rafmacalaba/data-use-mentions-extended huggingface dataset:
.30|27.68|
|1990s<br>15.30|11.39|3.44|22.72|
|_All 87 developing countries_||||
|1960s<br>2.09|1.83|0.35|18.00|
|1970s<br>3.03|2.03|0.72|30.49|
|1980s<br>5.04|4.11|0.92|27.76|
|1990s<br>6.02|4.19|1.47|25.17|

_Note:_ The number in parentheses represents the number of countries in the data. The table includes all developing countries with 1990 populations exceeding 800,000 and for which more than one-half the observations are available for all four decades ("11990s" refers to 1990-96). The latter criterion elimninates the economies in transition.

_Source and definitions: Net ODA_ is the sum of development assistance grants and net disbursements of concessional development assistance loans. TC is technical cooperation grants, a sub-category of development assistance grants. The data are from the DAC98 CD-ROM (OECD Development Assistance Committee, _Geographical Distribution of Financial Flows to Developing Countries, 1960-96)._ Development assistance loans are classified by the DAC as concessional if their grant element exceeds 25 percent; see DAC98 for details. _GNP_ in US dollars is ftom the World Bank as reported on DAC98.

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error of undernutrition indicators [28,29] . The models also include variables as number of children in household and children close in range, which capture within household correlation. See also Jones and Haslett [22], as well as Fujii [16], for alternative ad hoc modeling solutions for within household correlation.

The models have adjusted r-squares similar to those of other undernutrition regressions, particular those used for undernutrition maps (0.06 to 0.27 for stunting and between 0.08 and 0.14 for underweight). Some studies have 7
# **3. Data**

We are able to study the impacts of extreme weather events on rural households by merging household and community level data from the Indonesian Family Life Survey (IFLS) with daily rainfall data covering a 25 year period. The combined data set contains information on rainfall, household expenditures, household level socio-economic characteristics, and community level attributes.

Household and community surveys were fielded from late June to the end of October 2000 for IFLS3 and from August 1997 to January 1998 for IFLS2. The surveys include villagelevel data which allows the determination of the extent to which access to better infrastructure or social programs increases resiliency. The consumption aggregate consists of food and nonfood components. The food component consists of 37 food items (purchases and the value of own production or gifts) consumed within the last week. The nonfood component consists of frequently purchased goods and services (utilities, personal toiletries, household items, domestic services, recreation and entertainment, transport, sweepstakes), less frequent purchases and durables (clothing, furniture, medical, ceremonies, tax), housing, and educational expenditures for children living in the household. Transfers out of the household were excluded. All values are monthly figures and are in real terms. To obtain real values, both temporal and spatial deflators were used, using prices in December 2000 in Jakarta as the base.<sup>6</sup> Using daily rainfall data from 1979 to 2004, we calculated the 25 year mean and standard deviations for monsoon onset and the amount of post-onset rainfall for 32 weather stations. The rainfall data from these weather stations were then matched to communities in IFLS. Weather data were merged with household survey data at the community level based on proximity. Only weather stations with complete data for the 25 year period were used. The matched data contained a total of 267 communities and 32 WMO stations. In rural areas, 106 communities in 9 provinces were matched to 27 stations. In rural Java, 66 communities in 4 provinces were matched
2

# **1 Challenges in Tracking Poverty**

Interest in understanding how poverty evolves over time is longstanding. It has received additional impetus through the call to monitor progress towards halving poverty by 2015 - the first Millennium Development Goal. Tracking poverty is predicated on the availability of poverty estimates that are comparable over time. Such measures are typically derived from survey based household expenditure data. The simple act of constructing a survey-based consumption measure already poses considerable challenges (Deaton and Zaidi, 2002); these only multiply when consumption expenditures and poverty estimates need to be compared over time.

