rafmacalaba/data-use-mentions-extended huggingface dataset:
Market Panel Survey**

The ELMPS is a nationally-representative household survey that has tracked a panel of households (continually adding refresher samples) since 1998.<sup>9</sup> The ELMPS has, since its inception, included a nonagricultural household enterprise module. Information on agricultural activities is collected in other modules that are not relevant for our purpose. Any household which reports a member who is selfemployed, an employer, or unpaid family worker in either their primary or secondary jobs must respond to either the non-agricultural or agricultural enterprise module.<sup>10</sup> Households can report on multiple enterprises. There were 2,315 enterprises in 2,195 households in the 2012 wave of the ELMPS and 2,228 7 The public sector does not have informal firms, but it does, increasingly, have some informal employment (Selwaness & Ehab, 2019).

8 For more information on the data, see Krafft, Assaad, and Rahman (2019). The data are publicly available at <u>www.erfdataportal.com.</u> 9 The ELMPS does not cover the Frontier governorates of Matrouh, New Valley, Red Sea and North and South Sinai, which together comprise about 2% of Egypt’s population.

10 Households that did not initially report members as employed in these statuses may also report non-agricultural enterprises; indeed, allowing this structure identifies substantially more employment among women (Krafft, Keo, & Fedi, 2019).

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to the countries’ policymakers. Second, we obtain estimates for thousands of elasticities of substitution using highly disaggregated import data, which may be useful for other studies. For example, different elasticities may imply different responsiveness of imported products to demand shocks or exchange rate movements suggested by Chen and Ma (2012).

The rest of the paper is organized as follows. Section 2 describes the data and reviews import growth from 1995 to 2021. Section 3 reviews Broda and Weinstein’s (2006) model. Section 4 explains the estimation strategy and gives a brief overview of the importance of elasticities of substitution. Section 5 reports the results of the analysis and presents the welfare gain. Section 6 concludes the study.

# **2 Data and Descriptive Analysis**

We used trade data from BACI (Gaulier and Zignago, 2010).<sup>2</sup> We used the import data of the selected countries from 1995 to 2021, covering 27 continuous years. The data contains information on the total values, quantities, and exporters of registered products to the countries. Unspecified country data and products with data on their quantities are dropped. Furthermore, due to the insufficient numbers of observations, HS-6 products with fewer than 26 observations are dropped in the sample. This is due to the problem that many products were not imported to the countries constantly throughout the period. This left us with more than 40 million observations of 5,383 products.<sup>3</sup> Gross domestic product (GDP) data were taken from the World Bank’s World Development Indicators (WDI) Database.

To study the welfare implications of the drastic increase in imports of the countries, we should consider the increase in value of each product (i.e., the intensive margin) and the increase in the number of products and varieties for each product (i.e., the extensive margin).

Figure 1 shows average annual imports as shares of GDP between 1995 and 2021. The imports
.

> 7 A special issue of _The Journal of Development Studies_ (Volume 51, Issue 2) was dedicated to this issue. For example, on the enduring challenges associated with agricultural data – another sector with a long history of data collection experience – see <mark>Carletto, Jolliffe, and Banerjee (2015).</mark>

> 8 Popularly known as GIGO: garbage in, garbage out.

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introduction 5

# 1 introduction

Sri Lanka has enjoyed rapid progress on poverty reduction and shared prosperity since the cessation of the decades-long conflict in 2009. In the last 10 years, GDP per capita has doubled from US$2,000 to US$4,000 (current US), and the national poverty incidence has been halved, from 9% to 4%, as measured by the national household income & expenditures survey. Despite this progress, the country scored modestly on several indicators related to disaster risk management in the 2015 Country Policy and Institutional Assessment, including quality of public administration; fiscal policy and macroeconomic management; social protection; and transparency, accountability, and corruption in the public sector.

