rafmacalaba/datause-encoder-data huggingface dataset:
information from a couple of available programs), a web-based search was undertaken, which thoroughly combed through various existing lists and databases maintained by large donors and international agencies operating in different regions. In most cases, a survey non-experimental research method relying on questionnaires or interview protocols, directly contacting project officers and program managers, for data collection, led to a more detailed review of available secondary information and, to collect as much information as possible, some of which was not available on the web. In many cases, interviews took place via e-mail exchange in order to collect all of the information necessary to fill up items within the template.

# **III. The Inventory**

This section provides a summary of the main descriptive statistics of the program inventory. The inventory compiles information about 36 programs supported by IOs (56% by bilateral and 44% by multilateral organizations) (see Annex 1). The main descriptive statistics are as follows<sup>5</sup> :

- **Program Type** : The great majority (83%) of all programs included in the inventory provided access to finance while about half provided some form of training as well as coaching and incubation services. Financial support provided by the programs was mainly in the form of grants (63%) while 20% provided loans. Only 17% of all programs provided both grants and loans (Figure 1).

**Figure 1: Type of programs supported**

<!-- Start of picture text --> 20%<br>17%<br><!-- End of picture text -->

- **Target Region:** The majority of all programs in the inventory were implemented in the South Asia (45%) and Africa regions (42%); which are also the world regions with the highest poverty rates. About 17% of all programs were implemented in the East Asia and MENA regions; while the share of programs in high-middle income regions (notably ECA and LAC) was 14%. Only 6% of all programs in the inventory (2 programs only) were implemented in North America (US and Canada) (Figure 2).

> 5 Data collected are available upon request.

5
# **7. References**

- Baird, Sarah, Craig McIntosh, and Berk Özler.  2009.  “Designing Cost-Effective Cash Transfer Programs to Boost Schooling in Sub-Saharan Africa.” World Bank Policy Research Working Paper No. 5090.

- Beegle Kathleen and Berk Özler. 2007. “Young Women, Rich(er) Men, and the Spread of HIV.” _Unpublished manuscript_ .

- Bruhn, Miriam and McKenzie, David John. 2008. “In Pursuit of Balance: Randomization in Practice in Development Field Experiments.” World Bank Policy Research Working Paper No. 4752.

- Das, Jishnu & Quy-Toan Do & Özler, Berk. 2005. "Reassessing Conditional Cash Transfer Programs." _World Bank Research Observer_ 20: 57-80.

- De Janvry, Alain and Elisabeth Sadoulet. 2004. “Conditional Cash Transfer Programs: Are They Really Magic Bullets?” _ARE Update_ , Vol. 7, No. 6

- De Walque, Damien & Corno, Lucia, 2007. "The determinants of HIV infection and related sexual behaviors: evidence from Lesotho." Policy Research Working Paper Series 4421, The World Bank.

- De Walque, Damien. 2006. “Who Gets AIDS and How? The Determinants of HIV Infection and Sexual Behaviors in Burkina Faso, Cameroon, Ghana, Kenya and Tanzania.” World Bank Policy Research Working Paper No. 3844.

- Duflo Esther, Pascaline Dupas, Michael Kremer, and Samuel Sinei. 2006. “Education and HIV/AIDS Prevention: Evidence from a randomized evaluation in Western Kenya.” World Bank Policy Research Working Paper No. 4024.

- Filmer, Deon and Norbert Schady. 2009. “School Enrollment, Selection, and Test Scores.” _Unpublished manuscript._

- Glewwe, Paul, Michael Kremer, and Sylvie Moulin. 2008. “Many Children Left Behind? Textbooks and Test Scores in Kenya.” _American Economic Journal:  Applied Economics._ 1(1), pp. 112-135.

- Hallman, Kelly. 2004. “Socioeconomic Disadvantage and Unsafe Sexual Behaviors of Young Women and Men in South Africa,” Policy Research Division Working Paper No. 190, Population Council. New York, NY.

- Halperin, Daniel, and Helen Epstein. 2004. “The Opportunity to Capitalize on the Growing Access to HIV Treatment to Expand HIV Prevention.” _The Lancet_ 364: 4-6.

22
17

- Heston, A., R. Summers, and B. Aten, 2012, Penn World Table Version 7.1, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania.