First, consumption measures are often not available at regular intervals. For example, of the 48 countries in Sub-Saharan Africa (SSA) included in the World Bank‘s PovcalNet<sup>2</sup> database, only 18 countries possess more than one national household consumption survey since 1995. Second, in those settings where multiple consumption measures are available, they are frequently not directly comparable. Even slight differences in questionnaire or survey design can yield quite different poverty estimates (Lanjouw and Lanjouw, 2001; Gibson, Huang, and Rozelle, 2003; Beegle, de Weerdt, Friedman and Gibson, 2010). Finally, the price deflators needed to capture real changes in command over goods and services, are also often missing or of dubious validity. More often than not, official consumption price indices (CPIs) deviate from price deflators

> 2 Read on April 2010 http://iresearch.worldbank.org/PovcalNet/jsp/CChoiceControl.jsp?WDI_Year=2007
# **B.1 Computing corporate profits**

We start by creating a dataset of corporate income tax (CIT) declarations for every year in the period 2003 - 2019. We compute firms’ total profit as the difference between total revenue and total allowable costs. We include non-taxable revenue in that computation, so our measure of profit is broader than simply taxable profits. It also includes profits for firms that are exempt from income tax or have tax credits - these are separately documented after firms’ compute their tax liability.

In Table B1 we present some key descriptive statistics of that dataset. The number of corporations filing income taxes increases substantially in the period, from less than 11,000 in 2003 to over 33,000 in 2019. In Column (2), we present the aggregate profits declared by these firms. In the period 2016-2019, aggregate corporate profits fluctuate in the of L45 - L55 billion. in the levels of range Noteworthy changes aggregate profits happen in 2015, when profits increase by 60%, and then in 2018, when they fall by 20% from 2017. These large changes in aggregate declared profits are likely driven by the introduction and then phasing-out of a corporate minimum tax that dampened the incentives of large corporations to report low profit margins (cite minimum tax paper). Note that when we compare these values, computed from CIT declarations, with macroeconomic aggregate of property income flowing to corporations in Table C1, our numbers are much lower - in 2019, for example, corporate income is L113 billion in macro aggregates vs. L56 billion in our dataset. These discrepancies are likely driven both by the precise definition of these aggregates in each dataset, as well as the inherent challenge of estimating these quantities.

We also highlight three other features of our data. First, a non-negligible share of corporations declare losses – 20-30% of total CIT filings every year. That implies that when we assign undistributed corporate profits to individuals, some of them will be assigned negative incomes - in some
industry i’s output sold to industry k as intermediate input, and bi is the fraction sold for K fi nal consumption to households. Note that � bik + bi = 1. The coefficients bik and bi have k=1 been calculated based on the China national I/O table for 1997.

Similarly, market access to the rest of China is de fi ned as where each province’s consumption of industry’s i’s output is weighted by the inverse of distance.

# 3.5. Provincial characteristics

In addition to distance as a proxy for trade costs, we include in the estimation the number of river and sea berths and length of railroads, using information from the China Annual Statistical Yearbooks. The degree of openness of a province is constructed from international trade data from the Chinese Customs Office. The production cost variables at the provincial level include data on electricity prices and wages obtained from the NBS. Wages are calculated as the ratio of the total wage bill to employment by province and year. We include all locations in China except Tibet and Inner Mongolia because the latter two have very little industrial activity. This gives us 29 locations comprising 25 provinces and 4 directly administered cities: Shanghai, Beijing, Tianjin and Chongqing. Table 1 provides summary statistics of all the variables.

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**Table 3. Description of the variable used and descriptive statistics, Kagera region, 1991-1994**

