rafmacalaba/data-use-mentions-extended huggingface dataset:
with SMC members from the male and female head of the household. A student questionnaire was administered to sampled children regardless of school-going status. Standardized tests were administered at this time to primary school-age children (ages 7-13), for Math, English and Sindhi.<sup>29</sup>

# **3.2 Outcome variables**

As described in section 2.2, the pilot interventions could potentially impact a number of outcomes: school functioning; student enrollment; availability of teachers; teacher attendance; school infrastructure; and learning outcomes. In order to ensure accuracy and adherence to the study timeline, we measure all variables through directly observed survey data.

For school functioning, we measure whether schools were open at the unannounced visit at baseline and endline.<sup>30</sup> Since a number of schools switched status between the visits, it is important to capture the direction of change from open to closed and vice versa, in relation to the treatments. This generates three specific outcome indicators: number of schools open; number of schools open at baseline which were closed at endline; and number of schools closed at baseline which had reopened at endline.

> 29 Standardized tests for English, Math and Sindhi were administered to children ages 7-13 at the household level both for the baseline and endline. The instruments were designed, pre-tested and piloted based on students’ textbooks and curriculum standards for Grades 1-5 of primary school. We fitted a two-parameter item response theory (2pl-IRT) model for English and Math scores to account for what students of similar knowledge and skills ought to know, by distinguishing between difficulty level (see Appendix B for details). Sindhi tests include open-ended question items and hence an IRT score cannot be fitted.

> 30 If a school was found closed, verification was sought from community members. If a school was closed for one or two days only, a revisit was made when the school was reopened and the school was not recorded as having been closed. If a
gender or household land endowments as individual or hosuehold characteristics ( _Xidt_ ) to explore potential heterogeneity of effects in these dimensions. Standard errors are clustered at household level throughout.

In all cases, availability of panel data allows us to use household fixed effects to control for unobservable effects that are time invariant. But households in control and treatment districts may follow different growth paths even in the absence of the intervention. To see if this is a concern, we use the 1982 data to test for pre-program parallel trends in variables of interest by estimating (1) for the 1982 to 1999 period (i.e. _t_ =0 for the 1982 and _t_ =1 for 1999/2000). Results for individuals’ main occupation, agricultural output value, and input use in table 1 suggest that, except non-farm self-employment where coefficients are marginally significant (though suggesting lower rather than higher growth in treatment areas), the hypothesis of preprogram parallel trends between treatment and control cannot be rejected.

To make initial inferences on potential transmission channels, we introduce MNREGS exposure as a productivity shifter in a standard Cobb-Douglas production function of the form _YMidt_ = _α0_ + _α1 MNREGSdt + α2 MNREGSdt X Postid + λ_ **_X_** _idt + μi + εidt_ (2) where, beyond the variables discussed earlier, _YMidt_ is monetary output by farm _i_ in _t_ , **_X_** _idt_ a vector of inputs _m_ used by _i_ , _μi_ a farm-specific fixed effect for farm _i,_ and _εidt_ a white noise error term, and the _α_ s and _λ_ are coefficients or coefficient vectors to be estimated. To ensure robustness of results, we estimate not only the panel regression with household fixed effects but also an equivalent cross sectional equation that includes villages with positive treatment only.

Moreover, for the subset of villages where such information is available, we replace the zero/one indicator of program availability ( _MNREGSdt_ ) with the number of days of total or irrigation-related MNREGS works in a village. As the share of the latter
). Relatedely, there was little _within-firm_ growth among a panel of manufacturing firms in Ghana between 1987 and 2003 (Sandefur 2010).<sup>6</sup> Yet while these studies draw from extensive evidence, Ghana’s recent boon — with the 2010 discovery of oil reserves — may welcome in substantial job growth.

# **III. Data and Descriptive Statistics**

The firm-level data used in this paper are taken from the World Bank’s Enterprise Surveys (ES) project. These data were collected in Ghana in 2013–14 and refer to fiscal year periods between 2010 and 2012, though they are cross-sectional and utilize recall information as well as financial and employment records.<sup>7</sup> As cross-sectional data, however, the ES do not capture firm entry and, particularly, firm exit and thus there is the potential for an incumbency bias by only gathering information from surviving firms.

> 5 They sampled about 1,000 manufacturing firms from the Ghana National Industrial Census (2003) and surveyed them in 2013; new entrants in 2013 are not captured—only those firms operating in 2003. Results reported here refer to the exit measured as those firms that closed and that could not be found/tracked in 2013.

> 6 Using a panel data on manufacturing firms between 1987 and 2003 in Ghana, Sandefur (2010) showed that there was actually little evidence for within-firm growth: firms that were small in 1988 remained small in 2003, and the big firms operating in 2003 were already big in 1988.

> 7 The data necessarily rely on recall questions for sales revenues and employment, introducing an element of recall bias. When possible records are taken from books and employment records directly. Of 1,940 firms interviewed, 29 percent were taken fully or partially from books, while 63 percent were based on estimates computed with some precision. Eight percent were regarded as arbitrary and unreliable; all assessments are based on enumerator notes and opinion.

