rafmacalaba/data-use-mentions-extended huggingface dataset:
80<br>Micro (0-4) Small (5-19) Medium (20-99) Large (100+) Micro (0-4) Small (5-19) Medium (20-99) Large (100+)<br>SAR Rest of the world SAR Rest of the world<br>Change in sales (%)<br>Probability of being open (%)<br>Change in sales<br>Predicted probability of being open (%)<br><!-- End of picture text --> Panel (b): Comparison by firm size The observed variation within size-sector groupings can be associated with the transmission channel of COVID-19, that is, the type of shocks suffered by firms. Unreported analysis suggests that COVIDinduced demand shocks have the largest effect on South Asian firms relative to shocks pertaining to disruption in production chains and finance. This is in contrast to the rest of the world where disruptions in supply chains and finance shocks had a large and comparable impact relative to demand shocks. Globally, two-thirds of the within-country change in sales can be attributed to the nature of shock firms experienced, while size and sector explain only 20% (right panel, Figure 2).<sup>6</sup> This implies that firm size and sector are imprecise targets for designing firm support programs.

6Shapley values are computed to assess the relative power of size, sector, exporter status, and shocks suffered in explaining the change in sales for firms in SAR.

6
# **6 Discussion and Conclusions**

In this paper, we studied how unconditional cash and in-kind welfare transfers impacted the standardized test scores of primary school children from rural and marginalized areas in Mexico. We merged individual-level data from the randomized controlled trial of the _Programa de Apoyo Alimentario_ with administrative panel data on standardized test scores taken 4 to 10 years after transfers began. Despite the fact that both transfer modalities increased household consumption, and that in-kind transfers induced children to consume more key micronutrients, we find that in-kind transfers did not impact student learning, while cash transfers significantly reduced student learning.

There are many possible reasons why the improved micronutrients intake might have not led to long lasting impacts on test scores. First, for the children in our sample the improvement in micronutrients was not accompanied by a significant increase in the overall caloric intake. Due to the high share of children who were consuming less calories than the recommended daily allowance at the baseline, one possible explanation is that the quality of the intakes might not be sufficient to improve the cognitive function if the overall calorie consumption does not achieve a minimum threshold, and children do not improve their anthropometric measures. Second, most of the previous evidence on the beneficial effects of iron supplementation on cognitive outcomes is based on interventions that target individuals diagnosed with iron deficiency anemia (IDA) with high doses of iron. At the opposite, in our study all children from PAL beneficiary households are potentially exposed to improved intakes, but the doses do not seem sufficient to benefit those with IDA at the baseline. In fact, we did not observe any statistically significant reduction in anemia in the follow-up survey. Finally, previous evidence shows that zinc supplementation per se is not sufficient to improve cognitive development. Powell et al. (2005) find for a group of undernourished children in Jamaica that zinc supplementation increases cognitive development only when complemented by psychosocial stimulation. This result together with the evidence from other studies that analyze the combination of micronutrient supplementation and psychosocial stimulation (see Attanasio et al
is part of a broader churn in household composition among the population of international refugees in Turkey: while more than 50% of households in both the treatment and control groups have no change in their household size at the six-month follow-up, many have lost or gained at least one member, with a non-negligible share of households losing or gaining two or more members.

Appendix Table 6 shows that the households that lost at least one child by the six-month follow-up have a significantly higher number of children at baseline than the average household, lower per capita consumption, and a lower share of children enrolled in school. They are also more likely to be female-headed and resort to livelihoods-based coping strategies. In contrast, households that gained at least one child are much smaller, have higher consumption per capita, and a higher share of children in school. The table also makes it clear that vulnerable households are net senders of children regardless of beneficiary status, while better-off ones are net receivers. As mentioned in the introduction, we do not see a difference in the likelihood of boys or girls being sent; nor do we see that households with more elderly members are more likely to receive children. These findings make it less likely that the movement of children has to do with home production, e.g. motivated by facilitating the care of small children by teenage girls or elderly members, thus freeing adults in receiving households to participate in income-generating activities. Unfortunately, we do not have information in our phone surveys about the relationships between household members, so we are unable to provide information about the share of children living away from their parents – be it with relatives or others. Child fostering is not common practice in Syria or other countries in the Middle East, so the movement of children between 18
problems are exceedingly rare in urea fertilizer, nutrient shortages are more common and more likely in fertilizer blends including nitrogen, phosphorous, and potassium (NPK), calcium ammonium nitrate (CAN), and diammonium phosphate (DAP) and in glyphosate herbicide. The magnitude of the problem also varies by input: while evidence suggests nutrient shortages tend to be relatively modest in the fertilizer blends, economically and agronomically significant problems have been found in packaged glyphosate in the region.

