rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 23: Cost and poverty reduction of four CCT scenarios aimed at small children <!-- Start of picture text --> A. Absolute gain per HH by market income decile B. Absolute gains per HH by demographic<br>structure<br>21,000 12,000<br>18,000<br>9,000<br>15,000<br>12,000<br>6,000<br>9,000<br>6,000 3,000<br>3,000<br>0<br>0 No children No children, 1 child 2 children 3+ children<br>1 2 3 4 5 6 7 8 9 10 or elderly with elderly<br>CCT Version 5 CCT Version 5<br>CCT Verison 6 CCT Version 6<br>KHR/month<br>KHR/month<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

# **6. Conclusion**

This paper performs a fiscal incidence analysis for Cambodia using the CEQ approach. The present analysis relies on the 2019/20 Cambodia Socio-Economic Survey (CSES) and 2019 fiscal data. The CEQ approach is a tool that helps assess the redistributive effect of a fiscal system and its individual interventions, offering invaluable insights for policy. Moreover, it offers a platform to simulate policy scenarios and assess their distributional implications.

The 2019 fiscal system in Cambodia, per the CEQ model, reduces inequality yet lowers incomes of poor households in the short-term. Note, these results predate the substantial increase in cash transfers to poor and vulnerable households in response to the COVID-19 pandemic. The informal sector accounts for a large share of the economy in Cambodia, hence taxes are largely collected indirectly. Poorer households pay indirect taxes as part of the purchase price of goods and services, which reduces their purchasing power, even with tax exemptions on
# 1 Introduction

Over the past two and a half decades, global extreme poverty has decreased rapidly. Since 1990, the share of the world population living below the extreme poverty line of $1.90 per day has fallen from 35.6% in 1990 to 10.0% in 2015 (World Bank, 2018a). Against this backdrop, international development actors, bilateral development agencies and countries themselves have united around a goal of ‘ending’ extreme poverty by 2030. This goal has been defined as complete eradication (United Nations, 2014) or as reducing global extreme poverty to 3% of the world’s population (World Bank, 2014). Several bilateral development agencies, such as DFID and USAID, have also made such goals central to their focus and mission. At the same time, the development policy debate is increasingly paying attention to the level of inequality in countries around the world (International Monetary Fund, 2014; Ravallion, 2001; World Bank, 2016). As a result, the internationally agreed Sustainable Development Goals (SDGs) include both a goal to end poverty (SDG1) and a goal to reduce inequality within countries (SDG10).

We simulate global extreme poverty until 2030 under different scenarios about how inequality and growth evolve in each country. This serves to quantify the importance of reducing inequalities vis-à-vis increasing growth in achieving the goal of eradicating extreme poverty. Although previous papers have simulated poverty up to 2030, we offer four distinct contributions. _First_ , we use micro data for 119 countries and grouped data for an additional 45 countries, allowing for an unprecedented data coverage of 97% of the world’s population. _Second_ , we model the impact of distributional changes on future trajectories of global poverty by changing countries’ Gini index. The Gini index is arguably the most frequently used measure of inequality, and it makes for an intuitive way of modeling distributional changes which has direct policy relevance and conceptual simplicity. _Third_ , since there are infinitely many ways in which a change
. Greenberg, and L. J. Muglia (2015). Influence of interpregnancy interval on neonatal morbidity. _American Journal of Obstetrics and Gynecology 212_ (3), 386.e1 – 386.e9.

- DHS (2018). Republique du cameroun: Enquete demographique et de sante 2018. Technical report, Institut National de la Statistique du Cameroun (INS), and ICF, Yaounde, Cameroun.

16
Alderman (2004) deal with the additional impact of nutrition education over the more general impact of schooling.

This paper focuses on the role of income growth in combination with two types of nutrition interventions in northwestern Tanzania, community driven supplementary feeding of young children, feeding posts, and child feeding carried out in crèches run by an international NGO, Partage. The implementation of these programs was likely to be an emergency response yet these crises were not directly issues of food scarcity. In any case, the study does not assess the impact of the provision of food _per se_ , but of the package of services, comprising of the provision of food, awareness creation, day and medical care, that accompany the intervention.

The current study looks at anthropometric measures of child nutritional status. We study the degree to which increases in resources at household level and participation in nutrition programs contribute to the reduction of child malnutrition. Other determinants of nutritional status, such as access to health care, parental human capital and environmental factors are also considered. Our study examines the impact of the availability of program interventions on the nutritional status of children rather than the impact of participation itself. While both approaches convey useful information, Heckman, Lalonde, and Smith (1999) observe in their review of econometric methodologies for evaluation that often it is the former that is of policy relevance.

The remainder of the paper is as follows. Section 2 describes the data set used. Section 3 illustrates the children’s health status in the Kagera region of Tanzania. Section 4 presents the econometric specification used and the estimated results according to different specifications. Section 5 continues with policy implications that the analysis implies. Section 6 concludes the paper.

