rafmacalaba/datause-encoder-data huggingface dataset:
significance of the transparency<sup>indexsomewhat insomeof the casesbut generallytheresults</sup> stay the same: the transparency<sup>index issignificantly correlatedwith governanceindicators.</sup> Others have found that state ownership<sup>of the mediais associatedwithpoorereconomic</sup> and social outcomes. I use an index<sup>developed by Djankovet al(2001)andWorldBank(2002),</sup> to see whether the transparency<sup>indexisstillsignificantoncethisindexisadded (notshown).</sup> However, the data on: ownership of<sup>the media isonly availablefor a muchsmallersample.I find</sup> that.addition of state ownership of<sup>the mediato the right hand side of the regressionsreduces the</sup> impact of the transparency index<sup>in someof the regressions(some of the -versions using the KKZ</sup> indicators) but the general conclusions<sup>arestill valid.That is,evenin countrieswhere muchof</sup> information packaging and dissemination<sup>tothegeneralpubliciscontrolledbygovernment,a</sup> government that publishes more<sup>economicinformationgoverns better on average.</sup> The transparency index is significant<sup>atthe10%levelevenaftercontrollingfor</sup> newspaper circulation, freedom of<sup>the pressandthepresenceof a Freedomof Informationlaw</sup> (see Tables lOa-lOf).

# **Table lOa. Transparency and Freedom**<sup>**of Press**</sup>

_DATA SET: KAUFmANN, KRAAYANDZOIDO-LOBA_<sup>_TON_</sup> <!-- Start of picture text --> How authorities are selected and Capacity of the state to Respect of the state and<br>rep wced i*n.knwnt d Dolicies the citizens for the rules<br>Voice and PoCitical lstability Govemnment Regulatory Rule of Graft<br>._______________ Socialist Legal Code Accountability -.03 and Vioknce .08 Effectiveness -.51*** Burden -.55*** LAW -.40*** -.68**<br>___________________ _ . 2 (-.3S). .47) (-3.51) (4.15) (-3.15) (-5.17)<br>French Legal Code -.04 -.12 -.08 -.09 ..28** -.34***<br>.________ Gcmian Legal Code . (-.61) -.06 (-.92) .09 (-.76) .10 -.(-1.11) 41*** (-2.71) .21 (-3.19)-.22<br>.___ ~(-.70) . (.50) (.48) (-3.22) (1.29) (-.83)<br>Scandinavian Legal Code .05 .23 .45*** - -.25 .32* - .59.*<br>(.66) (1.53) (3.5) (-2.19)** (2.52) (3.58)<br>Income .13*** .41*0* .33*0* .18*** .48*** .360*0<br>(3.87) (5.19) (5.94) (4.06) (8.55) (6.88)<br>Transparency .030* .01 -.12** -.14*** -.009 -.09<br>(21.9) (.80) (-2.24) (-3.09) (-.20) (-2.14)<br>Freedom of press (reverse) .03*** .01*** .01*** .01*** .009*** .01***<br>(21.9) (418) (3.72) (5.16) (3.61) (4.40)<br>Constant -2.57*.* -4.2*0* -2.94*** -1.49*0* -4.27*** -3.19*0*<br>(-8.1) (-5.82) 1 (-5.45) (-3.37)' (-8.24) (-6.52)<br>#obs 135 124 125 130 130 124<br>.____________________ R.89 _ .62 .67 .63 .72 .74<br><!-- End of picture text --> 26
appears to be associated with higher remarriage rates. In a context where women’s rights and access to property remain linked to men, remarriage can be a life saver.

A small literature in economics documents the predicament of widows in various situations in other African countries. In Uganda, Zimbabwe and Mali, studies investigating the well-being of female-headed households find that among them, widow-headed households are particularly disadvantaged (Appleton 1996; Horrell and Krishnan 2007; and van de Walle 2013, respectively). In the context of HIV/AIDS deaths, Chapoto et al. (2010) find evidence that households headed by widows whose husbands succumbed to illness are more vulnerable to losing control of agricultural land in Zambia. A number of studies have documented similar situations in Kenya, Lesotho and Southern Africa more generally (Drimie 2002).

