rafmacalaba/data-use-mentions-extended huggingface dataset:
**<u>Table 7: Welfare Loss from the VAT by Area of Residence and Poverty Status</u>**

||**All**|**R**|**ural**|**Urb**|**an**|
|---|---|---|---|---|---|
|||Poor|Non-Poor|Poor|Non-Poor|
||8|% Increase|in cereal, meat, a|nd other foods||
|Compensating variation (x1000)|-106.979|-68.207|-116.034|-101.383|-170.638|
||[0.479]|[0.414]|[0.977]|[0.560]|[1.507]|
||1|2% Increase|in cereal, meat,|and other food|s|
|Compensating variation (x1000)|-126.648|-78.499|-134.753|-119.87|-209.263|
||[0.586]|[0.474]|[1.159]|[0.678]|[1.899]|
|||Expe|nditure ratio to ru|ral poor||
|Mean food expenditure|1.647|1.000|1.728|1.558|2.786|
|Observations|21,239|6,766|4,785|5,743|3,945|

Standard errors in brackets, 100 bootstrap replications Note: Gini coefficient of welfare loss = 0.32 and 0.33 Source: 2012 1-2-3 Survey, DRC Table 7 presents the welfare and equity impacts of price increases by poverty status and area of residence. The welfare impacts of the price changes are sizable for all analytical
# VII. Household descriptive statistics

<u>Table 7</u> summarizes basic household demographic and consumption statistics, by decile. Lower-income households are larger, though no clear correlation emerges between income level and age or sex of the household head. Households in the lower-income deciles are also less likely to smoke (proxied by the report of positive consumption of cigarettes). In turn, the wealthiest decile reports the highest smoking prevalence, for all cigarettes and for filtered cigarettes only. On the other hand, lower-income households observe the highest likelihoods of consuming unfiltered cigarettes only (Table 8).

The expenditure amount on cigarettes (for all cigarettes and filtered cigarettes) increases with income. The value of cigarette purchases among the top 10 percent is more than seven times the value of cigarette consumption by the poorest 10 percent of households. Averaging for all households, all cigarettes take up the largest consumption share in decile two (3.2 percent of aggregate consumption in the decile), though filtered cigarettes are most relevant for middle-income households. However, conditional on smoking, the poorest households spend the largest shares of their budgets on cigarettes (filtered, unfiltered and all cigarettes). These shares decrease consistently with income. The share of all cigarettes for smokinghouseholds in the bottom-10 percent (14.5 percent of household consumption) is more than double the share for smoking-households in the top-10 percent (7.1 percent of their consumption). It is possible to conclude that although wealthier households spend more GEL on cigarettes, those purchases represent much lower tolls on their incomes, than for lower-income households. Poorer households consistently spend more of their scare resources on tobacco.

15
integration using price differentials between Juba and the other major towns as the inverse measure of integration. The paper then uses these estimates to compute the possible effects of changes in these drivers on poverty.

The question on the poverty impact of market integration is important in South Sudan, which has one of the highest incidences of poverty in the world. Years of wars and instability, which continues unabated to this day, have killed thousands and profoundly affected all economic activities. This instability is compounded by an unfavorable location without access to the sea, unstable neighbors, and weak political institutions. The outcome is a country where more than half of the population is poor (according to estimates from the 2009 household survey, which have likely worsened after the latest rounds of violence).<sup>2</sup> Much of the population, including the poor, relies on markets for their basic consumption. Indeed South Sudan’s economy has a very low domestic production capacity – excluding oil. Before the oil closure, the oil sector represented around 66 percent of GDP, 98 percent of government

> 1 For example, governments or parastatal agencies could affect the price structure for a commodity produced and consumed within the country through arbitrage by operating on small amounts of easily controlled trade flows (Timmer, 1986).

> 2 World Bank (2011), “A Poverty Profile for the Southern States of Sudan”. Africa Region, World Bank, and World Bank (2014), “South Sudan Poverty Note: Impact of a Continued Internal Conflict on Food Security and Poverty”, World Bank.
26,187|16.04|1.25|13.17|19.37|
|Log of size|26,187|2.74|1.14|-1.10|9.82|
|Direct exports 10% or more of sales Y/N|26,187|0.11|0.31|0.00|1.00|
|Foreign ownership Y/N|26,187|0.09|0.28|0.00|1.00|
|Establishment has checking or savings account Y/N|26,187|0.87|0.34|0.00|1.00|

Source: Authors’ calculation from the Enterprise Surveys.