Third, input quality issues have different causes. While the largely modest nutrient shortages in fertilizer blends are likely attributable to problems with manufacturing and storage, problems in herbicide could be due to faulty manufacturing, counterfeiting, or adulteration. Glyphosate quality issues also exhibit considerable spatial variation. Research designed to understand and ultimately

> 3 We do not review the evidence on hybrid seed quality, which is still emerging. Measuring seed quality involves assessing three dimensions: analytical purity, germination rates, and varietal purity. Work studying hybrid seeds and farmer beliefs about hybrid seed fidelity includes Gharib et al. (2021) and Barriga and Fiala (2020), with related work on misperception and misreporting of crop variety by Wossen et al. (2022).

> 4 For example, data from Malawi’s Fifth Integrated Household Survey (2019/20) suggests that ~18% of cultivating households applied urea to at least one plot, while ~53% applied a different type of mineral fertilizer to at least one plot (rainy season; authors’ calculation). Similarly, data from Nigeria’s General Household Survey Panel (2018/19) suggests 24% of households applied urea on at least one plot, and 34% applied an alternative mineral fertilizer (authors’ calculation).

3
. Buying happiness in an unequal world: Rank of income more strongly predicts well-being in more unequal countries. _Personality and Social Psychology Bulletin_ , 46(5), 769-780.

Mikucka, M., Sarracino, F., & Dubrow, J. K. (2017). When does economic growth improve life satisfaction? Multilevel analysis of the roles of social trust and income inequality in 46 countries, 1981–2012. _World Development_ , 93, 447-459.

Melo, M. A., Barrientos, A., & Coelho, A. (2014). Taxation, redistribution and the social contract in Brazil. _IRIBA Working Paper_ 11.

Meneguello, R. (2019). Brazilian electoral study 2018. <u>https://doi.org/10.25824/redu/HWZVV6</u> Montagnoli, A., & Moro, M. (2018). The cost of banking crises: new evidence from life satisfaction data. _Kyklos_ , 71(2), 279-309.

Morgan, R., & O’Connor, K. J. (2021). Labor market policy and subjective well-being during the great recession. _Journal of Happiness Studies_ , 1-32.

Mutz, D. C. (2018). Status threat, not economic hardship, explains the 2016 presidential vote. _Proceedings of the National Academy of Sciences_ , 115(19), E4330-E4339.

Neri, M.C. (2009). Income policies, income distribution, and the distribution of opportunities in Brazil. In: L. Brainard & L. Martinez-Diaz (eds), _Brazil as an Economic Superpower_ (pp.221-269). Brookings Institution Press, Washington DC.

Neri, M. C. (2010). The decade of falling income inequality and formal employment generation in Brazil. In: _Tackling Inequalities in Brazil, China, India and South Africa. The Role of Labour Market and Social Policies_ (pp. 57-107). OECD Publishing, Paris.

O'Connor,
Most of the data sets used in this study contain information to construct a diversity score for the child and the minimum acceptable diet measure, but lack information relevant for the other dimensions of food security. The diversity scores measures the consumption from different food groups. In general there are seven groups (grains, roots and tubers; legumes and nuts; dairy products; flesh foods including organ meats; eggs; vitamin A rich fruits and vegetables including orange and yellow vegetables; and other fruits) of which an individual needs daily consume four.<sup>9</sup> The minimum acceptable diet combines dietary diversity, breastfeeding and meal frequencies.<sup>10</sup> The dietary diversity score depends on the age of the child. For example, those under six months of age should be exclusively breastfed whereas older infants should receive complementary feedings as well as milk.<sup>11</sup> For the Helen Keller data from Bangladesh, we are able to construct a household level food insecurity measure, but not a measure of minimum acceptable diet. A child has to meet both the dietary diversity and the minimum acceptable diet (or food security) definitions be considered adequate in food.

# **2.2 Measurement of Child Care**

## **2.2.1 Ideal Indicators for Child Care**

Adequate care measures the capacity of the child’s caregiver to provide a healthy environment for the child to grow up in. Ideally, the measure is based on information on (1) the caregiver’s education, knowledge, and beliefs; (2) the health and nutritional status of the caregiver; (3) the mental health, lack of stress, and self‐confidence of the caregiver; (4) the caregiver's autonomy and control of resources; (5) the workload and time constraints of the caregiver; and (6) the social support received by the caregiver from family members and the community. Below we expand on
the legal definition, which overlaps with the definition of the benefits category, and −0.51 for the productive definition.

Principal-component analysis further shows that the JQM using four dimensions captures four distinct components and cannot easily be reduced to fewer dimensions (annex table A7). Using the full set of wage workers in 40 countries with over 1.1 million observations, the four components account for 41.2 percent, 24.4 percent, 21.0 percent, and 13.4 percent of the variation in job quality.

