rafmacalaba/datause-encoder-data huggingface dataset:
# **Introduction**

Pay-for-performance schemes have been increasingly introduced in the health care systems of SubSaharan African countries and low- and middle-income countries more generally (Miller and Babiarz 2013; Witters et al. 2012). The schemes are introduced with the aim of improving health outcomes through increasing both the coverage and quality of health services. In some African countries, such as Rwanda, Burundi, Sierra Leone, Cameroon and Benin, such schemes targeting maternal and child health services are implemented at scale and are part of the national health care delivery system. Many more countries are either piloting such schemes or have them implemented at a regional level (World Bank 2015). In the African context, these schemes have mostly focused on incentivizing formal health providers at the health facility level. However, some programs have also incentivized other agents, such as users of the health services, local governments, community bodies and community health workers.

This study evaluates the impact of an intervention that rewarded cooperatives of volunteer community health workers (CHWs) for utilization of five targeted maternal and child health services by the communities they serve. The intervention was introduced in the context of the Community Performance-Based Financing (CPBF) program at the end of 2010. When cooperatives received payments through the CPBF program, a maximum of 30 percent could be immediately distributed to cooperative members while the rest had to be invested in income-generating activities of the cooperatives’ choosing. Revenues from these entrepreneurial activities, in turn, could be reinvested or distributed as dividends to the members.

The evaluation relies on an experimental design in which cooperatives in randomly selected sectors (sub-districts) received quarterly payments conditional on performance, defined by utilization rates in the catchment areas served by the cooperatives. Cooperatives in comparison sectors received quarterly payments that were not tied to performance. These payment amounts equaled the average amounts received by the cooperatives under the scheme. Data used in the analysis are from a baseline survey of households, CHWs and cooperatives conducted prior to the launch of the program, and from a follow-up survey conducted about three years after the launch of the scheme.

The results indicate no impact of the CHW cooperative performance payments on the targeted indicators of timely antenatal care, in-facility delivery and growth monitoring of children. Lack of impact on use of modern family planning methods cannot be rejected. Furthermore, no evidence was found that the intervention impacted the motivation, satisfaction or behaviors of CHWs, or the way 2
**<u>Table A.8: Relation between expenditure growth, poverty severity, and inequality</u>**

|||Dependent va|riable is the growth of pe|r capita expenditure (Log|Yt– Log Yt-1)||
|---|---|---|---|---|---|---|
|||FE|||GMM||
|Explanatory variables|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Lagged log of per capita expenditure|-0.116***|-0.804***|-0.891***|-0.113***|-0.241***|-0.782***|
||(0.008)|(0.060)|(0.062)|(0.012)|(0.092)|(0.128)|
|Lagged Gini index|-0.592*|0.131|0.180|-0.855**|-0.605|-0.263|
||(0.352)|(0.148)|(0.160)|(0.353)|(0.390)|(0.269)|
|Lagged poverty severity|0.387|-0.194|-1.002***|0.522|-0.600|-3.668***|
||(0.516)|(0.314)|(0.329)|(0.575)|(1.196)|(1.093)|
|Lagged log of other State spending|||0.002**|||0.002|
||||(0.001)|||(0.002)|
|Lagged log of investment spending|||0.003|||0.010|
||||(0.005)|||(0.009)|
|Lagged log of population density|||-0.099|||0.039***|
||||(0.068)|||(0.015)|
|Lagged share of urban population|||-0.001|||0.004***|
||||(0.001)|||(0.001)|
|Lagged share of population with high-school diploma|||0.034|||-0.019|
||||(0.265)|||(0.175)|
|Lagged share of ethnic minority population|||0.002***|||0.001|
||||(0.000)|||(0.000)|
|Lagged share of wage income|||0.270***|||0.399***|
||||(0.093)|||(0.106)|
|Lagged share of non-farm income|||0.150|||0.207*|
||||(0.102)|||(0.109)|
|Constant|1.343***|6.535***|6.925***|1.399***|2.276***|6.260***|
||(0.112)|(0.477)|(0.468)|(0.072)|(0.682)|(0.948)|
|Year FE|No|Yes|Yes||||
|Province FE|Yes|Yes|Yes||||
|Observations|567|567|567|567|567|567|
|R2/ Chi2|0.0744|0.420|0.246|1883|4033|6803|

47
protests) are reduced the further one moves away from Lagos. Third, various factors related to grievances significantly magnify the conflict-inducing effect of a rise in the price of oil. That is particularly the case for ethnic factors and economic inequality.

