rafmacalaba/data-use-mentions-extended huggingface dataset:
0.77]|99.37|(100.70)|1,605|
|Assets sold [before lockdown] (=1)|0.10|(0.08)|[0.34]|0.25|(0.43)|1,600|
|Assets sold [during lockdown] (=1)|0.04|(0.07)|[0.65]|0.28|(0.45)|1,602|
|Assets sold [after lockdown] (=1)|-0.02|(0.04)|[0.69]|0.11|(0.32)|1,608|
|Days of work/housework/school missed|0.47|(1.55)|[0.77]|4.48|(11.67)|1,407|
|Lost job (=1)|0.00|(0.06)|[0.83]|0.66|(0.48)|1,606|

**_Notes_** :

- The table shows the regression results on pre-specified COVID-19-related outcomes using the phone survey midline data. Each row is its own dependent variable.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The periods referred to in the tables are before lockdown: (January 15 - March 15, 2020); during lockdown: (March 15 - May 15, 2020); and after lockdown: (May 15 - interview date). Monetary values are in USD PPP and are winsorized at the top 1% of values in order to limit the influence of outliers.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level
Equation (7) is estimated based on national “use” (input-output) tables between 2000 and 2014 for China and India – countries which experienced high rates of growth in services value added between 2000 and 2014.

# <u>Results</u>

The contribution of final demand to the growth of professional, scientific and technical services between 2000 and 2014 in India – at almost 90 percent – indicates that the expansion of the sector was largely “stand-alone, i.e. direct transactions between service providers and final consumers. In fact, among the different components of final demand, net exports accounted for 66 percent of the subsector’s growth compared to 20 percent for consumption and 3 percent for investment (Table 9). This is indicative of India’s success in the export of offshore professional services, starting with BPO services such as contact and call centers, which laid the foundation for higher value-added services such as software, finance and accounting (Nayyar 2012).

In contrast, the contribution of final demand to the growth of professional, scientific and technical services in China between 2000 and 2014, was 24 percent and that of (net) exports was only 1 percent. The contribution of intermediate demand in the growth of these services was therefore paramount, but this was not driven by links with the manufacturing sector. In fact, what seemed to matter most was the input of professional scientific and technical services into mining, utilities and construction as well as other services (Table 9).

Final demand mattered less for the growth of other high-productivity services. For example, it accounted for 50 percent of the growth in wholesale and retail trade in China and India between 2000 and 2014 (Table 7). In the case of transportation and storage services, final demand mattered less, accounting for approximately 31 percent of the sector’s growth in China (Table 8). Furthermore, the large contribution inputs into manufacturing value added within the intermediate demand component indicates that the growth of these services subsectors
Policy Research Working Paper 5677

# **Abstract**

As free trade areas have proliferated and statutory tariffs have been dramatically reduced in recent decades, nontariff barriers (NTBs) to international trade have risen in importance. Destination-specific product standards are one of the major types of NTBs as they impose additional costs on exporters and increase the time required to bring a product to market. This paper examines the response of U.S. manufacturing firms to a reduction of this NTB by looking at the harmonization of European product standards to international norms in the electronics sector. Using a highly detailed dataset that links U.S. international trade transactions to U.S. firms and a new industry-level database of EU product standards, the author finds that harmonization increases U.S. exports to the EU and that this increase is due to more U.S. firms entering the EU market—the extensive margin of trade. New entrants to the EU region are drawn mainly from the most productive set of firms already exporting to developing markets before harmonization -the extensive margin of trade composition. These firms are characterized by being smaller and less productive than the firms that were already exporting to the EU before harmonization. Furthermore, harmonization decreases export sales at existing exporters -the intensive margin of trade. These findings are consistent with a model featuring the role of product standards heterogeneity across market destinations and productivity heterogeneity across firms. These results suggest that working toward a harmonization of product rules across markets could be a supportive policy to encourage small and medium size firms’ ability to enter new export markets.

This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network. 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 jreyes2@worldbank.org.

_The Policy Research
Treisman, Daniel. (2016). "Russia's Billionaires." _American Economic Review_ , 106(5): 236-241.

White House. (2013). _Remarks by the President on Economic Mobility._

- _<u>https://obamawhitehouse.archives.gov/the-press-office/2013/12/04/remarks-presidenteconomic-mobility</u>_

- ---. (2014). _President Barack Obama's State of the Union Address._ <u>https://obamawhitehouse.archives.gov/the-press-office/2014/01/28/president-barack-obamasstate-union-address</u>

- Wooldridge, Jeffrey M. (2002). “Inverse Probability Weighted M-Estimation for Sample Selection, Attrition and Stratification”. _Portuguese Economic Journal_ 1: 117–39.