# **2. The Data Set**

The data used for this study come from a longitudinal living standards survey of households conducted in Kagera, a region of Tanzania with comparatively high adult mortality in the first decade of the AIDS epidemic. The region is located to the west of Lake Victoria and borders the
was +9.0%. Now consider what d would have been with a r=3.0%, modest by African standards and still well below g. It would have been 61.4% of GDP instead of the 56.9% shown in Table 5.<sup>12</sup>

**Table 5: Ethiopia’s Public Debt Dynamics 2017-19**

||2017|2018|2019|
|---|---|---|---|
|Public debt/GDP %<br>|56.5|60.8|56.9|
|Primarydeficit/GDP %<br><br>|4.5|4.00|3.3|
|Real interest rate %(r<sup>c</sup>)<br><br>|-0.74|1.22|-4.98|
|---Local currency (r<sup>d</sup>)<br>|-2.40|-5.20|-6.70|
|---Foreign currency (r<sup>f</sup>)|3.21|7.74|-3.40|
|Realgrowth rate %(g)<br>**_Notes_**: Own calculations based o|10.20<br>Table 2 of the Debt Sus|7.70<br>tainability Analysis in IMF Co|9.00<br>untry Report 20/150 (IMF|

**_Notes_** : Own calculations based on Table 2 of the Debt Sustainability Analysis in IMF Country Report 20/150 (IMF 2020). See Annex 1 for the derivation of the formulas for the interest rate calculations. For 2017, the shares of birr and fx debt at the end of 2016 (not provided in the DSA table) were inferred from the real
Europe suffered the largest declines in growth. Latvia and Lithuania in particular suffered tremendous economic disruptions, as growth rates in these countries slowed by roughly 20 percentage points in 2009, compared to the average growth rates in the two prior years. Turkey, Romania, Bulgaria, and Mexico also suffered severe slowdowns in growth, due to a decline in exports to Western Europe and the US. In contrast, a larger group of seven countries, including the non-Mexican Latin countries, Poland, Jordan, the Philippines, and South Africa were only moderately affected. Finally, Indonesia and Egypt escaped the brunt of the crisis, as growth only fell around 2 percentage points.

_The size of the adjustment in employment and unemployment may be related to countries’ labor market institutions._ Employment losses varied considerably across countries. Poland, Costa Rica, and Bulgaria, for example, experienced unexpectedly large employment losses, given their moderate decline in growth, while Romania and Turkey experienced larger slowdowns in growth but substantially smaller employment reductions (Figure 1).<sup>26</sup> These patterns suggest that countries with more rigid labor markets fared somewhat better in maintaining employment during the crisis. For example, employment and wage employment growth fell relatively little in Turkey and Romania, even though Turkey is probably among the most regulated labor markets in the OECD, with a low _Doing Business_ ranking in employing workers, and 40 percent of Romanian workers belong to a union.<sup>27</sup> Costa Rica and Bulgaria, where employment fell more, have relatively free labor markets among countries analyzed here: both countries score relatively highly on the Fraser index of labor market freedom, and only 13 percent of Costa Rican workers belong to a union (Freeman, 2009).<sup>28</sup> If labor markets with high firing costs responded to the crisis by reducing hours rather than shedding employment, employment loss may have been smaller in more rigid countries.<sup>29</sup> This is line
find that while cash transfers to mothers did not lead to any significant difference, transfers to fathers improved young children’s health, increased livestock and cash crop investment, and led to improvements in housing.

We use a panel of 1,214 married households that we track over three rounds of data collection. Using an Analysis of Covariance (ANCOVA) model, we find that whom you target determines the type of impact achieved and the channels through which this impact is realized. From our results, we find that women’s access to extension services increases when only the male or female spouse is targeted, though the size of the increase is larger when the female spouse is targeted directly. We see no similar result for men, which could be because they already have higher access levels. Targeting both spouses also has no effect on access to extension services.

Similarly, we find that the gender of the targeted individual is important for the take-up of new technology. We find a significant increase in the take-up of new technology by women across all agricultural domains when only the male spouse is targeted. We also find a positive and significant increase in men’s overall technology take-up, and specifically for livestock, when the male spouse is targeted. However, when the female spouse is targeted directly, there is no effect on her technology take-up and instead, we find less adoption of technology by men in farming methods. Targeting both spouses does little for women but does have a complementary effect on men’s takeup of new technology in farming methods.

Targeting both spouses initially creates a positive interaction effect on agricultural productivity. This could be driven by the positive effect on technology use in farming methods for the men when both spouses are targeted. This impact is no longer significant by the endline, although it does remain positive. When only one spouse is targeted, we find no significant effect on agricultural productivity.

