rafmacalaba/data-use-mentions-extended huggingface dataset:
the result of two factors: a) wages are more flexible in the informal sector, and hence falling productivity results in a larger hiring drop in the formal sector; and b) the shock has been primarily to the tradable sector, composed mostly of formal-sector firms. The rise in informality is worrisome because the working conditions of the jobs are in general more precarious than in the formal sector. Herrera and Mohamed (2010) also find that wages in the informal sector are 13 to 14 percent lower than their equivalents in the formal sector.

The impact of the global crisis on poverty in Egypt is much less clear-cut than the impact of the crisis on employment and unemployment. Given the low elasticity of employment to output growth and the small size of the Okun coefficient described above, the only likely channel for the crisis to affect poverty is through the household income reduction originated by the fall in remittances from abroad. The balance of payments (BoP) shows that private remittances fell by 10 percent between FY08 and FY09, from US$8.6 billion to US$7.8 billion. Though the fall is moderate, it still leaves the level of post-crisis remittances above historical values. Using household income data for 2009, we found that only 7 percent of households that receive remittances from abroad are poor.<sup>16</sup> How this reduction affects poverty is fuzzy for two reasons. First, because most households that have a migrant are non poor; Roushdy et al. (2008) report that 87 percent of the migrants belong to non-poor households. Our estimates based on household income and expenditure survey reported in the previous paragraph show that in 2009, only 6.6 percent of the households who received remittances were poor. We did a simple partial equilibrium exercise of reducing the value of remittances by 10 percent, and examining how this would change the number of poor households: it increased to 6.7 percent, which is a trivial change. This result confirms Roushdy et.al.
objective deprivation levels and is strongly supported by recent evidence from a qualitative study in Morocco that found both genders arguing that men suffer much more than women from labor deprivation.

Furthermore, we find our measure of relative labor deprivation to be strongly associated with subjective responses in the household and youth survey to questions about social status and emigration. For example, the satisfaction levels reported by youth regarding their opportunities to improve their social status in the future are inversely related to the paper’s estimated levels of relative labor deprivation. These findings underline the importance of the reference group in transforming objective conditions of deprivation into subjective feelings of deprivation, and they are relevant for other countries in the region and call for further research in this area.

The paper is organized as follows. In section two we provide an overview of youth and gender in the Moroccan labor market. In section three, we present the relative deprivation framework that we intend to apply in the paper, while section four describes the data we use. Section five illustrates the results and section six concludes.

4
# **REFERENCES**

<mark>Abadie, Alberto. “Statistical nonsignificance in empirical economics.”</mark> _<mark>American Economic Review: Insights</mark>_ <mark>2, no. 2 (2020): 193-208.</mark> <mark>Afriat, Sydney N. “The construction of utility functions from expenditure data.”</mark> _<mark>I</mark> nternational economic review_ <mark>8, no. 1 (1967): 67-77.</mark> <mark>Anderson, Michael L. “Multiple inference and gender differences in the effects of early intervention: A reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects.”</mark> _<mark>J</mark> ournal of the American statistical Association_ <mark>103, no. 484 (2008): 1481-1495.</mark> <mark>Andreoni, James, and Lise Vesterlund. “Which is the fair sex? Gender differences in altruism.”</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>116, no. 1 (2001): 293-312.</mark> <mark>Andreoni, James, Michael A. Kuhn, John A. List, Anya Samek, Kevin Sokal, and Charles Sprenger. “Toward an understanding of the development of time preferences: Evidence from field experiments.”</mark> _<mark>J</mark> ournal of public economics_ <mark>177 (2019): 104039.</mark> Ashdown, D.and Bernard, M. (2011). Can explicit instruction in social and emotional learning skills benefit the social-emotional development, well-being, and academic achievement of young children? Early Childhood Education Journal, 39(6), 397-405.

Attanasio, Orazio, Ricardo Paes de Barros, Pedro Carneiro, David K. Evans, Lycia
4|
|29|2|3|89.4|
|37|1|1.5|90.9|
|39|2|3|93.9|
|40|1|1.5|95.5|
|41|1|1.5|97|
|43|2|3|100|
|Total|66|100||

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

_Notes_ : The table shows the number of work days as enumerator during the whole survey period.

33
American Science and Technology Indicators Network (Red Iberoamericana de Indicadores  de Ciencia y Tecnología). This source provides two alternative definitions of R&D, but these are actually two sub-components of R&D – investments in research and investments in development for a handful of countries. We use the total, which is available for most countries of the region. The data for Taiwan (China) are from the Taiwan (China) Statistical Yearbook.

> 3 UNESCO Statistical Yearbook (1980, p. 742). Definitions are common to the OECD, RICYT, and all are based on the Frascatti manual developed by the OECD in the early 1960s.

