rafmacalaba/data-use-mentions-extended huggingface dataset:
<br>20 20<br>0 0<br>No Yes No Yes No Yes No Yes No Yes No Yes<br>Ethiopia Malawi Uganda Ethiopia Malawi Uganda<br>First Quintile Second Quintile Third Quintile<br>Fourth Quintile Fifth Quintile<br>Prevalence of moderate or severe food insecurity Prevalence of moderate or severe food insecurity<br>Prevalence of severe food insecurity Prevalence of severe food insecurity<br><!-- End of picture text -->
//biogeo.ucdavis.edu/data/gadm3.<br>6/gadm36_shp.zip|Global Administrative Areas. 2012.<br>GADM database of Global<br>Administrative Areas, version 2.0.|
|GAUL – Global<br>Administrative<br>Unit Layers<br>(Gaul0–Gaul1)|Information is provided at two spatial levels:<br>national (Gaul0) and subnational (Gaul1). The<br>reference spatial layers are derived from the<br>GAUL data set, implemented by the UN Food<br>and Agriculture Organization (FAO) within the<br>CountrySTAT and Agricultural Market<br>Information System(AMIS) projects.|https://data.europa.eu/euodp/en/data/<br>dataset/jrc-10112-<br>10004/resource/609a472e-7e26-4b63-<br>8003-17298cb45e2a<br>https://mars.jrc.ec.europa.eu/asap/files/<br>gaul1_asap.zip|Europa.EU<br>http://data.europa.eu/eli/dec/2011/8<br>33/oj.|
|Crop coverage –<br>Copernicus Global<br>Land Cover Layers|Mapped crop coverage data layer (2019<br>vintage).|https://land.copernicus.eu/global/produ<br>cts/lc|Buchhorn, M., B. Smets, L. Bertels, B.<br>De Roo, M. Lesiv, N.-E. Tsendbazar, M.<br>Herold, and S. Fritz. Copernicus Global<br>Land Service: Land Cover 100m:<br>Collection 3: epoch 2019: Globe 2020.|
|Population – GHS-<br>POP R2019A|This spatial raster data set depicts the<br>distribution of population, expressed as the<
, SDG4, SDG8, SDG9 and SDG10. Access to and use of the internet have a positive impact on labor market outcomes in general, and especially for women, by driving economic opportunities and facilitating access to knowledge and information. Access to affordable and relevant digital devices and information provides more flexible work arrangement and saves workers time and money (Dettling, 2013; Antonio and Tuffley, 2014; Ben Moussa and Seraphim, 2017; Valberg, 2020; Viollaz and Winkler, 2020; The Economist Intelligence Unit, 2021b). Such flexible arrangements may be more needed in developing countries, such as those in the MENA region, where women are the main caregivers (Chen, 2004; Antonio and Tuffley, 2014; World Bank, 2016).

In spite of a range of evidence suggesting the impact of digital divide on gender equality and women’s economic empowerment in developing countries, empirical literature on the question is scarce, particularly in the MENA region. A study in rural areas of South Africa showed that full coverage by a mobile phone network leads to a 15-percentage point increase in employment and that women accounted for most of the change, although most of the women entering the workforce were without significant childcare obligations (Kloner and Nolen, 2010). A study of the implications of ICT diffusion in the United Arab Emirates on women’s empowerment showed it promoted access to education and job market opportunities and other resources (Ben Moussa and Seraphim, 2017). Similarly, a study using the Jordanian Labor Market Panel Survey found that female labor force participation increases with internet adoption, mainly among older skilled women, a result researchers attributed to their access to online job search (Viollaz and Winkler, 2020).

