rafmacalaba/data-use-mentions-extended huggingface dataset:
Second, to the best of our knowledge, this paper is the first to investigate the impact of the anticipation or threat of polygamy on economic decisions of monogamous spouses. The only studies on the effects of polygamy on household economic behavior focus on polygamous households and do not study the impact of the risk of polygamy on nonpolygamous households. Yet, papers analyzing the economic impact of polygamy are scarce, with the exception of Dauphin (2013) and Dauphin and Fortin (2001), who focus on the effect of polygamy on the efficiency of agricultural households, or Grossbard-Shechtman (1986), who studies the impact of polygamy on fertility. At the macroeconomic level, the relationship between polygamy and savings has been explored in a theoretical model by Tertilt (2005). In the authors’ model, polygamy leads in particular to high bride prices and high fertility, which crowd out other investments. Descriptive evidence of the relationship between polygamy and savings at the microlevel is provided by De Laiglesia and Morrison (2008). Using household survey data from Ghana, Indonesia, and Côte d’Ivoire, the authors find that polygamous households have lower assets per capita than monogamous ones, but they do not account for self-selection into polygamy. Our paper tests another channel for the impact of polygamy on saving decisions based on the strategic behaviors of monogamous wives “at risk” of polygamy.

The article is structured as follows. Section II discusses evidence of noncooperative behaviors between spouses. In section III, we present our empirical model and identification strategy. The data are described in section IV. Results on the determinants of polygamy and on the impact of the risk on polygamy on savings are presented and discussed in section V. Finally, section VI concludes.

II. THE RISK OF POLYGAMY AND SAVINGS: DISCUSSION OF POTENTIAL CHANNELS Several works have shown that spouses in developing countries do not behave cooperatively, especially in the presence of asymmetries of information (Udry 1996; Ashraf 2009; Castilla and Walker 2013, among others), and that spouses may have
need to be ongoing and effectively targeted. Lastly, our findings also highlight that vaccine hesitancy is a country-specific phenomenon with at times substantial variation in vaccine attitudes and their dynamics, correlates of hesitancy and hesitancy reasons. Any policy recommendations thus need to take into account the specific country context.

Our study faces the challenges and limitations of phone survey data collection on vaccination. There is some sample selection at the household level due to under-coverage, non-response, and attrition, whose potential impacts however our recalibrated sampling weights limit.[19] The purposive selection of respondents leads to the over-representation of certain population groups and the under-representation of others.[20] Survey data relies on respondent declarations, a method that is vulnerable to respondents’ incentives, misreporting, and misperceptions. Finally, our estimates cover five countries with a population of around 433 million, but they need not be representative of the entire region.

# **Conclusions**

We find overall high rates of vaccine acceptance across our study countries and over a 2-year time horizon, never dipping below 68%. Yet, we notice a decline of several percentage points in the willingness to be vaccinated in three countries from 2020 to 2022, while in one country vaccine acceptance increases. Overall, women, better educated individuals and those living in better-off households are more likely to express vaccine hesitancy, though these differences reduce over time. Vaccine hesitancy is lower in larger households and among heads of the household. Underlying vaccine acceptance and its correlates, there are important country differences. The main reasons for vaccine hesitancy are concerns about side effects and vaccine safety as well as perceptions around COVID-19 risk.

While this is not the only study analyzing COVID-19 vaccine attitudes in Sub-Saharan Africa, there has been lack of longitudinal evidence to assess dynamics over time. This study fills this gap by using panel survey data collected in high-frequency phone surveys over the span of almost two years.

9
|21,357.3|79,220.1|51,525.3|41.45|
|Radio and TV Receivers|4,854.5|3,157.4|13,843.0|9,003.6|35.06|
|Motor Vehicles|39,425.6<br>___<br>-----------------------|25,642.7<br>------------------------------|88,854.4|57.791.5|44.37|
|Total in our sample<br>|202,002.0|131,383.4|457,911.9|297,828.9|44.14|
|as%of Total Manufacturing||||||
|Output|42.|08|38|.05||

a) indirect taxes and subsidies are deducted (see table 2.3).

b) includes 25,008.3 million pesos (excl. indirect taxes and subsidies) for petroleum refining which are not shown in the census Resumen General but taken from Sistema de Cuentas Nacionales de Mexico.

