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

This article analyzes the extent to which the Basic Benefit Package (BBP), a subsidized health program in Armenia, increases health care utilization and affordability among health users. Health care utilization rates in Armenia have been declining at alarming rates. According to the WHO (2006), while in the early 1990s outpatient utilization in Armenia was at 7 contacts per person per year, in 2005 it had declined to 2 contacts per year (see Figure 1). This indicator is below the regional average, which oscillates between 6 and 10 contacts per person per year. Inpatient utilization is also very low. While inpatient care admissions in the region vary between 15 and 20 per 100 inhabitants per year, in Armenia the rate is less than 8 per 100 inhabitants (i.e. about half of the regional average compared to the 1990 or 1995 level). Declining utilization has occurred mainly as a consequence of a quasi-privatization of health services in Armenia after the end of the Soviet era, which permitted hospitals and doctors to generate revenues by selling health services to the public.

Decreasing levels of health care utilization in Armenia may also reflect both the low level of public expenditure on health and affordability constraints (Armenia National Statistical Services and World Bank, 2006; Angel-Urdinola et al., 2006; Bonilla-Chacin et al., 2005; Hayrapetyan and Khanjian, 2004). According to Armenia National Statistical Services and World Bank (2006), household survey estimates suggest that while public expenditure on health as a percentage of GDP in Armenia was at 1.32 percent in 2004 (one of the lowest in the Europe and Central Asia, ECA, region), private expenditure as a percentage was at 4.55 percent of GDP. This is a reflection of the fact that health service patients in Armenia generally pay for their treatment “out-of pocket”. One could argue that health care utilization in Armenia has reached efficiency and that the higher rates of utilization that existed in the past were a result of excessive usage of the system during the Soviet period. It may also be that the system may have become underutilized as affordability constraints forced sick individuals to seek informal care or not to seek care at all. Angel-Urdinola et al. (2006) find that expenditures on health among poor 2
Table 3: FDI and Growth, Top 20 Percent Most Regulated Countries (2SLS)

||||De|pendent Vari|able: Growth|in current per|iod|||
|---|---|---|---|---|---|---|---|---|---|
|Independent Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Constant|-0.07|0.39|0.47|0.49|0.43|0.37|0.27|0.40|0.45|
||(-0.14)|(0.80)|(1.00)|(0.88)|(0.84)|(0.77)|(0.57)|(0.64)|(0.85)|
|Change over previous period in average FDI|0.32**|0.43***|0.48***|0.42***|0.41***|0.41***|0.44***|<br>0.42***|<br>0.45***|
||(2.19)|(2.86)|(3.02)|(2.75)|(2.73)|(2.85)|(3.05)|(2.90)|(3.02)|
|Change over previous period in average||-0.50**|<br>-0.50**|<br>-0.52**|<br>-0.49**|<br>-0.46**|<br>-0.47**|<br>-0.48**|<br>-0.48**|
|<br>FDI*Regulation Dummy||(-2.36)|(-2.37)|(-2.44)|(-2.27)|(-2.11)|(-2.28)|(-2.32)|(-2.20)|
|Growth in previous period|0.54***|0.43***|0.38***|0.42***|0.43***|0.45***|0.40***|<br>0.47***|<br>0.40***|
||(4.99)|(3.67)|(3.08)|(3.48)|(3.40)|(4.15)|(3.46)|(3.73)|(3.56)|
|Change over previous period in average rule of law|0.77**|0.73**|0.82***|0.75**|0.75**|0.72***|0.68**|<br>0.43|<br>0.71***|
||(2.35)|(2.26)|(2.49)|(2.28)|(2.27)|(2.65)|(2.18)|(1.11)|(2.65)|
|Change over previous period in average inflation rate|||0.44||||||0.51|
||||(1.27)||||||(1.47)|
|Change over previous period in average population||||0.19|||||<br>0.18|
|growth||||(0.47)|||||(0.43)|
|Change over previous period in average government|||||0.04||||0.05|
|consumption|||||(0.80)||||(0.91)|
|<br>Change over previous period in average BMP||||||0.02|||-0.25|
|||||||(0.06)|||(-0.78)|
|Change over previous period in average trade|||||||1.28*<br>(1.67)||0.77<br>(0.92)|
|Regional dummies<sup>1</sup><br>|No|No|No|No|No|No|No|Yes|No|
|Shea partial R<sup>2</sup>(first-stage)||||||||||
|Change over previous period in average FDI|0.31|0.32|0.31|0.34|0.32|0.39|0.33|0.38|0.39|
|Change over previous period in average||0.47|0.50|0.46|0.46|0.48|0.47|0.47|0.50|
|FDI*Regulation Dummy||||||||||
|<br>Growth in previous period|0.41|0.36|0.32|0.35|0.33|0.44|0.35|0.40|0.42|
|<br>Change over previous period in average rule of law|0.21|0.21|0.22|0.21|0.20|0.35|0.21|0.16|0.36|
|<br>Hansen-Sargan overidentification test|18.8|14.1|12.8|13.9|15.3|13.9|13.6|17.2|14.4|
|<br>(χ<sup>2</sup>p-value)|(0.22)|(0.44)|(0.55)|(0.45)|(0.36)|(0.46)|(0.48)|(0.25)|(0.42)|
|R<sup>2</sup>|0.31|0.34|0.33|0.34|0.35|0.35|0.38|0.45|0.40|
|Observations|86|86|86|86|86|82|86|86|82|

