rafmacalaba/data-use-mentions-extended huggingface dataset:
as web design, software programming, or network management). The surveys also include items to determine the use of computer skills in daily life, which could be a broader measure of the availability of such skills in the workforce—although it is also plausible that such skills may not be required in their jobs.

The STEP surveys also gather information on noncognitive skills. Information on the Big Five personality traits (openness, conscientiousness, extroversion, agreeableness and neuroticism), grit, and behaviors such as decision-making and hostility bias is gathered through a series of Likert-type items with four possible responses ranging from “almost never” to “almost always.” Items measuring these traits and behaviors are rated on a four-point Likert scale from “Almost never” to “Almost always.” Along with the skills measures described above, the STEP surveys gather extensive information on individual education and employment outcomes, labor market trajectories and on family background characteristics.

# **_Analytic sample_**

The STEP surveys are targeted to the urban working-age population (those between the ages of 15 and 64). For this study, we limited the sample to adults between the ages of 25 and 64, excluding those currently attending an educational program. The sample was further restricted to include only wage and salaried workers and those who are self-employed. Employers were excluded from the self-employed group to avoid biased estimates due to measurement error in earnings. Part-time workers (those working less than 40 hours a week) and unpaid workers were also excluded from the analytic sample, conforming to standard practice in these types of analyses. The proportion of salaried and wage workers and the self-employed group in each of the country samples is presented in Table 1.<sup>9</sup> As shown, the self-employed in Armenia, Georgia, and Ukraine constitute a much smaller group compared to those in the other countries in the sample.

> 9 Also, see Tables A.2 and A.3 in Appendix A for differences between the two worker types. These
19 pandemic (since March 2020).<sup>19</sup> In the follow-up phone surveys (starting from May 2020 to May 2021), the above labor market participation questions were asked referencing the time as since last call, which is approximately one month.<sup>20</sup> As far as these changes in reference period trigger similar implications across all regions, they will be captured by round dummies in our estimations.

We define three indicator variables for each of the major employment activities, taking a value of 1 if any member of the household participated in farming, non-farm own business, and wage employment, respectively, and 0 otherwise. We also generate an indicator variable for participation in any of these livelihood activities.<sup>21</sup> Figure 5 (in Section 3) provides temporally and spatially disaggregated trends in households’ participation in these activities.

# **3. Descriptive Trends in Welfare Outcomes**

## **3.1 Food Insecurity**

We start by describing trends in household level aggregate food security outcomes. Figure 2 reports trends in overall prevalence of food insecurity across survey rounds and the four major highland regions in Ethiopia, Amhara, Oromia, SNNP and Tigray. The two measures of food insecurity, prevalence of moderate or severe food insecurity and probability of food insecurity, were constructed using the eight FIES questions as described in section 2.3. Panel (a) shows that prevalence of moderate or severe food insecurity (experienced during the previous month) more than doubled in Tigray after the outbreak of the civil war, jumping from about 30 percent during the immediate pre-war round to about 67 percent in May 2021. In comparison, food insecurity slightly declined in Amhara (from 37 percent to 29 percent) but slightly increased in Oromia and SNNP (from 45 percent to 49 percent, and from 45 percent to 48 percent, respectively).<sup>22</sup>

> 19 For wage-related activities this continued to
instance, specific social welfare programs may be intended to support families with children, but the allocation of those resources among children or parents could vary significantly.

**Several approaches in the literature rely on household-level data and apply various methodologies to estimate intra-household resource allocation.** These methods usually involve assuming equal sharing, per capita allocation, or considering specific demographic factors influencing household resource distribution, such as adult equivalent scales. However, it is crucial to recognize that these assumptions are not universally applicable, and the actual distribution of resources within households may differ based on cultural norms, income dynamics, and individual preferences. For example, the equal sharing approach is generally not suitable for examining the gender or child impact of taxes and transfers since it overlooks the reality that resources within a household are not always equally distributed across genders or among adults and children. Therefore, while analyzing fiscal incidence at the household level is pragmatic, it is essential to acknowledge the limitations and uncertainties arising from making assumptions about household resource allocation. As research progresses, efforts to refine methodologies and gather more granular data will aid in developing a more accurate understanding of how fiscal policies impact individuals within diverse household settings.

**Prior initiatives have aimed at customizing the CEQ methodology specifically to examine child poverty.** Notably, the CEQ for Children (CEQ4C) approach, as described by Cuesta et al. in 2020 and Bournokova et al. in 2022, tailors the fiscal incidence analysis to focus on child poverty-related policies and public finances. The CEQ4C approach includes three elements: (a) identify child-relevant budgets, (b) use multidimensional child poverty metrics, and c) take children as the unit of analysis.