Note: Figures are converted at the exchange rate of 1.5375 pesos to the cruzeiro. Source: Figures for Brazil from Censo Industrial, and for Mexico from Resumen General (except figures mentioned under footnotes a) and b)).
test is 64 _/_ 100), and the same applies to their knowledge related to HIV. This maps back to the low levels of contraceptive use and frequency of teenage pregnancy and childbearing.

The economic activities of young women that foster human capital are best described using a four-way type distribution: around 27% of young girls in control villages are in school; 34% are exclusively engaged in income generating activities; 18% are engaged in both schooling and income generation, while 20% report being engaged in neither activity, hence spending their time engaged in household chores or as caregivers.<sup>27</sup> Figure 4A shows how these activities vary by age at baseline. The majority of girls aged 12-14 are in school at baseline, pre-Ebola. There are no sharp discontinuities in enrolment rates at key cut-off stages of the school system (ages 15 and 18) but rather gradual declines in age. Presumably this smoothing is partly due to grade retention. There is a steady increase in specialization in 26We can compare the characteristics of our sample to those surveyed in the 2013 DHS data, in the same age range and resident in the same districts (although covering urban areas too). We find the samples have similar characteristics in terms of likelihood to be married, having children, being sexually active, age of sexual debut and the frequency of unwanted sex.

> 27The kinds of income generating activity conducted at baseline are all forms of self-employment, in line with the results in Casey _et al._ [2016]. The most frequent activities are small trade/business (40%), food processing (20%) and household land cultivation (15%).

16
payment will occur in exactly one year’s time (and will be adjusted for actual inflation), and the guarantee is issued today.

We assume next that advice provided by macroeconomists and electricity engineers familiar with the project suggests the following two assumptions:

- Peso inflation is forecast to be 5 percent a year over the term of the PPA.

- The annual electricity production of the plant is forecast to be 300,000 MWh. (This forecast might be derived from data on the maximum

11
is outside of the scope of this paper; (ii) the engineers evaluated by employers should be seen as the end product of the entire education system, not just engineering education. The engineering colleges receive graduates from the secondary education system with a set of skills upon which they add. In particular, the _Soft Skills_ are influenced by a prior schooling and the family setting; and (iii) although the sample size is fairly large compared to similar surveys, it is relatively small compared to the large population of Indian firms. Further, the survey may be biased due to a small size of convenience sampling and a possible oversampling of large firms. Keeping these important caveats in mind, we limit the recommendations to a set of broad actions within engineering education to improve the skill set of future engineers.

**There is substantial dissatisfaction with the quality of graduates.** 64% of employers are only somewhat satisfied or worse with the current engineering graduate skills. This confirms the finding of a number of other surveys showing that the skills set of fresh engineers is inadequate. Although, there are always caveats when comparing satisfaction surveys internationally, we find that Indian employers are less satisfied with their engineers compared to US employers. Obviously, the dissatisfaction suggests that renewed

> 14 The same question should be posed as to graduates of other stream of higher education in India and to to other levels of the education system. Clark, 2001 examines the culture of pedagogy in the primary and secondary education system. She finds a pervasive focus of teaching and learning in India on lower order thinking-typified by repetition and memorization-and the lack of attention given to the development of higher-order thinking.

26
have other similarities, we highlight the key distinctions of the two “surprises” of COVID-19. Section 3 briefly discusses the “economic surprise” of the short recession and fast turnaround in the U.S. economy long before the vaccines were available. Section 4 documents the “financial surprise” in the banking industry, focusing primarily on regulatory accounting data, and backing up our conclusions with some market data. Sections 5 and 6 discuss one key reason each for the two “surprises” – government stimulus programs during COVID-19 for the “economic surprise” and bank prudential policy responses to the GFC for the “financial surprise.” We primarily cover just one COVID-19 stimulus program in Section 5 for brevity, comparing the Paycheck Protection Program (PPP) to the Troubled Asset Relief Program (TARP) from the GFC. In Section 6, we describe and discuss research on four different methods of strengthening prudential policies in response to the GFC – higher capital requirements, introduction of stress tests, increased microprudential supervisory scrutiny, and shift to the Orderly Liquidation Authority (OLA) bailin regime. Section 7 goes beyond the simplistic “surprises” and the key reasons behind them and discusses some of the interactions among them. Finally, Section 8 concludes by suggesting some additional directions for future research.