- Hummels, D., and P. Klenow, 2005, “The Variety and Quality of a Nation’s Exports,” _American Economic Review,_ Vol. 95, pp. 704–723.

- Hwang, J., 2007, “Introduction of New Goods, Convergence and Growth,” Job Market Paper, Department of Economics, Harvard University.

- Imbs, J., and R. Wacziarg, 2003, “Stages of Diversification,” _American Economic Review,_ Vol. 93, pp. 63– 86.

- Khandelwal, A., 2010, “The Long and Short (of) Quality Ladders,” _Review of Economic Studies_ , Vol. 77, pp. 1450–1476.

- Klinger, B., and D. Lederman, 2004, “Discovery and Development: an Empirical Exploration of “New” Products,” World Bank Policy Research Working Paper Series No. 3450.

- Klinger, B., and D. Lederman, 2006, “Diversification, Innovation, and Imitation Inside the Global Technological Frontier,” World Bank Policy Research Working Paper No. 3872.

- Lederman, D., and W. Maloney, 2012, _Does What You Export Matter? In Search of Empirical Guidance for Industrial Policies_ (Washington: World Bank).

- Mukerji, P., and A. Panagariya, 2009, “Within- and Across-Product Specialization Revisited,” Columbia University, New York.

- Prati, A., M. Gaetano Onorato and C. Papageorgiou, 2013, “Which Reforms Work and under What Institutional Environment: Evidence from a New Dataset on Structural Reforms,” _Review of Economics and Statistics_ , Vol. 95, pp. 946–968.

- Schott, P., 2004, “Across-product Versus Within-product Specialization in International Trade,” _Quarterly Journal of Economics,_ Vol. 119, pp. 646–677.

- Sutton, J., and D. Trefler, 2011, “Deductions from the Export Basket: Capabilities, Wealth and Trade,” NBER Working Paper No. 16834.

World Development Indicators (Washington: World Bank).

World Trade Organization, Regional Trade Agreements Database (Geneva, Switzerland: WTO).
# **1. Introduction**

Are female-headed households (FHHs) in the Arab region more likely to be poor, and increasingly so? Social and cultural barriers often hinder women’s economic participation in the region and several recent studies find that women are at an increasing disadvantage compared to men in labor markets (Amara and Jemmali, 2018; AlAzzawi and Hlasny, 2022). The COVID-19 pandemic further deepened gender inequality in many countries (Dang and Nguyen, 2021; Alon _et al._ , 2022). Yet, few studies have investigated the topic of poverty feminization in the region. These are important policy questions since many countries in the region specifically target FHHs based on the premise that they are more vulnerable to poverty and probably increasingly vulnerable to poverty, particularly during the pandemic.<sup>1</sup> Furthermore, the Arab region is home to countries of different income levels with diverse social and cultural circumstances, resulting in different context-specific factors contributing to poverty feminization.

That poverty is more prevalent among women than men is widely assumed, and various explanations are offered for it. These include lower school enrollment rates and less work experience (Grant and Behrman, 2010), limited access to income-generating assets such as land (Deere and Leon, 2003), credit and other financial services (Demirguc-Kunt _et al._ , 2013), physical and social capital, and technology (World Bank, 2011; Klasen _et al._ , 2015), and market discrimination (Buvinic and Gupta, 1997).

There is, however, far less agreement on the existence of “feminization of poverty” affecting FHHs (Chant, 2010; Duflo, 2012; Klasen _et al._ , 2015; Bradshaw _et al._ , 2017). Buvinic and Gupta (1997) observe that of 65 studies covering Africa, Asia, Latin America and the Caribbean, 38

> 1 These include the Arab Republic of Egypt’s largest poverty-targeting cash transfer program, Takaful, and other subsidy programs in Jordan, Lebanon, and Tunisia (NAF, 2020; Nasri, 2020; ESCWA, 2021; World Bank, 2022).

