rafmacalaba/datause-encoder-data huggingface dataset:
Also included in more recent waves of DHS surveys is a geographic identifier. This identifier provides the coordinate location of the survey cluster. There are typically 500‐1000 survey clusters in a country, with each cluster containing around 25 households. To guarantee anonymity of the interviewed households, the geographical locations of the clusters have been randomly allocated by DHS within a radius from the real location of maximum 2 km for urban areas, and 5 km for rural areas.

DHS data is available for over 90 countries from over 300 surveys (numerous rounds are conducted within the same country). Of this, surveys in 52 countries contain _both_ a geographic identifier at the survey cluster level and a wealth index at the household level, conditions necessary to estimate where poorer households and richer households live within a country (this is an estimate since the survey clusters are offset, for full details and limitations of the method, see Appendix A). Thus, our sample contains DHS household survey data from 52 countries, with the most recent survey in each country selected. The year in which the survey data was collected varies somewhat by country (between 1994 and 2013), but the vast majority (75%) in our sample are surveys conducted after 2008. Our final sample consists of household wealth scores from 690,745 households (which also contain coordinates of the survey cluster) in 52 countries covered by the DHS.

We extract household wealth scores for the universe of survey respondents. DHS provides each household with a wealth score, with the distribution of scores within a country ranging from about ‐200,000 (asset poorest households) to +200,000 (asset richest households), with a median of zero. We express this score as percentiles of the country‐specific wealth distribution (for ease of interpretation), and use wealth quintiles – defining the poor as the bottom 20 percent in terms of wealth index.

The wealth index provided by the DHS has been used previously to represent poverty (Barros et al., 2012; Fox, 2012; Ward and Kaczan, 2014). However, it should be considered as an estimate of “structural” poverty, since asset indices take a long time to change, as opposed to measures like income or consumption, which are more “stochastic” measures of poverty. Additionally, due to the relative nature of the index, defensible comparisons between countries are challenging, and understanding within‐country results in terms of real variables (dollars, for example) can be difficult.<sup>20</sup> Wealth scores are thus reported as percentiles of the country‐specific wealth distribution, and the results we show below compare poor and non‐poor households _within_ the 52 countries.

# _Occupational Exposure Data_

> 20 Adjusting income data, which is a flow variable that is often a derivative of recorded market activities, for cross‐country differences in purchasing power is hard enough (Deaton and Heston, 2010). Creating purchasing power‐corrected wealth indices for poor households in developing countries is a tall order that we do not attempt here.

13
# **Distinctive Features**

The existing attempts at forecasting PPP between benchmark years can be divided into two broad approaches. The first is largely an accounting exercise that adjusts PPPs for changes in the relative purchasing power of currencies by using inflation differentials between countries. The second, typified by Ravallion (2013a), links the PPP changes to changes in the real GDP of the countries.<sup>5</sup> The present study deviates from these approaches by attempting to link the PPP changes with that in consumer preferences over time. In doing so, the paper deviates from the Balassa Samuelson framework that has dominated the PPP literature and has given primacy, almost exclusively, to the role of productivity differences in driving differences between PPP and exchange rates. As this paper shows, extending the Balassa Samuelson framework to explicitly bring in consumer preferences allows the introduction of distributional variables such as intra-country Gini inequality in studying PPP changes. While in some cases changing consumer preferences can add to the Balassa-Samuelson effects on PPP, in others they can ameliorate such effects. As the empirical evidence presented later shows, one of the consistently robust results of this study is the significant effect of inequality changes on PPP movements in the case of low income countries. These results acquire significance in the context of the recent contribution of Piketty (2015) that has put distribution back at the center of international policy debates.