Notes:<sup>1</sup> North America has been excluded; z-values are reported in parentheses; *** significant at 1% level; ** significant at 5% level; * significant at 10% level. Instrumented variables: Change over previous period in average FDI, change over previous period in average FDI*Regulation Dummy (except in column 1), growth in previous period, and change over previous period in average rule of law. Instruments: Distance to equator, dummy landlocked country, log of GDP per capita in 1984, FDI in 1984, average FDI in the previous period, rule of law in 1984, dummies for British and French law, interactive terms between legal dummies and FDI, legal dummies and regulation dummy, and included exogenous variables.

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_γ_ , imply different optimal premiums, which in turn impact the optimal talent allocation in the economy. In our exercises, we treat _γ_ as a free parameter that represents the level of public sector efficiency, and compute the optimal premium for a given level of _γ_ .

Table 1 lists the model parameters and their respective sources, while Table 2 summarizes the performance of the model in terms of the targeted moments.<sup>13</sup> Notice that in our baseline economy, the calibrated public sector premium averages 37% for women but less than 1% for men. The calibrated average across men and women is 22%.

Table 1: Parameters of the model

|Share of capital, private sector (_ηeα_)|0.3|Literature, e.g., Gollin, 2002|
|---|---|---|
|Share of capital, public frm (_ηgκ_)|0.3|Literature, e.g., Gollin, 2002|
|Fraction of women (_φm_)|0.5|Data (exogenous)|
|Fraction of women (_φm_)|0.5|Data (exogenous)|
|Discount factor (_β_)|0.95|Literature<br>Literature, e.g., Cooley, 1995;|
|Depreciation rate (_δ_)|0.06|Stokey and Rebelo, 1995;<br>Heathcote et al., 2010; Buera and Shin, 2013<br>Cagetti and De Nardi, 2006|
|CES elasticity (_σ_)|8.6|Calibrated to match data|
|Mean log-entrepreneurship skills (_m_)|–0.4|Calibrated to match data|
|Variance of log-entrepreneurship skills (_sd_)|18.5|Calibrated to match data|
|Highest entrepreneurship ability (ˆ_s_)|185,000|Calibrated to match data|
|Mass of highest entrepreneurship ability (ˆ_p_)|0.1%|Normalized|
|Span-of-control, private sector (_ηe_)|0.53|Calibrated to match data|
|Span-of-control, public sector (_ηg_)|0.86|Calibrated to match data|
|Home production parameter I (<br>_h_)|42|Calibrated to match data|
|Home production parameter II (_ηh_)|0.41|Calibrated to match data|
|Public sector premium, women (_τf_)|0.76|Calibrated to match data|
|Public sector premium, men (_τm_)|0.07|Calibrated to match data|
|Public sector premium, average, women ( ̃_τf_)|0.37|Directly computed from the model<br>parameters post-calibration|
|Public sector premium, average, men ( ̃_τm_)|<0.01|Directly computed from the model<br>parameters post-calibration|

> 13The purpose of our paper is not to provide a theory to explain the calibrated gender wage gap. It likely encompasses a mix of the factors studied in the literature (e.g., Blau and Kahn, 2017).