2
46

|||||||Hos|pital Pay|ment Me|thods||
|---|---|---|---|---|---|---|---|---|---|---|
|||S|HI dum|my<br>|Fee|-for-ser|vice<br>|Pa|tient-ba|sed<br>|
|||||Impact|||Impact|||Impact|
||Dependentvariable|Coef|SE|<br>(%)|Coef|SE|<br>(%)|Coef|SE|<br>(%)|
||Pertussis incidence rate|1.61|1.39|0.395|-1.01|1.45|-0.248|**-1.76**|**0.82**|**-0.430**|
||Diphteria incidence rate|-0.23|0.97|-0.152|-0.16|0.88|-0.105|0.54|0.99|0.351|
||Tetanus incidence rate|0.01|0.02|0.107|**_0.03_**|**_0.01_**|**_0.300_**|0.00|0.02|0.050|
||Cancer incidence rate|-2.42|3.29|-0.010|3.45|3.87|0.014|6.35|4.65|0.026|
||Tuberculosis immunization rate|-1.26|1.82|-0.013|2.83|2.22|0.030|6.13|4.26|0.065|
||DPT immunization rate|-1.82|2.27|-0.020|2.19|2.60|0.024|4.47|4.67|0.048|
||Measles immunization rate|-0.25|0.84|-0.003|0.03|0.95|0.000|0.01|0.84|0.000|
||Polio immunization rate|1.09|1.67|0.012|1.14|1.78|0.012|-0.19|1.35|-0.002|
||Mumps immunization rate|4.88|6.21|0.059|7.31|6.13|0.088|2.44|7.67|0.030|
||Rubellaimmunization rate|6.88|7.17|0.076|3.83|3.84|0.042|3.15|6.21|0.035|
||Length of stay (total)|-0.17|0.18|-0.014|-0.24|0.22|-0.019|-0.27|0.28|-0.021|
||Length of stay (acute care)|0.06|0.16|0.005|**-0.33**|**0.14**|**-0.031**|-0.36|0.30|-0.033|
||Bed occupancy rate|**2.51**|**1.12**|**0.035**|0.37|1.43|0.005|-2.23|1.36|-0.031|
||Hospital beds|-0.11|0.15|-0.014|-0.11|0.18|-0.014|**-0.37**|**0.17**|**-0.047**|
||In-patient admissions|0.19|0.16|0.012|**0.63**|**0.17**|**0.040**|0.03|0.25|0.002|
||Acute care admissions|**0.38**|**0.18**|**0.025**|**0.48**|**0.21**|**0.031**|-0.02|0.31|-0.001|
||Hospital discharges - infectious|**_67.22_**|**_39.04_**|**_0.083_**|40.35|53.67|0.050|26.03|25.37|0.032|
|_s_|<br>Hosp discharges - cancers|25.09|23.21|0.031|31.03|36.28|0.039|-46.50|33.94|-0.058|
|_ital_|Hosp discharges - heart|3.17|10.82|0.005|32.32|22.66|0.051|23.81|15.62|0.038|
|_osp_|Hosp discharges - circulatory|-42.09|62.89|-0.023|**141.61 **|**66.66**|**0.076**|178.95|151.10|0.097|
|_H_|Hosp discharges - cerebrov|7.94|5.28|0.024|19.05|11.59|0.058|-0.10|7.72|0.000|
||Hosp discharges - respiratory|44.61|61.82|0.022|**_170.54 _**|**_85.36_**|**_0.086_**|27.25|79.85|0.014|
||Hosp discharges - digestive|-17.45|16.98|-0.011|**_96.63_**|**_53.35_**|**_0.061_**|11.42|26.48|0.007|
||Hosp discharges - musculo|4.48|10.96|0.006|**_48.61_**|**_25.08_**|**_0.064_**|-12.44|22.35|-0.016|
||<br>SDR appendicitis|-0.05|0.06|-0.164|-0.04|0.05|-0.126|0.06|0.07|0.202|
||<br>SDR hernia & intestinal|-0.16|0.13|-0.071|**_-0.19_**|**_0.11_**|**_-0.087_**|0.10|0.15|0.046|
||SDR adverse effects|-0.02|0.07|-0.103|-0.09|0.07|-0.436|0.10|0.07|0.459|
||Surgical infection rate|**-1.34**|**0.46**|**-1.446**|-0.04|0.16|-0.043||||

_Notes:_ Results refer to the coefficient (Coef) and standard-error (SE, cluster-adjusted) of the dummies for SHI status, fee-forservice and patient-based payment methods. Significant at **5%** and **_10%_** . The estimated impact (%) of each dummy on a given health outcome has been calculated over the average outcome in the corresponding estimating sub-sample.
- 17) Filmer, Deon and Lant Pritchett (2001). Estimating Wealth Effects without Expenditure Data—or Tears: An Application to Educational Enrollments in States of India. _Demography_ , Volume 38Number 1, February 2001: 115–132.

