rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1.  Introduction**

In this paper, we use information from recent Demographic and Health Surveys (DHS) for India and Bangladesh to identify regional differences in measures of maternal and child health that are not explained by commonly cited socioeconomic determinants. Our analysis assesses the health status of mothers and children using the incidence of anemia and wasting, respectively. These measures have two advantages for our purposes: They are widely cited as critical health indicators, and they are the best-recorded health measures in the DHS surveys.

Wasting, or a substandard weight/height ratio,<sup>1</sup> indicates nutritional deficiencies that significantly increase the risk of child morbidity and mortality. These risks are well-documented in the literature (Ghosh-Jerath et al. 2017; Tickell et al. 2017), and are attributable to diverse factors, including weight loss from infectious disease; loss of muscle and fat tissue from inadequate diets (Lenters et al. 2013); associated chronic infections (Bhutta et al. 2017; Scrimshaw 2016); and other forms of developmental impairment (Menon et al. 2012; Kara and Dolan, 2014). Wasting afflicts more than 50 million children worldwide (Harding, et al. 2018, citing UNICEF 2017) and causes about 875,000 preventable deaths annually (13% of the total) for children less than five years of age (Black et al. 2013). More than half of the children with wasting are in South Asia (Harding et al. 2018), where IFPRI (2016) identifies wasting as a critical public health problem because its incidence surpasses the 15% threshold. In related work, Galasso and Wagstaff (2018) find that stunting (substandard height/age ratio), another nutritional deficiency measure, has a significant impact on adult wages through its effects on schooling, cognitive skills and height. For developing countries, they estimate that the associated per capita income penalty from stunting is around 7 percent.

> 1 Wasting is technically defined as a weight-for-height
|
|Commerce sector|0.500|0.485|0.456|0.449|
||[0.503]|[0.501]|[0.501]|[0.498]|
|Service sector|0.372|0.327|0.405|0.452|
||[0.486]|[0.470]|[0.494]|[0.498]|
|Starting capital (USD)|9228.393|6778.421|10374.000|9638.463|
||[22576.267]|[9777.351]|[17413.359]|[19118.309]|
|N|86|336|79|345|

Notes: This table shows baseline summary statistics using administrative data from SEBRAE-PR, for 2017 and 2018, and RAIS, for 2015, the latest available year at the start of the study. Starting capital comes from the Federal Tax Authority (RFB). Columns 1 and 2 displays means and standard deviations for control firms and treatment firms using below the median percentage of business practices at baseline (50%), grouping all four different treatments together. Columns 3 and 4 display means and standard deviations for firms with percentage of business practice above the median at baseline. Material denotes receipt of written or recorded material on business practices, either during a visit to a SEBRAE service center or to SEBRAE's website. Training includes classes, consulting, and networking events. Business practices are only available for 846 observations, since recording errors led to missing observations for firms in one week. Stars refer to differences in means of the treatment group to the control group in each subsample. ***, **, and * indicate significance at the 1, 5, and 10 percent level.
diaries (which track the financial lives of poor households at short intervals) indicates strong demand for microcredit, but as part of a patchwork portfolio of products from formal and informal providers that are stitched together because the incomes of the poor are small and irregular, and needs to access reasonably large sums (for health emergencies, weddings, funerals) inevitably arise (Collins, et al., 2009). In short, while microcredit might not be a magic bullet in alleviating poverty, generally it has been shown to be a useful tool for poor households in managing their financial lives.<sup>2</sup> While microcredit products have proved useful to poor households, the industry (or at least a sizable part of it) has moved beyond the relatively rigid group liability products introduced by Grameen. For example, Beck et al. (2011) note that African MFIs have shifted away from group liability lending mechanisms and, in West Africa, only individual liability loans are used. The trend toward individual liability lending has also coincided with greater reliance on commercial sources of funding (see discussion of commercialization in Morduch, 2000; Yunus, 2007; Cull et al., 2009, 2011a). While there is little direct evidence, it stands to reason that the market-based returns that commercial funding requires provide a strong incentive for MFIs to make larger loans to somewhat wealthier borrowers. Those loans are more cost-effective per dollar lent and thus contribute to greater financial sustainability for the commercially-oriented MFIs that specialize in them (Cull et al., 2009).