5
workers, and papers on firm-worker matching which they claim has been mainly present in rich countries. Our literature review builds on these surveys and adds papers suggesting that the effects on labor are seen from different perspectives such as the creation of employment opportunities, changes in the labor structure, and demand for labor. The studies reviewed suggest that the creation of jobs from ICT adoption is an effect mainly present in developing countries while labor displacement (higher demand for people with ICT training) is observed in the developed world (Eichhorst et al., 2016). However, there is no evidence of substitution effects in developing countries. Despite the findings, there is evidence that ICT have a positive impact on labor through increased productivity. This is supported by the work of Cusolito et al. (2020), which shows that overall, ICT enhance labor and, in some cases, even lead to higher wages for both skilled and unskilled workers.

# **2. Economic growth effects at the country level**

Various studies emphasize the link between ICT adoption and economic growth using data from multiple countries, periods of time, estimation techniques, and technologies (Choi and Yi, 2009; Niebel, 2018; Stanley et al., 2018). ICT have transformed production in many sectors of the economy; however, growth effects have been hard to identify in the macroeconomic data (Maurseth, 2018). This phenomenon has been popularized as the Solow-paradox after Robert Solow noted that “You can see the computer everywhere but in the productivity statistics” (Solow, 1987). A similar phenomenon is noted by more recent studies like Brynjolfsson et al. (2019) who claim that despite the big developments with AI, measured productivity has declined in the last decade. To analyze this 5
effect of increases in the minimum wage and greater compliance with legislation on the employment of men in the formal sector. The results of this study again support the hypothesis that a higher minimum wage contributes to the growth of informal employment. Thus, minimum wage increases make it more probable that a formal worker will end up working in the informal sector or being self‐employed. It was also found that the minimum wage, while reducing the chances of unemployment and inactivity, also discouraged the hiring of new employees.

In Indonesia, Bird and Manning (2002)<sup>6</sup> estimated the impact of the minimum wage on the formal and informal employment of workers in the urban sector over the period 1990‐2000. Based on panel data from the provinces, this analysis concluded that the informal sector grew during the period, mainly due to the displacement of workers from the modern sector of the economy and the contraction of formal employment following increases in the minimum wage. These results for Indonesia were corroborated by Comola and de Mello (2011) in a recent study of individual workers covering the period 1996‐2004, but these authors also found that the increases in informal employment more than offset the losses in formal employment, resulting in a net positive effect.

These findings notwithstanding, a recent study on Indonesia by Magruder (2013) indicated that in economies with high labor productivity where the minimum wage is set on the basis of local conditions, introduction of this policy can stimulate an economy’s development—an outcome known in the economic literature as the _big push effect_ . Based on panel data for the 1990s, Magruder (2013) showed that increases in the minimum wage at the district (kabupaten) level generated greater local demand (measured in terms of expenditure), which led in turn to a greater demand for labor. Such results would be consistent with the expansion of formal employment and contraction of informal employment. However, these trends are only observed in non‐tradable sectors of the economy.
**E. Primary expenditure by population size, 2011-23**

# **F. Volatility of expenditure, 2000-23**

<!-- Start of picture text --> Percent of GDP Standard deviation<br>60 7<br>6<br>50 5<br>4<br>40<br>3<br>2<br>30<br>1<br>20 0<br>10<br>0<br>Micro states Other small states<br>Small states Small states: Commodity exporters Small states: Commodity importers EMDEs excl. small states<br><!-- End of picture text --> _Sources_ : GFS (database); International Monetary Fund; WEO (database); World Bank. _Note_ : EAP = East Asia and Pacific; EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean.

A. Average total expenditure to GDP ratios in 35 small states and 112 other EMDEs.

B. Average total expenditure to GDP ratios by major component of government spending for the period 2011-22. Sample includes 19 small states and 80 other EMDEs.

C.-E. Average primary expenditure to GDP ratios over the period 2011-23.

C. EAP small states include 10 economies, LAC small states include 11 economies, and other small states include 11 economies.

D. Small states commodity exporters include 13 economies, small states commodity importers include 19 economies, and tourism-reliant small states include 20 economies.

E. Micro states have a population of less than 200,000. Micro states include 12 economies, and other small states include 20 economies.

F. Standard deviation of annual change in total expenditure to GDP ratios in 35 small states and 117 other EMDEs for 2000-23. Small states commodity exporters include 13 economies, and small states commodity importers include 22 economies.