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Policy Research Working Paper 8426

# **Abstract**

The effect of demographics on poverty measurement based on per capita consumption is well known. The size and composition of the household can affect the well-being of everyone in the household, with respect to total consumption within that household. Failure to address this issue may often lead to an underestimation or overestimation of poverty, especially for children. Many studies have tried to address the issue, using the generic approach of equivalence scales. However, the choice of scale is controversial and may lead to comparability problems between countries because of the different demographic structures and choice of the pivot household for establishing the per capita poverty line. Based on the World Bank’s African poverty database, this study estimates poverty rates for African children using the new international poverty line of $1.90 a day defined in terms of 2011 purchasing power parity. The equivalence scales approach (Food and Agriculture Organization/World Health Organization) is used with the adjustment suggested by Deaton after the identification of the pivot household, which is defined as the household whose per capita consumption is around the international poverty line. This study shows that taking account of demographics results in downward adjustments of child poverty, adult poverty, and child-adult poverty gaps. Moreover, breakdowns by country show that poverty may vary significantly depending on demographics, which may cause some reranking when comparing poverty between African countries. Finally, sensitivity analyses reveal that child poverty is not sensitive to the child discount factor, unlike adult poverty, but, overall, taking account of demographics is helpful for better identifying poor children.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at ybatana@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
broken down into three components: (1) whether the intervention increased the duration, intensity, and frequency of engagement in intervention activities; (2) if yes, whether it led to any adverse changes in BMI (e.g., decreasing weights may indicate that energy intakes do not compensate for greater activity levels); and (3) whether the intervention caused any other undesirable effects on time, such as reduction in time for child care, relaxation, or leisure.<sup>16</sup> These questions are especially relevant in developing country settings where lack of publicly provided infrastructure, limited access to mechanical or animal power, or access to labor-saving devices is associated with higher work burdens and increased drudgery. This approach is quite different from the developed country literature, where the emphasis is on increasing activity levels to prevent overweight and obesity, rather than to save labor and reduce drudgery.

Although the technology exists to measure energy expenditure and physical activity, it is only recently that advances have been made that reduce costs of monitoring energy expenditure in a field setting. The “gold standard” for measuring energy expenditure, in which the individual drinks doubly labeled water with heavy isotopes, and decreases in their concentration are measured in the person’s urine, is costly and therefore infeasible to collect in large enough samples in a field setting, although studies have been conducted among smaller samples in developing countries.<sup>17</sup> Moreover, to get a picture of energy expenditure over an extended period of time—a period that could potentially affect weight or BMI—one should be able to measure the intensity (whether moderate or vigorous), duration (minutes or hours), and frequency (number of days per week) of activity. Several methods exist for obtaining this information (e.g., heart rate monitoring, pedometers or activity-tracking devices), but the most common is activity (or exercise) diaries. Activity diaries face similar issues as time diaries (for recording the quantity

> 16 This draws from notes prepared by Gina Kennedy on measuring energy expenditure and physical activity, see <u>http://www.a4nh.cgiar.org/files/2015/01/Energy-Expenditure-and-Physical-Activity-Reference-Notes1.pdf.</u>

> 17 A recent example of this approach in a developing country context is the study by Pontzer et al. (2012) in Northern Tanzania where they measured the total daily energy expenditure of 30 Hadza adults over an 11-day period.

22
influence government, political leaders, business leaders/groups or households/communities to lead or support institutional change.

# **<u>Innovation</u>**

Discoveries – new products, new ways of producing etc., bring economic gains to innovators and others in society. With each such invention, new rules demanded by inventors and producers, consumers, government or others in society, develop to oversee the use and spread of the invention. Inventors usually wish to capture as much as possible of the gains associated with their discoveries or creations.<sup>23</sup> It is this desire, combined with the desire of rulers to support innovation and capture some of the gains associated with it that, historically, led to the development of patent law in its various forms. As Abraham Lincoln once put it, “The Patent System added the fuel of interest to the fire of genius.”<sup>24</sup> The first such law was adopted in 500 BCE where chefs in Sybaris, Greece had the monopolized right (and associated profits) to produce a unique dish they had created. The Republic of Venice was the first to grant a patent for a technological invention in 1416.<sup>25</sup> Over time, in different contexts, and as innovations proliferated, different variations of these laws were adopted, reflecting the preferences and situations of countries. Even in the United States, which receives the second largest number of patents per year, the value and “philosophical basis” underpinning the patent system has been debated since its inception, leading to various changes in the system over time.<sup>26</sup> While inventors have sought to guard their profits, societal preferences reflected in public policy, have also effected change. However, many other types of legislation, not just patent law are affected by innovation: rules on drug testing and provision, rules on electricity pricing and provision, regulations restricting materials that can be used in home construction or airplane construction, regulations in financial markets, e-commerce and e-banking laws and so on.