Given this context, effective disaster risk management (DRM) strategies are essential to the continuation of and in Sri Lanka. Parts of the peace prosperity country are affected destructive monsoon regularly by floods, particularly during regional seasons. In 2017, for example, high winds, severe flooding, and landslides struck the southwestern districts of Sri Lanka–most directly, Galle, Kalutara, Matara, and Rathnapura–causing approximately 50-60 billion LKR (333-400 million USD) in damages, as estimated in the Ministry of Finance (2017) Annual Report. According to the World Bank post disaster needs assessment, the government disbursed US$6.6 million in emergency relief, and total recovery needs were later estimated at nearly US$800 million. Although the 2017 floods were exceptional, 2016 also saw unusually destructive and such events could become more in the future as a flooding, frequent result of climate change.

Asset losses are used to connote disasters’ but routinely scope, they provide only a partial measure of the total cost of any event. Other loss dimensions include lost wages and other income, insurance liabilities, days of school and doctor visits skipped, and meals foregone. For example: imagine that two identical, neighboring households experience the same property damage from a flood. If only one of the households is insured, that
displaced people; (iv) duration of displacement; (v) perceived/real disparities between different groups affected by forced displacement; and (vi) patterns of settlement.

> 8In a conjoint survey experiment, Allen et al. (2021) study preferred policy responses of Colombians in response to the large inflow of Venezuelans into their country. They find that those who have less contact with Venezuelans, those who put more weight on economic priorities, and those who see the situation in Venezuela as mainly an economic problem, tend to support policies that are more restrictive.

> 9There is also the literature on the effectiveness of cash transfers in refugee-hosting settings. Valli et al. (2019) examine whether a short-term transfer programme targeted to Colombian refugees and poor Ecuadorians led to changes in social cohesion measures. They find that the programme contributed to reported improvements in social cohesion among Colombian refugees in the hosting community but had no impact on social cohesion among Ecuadorians. See also Devereux et al. (2017) for review article on the targeting effectiveness of social protection programmes.

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local communities collectively proposing the sub-projects that they want; for a SF in Jamaica, the authors found that the process was often dominated by local elites.

In practice, it is very unlikely that all the relevant assumptions are testable (including alternative assumptions made by different theories that might yield similar impacts). Nor is it clear that the process determining the impact of a program can always be decomposed into a neat series of testable links within a unique causal chain; there may be more complex forms of interaction and simultaneity that do not lend themselves to this type of analysis. For these reasons, the so-called “theory-based evaluation” approach cannot be considered a serious substitute for assessing impacts on final outcomes by credible (experimental or NX) methods, although it can still be a useful complement to such evaluations, to better understanding measured impacts.

Project monitoring data bases are an important, under-utilized, source of information. Too often the project monitoring data and the information system have negligible evaluative content. This is not inevitably the case. For example, the idea of combining spending maps with poverty maps for rapid assessments of the targeting performance of a decentralized anti-poverty program is a promising illustration of how, at modest cost, standard monitoring data can be made more useful for providing information on how the program is working and in a way that provides sufficiently rapid feedback to a project to allow corrections along the way (Ravallion, 2001).

The _Proempleo_ experiment provides an example of how information external to the evaluation can carry important lessons for scaling up. Recall that _Proempleo_ randomly assigned vouchers for a wage subsidy across (typically poor) people currently in a workfare program and tracked their subsequent success in getting regular work. A randomized control group located the counterfactual. The results did indicate a significant impact of the wage-subsidy voucher on employment. But when cross-checks were made against central administrative data, supplemented by informal interviews with the hiring firms, it was found that there was very low take-up of the wage subsidy by firms (Galasso et al.
3%|89.4%|
|South|South|19.3%|34.1%|31.2%|32.9%|

**Panel C: Foreign Direct Investment**

|**Source**|**Destination**|**2001**|**2007**|**2008**|**2018**|
|---|---|---|---|---|---|
|North|North|89.7%|93.6%|93.4%|98.1%|
|North|South|28.4%|36.3%|36.8%|66.2%|
|South|North|31.5%|47.3%|49.9%|87.3%|
|South|South|4.8%|8.6%|8.8%|41.3%|

Note: This table shows, for each block, the year-end share of active links in all reported links. Active links refer to country pairs that have a positive value. All reported links refer to country pairs that have a positive or zero value. Country pairs with a missing value are not considered. Bilateral data are aggregated for all countries within a source region to all countries within a destination region.
we contribute to the behavioral literature on defaults, which shows that defaults increase organ donation (Johnson & Goldstein 2003) and retirement savings (Chetty et al. 2014, Thaler & Benartzi 2004, Madrian & Shea 2001). We show that a default can also be used to induce people into a behavior such as tax compliance that even rational agents may not display.