**Table 7: Mediating factors** **<u>affecting the impact of price shocks on conflict</u>**

||Any<br>events|Events<br>with fat.|Battles|Protests<br>and riots|civ.<br>violence|
|---|---|---|---|---|---|
|election x CI (t-1)|0.034**|0.034**|0.030*|-0.003|0.030**|
|election x PI (t-1)|-0.023|-0.036**|0.010|-0.024|-0.049***|
|election x Oil Ind(t-1)|0.002***|0.002|0.001|0.002|0.002**|
|cost_lagos x CI (t-1)|-0.000|0.000|-0.001|-0.001**|-0.000|
|cost_lagos x PI(t-1)|0.001**|0.000|0.001**|-0.001**|0.001|
|president x CI (t-1)|-0.001|-0.019|-0.018|0.006|0.020|
|president x PI (t-1)|-0.002|0.008|0.009|-0.015|-0.013|
|president x Oil Ind(t-1)|-0.002|-0.000|-0.003***|0.002*|-0.001|
|unem03 x CI (t-1)|-0.000|-0.001|-0.001|-0.000|-0.000|
|unem03 x PI (t-1)|0.001|0.001|0.001|0.001|0.001|
|unem03 x Oil Ind(t-1)|-0.000|-0.000|-0.000|0.000**|-0.000|
|mult_domin x CI (t-1)|-0.016|0.022|0.003|-0.002|-0.006|
|mult_domin x PI (t-1)|-0.008|-0.035**|-0.039|-0.015|-0.015|
|mult_domin x Oil Ind(t-1)|0.003***|0.002**|0.004***|0.001|0.003***|
|many_minor x CI (t-1)|-0.011|0.029|-0.002|-0.045|0.009|
|many_minor x PI (t-1)|0.007|-0.027|-0.000|0.022|-0.000|
|many_minor x Oil Ind(t-1)|0.004***|0.002***|0.005***|0.001|0.004***|
|pov03 x CI (t-1)|0.000|-0.000|-0.000|0.000|0.000|
|pov03 x PI (t-1)|-0.000|0.000|-0.000|-0.000|-0.000|
|pov03 x Oil Ind(t-1)|-0.000|-0.000|-0.000|-0.000|-0.000|
|gini03 x CI (t-1)|-0.270**|-0.223|-0.187|-0.072|-0.708***|
|gini03 x PI (t-1)|0.256*|0.270|0.184|0.070|0.729***|
|gini03 x Oil Ind(t-1)|0.038***|0.046***|0.063***|0.002|0.030***|
|Past conflict x CI (t-1)|-0.000|-0.000|-0.001**|0.002*|-0.001|
|Past conflict x PI (t-1)|-0.000|-0.000|0.001*|-0.003**|0.001|
|Past conflict x Oil ind(t-1)|-0.000|-0.000|-0.000|-0.000***|0.000|

_The table reports the coefficients of the interaction terms between the price indices and various conditioning factors obtained from different regressions; all regressions include residuals from the first stage regressions of the endogenous price index on the excluded instruments as a control with appropriate standard errors (clustered at the state level) calculated; *, **, *** indicate statistical significance at the 10, 5, and 1 percent level respectively (based on adjusted standard errors). Data are for 37 states for 8 years (2004-11). All regressions include year and region effects, as well as region-year effects and a full set of controls as in Tables 4 and 5. The models are estimated through the population-averaged negative binomial estimator for panel data._ 25
Nevertheless, the results support the view that human capital policies in Guatemala should place a strong emphasis on the health status of the population. This finding is equally echoed by the World Bank (2003a) that places Guatemala among the worst performers in terms of health outcome in Latin America, and particular poor in child nutrition.