World Bank. (2013). _Jobs_ . World Development Report. Washington DC: World Bank.

- ---. (2016). _Russian Federation - Systematic country diagnostic: pathways to inclusive growth_ . Washington, D.C.: World Bank.

- ---. (2017). Country Income Classification.  Retrieved on July 1, 2017 at

- <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-</u> -

- <u>lending groups</u>

- Yemtsov, Ruslan. (2008). “Through the Looking-Glass: What is behind official data on inequality in Russia over 1992-2003?” _Paper Prepared for the 30_<sup>_th_</sup> _General Conference of The International Association for Research in Income and Wealth_ . Portoroz, Slovenia, August 24– 30.

33
make) efforts to gather and upload case information.

In order to build our dataset for analysis from this database, we scraped the metadata and full text decision associated with each case. In doing so, we were able to directly extract the following for most cases: the names of plaintiffs, defendants, and judges; the type of case; the court in which the case was heard; whether the case was appealed; and the year the judgement was delivered. We also used the history associated with each case to determine whether it was an appeal.

To determine gender and ethnicity and remove non-human cases (i.e., cases with companies, organizations, or the state as litigants), we used the name information scraped from the database. Cases without gender or

> 7According to our data, the Court of Appeals almost exclusively hears civil cases; the Environment and Land Courts are largely split between civil cases and environment and land cases; the Employment and Labour Relations Courts are largely split between labor cases and civil cases; and the High Courts frequently hear a wide range of cases, including civil cases, land and environment cases, labor cases, criminal cases, and others. We have little data on Supreme Court cases, but it appears to hear mostly civil cases. Despite these general trends, the data appears to show that the courts are generally not restricted in the cases they hear, as they all tend to hear a wide range of case types.

> 8See http://kenyalaw.org/caselaw/.

5
To account for potential systematic non-response in the post-COVID-19 phone survey, we use the weights described in Section 2. By using these sampling weights, we can generate representative statistics under the assumption that data are “missing at random” conditional on the observables used in construction of weights (Wooldridge, 2007; Korinek et al., 2007). Access to PSNP and related unobserved factors are potentially correlated among households living in the same sampling unit, the kebele. To account for this, we cluster standard errors at the kebele level.

The impacts of the pandemic and the role of PSNP in mitigating them are likely to vary across households with varying socioeconomic characteristics. To uncover such potential differential impact of PSNP across various groups of households, we run our preferred specification in equation (1) for several dimensions of heterogeneity, including wealth quintiles and remoteness of location of residence.

# **5. Results**

We first discuss our results on the impact of the PSNP in protecting household food security before considering impacts on maternal and child diets.

# **_5.1. The role of PSNP in protecting household food security_**

Table 2 presents difference-in-difference estimates comparing the temporal evolution of food security of PSNP beneficiaries and non-beneficiaries under alternative definitions of PSNP participation and controlling for time invariant household characteristics. We report linear fixed effects regression results in the main text and present results from nonlinear models (fixed effects logit and fixed effects Poisson regression) in Appendix Table A5. We present results with and without sampling weights, although weighted regressions are our preferred specifications, to probe the robustness of our results. The first three columns of Table 2 report results for the food insecurity dummy variable and columns 4-6 show results for the continuous food gap measure. Columns 1, 3, 4 and 6 are based on weighted fixed effects specifications while columns 2 and 5 are based on unweighted fixed effects models. Panel A of Table 2 provides results using self-reported access to PSNP benefits; Panel B reports results using information on amount of transfers;
Commission for Reception, Truth and Reconciliation (CAVR).<sup>7</sup> This information has been collected from deponents to the Commission‘s statement-taking process.<sup>8</sup> We make use of data on the number of killings that occurred during the war in order to derive patterns and variation of violence in Timor Leste over time and across space. We use this data to identify districts and years that experienced high and low violence-intensity, both at the start of the occupation and following the withdrawal of Indonesian troops in 1999. This allows us to estimate both the impact of the first years of the conflict and the impact of the last wave of violence in 1999.