# **2. Putting the COVID-19 Crisis in Context – A Comparison with the GFC**

The COVID-19 crisis may be best understood by explaining how different it is from other crises. We focus particularly on differences from the GFC. This was also a very large international crisis and caused the most economic and financial harm since the Great Depression. The two “surprises” of the COVID-19 crisis are key distinctions from the GFC. In the interest of brevity, we keep the references to the research literature to a minimum in this background section.

# **2.1 The GFC**

The GFC was a banking crisis – a financial crisis that originated in the banking sector – 7
childhood stunting and underweight in all surveys. The DHS reports, by contrast, use whatever standard was in force at the time the survey was done. In addition to ensuring consistency, there was a second reason to re-analyze the microdata: to ensure we have data for different ‘wealth’ or income groups, and a summary measure of inequality, namely the concentration index and its standard error (Kakwani et al. 1997).

# **Overview of the 2018 HEFPI database**

Figure 2 gives an overview of the 2018 HEFPI database in terms of indicators. The darker shaded boxes contain indicators used already in the MDGs. The lighter shaded boxes contain
size of the carbon

# **1.1 Our approach**

To estimate the impact of adoption of no-till agriculture on local air pollution levels we leverage two recent advances in data collection and modeling. One obstacle to estimating the benefits of no-till agricultural has been poor data on the uptake of no-till over the entire U.S. Corn Belt growing region. Historically, analysis of the uptake of no-till practices has depended on five year surveys conducted by the Conservation Technology information Center and the National Resources Inventory database (e.g. ref.<sup>19,20</sup> ). These sources do not provide data that is spatially or temporally granular. Instead here we rely on advances in remote sensing and machine learning to measure uptake of no-till at approximately the field level and at an annual frequency.<sup>21</sup> This enables us to conduct an examination of how year-to-year changes in no-till usage in a given county impact the air quality of that county.

A second obstacle has been the lack of data on PM2 _._ 5 levels with sufficient spatial and temporal resolution. Data based on ground monitors generally offers daily, or even hourly, information on pollution levels but over a very limited geographic region. These monitors are rarely located in the rural regions that are most likely to be impacted by the changes in tillage practices that we examine here. The problem of limited geographic scope can be solved by using remotely sensed data on pollution levels that takes advantage of satellite data to provide estimates of pollutant levels across a large geographic area. However, the increase in geographic scope comes at the cost of low temporal frequency. Because we expect changes to local air pollutants due to the uptake of no-till to be concentrated at the times when no-till practices are most likely to occur, the lack of temporal frequency makes it difficult to detect impacts with existing remotely sensed pollution data.

In the current study, we utilize a recently released dataset of highly localized pollution estimates for the United States
# 1. Introduction

Zambia’s private sector must deliver quality jobs at scale to keep up with the expanding working age population, contribute to economic transformation, and reduce poverty. Based on population growth projections, Zambia needs to create over 10 million new jobs by 2050 to keep its labor force participation and employment rates from declining. The country’s working age population is projected to more than double between 2022 and 2050 (Merotto 2017). This growth will provide a demographic dividend if Zambia can drive productivity growth and economic transformation. However, if Zambia’s private sector fails to generate enough good jobs to absorb this expansion, economic growth will fail to permeate to the rest of the economy as new working-age Zambians will be unable to access productive opportunities. This will limit its impact on poverty reduction and increase the risk of upheaval.