The increasing commercialization of microfinance has fueled debates about mis-targeting and mission drift, from serving the poor and promoting social inclusion to pursuit of profitability (Yunus, 2007). While there is some evidence that MFIs benefit somewhat wealthier households rather than the poorest in some developing country contexts (Coleman, 2006; Kondo, et al., 2008; Takahashi, et al., 2010),<sup>3</sup> evidence from studies that use larger
Barro, R. J. (1979), On the Determination of Public Debt, _Journal of Political Economy_ , 87: 940-71. Barro, R. J. and G. Becker (1989), Fertility Choice in a Model of Economic Growth, _Econometrica_ , No. 57: 481-502.

- Bloom D. E. and D. Canning (2003), Contraception and the Celtic Tiger, _The Economic and Social Review_ , Vol. 34 (3): 229–247.

- ________ (2008), _Population Health and Economic Growth_ , The Growth Commission.

- Bloom, D.E., D. Canning, and G. Fink (2009), Population aging and economic growth, in M. Spence and D. Leipziger, eds., _Global Challenges and Growth_ , Washington, D.C., The World Bank.

- Bloom, D. E., D. Canning, G. Fink, J. E. Finlay (2007), _Demographic Change, Institutional Settings, and Labor Supply_ , PGDA Working Paper No. 42, Harvard School of Public Health.

- Bosworth, B., G. Burtless and J. Sabelhaus (1991), The Decline in Saving: Evidence from Household Surveys. _Brookings Papers on Economic Activity_ , vol. 1.

- Caldwell, J. C. (1982), _A Theory of Fertility Decline_ , Academic Press, New York.

- Callen, A.D.-G. and C. Thimann (1997), _Saving in Southeast Asia and Latin America Compared – Searching for Policy Lessons,_ IMF Working Papers, No. 97/110.

- Campbell, J. and M. Feldstein (2001), _Risk Aspects of Investment Based Social Security Reform_ , University of Chicago Press.

- Chakraborty, S. (2004), Endogenous Lifetime and Economic Growth, _Journal of Economic Theory_ , No. 116: 119-137.

- Chawla, M., G. Betcherman, and A. Banerji (2007
expenditures are found to have private benefits to firms in some circumstances (Cai et al. forthcoming). Alternatively, this measures the average regulatory burden, which should have negative effects on firm performance.

The second variable is the standard deviation of the share of senior managers’ time that is spent in dealing with government regulators in the same city (―deal uncertainty‖). A larger value means that the deal environment is more arbitrary and less certain. We expect this variable to have a negative effect on firm performance. Indeed, based on the World Bank Enterprise Surveys of around 100 countries, Hallward-Dreimeier et al. (2010) find evidence that the average time cost has positive effects while deal uncertainty has a negative effect on firm performance. However, as we emphasized, policy effects may differ in specific contexts. For instance, the effects of regulatory burdens and uncertainty may depend on the organizational details of bureaucracy. When firms have to go through decentralized corruption—that is, when firms have to bribe multiple, decentralized government regulators, and each regulator has discretion to stop the deal—the effect of regulatory burden and arbitrariness may be more pronounced (Shleifer and Vishney 1993, Berkowitz and Li 2000). Many observers of corruption have viewed India’s corruption and regulation as typical of a decentralized setup, while China as being typical of a centralized setup (Berkowitz and Li 2000, Sun and Johnson 2009). We therefore expect the effects of these two variables to be more pronounced in India than in China. Table 3 shows that China features both higher time costs and deal uncertainty. While Indian firms on average spent 14% of their senior managers’ time on dealing with regulators, the corresponding number of China is 20%. There are significant more variations in deal uncertainty in China as well: the standard deviation of this time cost is 13% in India, but 17% in China.