**Given the Bulgaria data available, identifying a child-relevant budget is not a trivial task.** This is more straightforward on education spending as they directly benefit children of different cohorts. However, many of the other benefits (health, social assistance) combine benefits aiming to reach children and benefit children indirectly.  On the revenue
households that expressed interest in participating in warrantage at the time of the village lottery. We present only the intention to treat estimates.

Second, we examined the spillover impact of warrantage access in a village for non-treated households (that is, households that did not express interest in participating in warrantage at the time of the lottery and the control households). Here we estimated: h1 5H E h0 + γ h0 (3) vv hvv YY = αα + ββ HHhE EEuuT EEHHE TTHHTT + δδYY + uu where H E is a binary indicator for whether the village was assigned to the high exposure treatment condition. Variation in village size and in village interest in warrantage HHhE EEuuT EEHHE TTHHTT participation imply that the village-level randomization does not result in precisely one-third and two-thirds of the village receiving access. Figure 3 plots the cumulative distribution functions (CDF) of the proportion of households, based on the census, treated for villages assigned to low exposure (solid line) and high exposure (dotted line), respectively. The CDF for the high exposure treatment condition clearly shifted to the right; that is, in high exposure villages, a larger fraction of the village obtained access to warrantage. The figure also clearly demonstrates variation in the fraction of the village treated: 40 percent less of all households in a village are treated in 20 percent of the high exposure villages.

Figure 3: Cumulative distribution functions (by village level treatment) of share of village treated <!-- Start of picture text --> 1<br>.8<br>.6<br>.4<br><!-- End of picture text --> 14
for growth and partially factor out this effect to understand whether it impacts other variables (for example, FDI or infrastructure).

Since demography can influence economic growth through various channels (Vandenbrouke 2021), we additionally experimented with other demographic variables, such as the share of urban population in the total population and population density (both from the WDI), but we did not obtain meaningful regression results. Their inclusion also does not considerably alter our key results.

# 3.3 Estimation

We follow the standard approach in the empirical growth literature (Araujo et al. 2016; Moller and Wacker 2017; Brueckner and Lederman 2018) and estimate equation (1) as nonoverlapping five-year averages in levels, which gives us: lnyct = θlnyc,t−1 + β1x1,ct + ⋯+ βkxk,ct + δt + αc + εct. (2)

-11-
and 25.3 percent at the tail-end of the pandemic and period of high inflation in 2020 and 2023, respectively, with a period of recovery in between. Forecasts predict that, following economic recovery, the poverty headcount will decrease to 18.7 percent by 2025.

The paper proceeds as follows: Section 2 outlines the data and projection methodology, Section 3 discusses the key results and trends, Section 4 compares results to conventional projection methods, and Section 5 concludes.

# **2. DATA AND METHODOLOGY**

# **2.1 Data**

We use both micro and macroeconomic data for the modeling. Microeconomic data provides the baseline levels of poverty and inequality as well as a set of household characteristics. On the other hand, macroeconomic data on sectoral growth and prices are the basis for predicting the evolution of household expenditure and the corresponding welfare changes, including poverty and inequality. The FY19 Household Integrated Economic Survey (HIES 2018/19), conducted by the Pakistan Bureau of Statistics (PBS), provides the expenditure vector to calculate the official

> 3 Inflation rates differ systematically across different types of households as their consumption patterns and different types of goods and services have different inflation rates.

3
One of the most salient problems facing legal systems around the world is still the lack of access to formal justice mechanisms. During the pandemic, those without legal representation remain defenseless against criminal activities. In a recent briefing, the New York University Center on International Cooperation (CIC) articulates the three factors contributing to the global justice gap: extreme conditions of injustice, including slavery, statelessness, or insecurity; unreported civil or administrative justice problems that cannot be resolved; and lack of identity or proof of property ownership that result in legal exclusion (Steven and De Langen, 2020). For justice systems to be more conscious of the gap’s human dimensions, the CIC calls for the collection of more person-centered justice data emphasizing the legal needs and victimization of the poorest and most disadvantaged members of
**Figure 3. The A index**

<!-- Start of picture text --> 400.00<br>350.00<br>300.00<br>250.00<br>200.00<br>150.00<br>100.00<br>50.00<br>0.00<br>Year<br>"Nominal A Index" "Real A Index"<br>Nominal US $<br>1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008<br><!-- End of picture text --> Source: World Bank Commodity Price Data Accessed, March 1, 2011: http://blogs.worldbank.org/files/prospects/PINK_DATA(3).xls 20
Relying on detailed farm-level data on shocks (both related and not directly related to agriculture) and the use of extension services from Sub-Saharan Africa, we then demonstrate that empirical observations are consistent with the predictions of our model.<sup>5</sup> In particular, we show that there is statistically strong and robust evidence that, in line with the model, non-agricultural shocks are indeed associated with increased demand for extension. For this purpose, we split shocks into two categories, namely shocks that are directly related to agriculture (e.g., pest outbreaks and irregular rainfalls) and shocks that are not directly related to agriculture (e.g., shocks to farmers’ wealth or health). In addition, our data allow us to separately identify extension services that are actively requested by farmers from unsolicited extension services of which the household is merely a passive recipient.