Finally, we draw on methodological contributions from two literatures. We follow the lead of Fisman & Wei (2004) in identifying misreporting by comparing two data reports on the same tax base. This approach is also used in Zucman (2013), Kumler et al. (2015), Best (2014) and Rijkers et al. (2015). Using the approach, we show that rms under-report sales as well as costs, consistent with Carrillo et al. (2016). We also construct novel estimates of compliance gaps on the extensive and payment margin. Shedding light on payment delays, we highlight the importance of analyzing payment data in addition to tax liability data, which previous studies have focused on. We also draw on the bunching literature in public nance, initiated by Saez (2010), Chetty et al. (2011) and Kleven & Waseem (2013), and summarized in Kleven (2016). This literature provides the techniques to estimate taxpayers' behavioral responses to discontinuities in the tax schedule, and translate them into income elasticities.

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power than women when not directly engaged in the decision: men have some form of effective power in 22 percent of decisions on average when not a decision-maker, while women hold it in 13 percent.

Our results indicate that traditional decision-making measures underestimate the agency of some individuals. This is asymmetrically true for husbands and fathers, who can disproportionately rely on their preferences being met without bearing the time cost and cognitive burden of actively engaging in decisionmaking through effective power by proxy. Because we have data on a diverse set of decisions, we can compare effective power in household decision-making domains that are typically women’s—such as water related activities—and those that are typically men’s domains—such as decisions over household purchases. In the two water-related decisions (on water source choice and water allocation) where men are the least involved in the decision directly, about 15 percent of those who did not make a decision hold effective power by proxy. Due to their position within the household, these men can expect water allocation to align with their preferences without direct involvement. Social norms favor the male heads’ preferences regarding water allocation more than women’s. In contrast, only two percent or fewer women have effective power by proxy when not involved in decisions in activities that are typically men’s domains (in water and household expenditures). Women are less likely to be in a position of power to delegate or to expect the outcome will happen as they would like when they are not directly involved in these decisions which men typically make.

Our survey data is also unique in that we interviewed all adults in the household and have information on the relationships among all individuals, not just their relationship with the primary respondent. This allowed us to explore relationships beyond the primary couple and to map out different household types beyond single woman-headed households and households with a husband and wife (male-headed households). Because of this we can show that decision-making power and agency varies with the household composition in households headed by men.
we only mention that **E** _sr_ is the _N ×_ 1 vector of exports of country _s_ to country _r_ , **X** _s_ is the _N ×_ 1 vector of gross output produced by country _s_ , **A** is the _GN ×GN_ global matrix of input coefficients, **B** is the global Leontief inverse matrix for the entire inter-country model and **V** _s_ is the 1 _×N_ vector that incorporates the value-added shares embedded in each unit of gross output produced by country _s_ .

In their seminal contribution, Hummels et al. (2001) propose to split gross exports between a share of domestically produced items and a share of imported inputs embedded in exports, where the latter takes into account both the direct and indirect production linkages within the domestic market. Hummels et al. (2001) define the share of import content in exports as the ‘Vertical Specialization Index’ ( **VS** ) which can also be computed for bilateral exports between _s_ and _r_ . In an ICIO framework the **VS** _sr_ indicator can be expressed as:<sup>3</sup> where **u** _N_ is the 1 _×N_ unit row vector and ( **I** _−_ **A** _ss_ )<sup>_−_1</sup> represents the domestic inverse Leontief matrix.