4
# **4 Constraints and Risks Faced by Household Enterprises**

36. Household enterprises face constraints and risks that inhibit their growth and trap them at a low level of productivity. When analyzing them, several important points should be borne in mind. First, HEs differ by location, nature of activity, and characteristics of operators, and may therefore be affected differently by different constraints. While recognizing these differences, it is certain that some constraints are most binding across all HEs. Second, while the study seeks to identify the many constraints HEs face in operating their business, the focus is more on highlighting the underlying differences in how they are affected by these constraints. Third, some operational difficulties faced by HEs are cited as constraints, but may in fact be a reflection of the HE operators‟ weak capacity to do business, the unfavorable business environment in which they operate, and/or their frustration with the government‟s failure to support them. Finally, the analysis of the constraints, risks, and coping strategies of HEs are based primarily on data drawn from the ILFS and the FGD surveys, but also made use of the limited literature available, most notably the studies conducted by Jin and Deininger (2008) and by Lyons and Msoka (2007).

37. Lack of access to credit (hence the lack of capital) was the most often reported constraint by HEs. This is not surprising, as it also applies across the MSME sector (Skof, 2008). Another most commonly cited constraint is the lack of workspace to conduct business. Because of their pervasiveness, these two issues are given greater prominence in the analysis that follows.

## **4.1 Types of Constraints**

## 4.1.1 **_Constraints by Sector/Nature of Activity_**

38. Some of the most binding constraints faced by HEs are specific to the nature of their activity. Business premise, power, and technology constraints are most binding among HEs engaged in manufacturing.
Policy Research Working Paper 8643

# **Abstract**

There is a global trend toward more production, use, and sale of services by manufacturing firms. This phenomenon is known as the servicification of manufacturing. Services inputs as well as services activities within manufacturing firms account for over half of the value of manufacturing exports. This paper uses a unique firm-level data set to analyze the link between servicification and productivity in Turkey. Although servicification has the potential to boost firm performance, the opposite appears to be the case in Turkey: manufacturing firms with service affiliates tend to be less productive. The type of services produced matters. For instance, firms that have post-manufacturing (transport and distribution) service affiliates are particularly less productive. Regulatory restrictions in services are explored as an explanatory factor. Productivity gaps appear in the same areas where services are more restricted, such as in post-manufacturing services.

This paper is a product of the Finance, Competitiveness and Innovation 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/research. The authors may be contacted at thaven@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 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
– but positive -- growth years after 1998. This difference persists even when the figures for changes in aggregate private consumption from NAS are considered rather than GDP growth. This is not a phenomenon unique to Poland and may be explained by the unusually high investment accumulation over the 1990s, and by its relatively high export rate.<sup>3</sup> Reconciling these two series is beyond the scope of this report, but it is important to note that the HBS shows declining values of average per capita consumption for the period 1999 – 2002 while the NAS data show moderate positive growth.Thus, the estimates of poverty, which are based on the HBS, show increases in poverty in these years despite the positive consumption growth rates shown by the NAS data.

When the responsiveness of poverty to growth in average consumption (rather than to GDP growth) is considered, the analysis shows a very strong link between growth and poverty reduction. Over the last decade the average estimated elasticity of poverty with respect to growth in average consumption from the HBS data has been 3.6 with a peak of 4.11 in 1999.<sup>4</sup> Given Poland’s GDP level, this value compares very well with the estimates for other transition countries and with those typically found in countries outside of the region (Bruno, Ravallion, and

> 3 Adams (2003). 4 These estimates are in line with the value of 3.5 given in World Bank (2002).

5
events served as a reminder that while the probability of a nuclear plant accident is quite low, its consequences can be extremely severe. Like the disasters at TMI and Chernobyl, Fukushima could cause political fallout, at the global level, which might in turn substantially derail a nuclear renaissance.

A public opinion survey sponsored by the industry and carried out across 24 countries in May 2011 found that 62 percent of the respondents opposed nuclear power, and one quarter (26 percent) of those opposed indicated that they had changed their previously held views because of the events in Japan.<sup>3</sup> However, the results varied significantly across countries. Opposition in much of Europe and some developing countries seems to be high: 81 percent of those surveyed in Italy are against nuclear power, 79 percent in Germany, 67 percent in France, 60 percent in Belgium, 81 percent in Mexico, 72 percent in Argentina, 69 percent in Brazil, 67 percent in Indonesia, 61 percent in South Korea, 60 percent in South Africa, and 58 percent in China and Saudi Arabia. Majorities in India, Poland, and the United States continue to support nuclear power (Ipsos, 2011), though support seems to be weakened. According to a Gallup poll conducted in summer 2011, 53 percent of Americans supported continued operation of the nuclear energy facilities that are closest to their homes despite the heightened concerns about safety (NEI, 2011a).