7
–38 (2021).

> 51 Liu, X., Huang, Y., Xu, X. _et al._ High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. _Nat Sustain_ **3,** 564–570 (2020).

52 Fang, J., Zhang, C., Fang, J., Liu, M. & Luan, Y. Increasing exposure to floods in China revealed by nighttime light data and flood susceptibility mapping. _Environ. Res. Lett._ **16** , 104044 (2021).

> 53 Ying, X., Bing, Z., Bo-Tao, Z., Si-Yan, D., Li, Y, and Rou-Ke, L. Projected flood risks in China based on CMIP5. _Adv. Clim. Chang. Res._ **5** (2), 57–65 (2014).

> 54 D'Errico, J. _SLM - Shape Language Modeling_ (MATLAB Central File Exchange, 2021).

https://www.mathworks.com/matlabcentral/fileexchange/24443-slm-shape-language-modeling 55 Dilling, L., Prakash, A., Zommers, Z., Ahmad, F., Singh, N., de Wit, S., Nalau, J., Daly, M., Bowman, K. Is adaptation success a flawed concept?. Nat. Clim. Chang. **9,** 572–574 (2019).

Economy-level summary statistics 22
_Notes:_ r esults are restricted to the sample of individuals aged between 15 and 24 in 2010 and between 23 and 32 in 2018. “Lower” and “Upper” refer to the two bounds on mobility; for immobility, “Lower” is the upper bound and “Upper” is the lower bound. Upper-bound estimates are based on 50 replications. Individual-level sampling weights are applied.

Some further validation checks were undertaken by means of comparison between transition matrices—both for the overall population and by population subgroups—using true panel data and the estimated synthetic panels for each of the consecutive year-pairs of data (2010/2011 to 2017/2018).<sup>11</sup> As we expected, the lower-bound estimates underestimate mobility—understating movements into and out of the NEET condition, and overstating the extent to which people remain NEET or non-NEET—while the upper-bound estimates overestimate mobility. Encouragingly, the “truth” (true rate) tends to lie about midway between these bounds. We find, thus, that our approach does indeed present bounds within which the “truth” can be observed.

# 6. Discussion and Conclusions

Several decades ago, Morocco embarked on a long-term process of political, social, economic, and environmental reforms, culminating in the adoption of the New Constitution in 2011. Because of these reforms, progress has been made, in particular, in the reduction of absolute poverty, better access to basic public services, and the considerable development of public infrastructures. The social landscape, however, remains marked by challenges yet to be met, particularly in terms of social cohesion. These challenges could pose the risk of exclusion of the most fragile components of Moroccan society, in particular women and young people. Young people unemployed, outside the school system and not undergoing any training—the so-called NEETs—form about 30 percent of the Moroccan population between ages 15 and 24. That number looks even grimmer when compared to NEET rates in the MENA region, already characterized by particularly
36 only 1.6 percent of household respondents report enrolling any of their school-age children in private schools.

The household survey reports two financial investments that households make in education. The focus here is on a variable constructed from the household roster: the number of children in the household attending school for whom the household purchases books. Approximately 42 percent of households with children in school report buying books. The reporting is likely to be accurate, since the number of children for which households buy books is significantly negatively correlated with the number of children in schools where government-supplied books are available; the majority of children are in schools where books are not available.

The household roster also records the number of children for whom the household paid matriculation fees for a child in school. However, two factors weaken the likely effects of radio access on these payments. First, education programming may increase household demand for better educational outcomes for their children (increasing the likelihood of payments), but in the case of the USAID programming described earlier, may also increase household confidence in the quality of schools and reduce the value they place on private payments to teachers. Second, in contrast to the book-buying information, responses to this question appear to exhibit substantial measurement error. The survey asks, ―Do parents in your area continue to pay fees to schools?‖ Of the respondents who indicate that they have paid fees for their children, though, 63 percent report that parents in their area are _not_ paying fees. In addition, income and wealth variables that are associated with book-buying are not associated with fee payment.

Table 7 reports the effects of radio access on households‘ education investments. The base regression is:
appropriate data.

Although agglomeration benefits are higher, urban costs may explain why cities do not expand further. Urban costs have been estimated for France and Colombia using land and housing price data (Combes et al., 2013; Combes et al., 2019; Duranton, 2016). In France, the elasticity of urban costs with respect to population ranges from 0.016 to 0.05, while the estimates for agglomeration effects in France range from 0.015 to 0.03 (Combes _et al._ , 2010). Similar results on the elasticity of urban costs are obtained for Colombia as well (Duranton, 2016). In both these countries, cities operate at near aggregate constant returns to scale, suggesting that the benefits of agglomeration are balanced by their costs.