Given that the agricultural and non-agricultural domains are likely not separable in the context we study (see Benjamin, 1992; LaFave and Thomas, 2016), one should be worried that there may be other channels than the one implied by our model that account for the observed link between non-agricultural shocks and demand for extension. Our data allow us to assess the empirical plausibility of several other potential channels in this context, but we do not find strong evidence in favor of those alternative explanations. In particular, there is evidence that suggests that the effect of non-agricultural shocks is not due to an increased need for credit (e.g., driven by shocks to non-farm income and remittances) or because non-agricultural shocks (some of which may affect household labor supply) work through a demand for advice on labor inputs. Overall, our modeling insights are more broadly applicable and the empirical support for the model provided in this paper suggests that scarce attention may be part of explanations for phenomena in economic development, including beyond the specific application studied here.

The remainder of the paper is organized as follows. Section 2 discusses related literature and provides further
and is higher for the poverty gap and squared poverty gap indices, reflecting gains to those living well below the poverty line. Rural poverty measures, that were historically higher than for urban areas, have been converging with urban measures over time, and the (distribution-sensitive) squared poverty gap index for urban India has actually overtaken that for rural India in recent years. There has been a marked urbanization of poverty in India over this period, from about one-in-eight of the poor living in urban areas in the early 1950s to one-in-three today.

Even though a trend decline in poverty emerged around the early 1970s, the year 1991-92 – the benchmark year for economic reforms in India – stands out as the year of the great divide. Markers of a structural break are many, even as an attribution to the reforms remains unclear. There was a significant spurt in economic growth, driven by growth in the tertiary sector and to a lesser extent, secondary sector. The pace of poverty reduction also accelerated, with a 3-4 fold increase in the proportionate rate of decline in the post-1991 period. The acceleration in rural poverty decline was even higher than that for urban poverty. This happened alongside a significant increase in inequality both within and between urban and rural areas, in contrast with a decline in rural inequality and no trend in urban inequality pre-1991. Despite the increase in inequality, we find greater post-1991 responsiveness of poverty to growth in the aggregate, regardless of whether growth is measured based on national accounts or survey-based consumption. Thus, faster growth also appears to have been more pro-poor when the latter is measured by the growth elasticity of poverty reduction.

Even more striking has been the structural break in the relationship between poverty and the composition of growth. Both urban-rural and sectoral (output) decompositions are suggestive of stronger inter-sectoral linkages, whereby growth in one sector transmits its gains elsewhere. Post-1991, urban growth has emerged as the primary driver of poverty reduction. Urban poverty has become significantly more responsive to urban growth, but (even more importantly)
|Tariff shocks|Y<br>Y<br>Y<br>Y|
|---|---|
|Excludingmigrants|Y<br>Y<br>Y<br>Y|

Note: This table reports the effect of FDI shock on city level school enrollment by gender. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

# 7. Conclusion

China’s prominent rise as a global manufacturing powerhouse during the past few decades has been accompanied by massive labor reallocation from agriculture to industry and services, as well as human capital accumulation. Inbound FDI has been instrumental in China’s economic development and export success. China has been the largest FDI recipient among developing countries since 1992. While many studies have assessed the productivity spillovers of FDI based on firm level data, very few papers have investigated the broader developmental impact of FDI. This paper constructs a city level panel data set spanning 1990-2005 to present the first causal evidence of the effect of FDI on local structural transformation and human capital accumulation in China. The paper exploits variations in FDI inflows across manufacturing sub-sectors and initial industry specialization patterns across cities as instrumental variables for local FDI shocks.

As far as we know, this paper is the first to analyze the causal effect of FDI on structural transformation and human capital accumulation in China at the city level. It joins the very limited literature assessing the role of FDI in catalyzing structural transformation, school enrollment and other local labor market effects. The paper highlights that in addition to trade shocks, FDI plays a pivotal role in driving local structural transformation and human capital accumulation, and the effect varies by the type of FDI.

Our results show that manufacturing FDI inflows greatly accelerated city level structural transformation and human capital accumulation. Cities that received more manufacturing FDI experienced higher increases in industry and services employment and GDP shares accompanied with faster declines in agricultural employment and
sup>1</sup> Income gaps with European destinations are huge, with The Gambia’s GDP in 2018 of $713 representing only two percent of Italy’s $34,319,<sup>2</sup> providing a strong incentive to undertake the perilous journey. We conducted a cluster-randomized experiment with 3,641 young males aged 18-30 living in 391 settlements in The Gambia to test the effectiveness of three different interventions designed to reduce the likelihood of backway migration, spur migration to safer alternative destinations, and improve the well-being of potential migrants.