As indicated above in ‘Motivation’, another key feature of this study is that it provides counterfactual PPPs, along with a proposed methodology for calculating an alternative set of PPPs, which allow comparison with the 2011 ICP PPPs that have come out only very recently. The need for such counterfactual PPPs has recently been emphasized by, for example, Inklaar and Rao (2015) in the context of findings that suggest that while the 2005 ICP PPPs made the developing countries suddenly look a lot ‘poorer’ than they did previously [Chen and Ravallion (2010)], the 2011 ICP PPPs did the very reverse leading to the Inklaar and Rao (2015) conclusion that ‘the (previous) international income inequality had been overstated’. Against a background of continuing controversy on the accuracy of the price information used in the 2005 and the 2011 ICPs, with one group arguing that the 2005 prices were ‘too high’, and the

> 5 Though neither  Ravallion (2013a, 2013b) nor Inklaar (2013) draws a distinction between the cross sectional/ cross country base of the ‘static Penn effect’ and the temporal context of the ‘dynamic Penn effect’, the present study does and provides separate estimates of the two quite  different types of ‘Penn effects’.

6
**Table 6: Loan Application During the Crisis, By Firm Size**

||**FullSample**|**Small Firms**|**Large Firms**|
|---|---|---|---|
|**_% that applied for a_**<br>**_loan_**||||
|2002|22.0%|15.4%|30.4%|
|2005|30.8%|25.8%|39.2%|
|2008/9|41.4%|26.7%|71.8%|
|**_Non-loan applicants:_**<br>**_% reporting no need_**||||
|**_for external finance_**||||
|2002|n.a.|n.a.|n.a.|
|2005|37.2%|38.4%|32.3%|
|2008/9|61.6%|56.9%|83.3%|

Notes: n.a. means not available.

45
**Table 2:** Summary Statistics, 1986-2000

|**Variable**|**Mean**|**Standard Deviation**|
|---|---|---|
|Actual Wage (In)<br>|11.57|0.44|
|Move ind|0.1|0.3|
|Move skill|0.1|0.3|
|Move skill-up|0.06|0.24|
|Move skill-down|0.01|0.11|
|Stayer|0.83|0.38|
|Real exchange rate|1.37|0.66|
|% change real exchange rate|0.03|0.67|
|Openness to trade in 88|0.37|0.27|
|Age|35.69|10.83|
|Age-square|1391.04|829.7|
|Tenure|9.97|8.8|
|Male|0.59|0.49|
|Schooling|5.52|3.01|
|Skilled (Schooling¿12)|0.02|0.14|
|1st Skill level|0.17|0.37|
|2nd Skill level|0.75|0.43|
|3rd Skill level|0.06|0.24|
|4th Skill level|0.02|0.15|
|Herfndal Index<br>|840.27|1425.81|
|Firm size (In)|4.88|1.64|
|Firm Average labor productivity|8.83|1.18|
|Firm Age|24.24|21.15|
|Fraction of Foreign Capital|0.11|0.3|
|**Number of observations**||5,561,687|

# **4.2 International competition and the returns to skill**

Table 3 presents the estimated results of equation (1) using the 1989-1992 appreciation as the exogenous change in international competition. Columns 1-4 present estimates for skill defined as the number of completed years of education, columns 5-8 report estimates for skill using the ILO’s skill definition. For each, we start by estimating the difference results (columns 1-2 and 5-6) and proceed by estimating difference in differences results (columns 3-4 and 7-8). The dependent variable in each column is the log real hourly wage. Each regression includes regional and year dummies to control for disparities in the returns to skill across regions and macro-shocks, respectively. To control for unobservable industry characteristics we include a full set of 74 industry fixed-effects. Additionally, we run each specification with worker fixed effects (where the effect is identified out of the within sector variation in competition) and proceed by estimating the models with spell (work-firm) fixed effects. Whereas in the first case identification comes from the within sector variation in competition, in the second it comes from within spell variation, that is, from variation over the period in which the worker is employed in a given firm. This procedure is important as we are interested in estimating the effect of within industry changes in international competition on relative wages.