- 18) Hicks, Joan Hamory, Michael Kremer, Isaac Mbiti, and Edward Miguel (2011). “Vocational Education Voucher Delivery and Labor Market Returns: A Randomized Evaluation among Kenyan Youth”. Working Paper.

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- 21) Ibarrarán, Pablo and David Rosas (2009).  Evaluating the Impact of Job Training Programs in Latin America: Evidence from IDB funded operation. _Journal of Development Effectiveness_ .

- 22) International Labour Organisation / World Bank (ILO/WB) (2010). _Joint Synthesis Report: Inventory of Policy Responses to the Financial and Economic Crisis_ . Geneva and Washington, DC.

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27
control for compositional changes and to explore heterogeneity—as well as those from the crosscountry approach—notably, to test whether our results are robust to different methods of accounting for deterministic trends or non-stationary processes in the data. We also show that the association between per capita GDP and mortality we observe is not a result of omitted variables that have received attention in the literature—including female education, droughts or floods, conflicts, or the quality of institutions.

We conclude by discussing two areas where our data impose limitations on the possible analysis we can conduct. The first of these is the timing of the GDP shocks. Our results suggests that it is macroeconomic conditions around birth, rather than in the early in utero period or in the later half of a child’s first year of life, which matter most for a child’s survival in her first year. However, because our data on GDP is annual, our results on the timing of the shock to aggregate income should be viewed as suggestive rather than definitive. Second, because we construct birth and death histories retrospectively, we do not have data on the utilization of health services before, during and after birth for the majority of births (and deaths) we observe. Further, the DHS data we use do not include information on other potential inputs into child health, such as the consumption of nutritious foods. We are therefore unable to explore in a satisfactory manner the transmission mechanisms from income shocks to infant mortality. Nevertheless, our results clearly indicate that short-term fluctuations in aggregate income can have important consequences for the likelihood that a child survives her first year of life. Earlier research has shown that volatility in aggregate income has implications for adult health and mortality (for example, van den Berg et al. 2006; Maccini and Yang 2007). Our research suggests that the benefits of policies that reduce the volatility of per capita GDP in developing countries, or that 27
# Teble of **joutents**

I* Introduction ***0* *******,..** . .. **...................... ..........** I 1 II. Theoretical Framework **...... ...... ............... *............. ...** 4 III. Empirical Findings ...................... 12 IV* Conclusion ......... 20 References 27

## List of Tables

Table 1 **.......... .... ..................................................** 18 Table 2 **.. . . ... .. ....... .*.. .....** 19

# List of Charts

Chart 1 ................ ................................................ 22 Chart 2 ......................... 23 C-hart 3 o.o...... ........... 24.... Chart 4 ***** .....o .... .........*........... ........ **..** ...... o..o.. 25 Chart 5 ***....o......** o ***ooo....o. .*o*o** o **o*.......*...... ............ * o.....** 26
To verify the robustness of the FE results obtained thus far we conduct a number of additional estimations. First, to account for the presence of potential outliers we drop observations at the top 1 and bottom 1 percent of the distribution for each variable (see Table 5). Second, to limit concerns about measurement error we report results for the period 1995-2003 (see Table 6). We speculate that the degree of measurement error is likely to be smaller during this later period, as opposed to during the 1970s and 1980s, given that countries have taken steps over time to improve their balance of payments statistics and, in particular, to better measure remittances. Also, in recent years competition in the remittance market has led to a decline in the cost of formal remittances that might have led to an increase in measured remittances (i.e., informal remittances could have declined as a result). Third, to control for common time effects, we run a two-way fixed effect model including country and time dummies (see Table 7). Fourth, to address the potential for reverse causation we conduct FE estimations substituting regressors for their lags (see Table 8) and we report dynamic system GMM estimations à la Arellano and Bover (1995), where lags of the regressors are used as instruments for the variables in the model (see Table 9). The problem with estimations including lagged regressors (either directly or as instruments like in the GMM case) is that they cannot correct for biases arising from measurement error, since these would also affect lags of the questionable variable/s. Hence, finally, in order to correct for endogeneity biases that might arise due to measurement error, we present separate instrumental variables regressions using economic conditions in the remittancesource countries as instruments (see Table 10).