# **_Military Expenditures and Governance_**

Given the strong influence of military rule in Guatemala’s recent history, it is finally imperative to discuss the role of military expenditure on growth.<sup>29</sup> This issue is particularly important since in the light of Guatemala’s low tax burden military expenditures will necessarily be met at the expense of other government services, such as education and health. Military spending shows the priority given to other fiscal functions by the government and serves as an indicator of the military’s power as a lobby. As such, _Guatemalan_ military expenditures may also indicate political corruption and other aspects of bad government.

However, a number of channels by which military spending can influence growth have been identified. According to Deger and Sen (1995), the defense sector can take skilled labor away from civilian production, but it can also train workers. It could crowd out resources for investment and impact negatively on the efficiency of resource allocation, but also provide positive externalities for the civilian sector, such as infrastructure development. It can stipulate civil strife, but also generate an increase in national security and strengthen property rights. Thus, the role of military expenditure is ambiguous and the direction of the overall effect remains an empirical question.

Given the historical and political context of Guatemala, however, it is hard to believe that military expenditure plays a positive role on economic growth. According to the Commission for Historical Clarification (1999) an overwhelming number of violent actions during the civil war was attributed to members of the army. In addition, forced displacement and mandatory civil defense patrols ( _Patrullas de Autodefensa Civil_ ? PACs) diverted a significant share of the economically active population from productive activities. Guatemalan defense spending reached its height during the peak of the civil war and declined in the advent of the peace process. They eventually began to rise again in 2000. Even without econometric analysis, a look at Figure 7e reveals that output growth is essentially opposite to the ratio of military expenditure to GDP. Moreover, the negative correlation of the share of foreign capital goods to investment suggests that a higher ratio of military expenditure to GDP is associated with a decrease in the efficiency of capital accumulation. When military

> 29 As a share of GDP, military expenditure in Guatemala is not excessively high, ranging from 0.7 up to 2 percent. However, its share of government expenditures is quite significant. According to Scheetz (2000) it has varied from approximately 14 up to 31 percent (in the 1980s) in terms of total resources controlled by the Treasury.

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Table 11: Average Job Quality between 2007-2021, by employment structure

|||Employment|Structure||
|---|---|---|---|---|
||(1)<br>Micro and Small Firms|(2)<br>Medium and Large|(3)<br>Self-employed|(4)<br>Employer|
|||Firms|||
|2007|0.25|0.74|0.38|0.54|
|2008|0.23|0.72|0.38|0.47|
|2009|0.24|0.71|0.39|0.51|
|2011|0.22|0.74|0.42|0.55|
|2012|0.26|0.75|0.40|0.51|
|2013|0.24|0.76|0.41|0.55|
|2014|0.22|0.75|0.40|0.49|
|2015|0.23|0.73|0.40|0.51|
|2016|0.23|0.77|0.41|0.50|
|2017|0.23|0.76|0.40|0.54|
|2018|0.22|0.77|0.40|0.54|
|2019|0.23|0.78|0.41|0.54|
|2020|0.22|0.79|0.43|0.58|
|2021|0.24|0.75|0.40|0.52|

_Notes:_ Based on data from EH 2007-2021. Estimation using a rescaled score from the first principal component of the PCA. Workers in micro and small firms are defined for people employed in a firm with less than 5 employees, domestic workers and unpaid workers. Workers in medium and large firms are defined for people employed in firms with more than 5 workers or workers in the public sector. Self-employed workers are defined for independent workers and production cooperative members.

On the other hand, when considering the Tax Identification Number (TIN) as a formality indicator for non-salaried workers, those with a TIN registration have higher quality jobs than their peers without any TIN registration (with a JQI pre-pandemic value of 0.56 vs. 0.41, respectively). While this gap is smaller than the one for salaried workers with and without a contract, these figures show that formal jobs are also higher-quality jobs, irrespective of which indicator is used.

26
Having been to a French school, having been fostered in childhood and having had a monogamous father all correlate with marrying a husband with a formal sector job, and more so for widows than for divorcees. Finally, living in a rural area at the time of dissolution is unfavorable as it reduces access to husbands with formal jobs and increases the likelihood of polygamy.