To summarize, relative to India, China has a better infrastructure (as measured by the share of losses
historical district level malaria prevalence rate. The data on historical malaria prevalence are taken from Newman (1965). The measure for malaria prevalence used in this paper is called Gabaldon’s endemicity index (see column 2 in Table 4, P.34, Newman, 1965). This index is based on the estimates of enlarged spleens in children due to malaria, and is a good indicator of the degree to which malaria is high and permanent in a district. However, we need a measure of malaria variations at the subdistrict level because the land restrictions vary at that level in the data. Also, we rely on district fixed effects in the IV regressions reported below in section 6 to control for unobserved land and labor 12
## **Employment growth patterns in South Asia**

**Some evidence from Interim Enterprise Survey data**<sup>**1**2</sup>

# Klaus Friesenbichler<sup>3</sup>

# JEL: D00, D04, D22, D92, J23, L11, L25, N95, F50

Key words: employment growth, firm growth, firm size, Gibrat’s law, expansion, South Asia, conflict, war, security, urban, rural

> 1 The findings, interpretations, and conclusions are the authors’ own and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member countries.

> 2 The author is grateful to Murat Seker, Judy Yang, Mehnaz Safavian, Kalpana Kochhar, Pablo Gottret, Reema Nayar and Michal Rutkowski for their valuable comments.

> 3 Affiliation 1: The World Bank, Finance and Private Sector Development Network, 1818 H Street, Washington, D.C. 20433, USA

Affiliation 2: Austrian Institute of Economic Research, Arsenal Object 20, A-1030 Vienna, Austria
as well as cash flow statement. We use the ASIF database in 2000 and 2005 to complement city statistical yearbooks in calculating foreign firms’ employment and output share at the industry and city level.

# **Tariff data**

We obtained China’s export and import tariff at 4-digit ISIC level for years 1992, 2000, and 2005 from the TRAINS database. Sector level tariff is based on weighted average of tariff from all import sources and export destinations. The weights are constructed using the trade flow data from three years earlier. Following Li (2018), we concord all 4-digit ISIC sectors into 3-digit Chinese Standard Industry Classification (CSIC) code, then calculate city level tariff shocks by interacting initial industry employment share with changes in import and export tariffs.

# **Summary statistics**

12
<!-- Start of picture text --> Alarms<br>100%<br>E<br>=3 50% \<br>E Me a. |<br>= \ WH j<br>7 RW auc, pad<br>on DR ApEE SA hn Mnotiar EAA<br>2010 2012 2014 2016 2018 2020 2022<br>Indicator —— Conflict —— Displacement —— Drought —— Exchange rate —— Food price —— Fuel price<br>Alerts<br>100%<br>2J<br>:2 50% i | i A<br>A a )<br>&° 0. NAMB4 r' QSShal \ NAvy LT \ainy’ ; ad<br>2010 2012 2014 2016 2018 2020 2022<br>Indicator —— Conflict —— Displacement —— Drought —— Exchange rate —— Food price —— Fuel price<br><!-- End of picture text -->

period of 2010-2011, marked by a sharp currency devaluation reflective of Yemen's severe internal conflict and the onset of the Arab Spring protests. The subsequent period from 2011 to 2014 appears displacement data only begins in 2014, and conflict data in 2015, implying that the full spectrum of and conflict data contribute to a stark rise in the issuance of alerts and alarms, with the food and fuel price data highlighting additional risks during the Saudi-led port blockade in 2015. A significant increase in currency devaluation and inflation from 2017, coupled with escalating conflict, culminated in a peak in alarm issuance in 2018. Lastly, a widespread inflationary surge across all price indicators triggered escalations in economic and conflict indicators, the drought alarms do not center on any specific year and the highest count of alarms occurs when economic and conflict risks coincide with periods of The univariate validation exercise confirmed the relevance of each indicator while also underscoring the
university education is based on student WASSCE exam scores. The WASSCE score is graded on a 9-point scale with 1 being the best score while 9 is a fail. A satisfactory grade locally known as “credit” is a score between 1 and 6, inclusive. Scoring “credit” in both Math and English” is a pre-requisite for entering university in The Gambia. We used the 2014 WASSCE data along with the EMIS data 11
> Ikt(Hearing Aid 2007) is an indicator variable which takes value 1 for HS code 902140 “hearing aid” from year 2007 onward – the year in which the technology became widely used in the production of hearing aids (see Section 2). γik and δit are country-product and country-year fixed effects, respectively. Country-product dummies capture the effect of time-invariant characteristics that determine the level of exports, including secular productivity and endowment differences that can determine the specialization trade patterns. While country-year fixed effects account for country specific shocks common to all products, including country-specific macroeconomic conditions.