As well as creating new jobs, Zambia’s private sector needs to make existing jobs more productive. Low productivity is limiting Zambia’s potential to diversify its sources of growth and to create quality jobs and firms. While shifts in labor to higher-productivity sectors and within-sector productivity improvements can contribute to labor productivity growth and higher wages, Zambia’s private sector has had limited success in both areas. Lower-productivity sectors such as agriculture (58.7 percent of employment) and wholesale and retail trade (14.1 percent) still account for most employment. Existing high-productivity sectors tend to generate few jobs. These include financial services (0.5 percent of jobs) and mining, which accounts for only 1.1 percent of jobs despite its role as Zambia’s primary export driver. Meanwhile, lower-productivity informal employment accounts for three-quarters of jobs, and within-sector productivity has generally been flat (Zambia MLSS 2022, World Bank 2024).

It is thus critical to identify the drivers of and barriers to productivity, formal employment, and formal wage growth in Zambia. Leveraging firm and worker administrative tax data from Zambia, this paper decomposes labor productivity and wage growth among formal firms
sets of covariates and 2021 and 2022 data to capture the stability of the relationship between a set of explanatory variables and the likelihood of experiencing these specific forms of deprivation and identify any emerging trends or shifts in socio-economic dynamics and the robustness of the results to the specification. We report marginal effects, which allows for a clearer interpretation of the model results by quantifying the change in the probability of the outcome variable associated with a one-unit change in the independent variables. This facilitates a deeper understanding of the factors associated with the likelihood of being at risk of poverty or social exclusion, facing low work intensity, or experiencing severe material and social deprivation, thereby informing targeted policy interventions aimed at alleviating these socio-economic challenges. Annex 4 describes the specifications of the regressions performed.

**To identify at-risk groups, we compare unconditional and conditional AROPE means. In our modeling approach, we incorporate several vulnerable groups**<sup>**34**</sup> **as covariates to assess whether they continue to face a higher probability of experiencing poverty, even when controlling for other observable characteristics.** By including these covariates in the probit model, we aim to examine the unique contribution of each vulnerable group to the likelihood of being at risk of poverty or social exclusion, facing low work intensity, or experiencing severe material and social deprivation. This allows us to identify persistent disparities in socio-

> 29 We do not include other services and infrastructure variables (such as quality of the dwelling or sanitation facilities) typically included in the EU SILC because these variables are not available in the 2021 and 2022 surveys.

> 30 We do not include self-reported environmental variables (noise from the neighborhood, pollution, crime) because these are not available in the 2021 and 2022 surveys. They were collected annually until 2020 and then again in 2023. 31 We use primary education as the deprivation threshold because the majority of the population has secondary education or above. Thise with other studies on Europe, including those of OPHI.
joining the scheme—were compulsorily enrolled by virtue of their employment with the government or a state-owned enterprise, or being a pensioner or a ‘person of merit’ (such as a war veteran). The bulk (95%) of those voluntarily enrolled were school children, whose schools are encouraged to enroll their pupils by the local Communist party. While there appears to have been very little _self_ -selection into the scheme during this period, there is the possibility that those selected into the scheme share unobservable attributes that make them healthier (or heavier users of health care) than those not selected. The pre-post longitudinal data allow us to replace the standard PSM assumption—that outcomes are independent of participation given the values of the relevant pre-intervention control variables—by a weaker assumption, namely that _changes_ in outcomes are independent of participation given the pre-intervention controls (cf. Blundell and Costa Dias 2000). We are thus able to eliminate any bias due to time-invariant unobservable heterogeneity, in addition to bias due to observable heterogeneity which PSM on cross-section data already eliminates (cf. Heckman, Ichimura and Todd 1997, Heckman et al. 1998, Jalan and Ravallion 2003a). We are, however, forced to assume that any unobserved time trend in our outcomes is the same for the insured and uninsured. Only randomization can completely rule out such a possibility, which in principle is a concern even in studies that exploit differences in speed of adoption across geographic units. The attraction of PSM over a regression analysis is that we do not need to specify any model for our outcomes and hence reduce the risk of biases due to inappropriate model specification, whether in the functional form or the covariates included. We do have to do some modeling, namely in the prediction of propensity scores—the probability of being enrolled in VHI.