Living in a rural area is associated with other negative outcomes (Tables 9 and 10): divorcees in rural areas are less likely to have a civil contract for their marriages (this is true for widows as well); they are more likely to live with their in-laws, and are more at risk of not living with children from their previous union. Having a son from the previous union is positively correlated with the probability of living with children from the previous union after remarriage.

Interestingly, when we compare these results with the correlates of marriage quality in these various dimensions for women in their first marriage, it appears that a woman’s education is associated with protection against polygamy and a higher probability of a civil marriage in the same way for them and for divorcees when they remarry. Conversely, it does not go hand-in-hand with such a protective role for widows upon remarriage (see Table A4 for women in their first marriage).

Finally, we examine whether a new marriage is associated with social mobility through the relative characteristics of ex- and current husbands. Concentrating on women whose first marriage was with a man working in the informal sector, we look at the correlates of a second husband working in the formal sector. Table 11 shows that the chances of marrying ‘upwards’ are higher for women whose ruptures occurred relatively late (after age 40) and who have some formal education. There are insufficient observations for women first married to men with formal sector jobs to explore the correlates of downward mobility.

# **6. Differences in welfare levels**

To investigate associations between marital status and women’s welfare, we first turn to some non-monetary individual welfare indicators as well as measures of decision making power and resource constraints available from the 2005 DHS. We then examine individualized measures of consumption using PSF1.

## **6.1 Measures of well-being, decision-making, and constraints**

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Figure 2. The country perspective: developing East Asian countries’ exports of goods and services in current U.S. dollars, 2005‐ 2016 (percent change from previous year, averaged by period) <!-- Start of picture text --> MYS<br>THACHN PHL<br>MMR<br>IDN<br>MNG<br><!-- End of picture text --> <!-- Start of picture text --> CHN<br>MNG<br>VNM<br>MMR LAO<br>THA KHM<br>IDN<br>MYS<br>PHL<br><!-- End of picture text --> <!-- Start of picture text --> CHN<br>MNG<br>VNM<br>MMR LAO<br>THA KHM<br>IDN<br>MYS<br>PHL<br><!-- End of picture text --> <!-- Start of picture text --> 35<br>30<br>25<br>VNM<br>20<br>15 MNG KHM<br>10 LAO<br>CHN<br>PHL THA<br>5<br>0<br>MYS<br>IDN<br>‐5<br><!-- End of picture text --> Sources: WTO, IMF Balance of Payments.

Notes: Each panel includes a 45‐degree line. Outer circles indicate goods and services trade values, and inner shaded circles denote services trade. Data for Vietnam not available before 1996. Lao PDR and Myanmar data are outliers in the pre‐Asian Financial Crisis period, hence are not shown.

7
**Table 3B: Export share and household and nonfinancial corporation credit as a share of GDP**

|Dep. Var.:|2SLS|2SLS|2SLS|2SLS-FE|
|---|---|---|---|---|
|export|(1)|(2)|(3)|(4)|
|ln_hhcredit|-7.7272|-10.1627*|-8.7530|-6.4144|
||(0.152)|(0.100)|(0.199)|(0.259)|
|ln_nfccredit|38.7256***|47.9198***|44.8425***|37.3259**|
||(0.004)|(0.002)|(0.009)|(0.011)|
|fdi|2.1595***|2.0675***|2.0680***|2.0645***|
||(0.007)|(0.005)|(0.005)|(0.005)|
|ln_initialgdppc|-6.2351|-6.2308|-6.0781|-4.7752|
||(0.117)|(0.133)|(0.152)|(0.265)|
|financialopenness|0.0005|0.0003|0.0005|0.0009|
||(0.803)|(0.879)|(0.816)|(0.650)|
|tot|-0.1562|-0.2651*|-0.2306|-0.2975*|
||(0.216)|(0.078)|(0.180)|(0.100)|
|financialcrisis||-20.8335**|-20.9521**|-27.7355***|
|||(0.012)|(0.011)|(0.007)|
|commodityexporter|||-4.1162|-4.7524|
||||(0.540)|(0.434)|
|Constant|-63.6293*|-78.3277*|-73.3892|-39.3398|
||(0.099)|(0.082)|(0.105)|(0.345)|
|Observations|141|138|138|138|
|R-squared|0.388|0.388|0.402|0.451|
|Prob > F|0.000|0.000|0.000|0.000|

Regressions (1) – (3) are 2SLS regressions with legal origin and religious composition as instruments. Regression (4) is a 2SLS regression with the same instruments adding with year fixed effects. Credit variables and initial GDP per capita are in logs. All the other variables are in levels. Five-year average data are used. Robust p-values are reported in parentheses. *, **, *** respectively indicate significance at the 10%, 5% and 1% level.