For livestock outcomes, targeting both spouses is better than just targeting one. Targeting both spouses has a positive effect
1)/2 = 276 correlations, for _N_ = 24. Panel B examines the stationarity properties of our panel time series by examining the unit root of factor nonstationarity and idiosyncratic nonstationarity. We use annual data on commodity prices from 1900 to 2008 ( _N_ = 24, _T_ = 109). We identify the factor structure using an information criterion from Bai and Ng (2002). For the factor unit root test, we reject the null hypothesis of a unit root for large negative (less than -2.89) and for the idiosyncratic component we reject the null hypothesis of a unit root for large positive values of the test statistic

# _4.2 Uniform Spacings Analysis_

To assess the pervasiveness of co-movement in commodity prices, we first apply the Ng‟s (2006) Spacings Variance Ratio (SVR) test. This test partitions a series of linear relationships in a panel data set into subsamples of „small‟ and „large‟ correlations. The proportion of small correlations is represented by <sup>ˆ</sup> . If the SVR test statistic is significantly different from zero, pairings in a particular subsample are significantly correlated. If the small group correlations are significant, so must the subsample of pairings with large correlations. If only the subsample of large correlations is significant, then we have only a degree of correlation in the data.

We find evidence of a significant degree of correlation in our panel (see Panel A, Table 1). The SVR for the large group of correlations, which represent about 70% of the data (i.e. <sup>ˆ</sup> = 0.31), has a test statistic of 4.504, when the 5% critical value is 1.65, and hence allows us to reject the null hypothesis of no significant correlation in our 24 commodity price indexes. For the remaining 30% of correlations in the small 14
**Table 3. Mean Child Exposure to Supplementary Feeding Program, Children 6-60 Months in 2000.**

||**All groups**|Ge|nder|Com|munity|
|---|---|---|---|---|---|
|||Boy|Girl|Urban|Rural|
|6-12 months|**0.34**|0.34|0.33|0.36|0.32|
|Observations|**525**|271|254|230|295|
|12-24 months|**0.26**|0.27|0.24|0.30|0.23|
|Observations|**1042**|526|516|474|568|
|24-60 months|**0.14**|0.14|0.14|0.14|0.14|
|Observations|**3221**|1625|1596|1364|1857|

Notes: Calculated from IFLS3 data.

29
The conditional posterior of _g_ given _Mj_ is simply<sup>9</sup> The advantage of using the Gibbs sampler on ( _g, Mj_ ) is that it does not rely on approximations that are hard to control and makes prediction quite straightforward: for every _g_ drawn in the sampler we predict as with a fixed _g_ (Fern ́andez _et al._ , 2001a), and predictions are simply mixed over values of _g_ in the sampler. With Laplace approximations this seems much less straightforward and the quality of these approximations is not that easy to assess. Also, truncation, as in the Bottolo-Richardson and Forte _et al._ priors, can be dealt with easily in a Gibbs sampling framework.

We use a simple random walk Metropolis-Hastings step for _g_ with a log-Normal proposal centred over the previous value. Finally, we control the acceptance probability for _g_ by making the Metropolis-Hastings step adaptive.

Throughout, we use MCMC chains of length 1,000,000 after a burn-in of 500,000, which was found to be more than sufficient for convergence. The Fortran code used for this paper is available from the authors upon request.<sup>10</sup>

# **7. Simulations**

First, we examine simulated datasets to mimic the kinds of situations typical for many applications in econometrics (and in other areas as well). We generate datasets from three different model structures, which closely correspond to those in Fern ́andez _et al._ (2001a). For Models 1 and 2 we generate an _n × k_ matrix _R_ for _k_ = 15 regressors where the first ten columns ( _r_ (1) _, . . . , r_ (10)) are drawn from independent standard normal distributions, and the next columns are constructed as where _E_ denotes an _n ×_ 5 matrix drawn from independent normal random variables. This induces correlations between the first five and the last five regressors ranging from 0.153 to 0.74. The demeaned version of
Cov**|||therapy (IPT) against Malaria<br>Proportion of pregnant women|
||ANC 4 (%)|5|receiving 4 or more antenatal care<br>visits|
||TB TSR (%)|5|Tuberculosis Treatment Success Rate|
||Approved posts filled (%)|10|-|
|**ent**|% Monthly reports sent on time|3|-|
|**em**|% Completeness monthly reports|2|-|
|**nag**|% Completeness facility reporting|3|-|
|**Ma**|Completeness of the annual report (%)|2|-|
||Medicine orders submitted timely (%)|5|-|

