|
||(0.014)|(0.055)|
|Local green management|-0.009|0.233***|
||(0.006)|(0.041)|
|Local green management x MENA|-0.057**|0.304***|
||(0.020)|(0.075)|
|MENA|0.951|0.153|
||(1.053)|(2.036)|
|Observations|17,902|17,902|
|Clusters|3,022|3,022|

Note: This table shows the first stage regressions corresponding to Tables 5 and 6. Regression controls for localitylevel credit market controls (log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius); an indicator for no firms in other sectors in a 15km radius with data on credit constraints; an indicator for no firms in a 15km radius in higher deciles with data on green management; population size class; and region and sector fixed effects. Locality-clustered bootstrapped standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

# _Type of green investment_

The second stage average marginal effect estimates in Table 5, Panel A show that once we account for potential endogeneity of credit constraints and green management, the only significant estimates are found for both capital- and non-capital intensive green investment category. A unit increase in the quality of green management practices, equivalent to moving from the 10<sup>th</sup> to 50<sup>th</sup> percentile of the distribution of the quality of green management practices, is associated with a whopping 42.6 percentage points higher probability of making both capital- and non-capital-intensive green investment. This is almost twice the size of the estimate in Panel A of Table 2 that does not account for endogeneity. Our hypothesis 1 is thus only partly confirmed in this setting.

Overall average marginal effect estimates also indicate that being credit constrained leads to a 30.9 percentage points lower probability of making both capital- and non-capital-intensive green investment, although the estimate is statistically significant only at 10% level of significance.

Estimates in Panel B show that the overall average marginal effect estimates mask differences at the regional level. They indicate that credit constraints matter for some types of green investment by firms in ECA (at least at 10% level of significance): being credit constrained leads to a 29.7 percentage points higher probability of making no investment and a 15.2 percentage points higher probability of making noncapital-intensive investment only. However, credit constraints do not play a role in MENA firms’ decisions about the type of green investment. In MENA, green management quality plays a more important role for either no green investment or both capital- and non-capital-intensive green investment. A unit increase in the quality of green management in the MENA region leads to a 46 percentage points decrease in the likelihood of making no green investment, compared with 28.4 percentage points decrease in ECA. The 25
# **Covariate Shocks and Child Undernutrition: A Review of Evidence from Low- and Middle-Income Countries**

Zelalem Yilma Debebe Dhushyanth Raju _JEL codes:_ O15, I15, I12 _Keywords:_ Covariate shocks, child nutrition, developing countries

> Debebe, World Bank, <u>zdebebe@worldbank.org; Raju, World Bank, draju2@worldbank.org. The authors thank</u> Rekha Menon, Martin Rama, and Hans Timmer for their support. The authors gratefully acknowledge funding by the United Kingdom Department for International Development and the European Commission, through the South Asia Food and Nutrition Security Initiative (grant number TF0A5366) administered by the World Bank.