13
Graham, J. (2000). How big are the tax benefits of debt? _Journal of Finance, 55_ , 1901– 1941.

IMF (2006). Awash with Cash: Why are Corporate Savings So High?, _World Economic Outlook_ . April, 135-159.

International Institute for Labour Studies ( 2011). Making profits work for investment and jobs. En _World of Work Report 2011_ . International Institute for Labour StudiesInternational Labour Organization, 31-54.

Irvine, P., Pontiff, J. (2008). Idiosyncratic return volatility, cash flows, and product market competition. _Review of Financial Studies,_ 22 _,_ 1149–1177.

Jensen, M. (1986), Agency Cost of Free Cash Flow, Corporate Finance, and Takeovers. _The American Economic Review,_ 76(2), 323-329.

Jensen, M. (1989). The eclipse of public corporation. _Harvard Business Review_ , September-October, 61-74.

Kahle, K., Stulz, R. (2013), _“Access to capital, investment, and the financial crisis” ,Journal of Financial Economics_ , in press.

Kaplan, S., Zingales, L. (1997). Do financing constraints explain why investment is correlated with cash flow? _Quarterly Journal of Economics,_ 112, 169-215.

Krishnamurthy, A., Vissing-Jorgensen, A. (2010).The Aggregate Demand for Treasury Debt. N _BER Working Paper No. 12881._ OECD (2007), Corporate Savings and Investment: Recent Trends and Prospects, _OECD Economic Outlook, Vol. 82._ Preliminary Edition.

Opler, T., y Titman, S. (1991). The Determinants of Leveraged Buyout Activity: Free Cash Flow vs. Financial Distress Costs. _The Journal of Finance, Vol. 48, No. 5,_ 1985-1999 Pozsar, Z. (2011). Institutional Cash Pools and the Triffin Dilemma of the U.S. Banking System, _IMF Working Paper 11/190._ Von Kalckreuth, U. (2000). Exploring the role of uncertainty for corporate investment decisions in Germany. Deutsche Bank, Discussion Paper 5/00.

Wei, S., Zhang, C., (2006). Why Did Individual Stocks Become More Volatile?, _The Journal of Business, Vol. 79_ , No. 1, 259-292.

Whited, T., Wu, G. (2006).Financial Constraints Risk. _The Review of Financial Economics_ , Vol. 19, No2, 531-559.

14
Student use of textbooks is often heavily contingent on teacher behavior. We examine program impacts on teacher behaviors with relation to textbooks in Table 10. This information also comes from student interviews. We see that while the program had modest impacts on the likelihood of teachers encouraging the use of textbooks by students, it did not impact any of the more direct predictors of textbook use, such as students reading from textbook in class, students being allowed to take textbooks home, and homework assignment in general.

Given the lack of program impacts on student textbook use, it is not surprising that we observe no impacts of the textbook provision program on student performance. We are, however, faced with a puzzle – when schools, by their own admission, received textbooks from the government, why then did the students’ actual use of these textbooks did not increase? What happened to the government-provided textbooks?

To answer this question, we again turn to directly observed data collected at endline. During unannounced classroom visits, data were collected on the number of textbooks per student in class. We find that, at endline, textbooks per capita in classrooms were not significantly higher in treatment schools than in control schools (Table 11 Columns 1 and 2).

During data collection, enumerators were instructed to request access from the head teacher to the place where textbooks were stored and to count the textbooks (of the 4 core subjects for grades 4 and 5) in storage. We find that in treatment schools the number of core textbooks stored per students present in school is significantly higher than in control schools (Table 11 Columns 4-6). Comparing books stored per capita at endline and baseline, we find that the program led to a strong increase in the number of books stored in treatment schools (Table 11 Columns 7-10). Therefore, a major reason for lack of student access to textbooks either in the classroom or at home could be that many books are stored by schools and not distributed to students.