Elsewhere in the developing world, a literature focuses on the plight of widows in South Asia, and particularly India where widows also face many social and economic constraints (Chen and Drèze 1995; Drèze and Srinivasan 1997; Chen 2000; Jensen 2005). Jensen (2005) analyzes the well-being of widows as proxied by their nutritional status. Wealthier upper-caste widows are found to be no better-off than poorer lower-caste widows. This is explained in part by the more severe restrictions placed on their behavior, socio-economic opportunities and remarriage options. Lloyd et al. (2015) compare various socio-economic and health attributes of widows aged 50 and older across five countries (China, India, the Russian Federation, Ghana and South Africa) and find evidence of disadvantage but also variance in its manifestation.

# **3. Data and descriptive statistics**

The analysis uses two nationally representative cross-sectional samples of women aged 15 to 49, pooled from the Nigeria Demographic and Health Surveys of 2008 and 2013. We create a data set that draws on the household-level questionnaire for information on each household, its head and other members, and on the women’s questionnaire for information on women’s anthropometrics, marital status, religion, age, education and other relevant individual characteristics. The latter questionnaire is administered to all women in the 15 to 49 age range.

**_Individual welfare indicators_** : An advantage of the DHS for this purpose is that it provides an individual measure of adult women’s welfare, namely nutritional status as indicated by her BMI. We also use an indicator for being underweight, equal to one if a woman’s BMI is lower than 18.5 and zero otherwise. It has become common to use nutritional status as an

10
Cox, D. R. 1972. "Regression Models and Life-Tables." Journal of the Royal Statistical Society B 34: 187—202.

Cox, D.R. 1975. "Partial Likelihood." Biometrika, 62: 269—76.

Currie, Janet, and Enrico Moretti. 2003. "Mother’s Education and the Intergenerational Transmission of Human Capital: Evidence from College Openings and Longitudinal Data." Quarterly Journal of Economics 118 (4): 1495—532.

Deaton, Angus, and Christina Paxson. 2003. "Mortality, Income, and Income Inequality over time in Britain and the United States." In David A. Wise, ed., Perspectives on The Economics of Aging, vol. 8. Chicago. Ill.: University of Chicago Press for NBER.

De Walque, Damien. 2003. "How Do Information and Education Affect Health Decisions? The Cases of HIV/AIDS and Smoking." Ph.D. Dissertation, University of Chicago. Chicago. Ill.

De Walque, Damien. 2004. "How Do Education and Information Affect Smoking Decisions: Evidence from Smoking Histories (1940—2000) in the United States." Development Research Group, World Bank, Washington, D.C. Processed.

Farell, Phillip, and Fuchs, Victor. 1986. "Schooling and Health: The Cigarette Connection." In Victor R. Fuchs, ed., The Health Economy. Cambridge, Mass., and London: Harvard University Press.

Finkelstein, Amy, and James Poterba. 2004. "Adverse Selection in Insurance Markets: Policyholder evidence from the U.K. Annuity Market." The Journal of Political Economy 112 (1,1): 183—208.

Fuchs, Victor. 1982. "Time Preference and Health: An Exploratory Study." In Victor Fuchs, ed., Economic Aspects of Health. Chicago, Ill.: University of Chicago Press.

Fylkesnes, Knut, Rosemary M. Musonda, Moses Sichone, Zacchaeus Ndhlovu, Francis Tembo, and Mwaka Nonze. 2001. "Declining HIV prevalence and risk behaviours in Zambia: evidence from surveillance and population-based surveys." AIDS 15: 907—16.

Fylkesnes, Knut, Rosemary Mubanga Musonda, Kelvin Kasumba, Zacchaeus Ndhlovu, Fred Mluanda, Lovemore Kaetano, and Chiluba C. Chipaila. 1997. "The HIV epidemic in Zambia: socio34
in coping strategies in different counties. The standard errors, _εhct_ , are clustered within counties.