Critics argue that the nuclear renaissance met economic reality and began faltering even before Fukushima. They claim that unlike other industries (e.g. aircraft manufacturing) whose unit costs decline with cumulative technology deployment and output, the nuclear industry actually has been characterized by a negative learning curve and a substantial escalation of construction costs (figure 3).<sup>4</sup> It is pointed out that the history of the nuclear industry is replete with construction delays and cost overruns. The flagship EPR project at Olkiluoto, Finland—the first nuclear plant ordered in Western
-2-

reveal the contributions to earnings made by general education, formal VTE, and apprenticeships (informal VTE). The specification will bring out the role of diplomas and certificates obtained. Second, the role of VTE in gaining accezd to different types jobs is ana4yzed. This will be portrayed by binary choice models which, successively, will sz.0w the effects of VTE on the choice between self-employment and wage-employment, the choice between private and public sector employmenc, and on the choice between formal and informal sector wage employment. The explicit consideration of the employment selection process permits to test for and, if needed, to correct selection bias in the estimated earnings functions, at each stage of the analysis. Third, the cost structures of both formal and informal VTE are analyzed: out-of-pocket costs, opportunity costs, and institutional costs. In combination with the earnings analysis, this will permit to compute private and social rates of return to investment in diferent types of vocational and technical education.

The plan of the paper is as follow. In the next section, we discuss the household survey which is the main data source for the analysis. Sections 3 to 5 are devoted to a description of, respectively, the general education sector in C8te d'Ivoire, the formal system of vocational and technical education, and the system of informal apprenticeships. Section 6 outlines the Bank's involvement in the education sector in the country. The empirical analysis starts in section 7 with the presentation of results at the national level. The discussion covers the impact of VTE on earnings and on access to jobs, the private and
placements after the completion of the program. Although these job agreements were not legally binding, the institutional change created an important slowdown in registration and enrollment, which in turn affected the program’s take-up rates as shown in Section 5.

# **A Complementary Treatment Design**

Because no information exists regarding the effectiveness of this ALMP, or any similar intervention in Mongolia, the average participant might not be aware of the labor-market benefits of completing training. Indeed, administrative data showed that dropout rates were particularly high for the MVTP. This lack of information about the benefits of vocational training is even more important in the context of Mongolia’s very rapid transition from a state-controlled to a market-based economy. Less than 20 percent of unemployed Mongolian youth in Ulaanbaatar indicated they were aware of public-information sources regarding employment, while less than 5 percent made use of them in 2014 (Shatz et al., 2015).

Thus, inspired by the work of Jensen (2010) in the context of educational interventions, an information treatment is added to the study’s evaluation framework. Participants are randomly selected to receive weekly newsletters with information about labor-market outcomes among skilled and unskilled workers in Mongolia.<sup>5</sup> The newsletters stated market wages for occupations in sectors that were similar to those of the trainees and compared wages for jobs filled by unskilled workers. This information was estimated based on the 2012 Mongolian Household Survey. Figure S1.1 in the supplementary appendix available with this article at The World Bank Economic Review website shows a typical newsletter submitted to trainees.

> 5 In the context of formal schooling in the Dominican Republic, Jensen (2010) showed that students tended to underestimate the returns to formal schooling. When they were correctly informed, however, both enrollment in the subsequent year and the average length of formal schooling increased.