41
# Survey waves used|3|3|2*|2|2*|2|2|2|2*|2|
|# Observations|931|8220|888|1214|1204|887|1438|1492|673|1232|
|# Enterprises|799|7619|673|1166|1052|742|1199|1120|582|1117|
|# Exporters|192|608|72|207|185|99|437|129|86|182|
|# Importers|379|1024|160|379|425|217|415|270|158|337|
|# Exporters or<br>importers|434|1417|230|466|511|258|587|344|193|430|
|# Exporters and<br>importers|133|207|26|118|92|52|259|52|47|88|
|# Holders of|55|445|24|89|141|16|146|151|65|101|
|TechnologyLicensee|||||||||||

* The reported results use data from the last two waves.

36
49 consumption cities (Bogot ́a is a “medium” consumption city but Barranquilla is an “extreme” consumption city).

# **B Robustness for the Mapping Analysis**

**Non-Linearities.** We obtain similar residuals to those in the main text if we presented also include the square, cube, and perfect fourth of the urban share in 2000, and their interactions with the population dummies (coefficient of correlation = 0.99), in case there are non-linearities in the relationship between MFG+FIRE employment, urban economic development, and city size.

**Per Capita GDP.** We obtain a coefficient of correlation of 0.97 if we use log per capita GDP in 2000 (PPP and constant international dollars) instead of urbanization (including the square, cube, and perfect fourth of log per capita GDP, and their interactions with the population dummies). This is not surprising since urbanization rates and log per capita GDP are highly correlated cross-sectionally (correlation of 0.91 in 2000; N = 178).

**Omitting Urbanization.** The correlation is 0.99 if we do not control for the urban share and do not interact the population dummies with it. We then do not allow the relationship between city employment and size to change with urban economic development.

**Using the Raw Employment Shares.** We obtain a coefficient of correlation of 0.89 if we simply consider the raw, i.e. non-residualized, city-specific employment shares. While the residualization was a priori important to ensure we compare apples with apples, the very high correlation indicates the residualization is not entirely necessary.

**Weights.** We obtain a coefficient of correlation of 1.00 if we do not modify the weights so as to over-weigh developed countries (which are under-represented in IPUMS). In that case, the weights are only based on the FUAs’ population levels c. 2000. We obtain a coefficient of correlation of 1.00
Policy Research Working Paper 10025

# **Abstract**

Previous literature found overwhelming evidence of an agricultural gender gap in favor of male farmers. The case of Sri Lanka is unique as agricultural productivity, measured by yield per unit of land, is 25.4 percent higher among female farmers than male farmers. Using the nationally representative 2016 Sri Lanka Household Income and Expenditure Survey and the Oaxaca-Blinder decomposition technique, the paper explores the sources of this unconditional female productivity advantage. The analysis finds that the smaller plot size cultivated by women is the leading source of female productivity advantage, reflecting the inverse relationship between cultivated area and productivity. However, this productivity advantage does not translate into women’s higher crop earnings. Another important source is the gendered pattern of crop mix as women tend to cultivate more high-value, export-oriented crops, while men are more likely to grow paddy with low productivity. Once controlling for plot size and crop mix, a conditional male productivity advantage emerges, reflecting men’s greater access to agricultural resources and potentially an unequal pattern of division of labor associated with social and gender norms. Policies to promote equitable access to resources and address other constraints to women’s productivity in agriculture continue to be important in promoting gender equality.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at ykim8@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this
1_._000<sup>_∗∗∗_</sup><br>(0_._000)|
|Constant|2_._060<sup>_∗∗∗_</sup>|2_._748<sup>_∗∗∗_</sup>|2_._197<sup>_∗∗∗_</sup>|2_._996<sup>_∗∗∗_</sup>|
||(0_._012)|(0_._004)|(0_._004)|(0_._000)|
|_R_<sup>2</sup>|0.213|0.204|0.518|1.000|
|Num. Obs.|123,101|127,044|127,044|127,044|

_Source_ : OLS estimation using SPADIES for actual data and model’s baseline predictions for simulated data. _Notes_ : Dependent variable is ln(classes completed per year), or ln( _xt_ ) in the model. An observation is a student-year; years 1-8 are included. Ability is _θ_ , effort is _e_<sup>_∗_</sup> _t_<sup>,andshocktoclassescompletedis</sup><sup>_zt_.All</sup> regressions include year fixed effects (not shown). Standard errors (in parentheses) are clustered by student. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

One advantage of our structural model is that we can recover variables—effort and shocks— not observed in the administrative data. This allows us to expand regression (2) in order to gauge the relative roles of effort, performance shocks, and ability. Adding effort (column 3), we explain an additional 30 percent of the variation in classes completed. As expected, we explain all the variation in
**<u>Table A.3 Attrition</u>**

||**Total Households**<br>**at Baseline**|**Not Found at**<br>**Midline**|**Attrition Rates**<br>**(%)**|
|---|---|---|---|
|Control|1,872|267|14.3|
|Arm 1|1,406|198|14.1|
|Arm 2|1,394|175|12.6|
|Arm 3|1,395|152|10.9|
|Full sample|6,067|792|13.1|