Removing potential outliers does not change our results in any significant way. Table 5 shows that both the significance and the magnitude of the remittance variable remain unchanged when we drop observations in the top and bottom one percent of the distribution for each 16
# **Appendix A: National Agricultural Insurance Scheme (NAIS)**

This Appendix presents additional information about the main Indian crop insurance scheme, NAIS, and compares the costs and benefits of rainfall insurance and area yield insurance (This discussion was removed from the main text due to space constraints.) NAIS is the most widespread form of index-like agricultural insurance currently available in India. In participating states, farmers are required to purchase NAIS insurance if they take a crop loan (typically for seeds) from a formal financial institution; other farmers can also choose to purchase the insurance voluntarily (Kalavakonda and Mahul, 2005; Mahul and Rao, 2005). NAIS insurance payouts are based on area yields on individual crops, measured via crop-cutting experiments. Policyholders in each designated policy area are given a payout based on the shortfall (if any) on the measured crop yield relative to a threshold value set according to historical yields, which are estimated over a rolling window (the window depends on the crop, but is generally 3-5 years).

Like most government crop insurance programs, NAIS operates at a substantial loss. Between late 1999 and early 2004, NAIS collected premia of Rs. 12.5 billion, but paid Rs. 47.5 billion in claims (Mahul and Rao, 2005). Kalavakonda and Mahul (2005), who present a detailed case-study of the operation of NAIS in the southern Indian state of Karnataka, find a claims-topremia ratio of approximate 7 to 1 for the between 2000 and 2002; taking administrative costs into account, policy premia provide only 12 percent of program costs.<sup>1</sup> Despite these heavy subsidies and the scheme’s availability to all farmers, NAIS has a relatively low penetration rate. In the 2004 Kharif, 12.7 million farmers across India were even partially covered by the program, representing 9 percent of the total rural population of 138 million households (sources: Mahul and Rao, 2005; 2001 Indian Census). Moreover, insurance participation is particularly low among small and marginal farmers. In Karnathaka in 2002, Kalavakonda and Mahul estimate that 11.6 percent of small and marginal farmers participated in NAIS, compared to 27.0 percent of medium and large farmers. This disparity exists despite explicitly targeted subsidies; small and marginal farmers received a 40 percent premium subsidy in 2002 (Kalavakonda and Mahul, 2005).

This low participation rate likely in part reflects shortcomings in the design and marketing of NAIS insurance contracts. First, NAIS applies a uniform premium rate throughout India for each

> 1 NAIS was introduced in 2000, replacing the Comprehensive Crop Insurance Scheme (CCIS), which covered only farmers borrowing from formal financial institutions. The CCIS also generally operated at a substantial loss. Over the period 1985-2001, the two schemes combined paid out claims in excess of premia collected in all but three years (1988, 1994 and 2000).

33
# **FIGURE 1: MEAN YEAR OF ARRIVAL IN BRAZIL AND MEAN AGE AMONG YELLOW RACE FOREIGN-BORN IN BRAZIL (2000 CENSUS)**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>MEAN YEAR OF ARRIVAL<br>Japan<br>Vietnam Thailand<br>China<br>Malaysia<br>Indonesia<br>Singapore<br>Taiwan<br>MEAN AGE Hong Kong Macau South Korea Philippines<br><!-- End of picture text --> Source: 2000 Brazilian Census These Nikkei estimates were then used to select 50 census tracts in São Paulo state and 25 census tracts in Parana state as follows. First, municipalities were randomly selected according to probability proportional to size (PPS) sampling with replacement, where size is the number of Nikkei households. Secondly, within each municipality selected, APs were sampled with PPS. Then finally, census tracts were sampled with PPS within the APs. In order to ensure coverage of both census tracts with high concentrations of Nikkei, and lower concentrations, we stratified so that in São Paulo, 30 out of the 50 census tracts were selected from among census tracts estimated to have 15 or more Nikkei households living in them, and 20 census tracts were estimated to have 4-15 Nikkei households living in them. In Parana, 15 out of the 25 census tracts were chosen from those with 15 or more Nikkei households, and the remaining 10 census tracts chosen from those with 4-15 Nikkei households. We did not include census tracts with 3 or fewer estimated Nikkei, as they are estimated to cover only 1 to 3 percent of the Nikkei population in the two states, and listing such census tracts would increase the survey cost with little additional increase in sample. Survey weights which take account 14

-  -