# 2.2 Econometric specification

The following equation is estimated using the panel data set: I I I i 0 1G i 1 2 i (1) cc,ii−1 xx ccii yy ii ii = ββ + ββ IIGGIIGGII + ββ ZZ + ββ ZZ + γγ GG + γγ GG + εε where I I I is a measure of investment by firm at time t; G is the central government debt as a percentage of GDP for country c, lagged by 1 yearii<sup>2</sup> ; Other control variables at the country level (IIGGIIGGII ) ccii include log of real GDP per capita and log of total population. Other control variables at the firm-level ZZ include log of age of firm, log of firm size, whether the firm exports 10 percent or more of sales (direct

> 2 This is to capture pre-existing government debt. Using further lags of GovDebt does not change the findings.

8
583|0.583|0.639|0.583|

Note: Sample includes all mothers with and without any preschool-aged eligible child (age 3-6). Comparison group includes non-mothers and mothers of children with the wrong ages. Sample is restricted to PODES years with preschool enrollment data (2003, 2005, 2011, 2014). Preschool enrollment is defined among children aged 3-6. Median preschool enrollment rate at the district-level is 15.6 percent and the 99<sup>th</sup> percentile at 49.8 percent. Median preschool enrollment rate at the individual-level is 17.2 percent. All columns regress work participation on preschool density, preschool eligibility dummy, and their interaction. All regressions include district, year, mother’s age fixed effects and an urban residence dummy. DDD excludes while DDD-FE includes individual fixed effects. Standard errors clustered at the district-level is shown in parentheses; *, **, and *** indicate statistical significance at 10, 5, and 1 percent, respectively. Number of observations and work participation means for each column are indicated at the last two rows.

58
<mark>Finally, actual 2020 micro data for Brazil shows that the simulations underestimated the magnitude of the shock throughout the distribution, especially for the wealthy, because they failed to account for the extent of the earnings decline. Consequently, it underestimated the increase in poverty. The evidence shows that during shocks employers or the government may protect employment, but use reduced hours and reduced earnings as the margin of adjustment to the shock, and this needs to be better taken into account in these models. The underestimation of the shock was even larger when</mark> using employment projections based on elasticities instead of actual employment data.

<mark>This work provides valuable lessons about conducting microsimulations to analyze the distributional impacts of shocks. First, the use of employment projections based on past data could underestimate the magnitude of job losses and, consequently, poverty increases. The use of actual employment data when available is advised to reflect changes in employment levels more</mark> precisely.

<mark>Second, even when using actual employment data, projections of income changes can be biased if the adjustment mechanisms used during crises are not captured by the model. Adding more complex features to the model, such as the possibility of labor adjustments in the intensive margin, may allow to obtain a better simulation (closer to the real values) of income changes. A similar caveat applies to other sources of income that our model did not capture, such as non-labor incomes different from remittances. In the same line, using a sector-specific scale factor to adjust labor incomes up or down (instead of a single scale factor) could be an improvement. Such data could be obtained from ILO sectoral-specific wage data, although it would only capture earnings of wage</mark> employees.

<mark>Third, in the context of the COVID-19 crisis, only the agriculture sector was growing in some countries. This allowed us to implement a simplified model where there was no sectoral reallocation. However, in other contexts, where there are
governance structures were also found to insulate firms during the financial crisis (Minichilli et al., 2016). The one study to examine the management-crisis response nexus found that managerial ability helped dampen the negative effect of crude oil price uncertainty on firm performance in the United States between 1983 and 2016 (Phan et al., 2020).

The pandemic has prompted a new wave of studies quantifying effects on firms, primarily using surveys. Baker et al. (2020) show that COVID-19 increased economic uncertainty. Revenue loss, mass layoffs, closure, and liquidity have been impacted for firms in China (Dai et al., 2020), the U.S., and Europe (Bartik et al., 2020; Humphries et al., 2020; Abigail Adams-Prassl, Teodora Boneva, Marta Golin, 2020; Fairlie, 2020). While many firms confronted multiple effects at once, depressed demand is most often reported and has become a more prominent concern where pandemic disruption persists (Dai et al., 2020; Balleer et al., 2020). Firms in the United States show increases in cash holdings in March 2020 as COVID19 fears mounted (Acharya and Steffen, 2020), while revenue loss has led to financial distress among SMEs in hard-hit locations (Bartik et al., 2020; Zhang). Among these studies, there are very few systematic surveys of firm responses across sectors and countries. Beck et al. (2020) show evidence from 500 firm survey responses across 10 emerging countries that firms responded to the pandemic by reducing investment than by reducing payrolls, while surveys of firms operating on e-commerce platforms reveal sales and employment suffered among SMEs (Facebook and World Bank, 2020). These are not unexpected findings, given that the pandemic has been strongly associated with increased uncertainty among firms (Altig et al., 2020). Data collected by the World Bank from over 100,000 firms in 51 primarily lowand middle-income countries using short COVID-19 Business Pulse Surveys (COV-BPS), reveal that
secondary school is 12 to 14 years, then the TNER measures all children aged 12 to 14 who are enrolled in any level of school as a fraction of all children aged 12 to 14. In this way, the TNER best approximates the age‐specific enrollment rates for ages 12 through 14 since it captures the enrollment status of all 12 to 14 year‐olds, irrespective of what level of school they are in. Unfortunately however, data on TNER are missing for many countries and years in the UNESCO database, and, depending on the country and year, one or more of three other enrollment rates are more widely available. These are (i) the “adjusted net enrollment rate” (ANER), measuring the fraction of children in the theoretical age range for a given level 25
# **4. Results**