Presumably, this lack of access did not extend to teachers, given that the program positively impacted teacher behavior on a number of dimensions.<sup>6</sup> This suggests that teachers may have used a few books to facilitate teaching (hence the significantly higher rates of teaching in treatment schools) but without providing broad access to students.

> 6 These results suggest that it would be of interest to derive impacts of textbooks on student performance after controlling for storage. However, book storage is endogenous to other factors (e.g., school needs, teacher capacity) and the authors were unable to find a credible instrument for storage.

14
# Poverty and Undernutrition in Indonesia during the 1980s*

by Martin Ravallion and Monika Huppi

# Table of Contents

|1.<br>Introduction|1|
|---|---|
|2.<br>Measuring Poverty and Undernutrition|3|
|3.<br>The Data and Results|7|
|4.<br>Proximate Causes|12|
|5.<br>An Alternative Poverty Assessment|18|
|6.<br>Implications for Future Poverty Alleviation Prospects|20|
|7.<br>Conclusions|22|
|Appendix 1: The Distributions|25|
|Appendix 2: Lorenz Curve Parameterizations|33|
|v!otes|35|
|References|42|
|Tables|46|
|Figures|52|

* This Is the first In a series of papers reporting results of an ongoing World Bank research project, "Policy Analysis and Poverty: Applicable Methods and Case Studies" (675-04), based in the Agricultural Policies Division, Agriculture and Rural Development Department. We are grateful for the financial support and encouragement of the Bank's Research Committee. The paper also provides background support for the 'Indonesia: Poverty Assessment and Strategy Report" being prepared by Asia-Country Department V. The paper has benefited in many ways from the assistance and comments of staff of the Country Department; we are particularly grateful to Kyle Peters and Nicholas Prescott. We have also had useful comments on the paper from Anne Booth, Francois Bourguignon, Gaurav Datt, Paul Glewwe, Nanak Kakwanl, Lyn Squire, and Dominique van de Walle. The household level data tapes used here were provided by the Central Bureau of Statistics, Jakarta. We are most grateful to the Bureau's staff for their considerable and able help at various stages of this study.
Figure 4: Illustraffon of<sup>the Impact of the Uruguay Round on the Profile of Nontariff Protecton</sup> Facing African Countries <!-- Start of picture text -->
- Pre-Uruguay -U- Post-Uruguay|<br>100<br>90<br>80<br>Nb<br>70<br>An60<br>seo<br>N<br>30<br>20<br>10- -- -<br>0 o- an ~ ~ ~ ~ ~ ..<br>African Countries Ranked In Terms of Increasing Pre-Uruguay NTB Coverage Ratio<br><!-- End of picture text -->

<!-- Start of picture text --> L<br><!-- End of picture text -->
# **Table A21. Price elasticity of fertilizer purchases.**

|(1)<br>Variable<br>Ethiopia|(2)<br>Tanzania|
|---|---|
|Log fertilizer price, winsorized<br>-0.106|-1.069***|
|(0.185)|(0.314)|
|Constant<br>5.531***|12.73***|
|(0.722)|(2.298)|
|Observations<br>546|335|
|R-squared<br>0.002|0.070|
|Note: Results from OLS regression of log total fertilizer purc<br>hectare on log fertilizer price. Robust standard errors in paren<br>p<0.01, ** p<0.05, * p<0.1.|hased per<br>theses. ***|