Although the **VS** indicator provides valuable information for several empirical applications,<sup>4</sup> there are relevant aspects that are not taken into account. In particular, imported inputs in exports are considered as a single category, without distinguishing between the part that originated abroad and the part that was originally produced by _s_ itself and then re-imported.

Indeed, by exploiting Inter-Country Input-Output tables, gross exports can be broken down according to the country that initially produced each component. Koopman et al. (2010) use the global
or non-poor. We set the threshold at the 4<sup>th</sup> percentile of non-monetary welfare in Sri Lanka and the 20<sup>th</sup> percentile in Tanzania, to reflect prevailing national monetary poverty rates. In other words, households were classified as non-monetarily poor if their non-monetary welfare, defined as the score of the first principal component, fell in the bottom four percentiles in Sri Lanka and the bottom quintile in Tanzania. Below in the robustness checks section, we also consider results when the poverty rates of the two countries are swapped.

_Table 1: Factor loadings for census-based welfare index by country_

|Sri Lanka||Tanzania||
|---|---|---|---|
|Variable|Scoring<br>Coefficients|Variable|Scoring<br>Coefficients|
|Household size|0.09|Head Literate|0.45|
|Dependency ratio||Head Ever attended<br>school|0.45|
|Children 0-14 and 65+|0.04|Head age|-0.20|
|Children 0-14 Only|-0.05|Household size|-0.22|
|Gender ratio|0.04|Dependency ratio|-0.41|
|Household education||Male head|0.12|

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# **4.4 Industry-Level TFP Changes and Policy Reforms**

To what extent can the increase in productivity be attributed to the trade and other policy reforms that occurred during the 1990’s? To answer this question, we exploit the variation in those policies across industries to examine whether changes in the individual components of productivity were systematically related to specific reforms.

In order to use the policy variation across industries, we re-create our aggregate, average, and reallocation TFP measures at the state-industry level. We use the state-industry level because this level of disaggregation allows us to consider variations in policies and other characteristics across both industries and states, and because the ASI survey is designed to be representative at this level. Figure 7 and Table 5 present the average results across all groups, weighting each group by the total number of firms that appear in that group across all years. Doing so ensures that the results are more comparable to the all-India results, since larger state-industry groups are given more weight.

This weighting scheme ensures that average productivity is nearly the same at the state-industry and all-India levels. However, the reallocation component is lower across most years at the stateindustry level. The reason is that at this level, we can only measure reallocation _within_ stateindustry groups. For example, suppose that the steel industry is more productive than the chemical industry, and that all firms in the steel industry increase output by 10%, while all firms in the chemical industry reduce output by 10%. The all-India reallocation measure will increase, but the state-industry reallocation measure will not. While it would be ideal to capture between-industry as well as within-industry market-share reallocations, our identification strategy (described below) does not allow us to use an all-India measure of productivity. Nonetheless, despite some differences, the reallocation component at the state-industry level follows the same basic pattern as the all-India measure.

We exploit the fact that the trade, licensing, and FDI reforms occurred differentially across industries to isolate the impacts of each policy on each productivity measure.
measures are then applied to data from the U.S. Bureau of Labor Statistics (BLS), and to data from several countries from the International Labour Organization (ILO). They find that lower-income countries have a lower prevalence of jobs that can be done at home.

Several articles have implemented different adaptations of DN2020. For example, Gottlieb, Grobovsek, and Poschke (2020) use labor force surveys from 57 countries and point out that lowincome countries have a high share of self-employed agricultural workers. Their ability to work from home impacts the overall labor market effect of COVID-19 in lower-income countries. The DN2020 measure is based on the data from the United States, where farms are typically large and more reliant on hired labor. As a result, DN2020 assumes that only 8.3% of all agricultural workers can work from home. In poor countries farms are much smaller and a large share of agricultural workers is self-employed, which could imply that farming may be possible from home if plots are located very close to home (or perhaps, more relevant, may be feasible while respecting social distancing guidelines). Assuming that all these self-employed agricultural workers can do their jobs from home leads to a negative association between home-based work and GDP per capita. However, suing PIAAC data, Hatayama, Viollaz and Winkler (2020) show that the positive correlation between home-based work and GDP per capita remains positive when using countryspecific measures of tasks instead of O*NET. Their sample includes countries at different levels of development such as Ecuador and Peru (where agricultural employment represents almost a third of total employment) and the United States and Germany (where the corresponding figure is about 1 percent).<sup>3</sup> The Appendix to this paper provides extended arguments regarding these measures and shows that the ability of agricultural workers to work from home is limited and, moreover, tends to increase, rather than to fall, with income per capita.<sup>4</sup>