> 10 We use product level bilateral trade data from WITS (UN Comtrade) reported at the 6-digit level in the HS 1988/92 classification for the period 1995-2016 .  As common in the literature, we drop countries with population less than 5 million. Additionally, we keep only countries for which the World Bank provides information on the income level (see Table 9 or <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/378834-how-does-the-world-bank-classifycountries).  The available data cover 117 countries and 330 products that are either classified as high-tech</u> (see Table 5) or in HS chapter 90.
.283***<br>(0.068)|||
|Pastoralists/nomads|||||||||||-0.036<br>(0.033)|-0.012<br>(0.030)|
|Observations|717|643|717|643|717|643|717|643|717|643|717|643|
|R-squared|0.010|0.185|0.000|0.178|0.010|0.188|0.016|0.186|0.007|0.190|0.002|0.177|
|HH controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|Geo controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|ZoneFE|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|

_Note:_ Dependent variable is the log CES-D index collected in wave 3 of the GHS panel. Conflict variables are cumulative for the years 2010–16. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Household variables include controls for waves 1, 2, and 3, and geographical controls are for wave 3. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 39
**Figure 5: Welfare impacts of economic contraction FY22-FY23**

<!-- Start of picture text --> 0.0%<br>-1.0% -0.2%<br>-2.0%<br>-3.0%<br>-4.0%<br>-5.0%<br>-6.0% -5.1%<br>-7.0%<br>-6.5%<br>-8.0%<br>-7.5%<br>-9.0% -8.0%<br>Bottom 20% 2 3 4 Top 20%<br>(PAE)<br>FY23-FY22 Change in Real Household Consumption<br><!-- End of picture text --> Source: Authors’ calculation from HIES 2018/19 data.

Note: Projections use preferred specification following equation (1), including information from nominal growth from eleven economic sectors to model labor income and headline inflation.

# **4. ROBUSTNESS CHECKS**

Our modeling assumptions are generally aligned with the overall forecasting/nowcasting literature. Alternative methods are broadly divided into distributionally neutral (poverty growth elasticity, neutral distribution growth) and non-distributionally neutral, where heterogenous growth rates across households introduce changes in the welfare distribution (quintile growth contribution, sectoral growth models). A detailed description of each method is provided in Appendix A. Poverty-growth elasticity (PGE) uses the ratio between the percent change in the poverty headcount and the percent change in real per capita GDP to project poverty. Evidence shows that the value of the PGE is dependent on the period used. Meanwhile, the neutral distribution method (NDG) uses the growth in real per capita consumption (with a pass-through) and applies it to every household. These methods keep inequality constant as they only shift the welfare distribution to the right (left) as positive
,0,1)|0.33***|(0.11)|[0.02]|-0.30|(0.79)|1,606|
|<br>Change in cereal consumption (-1,0,1)|0.17|(0.11)|[0.31]|-0.17|(0.81)|1,603|
|<br>Change in meat consumption (-1,0,1)|0.11|(0.09)|[0.40]|-0.61|(0.61)|1,606|
|<br>Number of learning activities|0.19**|(0.09)|[0.08]|0.22|(0.56)|1,385|
|<br>Learning activities (=1)|0.15***|(0.06)|[0.03]|0.17|(0.37)|1,385|
|Hours spent on learning activities (average)|0.07|(0.18)|[0.77]|0.43|(1.17)|1,248|
|Child happiness (Scale 1-7)|-0.22|(0.38)|[0.65]|5.07|(1.90)|974|
|Child alertness (s.d. units)|-0.73***|(0.20)|[0.00]|0.00|(1.00)|975|

**_Notes_** :

- The table shows the regression results on pre-specified COVID-19-related outcomes using the phone survey midline data. Each row is its own dependent variable.