42
|**Table 5:** **Marginal**|**Effects of L**|**ogit Esti**|**mates of W**|**ork(Child**|**ren 10-13**|**and 14years)**|
|---|---|---|---|---|---|---|
|**PANEL A**|||**Pakistan**||||
||W<br>|ithout contro<br>|ls<br>||Withcontrol<br>|s<br>|
||Total|Male|Female|Total|Male|Female|
|May-Dec.|0.025|0.087*|-0.039|0.018|0.079*|-0.021|
||(0.031)|(0.043)|(0.044)|(0.029)|(0.039)|(0.042)|
|Age 10-13|-0.100**|-0.057|-0.141**|-0.096**|-0.066|-0.104*|
||(0.030)|(0.042)|(0.044)|(0.029)|(0.038)|(0.042)|
|May-Dec. * Age10-13|<br>-0.020|<br>-0.096*|<br>0.059|<br>-0.024|<br>-0.093*|<br>0.025|
||(0.035)|(0.048)|(0.051)|(0.032)|(0.043)|(0.046)|
|Observations|4480|2348|2132|4480|2348|2132|
|Pseudo R<sup>2</sup>|0.01|0.02|0.01|0.18|0.12|0.30|
|LogLikelihood|-2380.1|-1190.6|-1182.3|-1983.6|-1067.1|-826.1|
|**PANEL B**|||**Urban**||||
||W|ithout contro|ls||Withcontrol|s|
||Total|Male|Female|Total|Male|Female|
|May-Dec.|-0.052*|0.054|-0.131**|-0.021|0.081*|-0.055**|
||(0.026)|(0.044)|(0.033)|(0.022)|(0.041)|(0.019)|
|Age 10-13|<br>-0.098**|<br>-0.061|<br>-0.111**|<br>-0.080**|<br>-0.046|<br>-0.050**|
||(0.025)|(0.044)|(0.030)|(0.021)|(0.040)|(0.017)|
|May-Dec. * Age10-13|<br>0.010|<br>-0.056|<br>0.054|<br>-0.008|<br>-0.080|<br>0.023|
||(0.031)|(0.050)|(0.040)|(0.025)|(0.046)|(0.019)|
|Observations|2328|1195|1133|2328|1195|1133|
|Pseudo R<sup>2</sup>|0.03|0.02|0.05|0.15|0.11|0.37|
|LogLikelihood|-850.4|-455.2|-383.9|-741.0|-414.8|-256.8|
|**PANEL C**|||**Rural**||||
||W<br>|ithout contro<br>|ls<br>||Withcontrol<br>|s<br>|
||Total|Male|Female|Total|Male|Female|
|May-Dec.|0.137*|0.101|0.174|0.119|0.058|0.232*|
||(0.058)|(0.074)|(0.089)|(0.064)|(0.074)|(0.110)|
|Age 10-13|<br>-0.064|<br>-0.060|<br>-0.067|<br>-0.067|<br>-0.091|<br>-0.017|
||(0.057)|(0.072)|(0.088)|(0.064)|(0.072)|(0.111)|
|May-Dec. * Age10-13|<br>-0.104|<br>-0.130|<br>-0.069|<br>-0.102|<br>-0.100|<br>-0.137|
||(0.064)|(0.082)|(0.099)|(0.070)|(0.081)|(0.120)|
|Observations|2152|1153|999|2152|1153|999|
|Pseudo R<sup>2</sup>|0.01|0.02|0.01|0.14|0.09|0.22|
|LogLikelihood|-1362.7|-686.8|-660.6|-1192.1|-633.7|-520.4|

Note: Robust standard errors in parentheses; * significant at 5%; ** significant at 1% For boys, there appears to be an increase in probability of work in the control group, in the post-intervention period, where they are almost 8 percentage points more likely to work in the months of May-December. The difference-in-difference estimate, however, is significant and negative, suggesting that as a result of the enactment of the EoCA 1991, the relative probability of working fell by more than 9 percentage points and the actual probability of working for children in the age cohort 10-13 decreased by almost 0.9 percentage points (i.e., the difference in 22
through 6, indicating that the positive impact of this treatment on deposits, inputs, and crop output is magnified for households that had higher assets at baseline. For farmers in households with a one-point higher baseline asset index (which has a standard deviation of 1.86), the commitment (no raffle) treatment effect is larger for total OIBM deposits by MK7,170, for total value of inputs by MK14,939, and value of crop output by MK20,986. This pattern of heterogeneity is less strong for the ordinary (no raffle) treatment: corresponding coefficients are statistically significant in regressions for total deposits and value of inputs, but not for the value of crop output regression.