> construct an index
Figure 7: Regional Level Education Deprivation a) b) <!-- Start of picture text --> Urban Educational Deprivation by Geopolitical Zone - GHS Rural Educational Deprivation by Geopolitical Zone - GHS<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Wave 1_NC Wave 2_NC Wave 3_NC Wave 4_NC Wave 1_NC Wave 2_NC Wave 3_NC Wave 4_NC<br>Wave 1_NE Wave 2_NE Wave 3_NE Wave 4_NE Wave 1_NE Wave 2_NE Wave 3_NE Wave 4_NE<br>Wave 1_NW Wave 2_NW Wave 3_NW Wave 4_NW Wave 1_NW Wave 2_NW Wave 3_NW Wave 4_NW<br>Wave 1_SE Wave 2_SE Wave 3_SE Wave 4_SE Wave 1_SE Wave 2_SE Wave 3_SE Wave 4_SE<br>Wave 1_SS Wave 2_SS Wave 3_SS Wave 4_SS Wave 1_SS Wave 2_SS Wave 3_SS Wave 4_SS<br>Wave 1_SW Wave 2_SW Wave 3_SW Wave 4_SW Wave 1_SW Wave 2_SW Wave 3_SW Wave 4_SW<br>c) d)<br>Urban Educational Deprivation - by Geopolitical Zone - MICS Rural Educational Deprivation - by Geopolitical Zone - MICS<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>MICS 2016/2017_NC MICS 2016/2017_NE MICS 2016/2017_NW MICS 2016/2017_NC MICS 2016/2017_NE MICS 2016/2017_NW<br>MICS 2016/2017_SE MICS 2016/2017_SS MICS 2016/2017_SW MICS 2016
whether the individual has social security coverage or not, and the last one is the size of the employing firm or establishment (firms with fewer than 5 employees are often assumed to be informal). In addition to these measures of informality, we also consider other aspects of employment quality to describe employment trajectories post graduation such as: full wages, time employment, whether the individual is a wage worker or not, employed in the public sector, or employed in the agriculture sector. To complete the picture, we also consider the quantity of employment, namely whether an individual is employed and the number of hours worked per week.

We use Labor Force Surveys (LFS) between 2006 and 2019 to construct the employment outcomes.<sup>3</sup> Labor Force Surveys were first conducted in Egypt in 1957 by the Central Agency for Public Mobilization and Statistics ”CAPMAS”, on a quarterly, biannual, and annual basis. In 2006, CAPMAS improved the questionnaire by re-ordering existing questions and creating new ones to better capture labor market conditions.<sup>4</sup> The Economic Research Forum (ERF) cleaned and harmonized the data as a part of a project that started in 2009, in which major efforts were devoted to facilitate micro data access and use in Arab countries (OAMDIA (2018b)).

In our main model we use a labor market indicator (the regional rate of employment) that is constructed directly from the 2006 through 2019 LFS survey rounds. Therefore we must restrict the estimation sample to individuals who exited the schooling system during those years. The indicators used in our second model (youth employment rate, GDP growth, oil and wheat prices) are only available at the national level but cover 1990 onwards. In that case we can include all individuals who graduated after 1990. Besides 3Data were obtained from the Economic Research Forum: OAMDIA (2019a), OAMDIA (2019b), OAMDIA (2019c), OAMDIA (2018a), OAMDIA (2018b), OAMDIA