## **4.1. Incidence of Taxes and Transfers**

The incidence of taxes and spending by gender is determined using the distribution of the share of taxes paid and transfers received as a proportion of market income. Figures 2a and 2b show a relatively equalizing distribution of income for both men and women. The poorest 10 percent gained more than they lost from the fiscal system, and women gained more than men relative to their market income. This result is mainly driven by in-kind transfers in the form of education and health, followed by direct transfers.

**Figure 2: Incidence of Taxes and Transfers by Income Decile**

<!-- Start of picture text --> a. Men<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>1 2 3 4 5 6 7 8 9 10<br>Market income decile<br>Direct transfers Direct taxes (broad)<br>Indirect taxes Indirect subsidies<br>In-kind transfer Co-payment<br>Total net benefit Direct transfers net of direct taxes<br>b. Women<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>1 2 3 4 5 6 7 8 9 10<br>Market income decile<br>Direct transfers Direct taxes (broad)<br>Indirect taxes Indirect subsidies<br>In-kind transfer Co-payment<br>Total net benefit Direct transfers net of direct taxes<br>Share of market income, percent<br>Share of market income, percent<br><!-- End of picture text --> _Source_ : Authors’ calculations; ESS 2018/19 data.

9
GHG inventories, which in turn influence the net demand for CDM credits, could be adapted to assess the benefits of land-use projects.

Even so, the task of managing land resources perhaps fits best within a cluster of national policies. This is especially the case in ECA, where large spans of degraded land, which create ancillary problems for water and soil resources, could be restored for productive use.

Agricultural policies, natural resource management policies and climate change policies are closely linked, and actions taken with the intent to influence outcomes in one domain often have consequences for the others, which is why a coordinated set of policies can be effective. In the case of good agricultural policy, activities pursued to promote sustainable growth can generate largely unintended mitigation benefits. For example, strengthen land markets can encourage land owners to better manage their cropland and pastureland for their own benefits, but with the consequence that soil- carbon is built up as well. Good natural resource management can work in the same way. For example, setting aside forests in order to preserve endangered plants and animals also prevents the release of carbon stored in the forest‘s plants and soils.

Even so, GHG mitigation efforts are unique, in that they are supported by carbon markets and incentives to invest in mitigation projects, a consequence of the Kyoto Protocol‘s flexibility mechanisms. This opens the door for using mitigation finance streams to supplement the costs of projects designed to deliver multiple pubic goods. Positive lessons on how to do this can be found in ECA countries, where public and private financing has been combined to restore degraded lands. National governments have the largest stake in managing their natural resources, but the global community benefits as well, since these activities promote biodiversity and reclaim land and water resources that sustain a global food system. Natural resource management plans, linked with good agricultural policy and national mitigation and adaptation plans, can help identify local and global needs and benefits and provide a framework for organizing and financing joint efforts.

# **Bibliography
the stratum has an important effect on the accuracy of the ML measure. The choice of the first stratum as the source of the training data results in an the _R_<sup>2</sup> value of 0.24 from the bivariate regression of observed CC yield against imputed CC yield in the test sample. The comparable _R_<sup>2</sup> estimate is 0.04 while working with the fourth stratum as the source of the training data. The reason behind these differences in performance might be the fact that the first stratum was the most heterogeneous unit in terms of CC yields: the kernel density of CC yields is more spread out within the first stratum vis-`a-vis the rest of the strata (see bottom right panel on Figure 8).