- The independent variable of interest is the TOT treatment indicator
Categorical(0-10)|
||How often your work<br>involves learning new<br>things|m5b_q1<br>7|Importance of<br>repeating the same<br>task(inverse)|Categorical(0-5)|
|Routine<br>Cognitive|Autonomy|m5b_q1<br>4|Structured vs<br>unstructured work|Categorical (1-10)|
||Repetitiveness|m5b_q1<br>6|Importance of<br>repeating the same<br>task|Categorical (1-4)|
|Routine|Operate|m5b_q0<br>9|Controlling Machines<br>and processes|Dummy|
|Manual|Physical demanding|m5b_q0<br>3|**-**|Categorical (1-10)|
|Non-<br>|Driving|m5b_q0<br>7|Operating vehicles|Dummy|
|Routine<br>Manual|Repair|m5b_q0<br>8|Control/Feel objects;<br>Manual dexterity|Dummy|

**Note** : the question codes are for Wave2 of the STEP Questionnaire (they do not coincide with the codes of Wave1).

To construct the indexes using STEP, each variable is standardized over the entire population of the pooled STEP surveys for all countries, where all countries are equally weighted. We then sum 8
a crude approximation of the actual figure. An instrumental variables two-stage least squares approach (IV 2SLS) is used to address possible measurement error. The foreign aid variable is therefore instrumented based on more reliable information about USAID aid flows at the local level. The first-stage regression also includes provincial fixed effects.

The preferred estimation includes the maximum number of control variables and corrects for measurement error (table 3a). As an additional robustness check, the indicator on conflict intensity is replaced by with the proportion of people at the provincial level who raised concerns about bad security conditions in their village (table 3b). The signs and magnitudes of the estimated coefficients are similar, although the high correlation between conflict-related indicators makes them less precise than would be ideal. However, the conflict-related indicators are jointly significant at the 5 percent level when using the indicator on casualties, and at the 1 percent level when using the indicator on perceptions of bad security.

In line with previous research, expenditures per capita increase with foreign aid and with the presence of troops, and decrease with conflict intensity (figure 6). Impact estimates vary across specifications. For instance, when controlling for the 2007 poverty rate at the district level, the positive impact of foreign aid is amplified and the negative impact of conflict intensity is substantially dampened. However, the positive impact of troops is relatively stable across specifications. In the case of Afghan troops, it is significant at the 1 percent level in all cases. The impact is two to three times higher than that of international troops. This is possibly a result of Afghan troops procuring and spending more at the local level.

# **6. The overall impact of conflict**

The estimates for Model 2 above show that conflict-related variables have opposite effects on household expenditures. Foreign aid and the presence of troops have a positive effect, whereas conflict intensity has a negative effect. But these conflict-related variables are not independent from each other. Indeed, the estimates for Model 1 imply that there is a strong
. Home visits.

_Source:_ SSB: Urban Income Expenditure Survey, 1992.

_Table 4: Quality - Medical Personnel Input Mixes by Province (1994)_

|_Province_<br>_by quintile_<br>_ofper capit_<br>_income_|_a_<br>_Doctor_|_Special_<br>_Nurse_|_Relative_<br>_Asst._<br>_Doctor_|_Personnel Rat_<br>_Nurse_|_io(RPI)_<br>_Midwife_|
|---|---|---|---|---|---|
|I|0.81|0.77|0.82|0.67|0.74|
|II|0.94|0.95|1.07|0.90|0.61|
|III|1.34|1.23|1.34|1.26|1.05|
|IV|0.86|1.15|1.24|1.16|1.40|
|V|1.90|2.38|1.76|2.48|1.33|

RPR= (Provincial Staff/Provincial Population)/(National StafflNational Population) RPR exceeding one indicates oversupply and less ihan one indicates undersupply of medical personnel.

**_Source:_** Table 9 (Annex).
Policy Research Working Paper 5808

# **Abstract**

In light of the recent microfinance crisis in South India, government-run institutions in general, and primary agricultural credit cooperatives in particular, may end up playing a larger role in the provision of financial services for the poor. Using survey data collected in 2007 from three districts in Andhra Pradesh, this paper assesses the performance of 72 primary agricultural credit cooperatives and finds lack of training among the management. In addition, primary agricultural credit cooperatives tend to be used as political instruments and, as a result, borrowers prioritize all debt obligations (microfinance institutions, informal lenders, etc.) before repaying their primary agricultural credit cooperative loans. The authors suggest that if the performance of primary agricultural credit cooperatives does not improve, a larger government role in the supply of credit may undermine the culture of repayment.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at xgine@worldbank.org and tvishwanath@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