13
Figure 5.2: Life Expectancy for Women and Men in Haiti, 1965-2002 <!-- Start of picture text --> 60<br>50<br>40<br>30<br>Female<br>20 Male<br>Total<br>10<br>0<br>1965 1967 1970 1972 1975 1977 1980 1982 1985 1987 1990 1992 1995 1997 2000 2002<br>Years<br><!-- End of picture text --> _Source:_ WDI, World Bank 2004.

Like adult illiteracy and infant mortality, the prevalence of child malnutrition declined in the 1978-2000 period, although it remains very high. Child malnutrition is measured by two variables—weight-for-height and-height-for-age—that fell by about 17 and 20 percentage points, respectively, to 17.3 and 22.7 percent in 2000 (Table 5.4). Child malnutrition, however, is still significantly higher than the regional LAC average of 9 percent. Efforts to lower the prevalence of child malnutrition are hampered by income poverty and by limited access to quality water, micronutrients, and general healthcare, among other considerations.

Table 5.4: Child Malnutrition in Haiti, 1978-2000

|1978<br>1990<br>1995<br>2000|
|---|
|Height for age (% of children under 5)<br>39.6<br>33.9<br>31.9<br>22.7|
|Weight for age(% of children under 5)<br>37.4<br>26.8<br>27.5<br>17.3|
|_Note:_Weight for height is the percentage of children under five whose weight for age is more<br>than two standard deviations below the median reference standard for their age. <sup>16</sup><br>Height for age is the percentage of children under five whose height for age is more than two<br>standard deviations below the median for the international reference population ages 0 to 59<br>months. For children up to two years of age, height is measured by recumbent length. For older<br>children,height is measured bystature while standing. <sup>17</sup>|

_Source:_ WDI, World Bank 2004.

16 As established by the World Health Organization, the U.S. Centers for Disease Control and Prevention, and the U.S. National Center for Health Statistics. Figures are based on children under age three, four, and five years of age.

17 The reference population adopted by the WHO in 1983, is based on children from the United States, who are assumed to be well nourished.

24
# **Annex table 3: Impact of a change in the price of cereals on consumers and  producers- Urban and Rural Areas**

||**-25%**|**-12.5%**|**No change **|**12.5%**|**25%**|**50%**|**100%**|
|---|---|---|---|---|---|---|---|
|**Pt b**||||**Rice**||||
|**overy, uran**||||||||
|Headcount index of poverty|23.95|24.47|**25.51**|26.63|27.27|28.60|31.81|
|Poverty gap|7.50|7.61|**7.76**|7.98|8.24|8.81|10.17|
|Squared poverty gap|3.32|3.30|**3.35**|3.44|3.54|3.78|4.44|
|**Poverty, rural**||||||||
|Headcount index of poverty|57.48|57.65|**57.63**|57.51|58.11|58.62|59.96|
|Poverty gap|21.02|20.82|**20.78**|20.82|20.92|21.20|21.97|
|Squared poverty gap|10.39|10.19|**10.17**|10.19|10.24|10.37|10.80|
||||**Mille**|**t and sorghu**|**m**|||
|**Poverty, urban**||||||||
|Headcount index of poverty|24.89|25.19|**25.51**|25.71|25.72|27.03|28.72|
|Poverty gap|7.62|7.68|**7.76**|7.83|7.91|8.09|8.52|
|Squared poverty gap|3.30|3.32|**3.35**|3.38|3.41|3.49|3.67|
|**Poverty, rural**||||||||
|Headcount index of poverty|57.07|57.35|**57.63**|58.30|58.20|58.87|60.04|
|Poverty gap|20.68|20.73|**20.78**|20.86|20.95|21.17|21.83|
|<br>Squared poverty gap|10.16|10.16|**10.17**|10.19|10.23|10.33|10.69|
|||||**Corn**||||
|**Poverty, urban**||||||||
|Headcount index of poverty|25.43|25.51|**25.51**|25.40|25.40|25.52|26.05|
|Poverty gap|7.86|7.81|**7.76**|7.71|7.73|7.77|7.88|
|Squared poverty gap|3.54|3.41|**3.35**|3.34|3.35|3.38|3.45|
|**Poverty, rural**||||||||
|Headcount index of poverty|57.64|57.64|**57.63**|57.59|57.59|57.54|57.28|
|Poverty gap|20.90|20.84|**20.78**|20.73|20.67|20.56|20.39|
|Squared poverty gap|10.30|10.23|**10.17**|10.11|10.06|9.97|9.84|
||||**All id**|**entified cerea**|**ls**|||
|**Poverty, urban**||||||||
|Headcount index of poverty|22.83|24.23|**25.51**|27.01|28.39|30.69|36.17|
|<br>Poverty gap|7.41|7.55|**7.76**|8.07|8.51|9.54|12.28|
|<br>Squared poverty gap|3.43|3.32|**3.35**|3.48|3.67|4.12|5.56|
|**Poverty, rural**||||||||
|Headcount index of poverty|56.70|57.16|**57.63**|58.21|58.84|60.26|63.93|
|Poverty gap|20.95|20.77|**20.78**|20.90|21.10|21.65|23.43|
|Squaredpoverty gap|10.46|10.22|**10.17**|10.19|10.25|10.51|11.54|