# **4.1. Identification strategy: The impact of violence on school attendance in 2001**

We first investigate the short-term impact of the 1999 violence. The empirical questions being addressed are: (i) whether the violence in 1999 imperiled school attendance<sup>9</sup> and school grade deficit, and (ii) whether different channels of exposure to conflict – displacement and house destruction – affected boys and girls and different age groups differently.

# **_4.1.1. Primary school attendance and grade deficit rates in 2001_**

We make use of information in TLSS 2001 collected at the individual and household levels on displacement and house destruction to identify conflict-affected individuals. We have constructed two different variables that try to account for the degree of severity of the conflict.<sup>10</sup> The first variable identifies individuals belonging to households that were displaced due to the 1999 wave of violence (all members displaced). The second variable identifies individuals in households that report having their house completely destroyed by the violent attacks in 1999.

The TLSS 2001 contains also useful retrospective information on school attendance and grade attained across three different academic years: 1998/99, 1999/00 and 2000/01. We are

> 7 Commission for Reception, Truth and
four-digit level. The survey covers all establishments with at least 10 employees or above a revenue threshold around 340 million Colombian pesos (at 2009 prices), which is close to 100,000 USD. The dataset contains rich information on key variables including plants’ geographical location, output production and sales for each product, total sales (local and export), material inputs, industrial and non-industrial costs, employee composition, and fixed asset holdings.

Gross output is the value of production of all products. Value-added is gross output after deducting material cost. Capital is measured using the perpetual inventory method, assuming a 5% depreciation rate, with the exception of land for which there is no depreciation. Employment size is the number of persons employed by the establishment. These include workers engaged in production and in non-production jobs. Labor cost includes wages and fringe benefits. Material costs include material input costs, electric energy expenditure, and land and building rental costs. Gross profit margin is gross output net of material cost and labor costs of workers associated with production, as a share of gross output. And net profit margin is gross output minus material cost, all labor cost, industrial and non-industrial costs, and taxes paid as a share of gross output. Nominal values are converted to 2014 Colombian pesos using the industrial production index reported by DANE which is the same for all industries. We deflate capital with the industrial production index reported by DANE for gross capital formation.

After merging manufacturing census data with the geospatial variables, the establishment-level data has complete information on 12,397 establishments engaged in 148 industries located across 267 municipalities. Similarly, the establishment-product level data contains the same number of establishments, and 6,711 products.

# _Ethiopia_

The Central Statistical Agency of Ethiopia conducts annual surveys of large and medium scale establishments engaged in manufacturing. In the surveys, economic activities are categorized as manufacturing based on United Nations ISIC-Rev.3 classification, and encompass industries 15-37 at the two-digit level. The survey covers all establishments with at least 10
# **3.1 Living Standards Measurement Study (LSMS)**

LSMS surveys are conducted by the World Bank in association with country statistical offices. We use 26 LSMS surveys from 7 sub-Saharan African countries: Ethiopia, Cˆote d’Ivoire, Malawi, Mali, Niger, Tanzania, and Uganda. All the surveys and their corresponding years are listed in Table 9. The collection of information and follow-up across subsequent surveys vary across countries. In our sample, 54 percent of respondents are women, and 86 percent of respondents reside in rural areas. Roughly 55 percent of individuals who responded to the survey were working at that time. Among these individuals, around 44 percent were working in the agricultural sector. Over 50 percent of individuals have at least a secondary level education.<sup>1</sup> The average age of a respondent is 36. Around 78 percent of sampled individuals are between 20 and 45; around 22 percent of individuals are between 46 and 66.

We primarily use data on respondents’ occupation. Following Hjort and Poulsen (2019), we categorize employed individuals as being either high-skilled or low-skilled workers. Also, given the importance of agriculture in African economies, agricultural employment is distinguished from other types of employment in part of the analysis. Respondents were classified as low-skilled workers if they worked in any of the following occupation types: farming, fishing, domestic employment. At the time of the survey, 55 percent of the sample reported being employed. Among employed individuals, 26 percent are classified as high-skilled workers, and 74 percent are classified as low-skilled workers. Farmers represented 42 percent of workers.

Using the geo-referenced information (latitude and longitude) associated with these surveys, we combine them with the roads and electricity datasets. An average household is located 14.6 km away from a main road and 7.9 km away from an electric grid.

# **3.2 Demographic and Health Surveys (DHS)**

Demographic and Health Surveys are repeated, cross-section
microeconomic point of view, the benefits are more nuanced. On the one hand, when women participate in the labor force, they gain a voice in society, and as a consequence, households invest more in education and health (Duflo, 2012). On the other hand, while higher FLFP may imply greater welfare, this may not always be true if working conditions are not ideal. Evidence from developing countries suggests that many women work in low-paid jobs or unsafe working conditions out of necessity.