11
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 44 4. Repeat steps 2-3 for _l_ = 1 _, . . . , L_ . The MC approximation of the EB predictor of _δd_ is obtained by averaging the indicators over the _L_ simulated censuses, that is, In step 2, it is necessary to simulate _L_ vectors **y** _dr_<sup>(</sup><sup>_l_)fromamultivariate</sup> Normal distribution of size _Nd − nd_ (which can be huge in many real applications) with covariance matrix **V** _dr|s_ , making the process computationally unfeasible. Molina and Rao (2010) proposed a procedure that avoids the generation of huge multivariate normal vectors, by noting that **V** _dr|s_ given in (6.8) is the covariance matrix of a random vector **y** _dr_<sup>(</sup><sup>_l_), whose</sup> elements arise from the following nested error model,

where _v_<sup>(</sup><sup>_l_)</sup> and _ε_<sup>(</sup><sup>_l_)allindependentandsatisfy,respectively,</sup> _d di_<sup>are</sup> Using the model (6.13)–(6.14) to generate the non-sample elements _ydi_<sup>(</sup><sup>_l_),</sup> _i ∈ rd_ , instead of generating a multivariate Normal vector of size _Nd − nd_ , we only need to generate 1 + _Nd − nd_ independent Normal random _ind ind_ variables, _vd_<sup>(</sup><sup>_l_)</sup> _∼ N_ (0 _, σu_<sup>2(1</sup><sup>_−γd_))and</sup><sup>_ε_(</sup> _di_<
sensitivity of measurement to the differences in length and complexity between the base survey and target survey questionnaire design, provided that the identical questions are utilized across the surveys. It is thus reasonable that changes in the distributions of the predictor variables over time for these four models can capture the change in the poverty rate between the rounds (i.e., satisfying Assumption 2 in our imputation framework discussed in the next section).

On the other hand, Table 1 also shows that the food and non-food consumption aggregates that can be created with the TA 2 and TA 3 data, as explained above, present large and statistically significant differences vis-à-vis the TA 1 counterparts. Relative to TA 1, the TA 2 and TA 3 food consumption modules decrease the reported household food consumption expenditures, respectively, by 22 and 31 percent. The non-food consumption module administered in TA 3 decreases the reported household non-food consumption expenditures by 46 percent compared to TA 1. The results lower our expectations regarding the predictive accuracy of Models 3 and 4 that would be applied to target survey data with reduced food and non-food consumption modules. Hence, as discussed later, we also explore “standardizing” the distributions of the predictors in TA 2 and TA 3 as to match the distributions of the same variables that are obtained in the base survey (Dang _et al._ 2017). In practical terms, making the standard assumption that the variables to be standardized have a normal distribution, standardization implies (1) subtracting each variable from its mean (i.e., demeaning) in the target survey, (2) multiplying the demeaned variable with the ratio of the square root of the variable variances in the base survey and target survey, and (3) adding the base survey mean (see Appendix C for further discussion).<sup>9</sup>

> <mark>variation in questionnaire design provides identical poverty predictions irrespective of the short versus longer questionnaire treatment.</mark> 9 Alternatively, we can employ a
Figure 1. Exports of Goods and Services <!-- Start of picture text --> Ave. 1998-2000 Ave. 2008-2010<br>RUS CHL<br>BRA IND PER COL CHN ZAF CHL BRA COLINDARG PER RUSZAFCHN<br>ARG<br>0 25 50 75 100 125 150 175 0 25 50 75 100 125 150 175<br>200<br>200<br>150<br>150<br>100<br>100<br>50 50<br>0 0<br><!-- End of picture text --> _Source_ : World Bank. World Development Indicators.

Trade openness in Brazil, considering the level of per capita income, is among the lowest in the world. Larger economies do tend to be more dependent on their domestic markets, and thus it should not be a surprise to see Brazil below the predicted level in figure 2. Still, when compared to other BRICS, the level of trade integration remains below the expected line, with no signs of improvement. Over the past decade, trade flow (exports plus imports) to GDP in Brazil rose from 20.2 percent in 2000 to 22.8 percent in 2010, after peaking in 2005 at 29 percent.

Figure 2. Trade to GDP (%) <!-- Start of picture text --> CHL<br>ZAF RUSCHL IND PERCHNZAF RUS<br>CHN PER COL COL ARG<br>IND BRA ARG BRA<br>6 7 8 9 10 11 6 7 8 9 10 11<br>Log of GDP per capita (PPP, av. 1998-2000) Log of GDP per capita (PPP, av. 2008-2010)<br>250 250<br>200 200<br>150 150<br>100 100<br>50 50<br>0 0<br><!-- End of picture text --> _Source_ : World Bank. World Development Indicators.