The child-adult ratio is calculated by dividing the number of children who are under 18 years old by the total number of adults aged between 18 and 65 in the household. We use a dummy indicating whether the dwelling space of the household has a dirt floor or finished floor as an indicator for poverty, as this measure is highly correlated with other poverty indicators in Kenya and feasible to elicit in a phone interview.

Moreover, we hypothesize that female-headed households are poorer and have a closer relationship with social networks, and these cause the gender differences in coping. To formally check whether the gender differences in coping are due to these two reasons, we interact the household head gender dummy with the two hypothetical channels: the household poverty indicator, and the status of receiving transfers from families prior to the COVID-19 crisis.

A series of sensitivity analyses are performed. Firstly, Kenyan nationals might employ different coping strategies compared to refugees. Thus, we exclude the refugee sample from the analysis and cross check the results. Second, 17.72 percent of the households have reported different household head gender across the five waves, and we exclude those households whose head gender was changed.

All analyses are conducted in Stata v. 15.0 (College Station, Texas, USA) and take into account the sample design, particularly the sample weights and stratification.

# **4 Gender Differences in Coping Strategies**

## **4.1 Main Results**

As shown in the descriptive figures (Figure 1), male-headed households are more likely to cope using household internal resources but female-headed ones rely more on social networks, although the differences are not significantly different. For example, maleheaded households are more likely to sell assets to cope with a negative economic shock (Figure 1a). On average, about 10.4 percent of the male-headed households have sold any asset or livestock within the last 14 days, but only 6.9 percent of the female-headed 6
Appendix 3: Test of balancing of covariates between non-farm self-employment participants and non-participants, 2010-2013, rural Malawi

||Self-employment|Non participants|Self-employme|nt participants|t-test difference|
|---|---|---|---|---|---|
|Variables|Mean|SD|Mean|SD|p-value|
|**Treatment variables**||||||
|Non-farm wage employment (0/1)|15.05|35.76|15.33|36.04|0.668|
|Non-farm self-employment (0/1)|0.00|0.00|100.00|0.00||
|**Outcome variables**||||||
|HHPCE|54,741.65|43,462.77|68,724.30|55,600.92|0.000|
|Log of HHPCE|10.70|0.63|10.91|0.66|0.000|
|Poverty incidence|39.81|48.96|27.72|44.78|0.000|
|Poverty gap||||||
|Poverty severity||||||
|Food insecurity (0/1)|33.24|47.11|30.15|45.91|0.213|
|Food consumption adequacy||||||
|1|42.81|49.49|36.52|48.17|0.063|
|2|50.90|50.00|55.70|49.70|0.369|
|3|6.29|24.29|7.79|26.81|0.094|
|Seeds purchase decision (0/1)|39.27|48.84|47.51|49.96|0.000|
|Fertilizer purchase decision (0/1)|36.73|48.21|46.71|49.91|0.000|
|Inputs purchase decision (0/1)|57.48|49.44|69.98|45.85|0.000|
|Seed purchase per acre (1000MKW)|0.49|1.66|0.65|1.78|0.011|
|Fertilizer purchase per acre (1000MKW)|3.48|9.62|4.00|10.38|0.215|
|Inputs purchase per acre (1000MKW)|3.96|9.96|4.60|10.16|0.078|
|Land cultivated (acres)|4.09|51.38|5.67|55.60|0.903|
|**Covariates**||||||
|Age of the household head|44.72|17.01|41.58|14.37|0.000|
|Male headed-household|74.92|43.35|80.65|39.52|0.014|
|Highest level of formal education acquired by<br>household head||||||
|None|78.33|41.20|72.95|44.44|0.010|
|PSLC|9.70|29.61|11.22|31.58|0.194|