||||**Stan**<br>**dard**|
|---|---|---|---|
|**Variables**|**Definitions of Variables**|**Mean**|**Deviation**|
||**Community variables**|||
|Partage or feeding posts|= 1 if there is Partage and/or feeding post in the<br>village|0.120|0.325|
|Urban|=1 if the child lives in a rural area|0.163|0.370|
|Health worker|=1 if there is a health worker in the village|0.818|0.386|
|Distance to health center|<br>Distance to the nearest health center in km|3.131|3.495|
|Motoroad|=1 if there is a motor road in the village|0.954|0.210|
|Road impassable|<br>=1 if the road is often impassable|0.483|0.500|
|<br>Ratio of children vaccinated|<br>Ratio of 0 to 3 year old children in the community<br>who have been vaccinated|0.712|0.212|
||**Individual and household characteristics**|||
|Height for age z-score|Height for age z score statistics|-1.643|1.388|
|Weight for age z-score|Weight for age z score statistics|-1.293|1.221|
|Aged 12_23 months|=1 if Child’s age id between 12 and 23 months|0.171|0.377|
|<br>Aged 24_35 months|<br>=1 if Child’s
firms, and in states and industries that were relatively less exposed to external competition (through proximity to ports and trade exposure) prior to the reforms.

Finally, we use our panel dataset to isolate the impacts of the policy changes on within-firm changes in productivity. Once we control for time-invariant, firm-specific characteristics, the magnitude of the policy impacts is somewhat attenuated, suggesting that the changing composition of firms, and unobservable, firm-level characteristics, may play important roles in determining productivity growth. Nonetheless, our findings confirm that the trade and FDI liberalizations are associated with increased within-firm productivity.

Our study was motivated by the emphasis that the new trade theory places on the importance of market-share reallocations in increasing aggregate productivity. Although a number of papers have tested various implications of this literature (see, for example, Arkolakis (2010), Bernard et al. (2003), Bernard, Jensen and Schott (2006), Berthou and Fontagne (2010), Eaton, Kortum and Kramarz (2008), Helpman, Melitz and Yeaple (2004), Manova and Zhang (2010)), few are able to directly test the effect of a trade liberalization episode on market-share reallocations, and existing evidence on the role of reallocation is mixed. For example, Tybout and Westbrook (1995) find that the reallocation of market share to relatively low-cost firms explained little of the overall change in productivity following Mexico’s trade liberalization; however, Pavcnik (2002) and Menezes-Filho and Muendler (2007) find that market-share reallocation was an important driver of productivity growth following trade reforms in Chile and Brazil, respectively. Trefler (2004) documents that a fall in Canadian tariffs increased industry-level labor productivity, but not within-plant labor productivity, which he interprets as evidence that reallocation was more important than withinplant improvements. In Colombia, Fernandes (2007) finds that average productivity gains were more important than reallocation, but that reallocation became important in many industries during periods of tariff liberalization.

Our study also contributes to the substantial body of work examining India
<!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>1995 1996 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2013 2015 2016 2017 2018 2020<br><!-- End of picture text --> Source: Latinobarómetro Corporation, several years Participation in authorized demonstrations is also relatively uncommon, with the regional average reaching only 15 percent of the population (see Table 1). Chile leads in this regard, with almost a third of the population reporting having taken part in legal demonstrations—likely reflecting the mass demonstrations in 2019 and 2020 over the cost of public transport and education as well as economic inequality more broadly. Participation in demonstrations in the region has declined over the past twenty years, coming in line with the global average of 12 percent (Cuesta, Madrigal and Pecorari, 2022).

Another form of societal participation is working with others to solve problems at the community level on a consistent basis. In this sense, only about a quarter of Latin American and Caribbean citizens appear to be working on issues that impact them or their community regularly. National averages are more or less in line with the regional benchmark, with a narrow intercountry variation. The proportion of persons working to tackle community problems has remained stable since 2000, at roughly 20-25 percent of the population.