> 1 Calculated from FRONTEX data on illegal border crossings and World Development Indicators data on population. 2 Source: World Development Indicators (2019).

2
# **3. Data and Empirical Approach**

To investigate potential recall bias in agricultural harvest estimates, we make use of three nationally representative multi-topic household surveys from Sub-Saharan Africa, the 2004/2005 Malawi Integrated Household Survey (IHS), the 2005/2006 Kenya Integrated Household Budget Survey (KIHBS), and the 2001 Rwanda _Enquête Integrale sur les Conditions de Vie des Menages_ (EICV). These surveys were chosen because of their 12-month fieldwork calendar and because interview locations were randomized across regions throughout the data collection.<sup>3</sup> The 2004/2005 Malawi Integrated Household Survey (IHS) was collected over 13 months from March 2004 to March 2005, and covered all districts of Malawi excluding Likoma Island (Malawi NSO, 2005). Fieldwork was conducted concurrently in the three main agricultural regions of the country (north, central and south) to prevent regional averages from being distorted by seasonal bias. The sample was selected using a two-stage stratified sampling design based on a frame from the 1998 census, and was structured to be representative at the district level. The total number of households interviewed was 11,280. Of these households, the analysis was restricted to those households involved in rain-fed agriculture. Households reported agricultural information with respect to the most recently completed agricultural seasons (2002/03 or 2003/04).

The 2005/2006 Kenya Integrated Household Budget Survey (KIHBS) was collected over 12 months from May 2005 to April 2006, covering all eight provinces of the country concurrently (Kenya NBS, 2007). The KIHBS sample was also selected using a two-stage stratified sampling

> estimates of production, as concluded in the review of evidence by Fermont and Benson (2011). The same study notes that farmer reports were closer to actual production and of lower variance than crop cuts.

> 3 In the three surveys studied, households are clustered in enumeration areas (EAs, also referred to as primary sample units), with all households in the EA interviewed over the course
sector|0.0272<br>0.0168|0.157***||
||[0.0233]<br>[0.0244]|[0.0272]||
|Num cars|-0.0355*<br>-0.0567***|-0.0164||
||[0.0183]<br>[0.0193]|[0.0213]||
|Constant|0.814***<br>0.791***|0.271***|0.876***|
||[0.0658]<br>[0.0693]|[0.0756]|[0.129]|
|Observations|881<br>927|887|598|
|Adjusted R-squared|0.241<br>0.147|0.062|0.132|
|Standard errors in brackets|*** p<0.01, ** p<0.05, * p<0.1|||

**Notes:** Columns 1-3 represents personal beliefs of acceptability of working in the private sector, as well as preference for working in the private sector for both female and male respondents for both working and non-working respondents. Both of these of the dependent variables are re-scaled to between 0-1 for easier interpretation. Column 4 includes both male and female respondent responses on if non-working female (respondent or female counterpart) wants to work. “Social Normative” is a standardized variable of the single question of how many would speak badly of women working in the private sector, whereas “Social Normative Index” is a standardized aggregated variable including all the 4 questions asked about the private sector, including perceptions of risks and discrimination. The other perception variables should be in interpreted in a similar manner.
32. Trading, the most popular sector, is still a good choice for HE operators. Only two sectors -- mining and construction -- appear to generate more earnings for male-run HEs, and one sector – hotels and restaurants -- for female-run HEs. Note that with training, however, hotels and restaurants generate positive returns for men.

33. Other authors have found that access to infrastructure matters a lot for HE profitability (e.g., Jin and Deininger, 2008). However, these correlations are difficult to interpret since clearly infrastructure comes first to richer areas (e.g., Dar es Salaam). In our regression analysis, local conditions are very important in determining earnings. The coefficients on Dar es Salaam and on Other Urban areas are positive. in addition, the fixed effects variables account for the majority of the variance explained by the regression. This may be why we did not find access to electricity, to be significant, but we did find that being near a market matters to female HE operators. As noted above, De Weerdt (2008) found that rural households living near towns were much more likely to be successful in using an HE as a route out of poverty.

34. Surprisingly, apprenticeship training does not help earnings, and more formal types of training tend to help only male HE operators. The effect of training on earnings is also difficult to isolate as it involves a certain element of omitted variable – what is it that causes people to go get training in the first place? Some authors have found that training comes up negative in these types of regression because only poorly educated people go for training (Kahyarara and Teal, 2008). Testing this with the ILFS 2006 data, training is found to be a fairly rare event in Tanzania – only about 10 percent of female HE operators and 18 percent of male HE operators had any training at all. Those who received training tended to be concentrated in a few sectors. To clarify the effect of