41
# **Section IV Other Data**

# A. **Informal Arrangements**

Estimated Reliance on Informal Systems of Old Age Support. The introduction to Chapter 2 refers to estimates of reliance on informal systems for old age support. The statement suggests that roughly 60 percent of the world's labor force and 70 percent of the world's old people do not participate in formal pension systems. This estimate is also mentioned in the introductory chapter in endnote 3. While the detailed estimates are presented in Palacios (1996), the general approach was as follows: First, coverage data (see "Coverage" section above) were gathered for more than 50 countries for years ranging from 1985 to 1992. The relationship between coverage and income per capita measured in PPP dollars in 1990 from the 1992 World Development Report (table 30) was estimated based on a series of regressions. The bivariate regression which exhibited the best fit is the quadratic equation described in figure 1.7 in Chapter 1 (page 40). Further evidence and discussion of the detenminants of pension coverage can be found in Palacios (1996).

Second, data on the size of the labor force in 1990 were taken from the 1992 "Social Indicators of Development". For countries where labor force coverage was not known, the coefficients from equation A. I were applied to the known income per capita for countries where these data were available.

This resulted in estimates of coverage rates which were then multiplied by the labor force figures to arrive at an estimate of the number of covered workers in each country. These covered workers were summed for all countries and the estimated total number of covered workers was divided by the total world labor force to arrive at a global coverage rate. Those not covered by the formal system were assumed to depend on informal arrangements. This implies that workers not participating in the formal contributory schemes run by the public sector are also not covered by privately-managed occupational pension plans or personal retirement savings plans. In general, it seems that those covered by privatelymanaged schemes are also likely to be covered by the main public scheme but that those outside the public scheme are generally not participating in privately-managed plans.

50
Spillover Effects of China’s Trade and Growth Shocks on ASEAN Countries: Evidence from a GVAR Model Nazmus Sadat Khan* _Keywords:_ ASEAN countries, Chinese trade shocks, GVAR, Economic integration _JEL classification:_ C32, F43, O47

> _∗_ The World Bank, Plot E-32, Agargaon, Sher-E-Bangla Nagar, Dhaka-1207, Bangladesh, Email: nkhan12@worldbank.org, Phone: (+8802) 9604404210, Fax (+8802) 55667778
# **1. Introduction**

Governments often subsidize food in an effort to either augment the real incomes of constituents, or to improve the nutritional status of vulnerable groups, or both (PinstrupAndersen 1988). Criteria for evaluating such subsidy programs, however, differ for these two broad objectives. Evaluation of the formner focuses primarily on the incidence of transfers among subpopulations, while the impact on growth (preferable) or on incremental food consumption are the standard forms of evaluating the latter. While income transfers do affect nutrition itself, the impact of food subsidies is greater than that of a cash transfer to the degree that the commodity subsidy is extramarginal (that is, the subsidy decreases prices at the margin) and to the degree that price decreases lead to net increases in nutrient demand after substitution. Moreover, nutritional impacts are enhanced when program designs encourage behaviors that target food toward children at risk.

Milk is often believed to be a commodity particularly well suited to meeting nutritional objectives. This view prevails despite the fact that such subsidies are seldom targeted towards poorer households or towards the youngest children within populations (Kennedy and Alderman 1987). Peru's program to provide milk and milk substitutes to low income households is an example of a transfer that is motivated by this belief. The program, called the _Vaso de Leche_ (VL), uses community-based targeting, an approach that has proven progressive in other contexts (Conning and Kevane 2002, Alderman 2001, Galasso and Ravallion 2002). The question we thus address in this paper is: If targeting is propoor, is it possible that a program such as VL can achieve its nutritional objectives in a manner similar to the well documented impact of the Women Infant Children (WIC) program in the United States (Rush and others 1988)? Since randomized evaluations are often hard to implement in politically popular transfer programs, we approach this question by linking public expenditure data with household survey information to illustrate this approach to program impact assessment.

The next section depicts the details of the VL program. Section 3 describes the various data sources used in this evaluation, and is followed by a discussion of methodologies used to assess both the targeting efficiency of the program as well as its impact on the nutritional status of children. Section _5_ presents the results.