Section II outlines the salient features of Vietnam’s health insurance program. Section III sets out our estimation method, based on a combination of propensity score matching and double differencing on pre-intervention and post-intervention data
# **Figure 13: Investment Indicators**

A. Public and Private Expenditure on Infrastructure (% <u>of GDP), 2003 (Excludes spending on other urban)</u> B. Private and Public Investment since 1996, Percent <u>of GDP</u> <!-- Start of picture text --> 16 45<br>40<br>35<br>12<br>30<br>25<br>8<br>20<br>15<br>4 10<br>5<br>0 0<br>Thailand China Indonesia Philippines 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Transportation Telecommunication Water and Sanitation Power Private Investment (Percent of GDP) Public Investment (Percent of GDP)<br>C. Industrial Capacity utilization D. Private Investment Indicators<br>55,000 550,000<br>100 50,000 500,000<br>95 45,000 450,000<br>90 40,000 400,000<br>85 35,000 350,000<br>80 30,000 300,000<br>75 25,000 250,000<br>70 20,000 200,000<br>65 15,000 150,000<br>60 10,000 100,000<br>55 5,000 50,000<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>50 Domestic cement sales (1,000 tons) (LHS)<br>95Q1 95Q3 96Q1 96Q3 97Q1 97Q3 98Q1 98Q3 99Q1 99Q3 00Q1 00Q3 01Q1 01Q3 02Q1 02Q3 03Q1 03Q3 04Q1 04Q3 Construction areas permitted in municipal zone (1,000 sq. mets.) (LHS)<br>Domestic commercial car sales (units)<br>Import of capital goods at 1995 prices (
of economic status since it represents maternal genetic endowment in addition to her own prenatal and childhood exposure. Children born to mothers who are shorter are more likely to have lower birth weight/length and higher neonatal mortality than those born to taller mothers (Bhalotra and Rawlings 2013). To measure maternal receptive vocabulary, the same test used for children was used for mothers, the PPVT-III. Maternal vocabulary ability was estimated using item-response theory (IRT) to ensure a common metric for the variable across the sample (Masters and Wright 1984).

# _Parenting Inputs_

To measure parenting inputs, an adaptation of the Home Observation for Measurement of the Environment Inventory (HOME) for Middle Childhood (MC) in 2011 was administered. The adapted HOME scale captures learning environments which represent key pathways through which poverty can affect school readiness; the HOME scale captures both risk factors that increase vulnerability (e.g., harsh parental punishment, lack of sensitive parenting, limited books or toys in the home) as well as buffering factors that can help children thrive in spite of poverty (e.g., exposure to responsive parenting, presence of books and toys in the home) (Blair and Raver 2012;

> 3. <mark>Children whose mothers assessed their weight as small, and children with low measured weight have been shown to be significantly less likely to survive than are children who weigh more at birth (Alderman et al. 2011).</mark> 4. The prevalence of stunting in our sample is slightly higher than the prevalence reported by UNICEF (50%, 2013) and the DHS (53% in children 6–59 months old, 2008–9), as our sample is representative of rural and small urban centers in the country, excluding the capital and larger cities.
**Figure 1. Average Annual Real GDP Growth (Percent)**

<!-- Start of picture text --> 6.0<br>4.0<br>2.0<br>0.0<br>1990-1994 1995-1999 2000-2004 2005-2010<br>-2.0 EU15 North<br>EU15 Continental<br>-4.0<br>EU15 South<br>EU11<br>-6.0<br><!-- End of picture text --> _Source:_ For EU11: World Bank ECACE regional tables; for Western Europe, IMF World Economic Outlook<sup>6</sup>

**Figure 2. GDP per Capita, PPS Percent of EU15 Average**

<!-- Start of picture text --> 73 76<br>68<br>58 55 56 59 59 62<br>49<br>41 43<br><!-- End of picture text --> _Source:_ World Bank ECACE regional tables and IMF WEO _Note:_ Purchasing Power Standard (PPS)

**Table 1. Income Gap Change, EU15 Average and EU11 Economies**

||**1990-**|**1995-**|**2000-**|**2005-**|
|---|---|---|---|---|
||**1994**|**1999**|**2004**|**2010**|
|**EU11**|–**1.36%**|**0.08%**|**0.70%**|**0.42%**|
|Bulgaria|–0.36%|-0.29%|0.34%|0.29%|
|Croatia|–