# **5. Conclusions**

The vast majority of policy research work on labor markets across developing countries focuses on job creation, while less attention has been devoted to the quality of employment beyond a single dimension, such as formality or labor earnings. However, because not all jobs have the same implications for worker well-being, understanding the multiple characteristics that contribute to worker welfare, especially those that allow individuals to move out of poverty and into the middle class in a sustained way, is a crucial issue in designing and implementing policies to achieve inclusive growth across developing countries.

This paper proposes an index of the quality of wage employment across developing countries that combines four dimensions—widely discussed in the literature—that characterize a good job: sufficient earnings to move a family out of poverty, access to employment benefits, job stability, and acceptable working conditions. To develop this global JQM, the analysis harmonizes a common set of indicators available for each dimension in recent labor force and household surveys across 40 developing countries. A JQM that scores several important characteristics of employment as opposed to a single indicator, such as wages or formality (widely used in the previous literature), adds value to both empirical research and policy analysis. A globally harmonized comprehensive dataset also allows the quality of jobs to be analyzed across various countries, regions, industries, and sociodemographic groups, such as women and youth.

The results show a strong positive correlation between the JQM and the share of the wage
to resources. To design the questionnaire, we built on the fact that consumptions common to various cells in the household appeared clearly defined, as well as the responsibilities for paying for those consumptions, while cells’ own resources turned out to be not entirely pooled within the households. We conceived the survey so as to capture this detailed information.

Consumption data are collected in four distinct parts: food taken at home; household common consumption expenditures, including consumption of home-produced food; cell specific expenditures (such as expenses for clothing of the cell members, mobile phone, transportation, or food taken outside the home); and finally expenses shared between several cells (but not common to the whole household). From this data, per capita consumption is computed at the cell level. Common consumption expenditures are attributed to cells in proportion to the share of household members they include. Food expenditures are compiled based on a detailed account of who shares which meal and how much money is specifically used to prepare the meal.<sup>7</sup> Each cell is ascribed its share in the food expenditures for the meals it joined in. For those meals, we have to assume food is shared evenly among participants. In fact, meals are often taken collectively from one common dish and individual food intakes are not observable. Though food expenditures for meals at home are often shared by the whole household, in 17% of households, subgroups emerge that take some or all of their meals separately, making room for unequal food consumption among household members. In addition, some members take parts of their meals outside the home. In any event, non-food expenditures naturally offer wider possibilities of divergence within households.

All the analyses presented in this paper exclude housing expenses. In this survey, only a very small share of the sample declares paying a rent for their dwelling. In fact, when everyone lives in adobe houses they built, as is the case in many parts of the country, the market price for the rental
more sectors may be liberalized and will eventually be included. The second way, which allows us to focus on the fully liberalized sectors, is by reporting results on fully liberalized KBLI 2-digit sectors and zero percent liberalized sectors to draw our conclusions (i.e., the two extremes). By doing so and taking the two extremes, there is no variation that exists in the 2-digit sectors that does not exist at the 5-digit sub-sectors. In some instances, results for other levels of liberalization more than zero and below 100 percent may also be discussed for illustration purposes.

For robustness, we include other controls including proxies for the Covid-19 shock and macroeconomic variables to assess if the results remain consistent.

# **4 Results**

## **4.1 Effect of Investment Reforms on FDI Inflows**

### **_All Sectors_**

We first present the results of the response of investment to the reform regardless of whether these sectors were liberalized. Table 1 presents the DDD results for realized FDI, without accounting for whether these were liberalized or not. The Table shows the average treatment effect on the treated (ATET) by sector for all sectors. In the period following the policy, realized FDI in the secondary sectors increased by 67.8 percent on average compared to the period before the policy.

On the other hand, FDI in the tertiary sector decreased by 89 percent following the reform.

All graphical representations of the quarter-by-quarter results show that the positive response in the secondary sector was picked up starting from the first quarter of 2022. (Figure 5). FDI in the tertiary sectors however declined, in line with the descriptive statics where secondary sector FDI surpassed tertiary sector values from 2020 onward. These results. however, do not provide insight into the degree of liberalization and the role in this response. For that, we conduct analysis on the basis of the share of sub-sectors liberalized by the reform.

12
warning during the year or the county experienced wind speeds of at least 21 m/s - the speed at which structural damage begins to occur - during a hurricane in the year.<sup>_∗_</sup> p=0.1,<sup>_∗∗_</sup> p=0.05,<sup>_∗∗∗_</sup> p=0.01.