# 3.2. Respondents’ Demographic and Socioeconomic Status

Around 26 percent of the Latinobarómetro respondents have complete secondary education, while only 13 percent have completed tertiary education (see Table A2 in Annex A). In addition, almost 8 percent of respondents are illiterate. When it comes to respondents’ perceptions of their own socioeconomic status, the majority of Latin Americans and Caribbeans identify as middle (33.8 percent) or lower
02|0.06|0.09|
|Unemployed|0.06|0.00|0.01|0.00|0.04|0.00|0.04|0.02|0.01|0.00|
|Employed in Agriculture|0.34|0.66|0.32|0.52|0.15|0.72|0.14|0.42|0.41|0.47|
|Employed in Industry|0.09|0.08|0.18|0.09|0.16|0.05|0.14|0.14|0.15|0.10|
|Employed in Services|0.42|0.18|0.32|0.31|0.46|0.15|0.48|0.40|0.38|0.34|
|**Access to basic services**|||||||||||
|Improved water|0.68|0.81|0.97|0.78|0.98|0.57|0.88|0.59|0.79|0.78|
|Improved sanitation facility|0.46|0.30|0.46|0.33|0.88|0.04|0.32|0.20|0.29|0.37|
|Access to electricity|0.47|0.53|0.76|0.80|0.98|0.36|0.91|0.81|0.44|0.58|

Source: International Urban Poverty Database. Note: Real per capita consumption expenditures are deflated using a spatial deflator calculated based on the WorldPop 250m dataset and the DOU method.

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accounts).<sup>29</sup> This would give a poverty line of $2.38 for use with HFCE and $3.55 for use with GDP.<sup>30</sup> Figure 5, Panel B illustrates the poverty estimate for each country using these poverty lines. Although poverty estimates with the adjusted lines are more closely aligned to survey estimates than in Panel A, there is still substantial variation, resulting from the variation in the gaps across countries combined with the uniformly adjusted poverty lines.

Just as it does not make much sense to scale up survey means and not change the $1.90 poverty line, it is also not reasonable to increase the poverty line by the same proportion as the survey means. Part of the poverty line is based on estimating the cost of obtaining minimum nutrition needs and this estimate need not necessarily be affected by underreporting of consumption (or income). Chen and Ravallion (2010) clarify this point better by noting that typical methods for setting national poverty lines will underestimate the poverty line if non-food spending is underestimated in surveys, and thus any correction for the underestimation of nonfood spending would also lead to higher poverty lines. But this adjustment, by construction, would almost certainly be less than the entire gap between survey means and national accounts.

A different way of adjusting the IPL for poverty measurement based on national accounts means, is to set a country-specific IPL is based on the country-specific survey-national accounts gap observed in the data. Alternatively (and equivalently), one could use the national accounts series, scaled down by the gap, and use the $1.90 line. Such an approach could be justified from a perspective that national accounts may be a more comparable measure of changes in living standards over time, but that surveys, on average, do a better job at measuring both inequality and the level of poverty. Under this approach, large changes to survey methodology that affect the survey mean (but smaller changes to inequality
the case of the Latin American countries. Using geo-coded data on subjective well-being, which we obtain from multiple survey rounds of the Gallup World Poll and Latinobarómetro, we compute the average subnational (level-1 administrative region) subjective wellbeing scores in each large Latin American country and assign the same initial utility only to locations within the same subnational region. This way domestic migration costs between any two locations in different subnational level-1 administrative regions could be different not only due to differences in initial technology or amenities, but also to different initial welfare levels. Then, a small change in population in a location with low initial utility relative to other parts of the country can be correctly attributed to migration frictions restricting the movement of people out of this location rather than with good fundamental amenities in the location.

This modification allows us to assess more precisely the size of domestic migration frictions circa 2000. Our results suggest that in most Latin American countries the adjusted migration entry costs are 4
# **2.1 Unemployment in Egypt**