So, in view of national surveys that may consider implementing crop cutting on a sub-sample basis and deriving imputed CC yields for the rest of the sample, the findings imply that implementing crop cutting in a way that is confined to a particular region may only result in reliable predictions if the chosen region is sufficiently heterogenous in terms of crop yields.

35
standards and heavy regulation (Chen and Findlay 2008).Chinese exports in agricultural and food products are increasing. Between 1992 and 2008, total agricultural exports rose from 9.7 to 30.1 billion USD. Exports in the seven product categories represent about 40 percent of Chinese agricultural exports, and went up from 5.1 billion USD in 1992 to 12.1 billion USD in 2008.

The evolution of Chinese exports in the seven product categories is shown in Figure 1. Exports seem to increasingly follow China‟s comparative advantage in labor intensive product (Fang, Beghin 2000). Labor intensive products –such as vegetables and tea– register large growth rates especially after China‟s accession to the WTO in 2001. In contrast, exports of land-intensive products –such as sugar, cereal and meat– have increased to a lesser extent. In 2008, China‟s agriculture exports accounted for about 10 percent of all agriculture exports from low and middle income countries.

# [Insert Figure 1 here]

Data on mandatory and voluntary standards are compiled from the Standards Administration of the Peoples Republic of China (SAC), the Chinese official national standards body.<sup>7</sup> Chinese standards developed in SAC are accessible through an online database: the SAC National Standards Query (SAC 2011b). The database has recently been used in Mangelsdorf (2011). To ensure reliability and completeness, the standards have been cross checked with the “Chinese Bulletin of Standards” (SAC 2011a) and the German-Chinese Standards Portal (DIN and SAC 2011). The Chinese National Standards Query provides information on the date the standards came into effect and the date the standards have been withdrawn or replaced by newer versions. Each standard is classified according to the International Classification of Standards (ICS) nomenclature, which allows for matching standards to trade data. The database allows differentiation between Chinese standards harmonized to international norms such as Codex Alimentarius and standards that are unique to China.<sup>8</sup
Policy Research Working Paper 6287

# **Abstract**

Labor markets are increasingly global. Overseas work can enrich households but also split them geographically, with ambiguous net effects on decisions about work, investment, and education. These net effects, and their mechanisms, are poorly understood. This study investigates a policy discontinuity in the Philippines that resulted in quasi-random assignment of temporary, partial-household migration to high-wage jobs in Korea. This allows unusually reliable measurement of the reduced-form effect of these overseas jobs on migrant households. A purpose-built survey allows nonexperimental tests of different theoretical mechanisms for the reduced-form effect. The study also explores how reliably the reduced-form effect could be measured with standard observational estimators. It finds large effects on spending, borrowing, and human capital investment, but no effects on saving or entrepreneurship. Remittances appear to overwhelm household splitting as a causal mechanism.

This paper is a product of the Poverty Reduction and Economic Management, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at etiongson@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
We have organized the rest of our paper as follows. Section 2 describes our data. Section 3 discusses econometric issues. We present estimation results in Section 4. Section 5 is summary and conclusion.

# **II. Data sources and measurement**

Our data come primarily from the World Bank’s Investment Climate Survey of India for the year 2003.<sup>12</sup> The survey covered 1856 manufacturing establishments sampled from 40 cities, which are the top 3 or 4 cities of the largest industrial concentration from each of 12 of India’s 15 largest states. These 12 states are Andhra Pradesh, Delhi, Gujarat, Karnataka, Kerala, Haryana, Maharashtra, Madhya Pradesh, Punjab, Tamil Nadu, Uttar Pradesh, and West Bengal. Between them the 12 states account for well over 90 percent of India’s industrial GDP. The 3 or 4 cities covered in each state also accounted for the bulk of manufacturing outputs of their respective states. In each city, samples were drawn exclusively from the main exporting or import competing manufacturing industries: food, textiles, garments, leather goods, drugs and pharmaceuticals, chemical, consumer electronics, electrical white goods, auto parts, fabricated metals, and machinery.

Table 1 draws up the business profile of establishments in the sample in terms of scale, age, start up conditions and productivity. The average plant has just over a hundred workers over the full sample, but with significant variation in scale across sectors, ranging from average employment size of under forty in consumer electronics to more than 250 in textiles. We are thus dealing with what is essentially a population of small to medium sized establishments, although the sample does include a sizeable number of large scale plants. The vast majority of the full sample are also owner managed, most

> 12 However, we use these in conjunction with similar World Bank Surveys for other developing economies for the purpose of estimating the reference technology in terms of which the productivity indices we relate to business environment and