Source: Authors’ estimation using ELIM 2006.
ASIA

# 1. **Estimates** and Projections

|Y_|Ppsa^tion|PW.<br> ratio|Pwriod|Gra<br>rat|CO|CD|TFt|40<br>elo|SO<br>Femles|_Ei_|Z100O|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1965|2824765|65.3|196M-89|1.86|27.6|8.9|3.43|62.8|64.8|71|95|
|1990|3099413|61.3|1990-94|1.72|25.7|8.4|3.14|64.2|66.4|63|82|
|1995<br>2000|337725<br>3648496|60.3<br>58.6|1995-99<br>2000-04|1.54<br>1.40|23.4<br>21.5|7.9<br>7.5|2.88<br>2.71|65.6<br>67.0|68.1<br>69.9|54<br>47|70<br>60|
|2005<br>2010|3912188<br>4166876|SS.1<br>51.0|2005-09<br>2010-14|1.26<br>1.16|20.0<br>18.9|7.3<br>7.3|2.56<br>2.43|68.1<br>69.2|71.1<br>72.4|42<br>36|53<br>4|
|2015|415473|50.0|2014-19|1.09|18.1|7.2|2.35|70.3|73.8|30|38|
|2020<br>2025|4661806<br>4901771|50.1<br>50.7|2020-24<br>202S-49|1.00<br>0.68|17.3<br>15.0|7.2<br>8.2|2.29<br>2.16|71.5<br>74.5|75.3<br>78.9|24<br>14|31<br>17|
|2050<br>|5816905<br>|56.7|2050-74|0.32|13.1|9.9|2.08|77.8|82.9|6|8|
|2075<br>2100|630740<br>6560572|64.0<br>69.1|205-?9<br>2100-24|0.16<br>0.08|12.4<br>12.0|10.8<br>11.1|2.07<br>2.06|79.8<br>81.4|85.6<br>87.7|4<br>2|5<br>4|
|2125<br>2150|6696718<br>6766687|71.9<br>73.2|2125-49|0.04|11.8|11.4|2.06|82.0|88.6|2|3|

# 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75 +<br>70-74 1<br>65-09<br>60-84<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-" 4<br>5.9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 810<br><!-- End of picture text -->

# 3. Distribution of population (percent): 1990 and 2025

<!-- Start of picture text -->
*ItcA  C13 %)  FRICA (12  2?)  AeI  C12  5%)<br>/  \  ~~~~~~~~~~~~~~CAW.A^ (O SS)/<br>OWAUIA  Co  m<br>ELO  C  14 60<br>AS2  C57 M<br><!-- End of picture text -->

12