The analysis relies on data from the Egypt Labor Market Panel Survey (ELMPS). The Egypt Labor Market Panel Survey is a nationally representative survey conducted in Egypt since 1998 by the Economic Research Forum (ERF), in cooperation with Egypt’s statistical agency, the Central Agency for Public Mobilization and Statistics (CAPMAS). The ELMPS has been administered to nationally representative samples in 1988, 1998, 2006, 2012, and 2018, and is a wide-ranging panel survey that covers topics such as employment, unemployment, job dynamics and earnings, as in a typical labor force survey, but also provides very rich information on education, residential mobility, migration, and socio-economic characteristics.<sup>4</sup> This section <mark>sheds light on recent unemployment trends in Egypt between 2012 and 2018, after the Arab Spring uprisings. It highlights in particular important gender differences in unemployment in Egypt, helping to explain the differential results obtained for men and women. Moreover, the analysis shows important disparities between urban and rural areas, highly educated and less educated individuals, as well as between younger and older age cohorts.</mark> Following the 2011 uprisings, male unemployment rates increased in Egypt (Figure 2). It should be noted that unemployment rates vary depending on the definition used. The market definition of unemployment, for instance, counts subsistence workers as unemployed individuals, while the extended definition of unemployment counts them as employed. The broad definition of unemployment does not require active job search, whereas the standard definition does. Depending on which definition is used, male unemployment rates in Egypt vary between 4.7 and 5.8 percent in 2018. The different definitions and the results they produce are informative, as they capture distinct dynamics regarding active versus passive job search and engagement in subsistence activities, which are particularly relevant for women in Egypt. Female unemployment rates have actually fallen between 2012 and 2018, as opposed to those of men
During the verification exercise, the verifiers were unable to locate 98 women (and 27 households). This leaves us with a sample of 2,637 women and 991 households. We were able to verify all 727 confidants.

Attrition of women was unrelated to treatment assignment (see Appendix Table A3), although we note that T2 households are more likely to attrit. Since the overall household attrition rate is very low (2.6 percent), this is not a source of concern. In addition, 158 women claimed to have cast a vote but did not have the requisite ink mark. To be conservative, we treat these women as not having voted, although the results do not change if we consider them as voters.

Verification was followed by a post-election survey of the same 2,637 women in March 2008. We ensured that the team of enumerators that visited a given household at follow-up was different from the one that had delivered the awareness campaign. The survey collected information on household demographics, recall of the door-to-door visits, access to and use of various media, and knowledge of the balloting process and political candidates, among other issues. Finally, we collected the official electoral results by gender, candidate and political party for each of the 21 polling stations which served our sample villages.<sup>20</sup> Average turnout among women in our sample is 59 percent, while female turnout in the 21 polling stations is 47.3 percent. At the constituency level, total turnout was 39.2 percent in Khairpur and 48.6 percent in Sukkur. The corresponding turnout rate (including both males and females) in the sample polling stations was 41.7 percent and 43 percent, respectively. While there is significant variation in contestation at the polling station level, the share of PPPP at the constituency level was a large 70 percent in Khairpur and 73 percent in Sukkur.

Table 2 reports the difference in means of household and woman characteristics across different samples. See Appendix Table A1
**Table 1: Incidence of Poverty and Food Stamps in Jamaica**

||_November_<br>_1989a_|_November_<br>_1990_|_November_<br>_1991_|_November_<br>_1992_|
|---|---|---|---|---|
|Percent Poor|26.9|27.9|38.9|34.2|
|Poverty Gap|8.8|7.9|13.7|10.6|
|Poverty Severity|3.9|2.9|6.6|4.4|
||_Percent Households Re_|_ceiving Food S_|_tamps_||
|Poorest Quintile|33.7|29.3|42.6|45.0|
|2|26|20.4|27.6|36.6|
|3|17|14.2|20.3|27.1|
|4|8.7|9.2|11.7|16.3|
|Top Quintile|4.0|2.7|4.0|6.1|
|Jamaica|14.9|12.8|17.8|20.0|

a Quintile data for 1989 refers to the month of July of the Jamaican Survey of Living Conditions.

_Source:_ Jamaica: A Strategy for Growth and Poverty Reduction Country Economic Memorandum, 1994, World Bank; and Jamaica Survey of Living Conditions Report 1992, The Planning Institute of Jamaica and The Statistical Institute of Jamaica.

# **3 The Jamaican Food Stamp Program (FSP)**<sup>**2**</sup>

In order to mitigate the adverse short-run impacts of macro-economic stabilization measures on the poor, the Government of Jamaica (GOJ) implemented the FSP in 19843.

The food stamp program was targeted to two main categories of people who were considered at nutritional risk: (i