44
This paper contributes to several related strands of the literature. Recently published volumes such as Baldwin and di Mauro (2020) have reviewed the range of emerging policy responses to COVID-19 and provided advice on how policy frameworks should evolve – although most of the focus has been on high-income economies and China. Authors such as Cororaton and Rosen (2020), Granja et al. (2020), De Marco (2020), Kozeniauskas et al. (2020), and Cui et al. (2020) have focused on the reach and targeting of specific programs in China, Italy, Portugal, and the United States. Additionally, this paper contributes to the emerging literature by authors including Chetty et al. (2020), Granja et al. (2020), and Cui et al. (2020) that have provided some early evidence on the effectiveness of programs to help firms weather the impacts of the pandemic in the same countries, as well as previous studies such as De Mel et al. (2012) and Bruhn (2020) that focused on experimental and quasi-experimental evidence from previous crises in Sri Lanka and Mexico.

The rest of the paper is structured as follows. The next section presents the data. The following two sections present policies that have been announced, including evidence on access to policies, as well as policies that firms identify as more needed. Then, we present evidence on mismatches between the demand and supply of policies, mistargeting, and barriers to accessing public support. Recognizing that the COVID-19 crisis has significantly altered firms’ outlook and heightened uncertainty (Altig et al., 2020), the section that follows specifically focuses on the relationship between receiving policy support and firms’ expectations and uncertainty. Lastly, we present some preliminary evidence about the effectiveness of different policies. The paper concludes with a discussion of policy implications and directions for future research.

# **2. Data**

We rely on data from the first wave of the Business Pulse Surveys (BPS) developed by the
leading up to displacement and how they evolved after. Specifically, this analysis focuses on the 1,116 households that were interviewed at least once in the six months leading up to displacement and surveyed at least once in the month of displacement or the following year. These displaced households were surveyed an average of 10.4 times over the course of the survey, with a minimum of two interviews and a maximum of 38. Figure 1 reports the number of households surveyed by month surrounding displacement. The highest number of interviews were in the month of displacement (932) and the month before displacement (757), while the number of household interviews declines for months further before or after displacement.<sup>14</sup> In addition to reflecting a large displacement crisis, the data further illustrate the dire food security situation just prior to displacement. Figure 2 demonstrates that the average FCS of households that later become displaced was stable for the six months prior to displacement and was between 46 and 49, which is significantly below the preconflict average; and the average rCSI was between 20 and 21, which also represents a significant increase from prior to the conflict.<sup>15</sup> Furthermore, these averages for displaced households are lower than the average for non-displaced households in the same WFP survey (e.g., WFP 2019), which is consistent with a number of other data sources (e.g., FAO 2017; IPC 2017; World Bank 2017b; etc.).

However, it is difficult to attribute these patterns to the impacts of displacement alone and the difference is difficult to interpret. For example, if large changes in the humanitarian situation were causing a significant amount of migration, it could be the case that households that never became displaced might have been exposed to significant changes in the humanitarian situation that displaced households were able to avoid. Alternatively, even if forced displacement was only driven by violence, the difference in food access between displaced and non-displaced households could
# **1 Introduction**

For consumers, the challenge of identifying the quality and reliability of goods prior to purchase can be problematic, particularly in international markets. Consumers rely on quality and efficiency signals to reduce search costs. I look at three of the signaling tools available to firms: obtaining a quality certification from the International Standards Organization (ISO), buying a technical license from a foreign firm, and having an external auditor review financial statements. I also analyze firms’ use of email and/or a website to communicate with clients and suppliers. These technological tools work in the same way that signaling tools do—they directly reduce search costs for potential customers in foreign markets. These technologies make the firm more visible and accessible to potential customers located far away. The objective of this study is to identify how these tools affect the probability that a firm exports and amongst exporting firms how they affect the export intensity of these firms.

Using firm-level data from the World Bank’s Enterprise Survey database, which covers more than 100 developing countries, I find that a firm that uses the Internet and/or is ISO certified is more likely to export; and among exporters, those that use these tools sell a higher percentage of total sales in foreign markets. ISO certification, in all specifications, is the best tool for firms. Firms that are ISO certified are 22% more likely to export and have 41% higher export intensity than those that are not certified. Firms that use their own website to communicate with clients and suppliers are 11% more likely to export whereas firms that use email sell 31% more abroad than firms that do not use email.

2