# **2. Description of VL Program**

Totaling $97 million in 2001, the VL is the largest social transfer in Peru and the second largest component of transfers from the central government to municipalities (Instituto Apoyo and the World Bank 2002). Introduced as a pilot in Lima in 1984, the program expanded nationally during the economic crises in the late 1980s amd early part of the 1990s. By 1998, the program had expanded to reach 44 percent of households with children aged 3 through 11 through earmarked monthly transfer to municipalities (Younger 2002). The _I_
19 TABLE 2

**Are the** Capital Market Variables Jointly Significant in each Coutry? _Hx_ a 0 3' 0 for I - **Brazil,** Colombia...

(Critical Xi at 952 confidence and 2 **degrees** of freedoms 5.99)

|PROXIES<br>_ARE:_|BRAZIL|COLOMBIA|I1DIA|KOREA|TUSY|
|---|---|---|---|---|---|
|DCGDt<br>lINTt|11.59|6.86|1.93|32.47|25.24|
|DCGD.,<br>INT.<br>1|38.09|21.90|10.85|85.25|33.98|
|DCGDt.,<br>RINTt|23.31|11.50|13.25|68.61|18.41|
# **Contents**

|1.<br>Introduction .................................................................................................................................3|
|---|
|2.<br>The evolution of subsidies ...........................................................................................................3|
|3.<br>Baseline data ...............................................................................................................................8|
|4.<br>A distributional analysis of subsidies (October 2014) ................................................................9|
|5.<br>Simulation of subsidy reforms ..................................................................................................12|
|5.1.<br>Evaluation of the 2014 subsidy reforms .....................................................................................13|
|5.1.1.<br>Direct effects ..................................................................................................................... 14|
|5.1.2.<br>Indirect effects ................................................................................................................... 15|
|5.2.<br>Complete elimination of subsidies .............................................................................................17|
|5.2.1.<br>Direct effects ..................................................................................................................... 17|
|5.2.2.<br>Indirect effects ................................................................................................................... 19|
|6.<br>The political economy of reforms .............................................................................................21|
|7.<br>Conclusion .................................................................................................................................23|
|References ..............................................................................................................................................24|
|Annex 1: Major historical landmarks of the subsidy system ..................................................................25|

2
|absolute change w.r.t original income, p.p.|-14.9|-14.1|
|---|---|---|
|relative change w.r.t. original income, percent|-30.0|-28.5|

Source: Authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.

Among the components of the tax-benefit system, the most important one is pensions **.** The marginal contribution<sup>7</sup> of pensions to poverty reduction is almost 26 percentage points, meaning that poverty more than doubles if pensions are excluded from income (Table 2).<sup>8</sup> The Gini index in this case almost returns to the level recorded at the _original_ income. Other components have much smaller effect on poverty and inequality. The largest among these other components in terms of marginal contribution to poverty are social insurance contributions: poverty would have been lower by 4 percentage points if employees were able to retain their contributions. The second biggest marginal impact on inequality is coming from non-means-tested social assistance benefits.

**Table 2. Marginal contributions of key tax/benefit programs to disposable income, 2017**

||Disposable<br>Income(DPI)|DPI plus<br>SIC|DPI plus<br>direct taxes|DPI minus<br>pensions|DPI minus non-<br>means-tested SA|DPI minus<br>means-tested SA|
|---|---|---|---|---|---|---|
|**Poverty rate, percent**|14.7|11.0|12.6|39.3|16.8|15.0|
|absolute change w.r.t original income, p.p.||-3.7|-2.1|24.6|2.0|0.3|
|relative change w.r.t. original income, percent||-25.2|-14.2|167.1|13.9|1.8|
|**Gini index**|34.8|35.2|35.1|47.6|35.6|35.0|
|absolute change w.r.t original income, p.p.||0.5|0.3|12.9|0.9|0.2|
|relative change w.r.t. original income, percent||1.3|0.9|37.1|2.5|0.6|

Source: Authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.