The economic benefits
||Quintile 1|0.52***|0.48***|0.70***|0.46***|0.41***|0.63***|
|Unconditionalaverage|37.9|43.0|17.8||[39.0]|[33.0]|[21.6]|[32.3]|[26.3]|[18.7]|
|||||Quintile2|0.11***|=0.10***|0.14***|0.09***|0.09***|0.12***|
|Individualandhouseholdcharacte|ristics||||[16.9]|<br>[14.6]|[8.8]|[13.8]|[11.7]|[7.4]|
|||||Quintile 4|-0.06***|-0.06***|-0.04***|-0.04***|-0.05***|-0.03***|
|Householdincome|||||[-17.3]|_ [-17.0]|[-4.2]|[-12.7]|[-12.6]|[-3.1]|
|Quintile 1|58.5|63.4|37.0|Quintile5|-0.12***|-0.12***|-0.09***|-0.09***|-0.10***|-0.07***|
|Quintile2<br>|47.0<br>|53.0
**Log minimum wage**<br>**index (1995+)**||||||||0.0304<br>[0.0432]|||
|**Constant**|0.4214<br>[0.0264]***|0.2975<br>[0.0402]***|0.4813<br>[0.0739]***|0.7559<br>[0.2620]**|0.7601<br>[0.2569]***|0.7377<br>[0.2484]***|-0.2807<br>[1.0656]|-1.0093<br>[1.2676]|1.0445<br>[0.3181]***|0.7386<br>[0.3106]**|
|**Observations**|209|209|209|209|209|209|209|178|186|141|
|**R-squared**|0.03|0.37|0.48|0.49|0.50|0.50|0.51|0.44|0.52|0.58|
|**Year effects**|No|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Country effects**|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Additional controls**|No|No|No|No|No|No|Yes|Yes|No|No|

Source: own computations based on SEDLAC (CEDLAS and World Bank) and WDI (World Bank). Notes: Robust standard errors in brackets, clustered at the country level for
Denote by _Timej,i,t−_ 5 the travel time between districts _j_ and _i_ at time _t−_ 5. We estimate the following regression on the bilateral district migration data: We then use the residual from this regression, _Rj,i,t_<sup>_s_</sup> _−_ 5<sup>, as the share variable</sup> when constructing the migration instrument for skill level _s_ :

# **Instrument validity**

## **Instrument relevance**

Table A4 presents the first stage results. Columns (1) through (3) refer to the standard instrumental variable, while Columns (4) through (6) refer to the residual network approach. Columns (2) and (5) add the main set of control variables: population, weather (temperature and rainfall adverse events), number of bank branches and road length. Columns (3) and (6) add controls for the agro-climate zone, including soils, length of the growing season (in days) and reference ETo (a measure of water stress). We note that the instrument is strong across the board, with an F-statistic ranging from 13 to 39.

## **Instrument exogeneity**

Identification with shift-share instruments is based on an assumption of exogenous shifts (Borusyak, Hull and Jaravel (2022)) or exogenous shares (GoldsmithPinkham, Sorkin and Swift (2020)). Translated to our setting, the assumption of exogenous shifts means that total immigration into a district from all districts other than a given source district is uncorrelated with current land outcomes in that source district (conditional on controls). The exogenous shares assumption translates into assuming that patterns of out-migration in the past are unrelated with current land outcomes (again, conditional on controls).

28
**8** A useful discussion of the properties of this and other estimators commonly used in the impact evaluation literature can be found in Angrist and Krueger (1999).

> 9 The incidence of self-rated poverty is not calculated using a pre-determined poverty line but by asking households where would they place their family on a card marked with the words "poor" and "nonpoor" and a line in between. See Mangahas (1999) for further details.

'° This is however based on a much smaller survey of 430 households in 31 communities. The sample households were selected on the basis of spatial and sectoral categories using purposive rather than a probability sampling (Reyes et al. 1999).

_5_