Figure 12: DLT Score - Components’ weights and description : _2011 local and presidential elections_ we use data from the 2011 elections, compiled by the Uganda Electoral Commission. For Directly Elected Councilors, we compute all the results at the electoral area level and then obtain the district-level average across all areas. The Herfindahl Index is most commonly used as measure of firm competition in a given 68
.048|0.020|0.009|-0.001|0.000|
||(0.012)|(0.031)|(0.026)|(0.020)|(0.007)|(0.121)|
|Diff. Effect=_β_1+_β_3|0.011|-0.009|0.031|0.006|0.010**|0.109|
||(0.009)|(0.023)|(0.020)|(0.015)|(0.005)|(0.081)|
|R-squared|0.031|0.025|0.032|0.040|0.031|0.403|
|Mean Dep. Variable|0.165|0.294|0.215|0.112|0.011|0.781|
|<br>Observations|5,590|5,554|5,578|5,573|5,568|259|
|_Controls in All Panels_|||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Dependent variables: (i) Mental Issues Index is constructed using the average of the variables in columns (ii) to (
LFS)|2018|
|Senegal|Census|2013|
||DemographxandHealth Survey (DHS)|2018|
|SouthA<sup>frica</sup>|Demographic and Health Survey (DHS)|2016|
||GeneralHouseholdSurvey (GHS)|Yearly from 2009-2018|
|Tanzania|Household BudgetSurvey(HBS)|2011|
||<br>NationalPanelSurvey(NPS)|2010,2014|
|Uganda|<br>NationalPanelSurvey(NPS)|<br>2009,2010|
||<br>NationalHouseholdSurvey|<br>2009|
||Functional Difficulties Survey|2017|
||DemographxandHealth Survey (DHS)|2016|
||ChildLabor Baseline Survey|2009|
|Zimbabwe|IntercensalDanographicSurvey|2017<br>4|
were reported to engage in 0.19 more learning activities (q-value = 0.08) but attend 0.43 fewer days of school on average, at a time when most in-person schooling was suspended due to the COVID19 pandemic. Children in treated households also experienced a decrease in reported alertness of 0.73 standard deviations (q-value _<_ 0.01). Beyond child outcomes, other significant effects at midline include the probability that the respondent is living with a disability that complicates self-care (0.38 SD higher difficulties with self-care, q-value _<_ 0.08). Yet only the child alertness reductions and days of school attendance are robust to accounting for non-random attrition (Table A.2).

# **5.3 Impacts in the endline survey round**

The endline survey was carried out in-person in 2021 as pandemic conditions eased, shortly after program assistance had ended for most households. As noted above, we find that the program significantly reduced housing expenditures by 82.05 USD PPP at endline relative to the control group (q-value=0.07). Effects on measured housing quality, on the other hand, largely dissipate by this point, with no significant differences between the treatment and control groups in any dimension of the housing quality index. Some of this could have been due to catch-up investments in the control group, or the possibility that housing improvements like window repairs and mold removal depreciate relatively quickly. Likewise the endline survey data reveals no statistically significant effects on any of the other pre-specified primary outcomes: program impacts on total household consumption, respondent depression levels, and child well-being are all negative, in fact, although relatively small in magnitude and none are significant at traditional confidence levels.

By the endline survey, most impacts observed at midline – including on food security, household composition, child learning outcomes, and COVID-19 – were no longer statistically significant. Four statistically significant effects survive multiple hypothesis testing adjustments (Table 6). First, there was a notable
productivity. This is a fruitful area for future research.

# **References**

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- Benjamin,
premises same as household’s residence, and premises independent of household’s residence. Household-based establishments are defined using the second group above.<sup>8</sup> Our analysis primarily considers recent women entrepreneurs, who are a subset of women establishment owners. We identify entrepreneurs using a survey field which asks about the growth status of the enterprise over the past three years: expanding, stagnant, contracting, or operated for less than three years. Those owners who answer as having operated their business for less than three years constitute our subset of recent entrepreneurs. We also describe in the next section how the survey collects age information for older establishments.

It is important to note that the NSSO sampling frame is primarily built off India’s periodic Economic Censuses, and is enumerated across households. The core sample is comprised of unregistered (unorganized) plants. Unorganized sector firms in India are not required to register or pay taxes, and thus the sampling of the NSSO does not depend upon these types of data. As we discuss later, this data collection and sampling approach provides confidence that the economic patterns we observe are real and not the artificial consequence of changes in reporting behavior (e.g., switching tax status).<sup>9</sup> We supplement our investigation into women’s labor market dynamics using data from the Annual Survey of Industries (ASI). The ASI provides microdata on the organized manufacturing sector of the economy, which is not covered by NSSO. The two surveys are designed to be perfect complements in joint coverage of the entire manufacturing sector. We use ASI surveys from the same fiscal years as the NSSO data described above to investigate 8 The categories listed for the ownership and premises fields are taken from the 1994 survey instrument. Later surveys expanded the listed categories to include additional ownership categories that do not overlap with the primary male/female proprietary categories used in this work. There have been recent efforts to better measure and classify home-based work (Chen 2012