> 23 By inventors, I am referring to the person or persons that discover a thing and also develop it for commercial use.

> 24 https://ladas.com/education‐center/a‐brief‐history‐of‐the‐patent‐law‐of‐the‐united‐states‐2/

> 25 At least according to Western history. https://onlinellm.usc.edu/resources/infographics/history‐of‐patent‐law/

> 26 https://ladas.com/education‐center/a‐brief‐history‐of‐the‐patent‐law‐of‐the‐united‐states‐2/

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**Table S3.4: Random effects meta-regression on synthetic effect sizes with inverse variance weights**

||(1)<br>MM|(2)<br>MM|(3)<br>REML|(4)<br>REML|(5)<br>EB|(6)<br>EB|
|---|---|---|---|---|---|---|
|RCT<br>TOT|-0.066***<br>(0.021)<br>0.061***<br>(0.020)|-0.066***<br>(0.020)<br>0.061***<br>(0.019)|-0.065***<br>(0.021)<br>0.061***<br>(0.020)|-0.065***<br>(0.020)<br>0.061***<br>(0.019)|-0.066***<br>(0.022)<br>0.063***<br>(0.020)|-0.066***<br>(0.022)<br>0.063***<br>(0.020)|
|Delay|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Intensity /week|0.002<br>(0.002)|0.002<br>(0.002)|0.002<br>(0.002)|0.002<br>(0.001)|0.002<br>(0.002)|0.002<br>(0.002)|
|Duration|0.000<br>(0.001)|0.000<br>(0.001)|0.000<br>(0.001)|0.000<br>(0.001)|0.000<br>(0.001)|0.000<br>(0.001)|
|Low inc. clients|-0.024<br>(0.016)|-0.024<br>(0.016)|-0.024<br>(0.016)|-0.024<br>(0.015)|-0.025<br>(0.017)|-0.025<br>(0.017)|
|Years of schooling|-0.015**<br>(0.006)|-0.015***<br>(0.006)|-0.014**<br>(0.006)|-0.014***<br>(0.005)|-0.015**<br>(0.006)|-0.015**<br>(0.006)|
|Low/lower inc. econ.|-0.044<br>(0.039)|-0.044<br>(0.038)|-0.043<br>(0.039)|-0.043<br>(0.037)|-0.046<br>(0.040)|-0.046<br>(0.040)|
|Mandatory<br>Teach. moment|-0.052**<br>(0.020)<br>0.053***<br>(0.018)|-0.052***<br>(0.019)<br>0.053***<br>(0.017)|-0.051**<br>(0.020)<br>0.052***<br>(0.018)|-0.051***<br>(0.019)<br>0.052***<br>(0.017)|-0.053**<br>(0.021)<br>0.053***<br>(0.018)|-0.053**<br>(0.021)<br>0.053***<br>(0.018)|
|Constant|0.251***<br>(0.075)|0.251***<br>(0.073)|0.249***<br>(0.075)|0.249***<br>(0.071)|0.256***<br>(0.076)|0.256***<br>(0.076)|
|I<sup>2</sup>|81.14%|81.14%|81.14%|81.14%|81.14%|81.14%|
|Adj. R<sup>2</sup>|-|-|0.442|0.442|0.474|0.474|
|n (Studies)|90|90|90|90|90|90|
|n (Effect Sizes)|90|90|90|90|90|90|
|Adjusted errors|yes|no|yes|no|yes|no|

Notes: Results from random-effects meta-regression (DerSimonian and Larid 1986) with and without Knapp and Hartung (2003) adjusted standard errors, respectively. Dependent variable is effect size (Hedges’ g) on financial behavior weighted by its inverse variance. Columns (1) and (2) show results for method of moments (MM) estimates. Columns (3) and (4) show results for restricted maximum likelihood (REML) estimates. Columns (4) and (5) show results from empirical bayes estimates. The Stata command is _metareg_ (Hardbord and Higgins 2008). Standard errors in parentheses. ***, ** and * denote significance at the 1%, 5% and 10% level.