These patterns are sensible if wealthier farmers are more likely to face higher pressures to share from their social network. The fact that commitment (no raffle) treatment effect is larger for farmers in higher-asset households may reflect the fact that the commitment treatment helped these farmers decline demands for sharing from their social network, perhaps because it made claims that their funds were inaccessible more credible.

# **6.** **<u>Benefit-cost analysis</u>**

Even a deliberately conservative analysis, which we carry out in Table 8, reveals substantial net benefits to society from offering commitment savings accounts. We take into account the costs to OIBM for setting up and servicing these accounts, the fees and time costs that customers face in using the accounts, and the benefits farmers gain from increased farm profits in the subsequent season. In our attempt to be conservative in this analysis, we do not include any additional profits to the bank from holding additional deposits (that can be lent to other customers or otherwise invested for a profit), and we do not attempt to include the dynamic benefits that customers who have higher profits in one year might reap through larger investments and consequently larger profits in subsequent years. Our calculations are most likely to be relevant for cash-crop farmers who have access to a centralized marketing system for their products, and pertain to the simultaneous offer of commitment savings accounts and direct deposit of crop proceeds into those accounts.

The bank incurs costs related to creating and servicing commitment savings accounts. We separate these costs into one-time costs that include opening new accounts for each customer, and ongoing costs for each deposit or withdrawal a customer is likely to make in the course of one growing season. Our estimated time costs are based on our experience administering commitment savings accounts for this study, and take into account salaries for OIBM bank 22
# **1. Introduction**

Maternal depression is a major public health challenge due to its high prevalence and direct and indirect consequences.<sup>4</sup> Globally, depression is experienced by about 10 percent of pregnant women and by 13 percent of women who have just given birth. In developing countries, the prevalence of depression is almost 50 percent higher than in developed contexts: around 15.6 percent of women experience it during pregnancy and 19.8 percent after childbirth (WHO, 2020). Given the limited availability of data on maternal depression in developing countries (Parsons et al 2012) underscores the likelihood that it remains under-diagnosed and undertreated, these figures likely represent a lower bound of the scale of the problem.

Existing studies on maternal mental health warrant concern about the economic and human costs of maternal depression, not only to the women suffering from it, but also to the children in their care (Aizer et al 2016, Wachs et al 2005). Maternal depression may interfere with the consistent, attentive, and responsive caregiving associated with effective parenting (Paulson, Dauber, & Leiferman 2006). Because mother-child interactions during early life shape foundational neural circuits (Shonkoff and Phillips 2000; Knudsen et al. 2006; OECD 2007), neglect or maltreatment associated with maternal depression can undermine children’s brain development and lead to worse health (physical and mental), cognitive, and behavioral outcomes (Stratakis 2006; Gunnar and Quevedo 2007; Center for the Developing Child 2010). Given that it often ‘goes hand in hand with poverty’ (Shonkoff and Phillips 2000), a major concern about maternal depression is that it may increase poverty and contribute to its intergenerational transmission. In particular, maternal depression can intensify the negative effects of material deprivation and exposure to exogenous shocks associated with poverty, and confine children to substandard developmental trajectories and hence worse outcomes later in life. However, despite the potentially far-reaching harmful effects of maternal depression on the welfare of mothers and their children, there is still a limited amount of rigorous evidence that quantifies its consequences on child development, the channels through which it acts, and how to mitigate its impact on children, particularly in developing countries.

> 4 Maternal depression is characterized by sadness, negative affect, loss of interest in daily activities, fatigue, difficulty thinking clearly, and bouts of withdrawal and intrusiveness. We focus our analysis on depression affecting mothers because, in the context under analysis, they traditionally play a more prominent role in childrearing, especially while children are younger.

2