Pensioner households benefit the most from the tax-benefit system. RUSMOD is able to disentangle fiscal impact by population groups and by programs. Given the importance of pensions, it is not surprising that retirees are the biggest beneficiaries of the system (Figure 2). Among age groups, this corresponds to the elderly above the statutory pension age (55/60+). Other family types are net payers to the system. For example, families with two or more children, and single parent households receive other direct transfers (child benefits), but in amounts insufficient to offset the taxes they pay.

> 7 The marginal contribution of a tax (or transfer) is calculated by taking the difference between the inequality indicator without the tax (or transfer) and with it  (Lustig, 2018).

> 8 Under the hypothetical case where pensions are excluded from the disposable income, but all other programs remain in place, the eligibility and amounts are recalculated as if there were no pensions.

10
Appendix Table 4b: RDD estimates of gaps in student math test scores associated with teachers hired pre- and post-FPE (multiple <u>bandwidths).</u>

||Bandwidth:|1|2|3|4|5|6|7|8|9|10|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Pooled|Coef|0.000|0.007|-0.020|-0.072|-0.079|-0.095|-0.088|-0.085|-0.067|-0.038|
||SE|(0.181)|(0.130)|(0.109)|(0.094)|(0.087)|(0.080)|(0.077)|(0.070)|(0.067)|(0.064)|
||Pval|0.999|0.957|0.853|0.444|0.367|0.233|0.251|0.226|0.312|0.546|
|Kenya|Coef|0.359|0.380|0.027|0.195|0.106|0.179|0.188|0.216|0.290|0.291|
||SE|(0.281)|(0.289)|(0.238)|(0.221)|(0.205)|(0.188)|(0.180)|(0.171)|(0.161)|(0.160)|
||Pval|0.218|0.196|0.909|0.381|0.608|0.343|0.298|0.207|0.073|0.071|
|Madagascar|Coef|0.295|0.051|0.094|-0.014|-0.096|-0.184|-0.175|-0.156|-0.103|-0.105|
||SE|(0.388)|(0.290)|(0.239)|(0.210)|(0.185)|(0.173)|(0.165)|(0.152)|(0.139)|(0.134)|
||Pval|0.450|0.862|0.694|0.946|0.604|0.287|0.288|0.306|0.458|0.431|
|Mozambique|Coef|-0.434|-0.215|-0.300|-0.335|-0.255|-0.159|-0.118|-0.167|-0.181|-0.130|
||SE|(0.442)|(0.284)|(0.228)|(0.196)|(0.177)|(0.160)|(0.143)|(0.132)|(0.133)|(0.124)|
||Pval|0.343|0.456|0.197|0.092|0.154|0.322|0.412|0.207|0.174|0.296|
|Tanzania|Coef|0.163|0.470|0.295|0.140|0.173|0.036|0.056|0.011|0.025|0.208|
||SE|(0.585)|(0.435)|(0.374)|(0.286)|(0.264)|(0.238)|(0.224)|(0.203)|(0.203)|(0.187)|
||Pval|0.783|0.284|0.432|0.626|0.514|0.880|0.803|0.957|0.903|0.268|
|Togo|Coef|0.324|0.048|-0.073|-0.142|-0.106|-0.135|-0.062|0.067|0.095|0.022|
||SE|(0.385)|(0.269)|(0.260)|(0.217)|(0.209)|(0.207)|(0.215)|(0.209)|(0.190)|(0.169)|
||Pval|0.407|0.859|0.779|0.516|0.612|0.515|0.773|0.749|0.619|0.897|
|Uganda|Coef|-0.572|-0.506|-0.303|-0.286|-0.275|-0.192|-0.234|-0.246|-0.263|-0.358|
||SE|(0.310)|(0.245)|(0.211)|(0.182)|(0.170)|(0.159)|(0.152)|(0.143)|(0.134)|(0.130)|
||Pval|0.073|0.044|0.156|0.120|0.109|0.228|0.125|0.087|0.051|0.007|

Notes: Table reports coefficient estimates (Coef), Standard Errors (SE), and p-values derived from estimating Equation (1). Each estimate and associated standard error and p-value are the result of a separate regression model. Pooled model includes country fixed effects.

A-8