55
Note: 95% confidence intervals for difference from Q1 are shown (rather than from zero). Including controls for respondent characteristics do not qualitatively change results. See Appendix Table 2 for regression tables with controls. Source: HFPS _What may be driving unequal impacts on employment?_ In short, evidence from these seven EAP countries suggests that the pandemic may have exacerbated inequalities in employment, particularly when the economy was reopening, and mobility restrictions were less stringent. But in periods when restrictions were more severe (and overall mobility was low), work stoppages were not significantly different across income groups (Appendix Table 1). What might be driving these results? Information on economic structures in middle-income EAP countries and characteristics of jobs and workers may hint to the mediating factors behind the observed welfare gradient at times of recovery or relatively normalcy in mobility.

First, differences observed between top and bottom workers at times of high or low mobility may reflect welfare differences in economic sector of employment. In general, workers in the top 20 were more likely to be engaged in sectors that were generally less likely to experience work stoppages – for example, public administration, professional, financial sectors (Figure 7, Panel A). Indeed, when restrictions were relatively relaxed and the economy started to reopen, work stoppages were selective, with sectors with high top 20 participation such as professional and financial activities, experiencing low rates of work stoppages (Figure 7, Panel B). This is consistent with the unequal impacts we find during the periods of low mobility. However, during periods of _de facto_ high mobility restrictions, work stoppages were widespread across most economic sectors, even including typically less vulnerable sectors in which the wealthiest are more likely to be employed. This, combined with the fact that top 20 participation was still significant in sectors that were relatively hard hit,<sup>22</sup> may attenuate the (potential) regressive gradient in work stoppages, especially in times of stringent mobility restrictions. Nonetheless, sector of employment alone does not appear to explain differences between the top 20 and the bottom 20 in the likelihood to stop working during times of higher mobility. Table 1 in the Appendix shows that the coefficient on quintile 5 does not change significantly once we control for the most recent sector of employment. One possible explanation for this result is that the industry classification available in the HFPS is not granular enough to pick up differences across quintiles in employment sector. These results suggest that differences across the welfare distribution _within_ sectors (e.g., occupation, ability to work from home, job formality, job security) likely have contributed more to observed differences among the top 20 and bottom 20.

> 22Across countries in the sample, an average of 46 percent of workers in quintile 5 worked in manufacturing, transportation, retail, or other services pre-pandemic.

16
# 1.  INTRODUCTION

This paper hypothesizes that labor and credit market imperfections - by discouraging offfarm income-generating activities and restricting access to inputs, respectively- affect female farm productivity more deeply than male productivity, thus increasing the gender productivity gap. The paper theoretically models the relationship between gender differences in agricultural labor productivity and market imperfections and provides empirical evidence for Malawi consistent with the theoretical model. We find that agricultural labor productivity in Malawi is on average 44 percent lower on <mark>plots managed by female heads of household</mark> than on those managed by male heads of household; and that 34 percent of the observed agricultural labor productivity gap is explained by spillovers from labor market gender differences and 29 percent is explained by gender differences in the use of purchased inputs.

Two key features are prevalent in agriculture in Sub-Saharan Africa: the dominance of female labor in agriculture and the existence of a gender gap in agricultural productivity. In SubSaharan Africa women make up a higher proportion of agricultural labor than males, ranging from 30 to 60 percent of the total labor (UNECA 1982, FAO 1984, Doss 2011, Doss et al. 2011). In Malawi, in a 2004 survey, rural wages were 35% lower for females than males. Furthermore, around 89% of women working off-farm do so part time in contrast to only 67% of males. Women participating in rural wage employment tended to be concentrated in lower skill activities- about 61.4 % - in contrast to the corresponding figure of 37% for males (Hertz et al. 2009).

Estimates of gender differentials in agricultural productivity range from 4 to 40 percent depending on the country, the representativeness of the data, the empirical approach used, and the 2