We use 18 cross-sectional waves of the National Employment Survey between 2001 and 2018, three waves of the World Values Surveys between 2001 and 2011, and four waves of the Arab Barometer between 2006 and 2017. Our goal is to gain a better understanding of Morocco’s low FLFP and to initially explore the constraints and choices that women face regarding their labor market status. For example, a restricted demand for female employment may be associated with traditional attitudes toward women’s work, which pulls women out of the labor force—an understudied factor in Morocco.

The structure of the paper follows: Section II reviews the existing literature on the microdeterminants of FLFP, along with the role of values and identity in women labor. Section III describes Morocco’s position in context in the MENA region and the world. Section IV describes the data used and its sources. Section V describes the profile of active and inactive women and female workers in terms of participation, employment, and inclusion. Section VI describes the methodology used to summarize the interplay of multiple variables. Section VII presents the results. Finally, section VIII pulls the paper together with conclusions.

3
reporting electricity as a major constraint, and they consume more self-generated power. As expected, more firms in low-income countries face electricity constraints as opposed to high-income countries. According to the World Bank’s Enterprise Surveys, an average of 70 percent of firms in low-income countries reports electricity as a major constraint, compared to only 13 percent of firms in high-income countries (Figure 1).

It is important to note that the electricity constraints faced by firms in low-income countries might relate to both the lack of electricity and higher tariffs, whereas they primarily reflect high electricity costs in high-income countries. In addition, the average share of self-generated electricity by firms in low-income countries accounts for 26 percent of the total, while it comprises only 13 percent in high-income countries. Again, these numbers are not directly comparable, as firms might choose to self-generate electricity as part of their production process, not because of a lack of grid electricity. Finally, the IEA data show that electricity consumption is much higher in high-income countries as opposed to low income-countries because of availability of power, high production needs, and affordability for the general population, among other factors. In terms of sectoral consumption, manufacturing and services are more energy intensive than agriculture across all income groups.

Data examination shows that countries experienced much higher growth in electricity consumption compared to the value added in the long term, and these higher growth rates occurred primarily in lowincome countries. Figure 2 depicts the cumulative growth in the real value added and electricity consumption between 1996 and 2016 for three sectors: agriculture, manufacturing, and services.<sup>11</sup> Capturing growth rates instead of levels for the value added and electricity consumption reflects the selected empirical methodology, which uses first differences to estimate the relationship between these two indicators in addition to level regressions. As discussed above, the data set includes a limited number of low-income countries due to the lack of sectoral data for electricity consumption. Despite a large number of observations for advanced economies, the variation in growth of both
securities. In addition, if the market appetite for a type of security is expected to increase in the future, this is reflected through the increasing issuance amounts over time, within the decision horizon.

The strategy alternatives are chosen to ensure that each alternative represents a specific policy direction (with a story behind it) that can be pursued by DMs. Some of the alternatives tested previously include strategies with regard to ―cost minimization,‖ ―moderate risk reduction,‖ ―currency risk reduction,‖ ―ambitious risk reduction,‖ and ―maintain current profile.‖ Alternatives based solely on a single borrowing instrument are also tested to identify the different cost and risk characteristics of the securities (for example, 100% CPI-indexed bonds vs. 100% floating-rate notes). The strategy alternatives are designed by plugging in the appropriate weights in the financing strategy vector, described in section 4.3. For example, weights of local currency instruments are increased in the vector to develop a ―currency risk reduction‖ strategy.

# **5.3 Runs and Analysis of Results**

Following the creation of simulation paths and selection of alternative strategies to be tested, TDSM then calculates the cash flows associated with each strategy. This module also compiles summary statistics (mean, conditional-at-risk values for different percentiles, etc.) of the distributions for these strategies with respect to the measures discussed in Section 3.1. The future levels of debt structure metrics discussed in Section 3.3 is calculated for each strategy tested. As the model is capable of producing interest and principal payments and future debt stock levels for each strategy under each simulation path, other cost and risk indicators can easily be calculated.

Each alternative strategy following a different policy direction is then numerically presented in terms of the debt structure metrics. For example, the share of local debt will be higher for a ―currency risk reduction‖ strategy compared to a ―maintain current profile‖ strategy. Debt structure metrics for alternative strategies is then reported to senior management together