**Table SI-5:** Difference in storm exposure in storm-year migration, all storms

||Mean|SE|_t-stat_|
|---|---|---|---|
|Receiving county storms|7.39|4.38||
|Sending county storms|7.35|5.04||
|Diference|0.04||0.46|

Notes: For counties that experience a storm, we calculate the total number of storms that county experiences over our full sample (Sending county storms). In each year a sending county experiences a storm, we calculate the weighted average total number of storms that the counties receiving migrants from that county in that year experience over our sample (Receiving county storms). Receiving counties are weighted based on the number of migrants they receive from a given county in a given year. We report the difference (a positive difference indicating that receiving counties experience more storms on average) and a t-test of the significance of that difference.
mark>Lemieux, T. 2006. “Increasing Residual Wage Inequality: Composition Effects, Noisy Data, or Rising Demand for Skill?”</mark> _<mark>The American Economic Review</mark>_ <mark>96 (3): 461-498.</mark> Madrian, Brigitte C. and Lars John Lefgren. 2000. “An approach to longitudinally matching Current Population Survey (CPS) respondents.” _Journal of Economic and Social Measurement_ 26 (1): 31-62.

Paus, Eva, Nola Reinhardt, and Michael Dale Robinson. 2003. “Trade Liberalization and Productivity Growth in Latin American Manufacturing, 1970-98,” _Journal of Policy Reform_ , 6(1): 1-15.

Pavcnik, Nina. 2002. “Trade Liberalization, Exit, and Productivity Improvements: Evidence from Chilean Plants,” _The Review of Economic Studies_ 69: 245-76.

United States Department of Labor, Employment and Training Administration. 1977. “Dictionary of Occupational Titles: Fourth Edition.” Washington, DC: US Government Printing Office.
The purpose of the study is to examine gender and inclusion differences in education, and to suggest policy actions as well as future analytical and operational work to address these differences.

# 2. Methodology

The research design for the study was informed by several sources of data. Data examination of the National Social-economic Household Survey (Susenas), the National Labor Force Survey (Sakernas), Dapodik, EMIS, and the 2010 Population Census was conducted to collect gender-disaggregated data on student enrollment rates, and the composition of teacher and administrative staff in the workforce, as well as disability information. This review and analysis of existing data sources allowed for the identification of areas of subnational gender variation in education attainment and other indicators.

Susenas collects data at the household and individual levels on many aspects of social and economic characteristics, such as: consumption, labor, health and other household variables. Sakernas is a survey that is specifically designed for labor data collection. Both Susenas and Sakernas are nationally representative surveys conducted by BPS ( _Biro Pusat Statistik_ /Central Bureau of Statistics) and used by the government for national planning documents. The population census, also conducted by BPS, records the number, composition, distribution, and selected characteristics of the population with national coverage.

Administrative data used in this study is the MoEC’s Dapodik and the MoRA’s EMIS. Dapodik records selfreported information on: (i) school-level data (public-private, ownership status, establishment date, accreditation status, availability of Internet, the number of school facilities and their condition); (ii) student-level data (sex, learning groups, parents’ information); and (iii) teacher-level data (sex, employment status, certification status, education qualification). EMIS records data on Islamic schools under the MoRA, including: number of students (data available by grade depending on the published year), number and condition (good, mild-/medium-/heavy- damaged) of school facilities such as classrooms, library, laboratories (computer,
Island groups (Table 7). Whereas close to half all non-agricultural jobs went into manufacturing on Java, the corresponding share was around one-third to one-quarter outside Java.

This pattern is not unexpected. Much of the labour intensive-industry was concentrated on Java. Thus the employment elasticity - growth in employment per (percentage) unit growth in output - could also be expected to be considerably 21 The RGDP and empoyment data cannot be compared directly because they refer to different time periods. Also, as already noted, the growth rates implied by the employment data need to be interpreted with care, given quite large year to year fluctuations by sector - even on a national scale.
, Part I, Major Groups 20-26, Part II, Major Groups 27-34, Part III, Major Groups 35-59, Bureau of the Census, Washington DC, August.

- U.S. Dept. of Commerce (1981c), The NationRl Income and Product Accounts of the United States 1929-76: Statistical Tables, BEA, Washington DC, September.

- U.S. Dept. of Commerce (1982), 1982 US Industrial Outlook, Bureau of Industrial Statistics, Washington DC.

- U.S. Dept. of Commerce (1984a), "The Input Output Structure of the US Economy, 1977", Survey of Current Business, Washington D.C., May.

- U.S. Dept. of Commerce (1984b), The Detailed Input-Output Structure of the US Economy, 1)77, BEA, 2 vols., Washington D.C..

- U.S. Dept. of Commerce (1986), The National Income and Product Accounts of the United States 1929-82, Statistical Tables, BEA, Washington DC, September.

- M. Ward (1985), Purchasing Power Parities and Real Expenditures in the OECD, Paris.
<!-- Start of picture text --> Indirectly affected<br>— [_]J 0.00- 0.10<br>wee [5 0.10- 0.20<br>i GB 0.20- 0.30<br>PALS HH 0.30 - 0.40<br>ee MM 0.40- 0.50<br>ey p' HM 0.50- 0.60<br>ses oh SO Missing data<br><!-- End of picture text --> ~~<u>a</u>~~