The costs of density on outcomes such as pollution, crime, and congestion is significantly high, and often higher than productivity elasticities. Ahlfeldt and Pietrostefani (2019) suggest that a log- point increase in density is associated with higher rents (0.15), pollution concentration (0.13), mortality risk (0.09) and crime (0.085). Thus, to understand the true advantages of larger cities, it is critical to estimate agglomeration costs alongside benefits. Cities in developing countries face the same downsides of density, that is, congestion, crime and diseases, however, they lack the financial resources to invest in infrastructure such as transport network, clean water and air, and sewerage to mitigate these risks. They also lack the public capacity to enforce urban regulations (Glaeser and Porteba, 2020).

# 3. Data

We rely on two separate datasets: one for the meta-analysis of agglomeration elasticities, and another for the analysis of urban costs. First, we discuss the construction of the sample of papers used for the meta-analysis, and second, the urban cost data.

> 2 For more evidence see Bacolod et al. 2009; Abel et al., 2012; Lindley
its voting share in the legislature. A greater number implies greater polarization. This method of measuring polarization has been used by Franzese (2008) and Eslava and Nupia (2010). The data used to calculate government polarization are from the Database of Political Institutions (2010).<sup>6</sup> The second important variable of interest is the quality of institutions. Ideally this would require a measure that captures how the interests of powerful groups are translated into central government policies. Such powerful groups can be political leaders, provincial governments, labor unions, parastatal enterprises, and financial backers of the ruling government. This measure at its lowest would describe the case of a central government that is weak and surrenders to all the demands of the interest groups. At its highest value, it captures the case when the central government is strong and makes optimal decisions for the aggregate economy. As far as I know, there is no cross-country indicator of institutions or governments that measures that. Instead I used two proxies. The first is the standard market assessment measure of institutional quality: the International County Risk Guide (ICRG) institutional index. The index from the PRS Group is based on investors’ evaluation regarding the rule of law, bureaucratic quality, corruption, expropriation risk, and government repudiation of contracts. It ranges from 0 and 1, with high values representing better institutions. This measure of institutional quality includes several aspects of government structure. There are two potential disadvantages: 1) ICRG, as any market assessment measure of institutions, tends to improve with income level rather than reflect durable institutional constraints in the government; and 2) ICGR might already contain information about past defaults,<sup>7</sup> hence there would be an endogeneity problem.

The second proxy that I use is regulation of participation (parreg) from POLITY IV. It measures the degree of institutionalization or “regulation” applied regarding how political preferences are expressed by subordinates.<sup>8</sup> It
participants and non-participants, would provide an estimate of the program effect. Results of a t-test for _ΔPSG_ - _ΔNon-PSG_ = 0 reported in column 8 suggest that none of the other variables show a significant difference. To avoid potential bias from selective responses to the survey, we use cadastral data for the universe that is available for the universe of eligible SAR registrants to check if receipt of a PSG grant affects this variable.

# **4. Assessing PSG impacts on area cultivated and targeting**

Cadastral parcel data, together with satellite-based crop maps, suggest that, despite incentives to overstate cultivated area, targeting rules were largely adhered to. A difference in differences design that leverages the timing of program implementation using 5 years of panel data on area cultivated with agricultural crops by the universe of PSG-eligible SAR registrants suggest the program had significant, albeit modest benefits the incidence of which was concentrated among small producers near the frontline.

# **4.1 Data and evidence on targeting**

Overlaying cadastral data for all parcels to which a farm has registered rights with public crop maps (available at <u>https://ukraine-cropmaps.com/) for winter and summer seasons separately,</u><sup>22</sup> allows us to

> 22 Crop maps had been produced under the EU/WB program to ‘Improving Transparency of Land Governance in Ukraine’. Refer to (Kussul _et al._ 2022) for the methodology and recent applications. Land registered in the name of the owner but rented out informally could, in principle, have been falsely reported as self-cultivated and counted towards the owners’ eligibility but was considered not to be significant enough to matter.

12
other personnel was included in the central government category. Health employment data is taken from the same document. However, it indicates that 21,455 people work in the Ministry of Health, of which only 6,244 are doctors and paramedics. Since this proportion of supposed administrative to medical staff seems somewhat excessive, it is possible that some additional medical staff could be in this category. We have decided to include all 21,455 employees in the health employment category, conscious that this might be overestimating somewhat medical staff. . This figure should therefore, be treated with some caution. Employment is State Owned enterprises, accounts for 11,071 people. Military employment data do not include personnel in paramilitary units, e.g., the National Civilian Police (8,000).

Data on GDP at market prices is from IMF Government Finance Statistics and is for 1994. Average Public Sector wage corresponds to Average wage of Consolidated Central Government, is taken from IMF Country Report No. 96/39 of May 1996 and relates to 1994. Manufacturing wage is also taken from IMF Staff Background Country Report of May 1996 and relates to 1994.

# **Guatemala**

Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1993. Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995, refer to 1991 and source l1l, Code 1: _Employment office statistics_ and do not include persons who are already in employment. Military employment data include conscripts, but do not include personnel in paramilitary units, i.e., the National Police (9,800), the Treasury Police (2,500), and the Territorial Militia (300,000).

Guyana Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995, refer to 1993 and source Illi, Code 2: _Employment office statistics_ and include persons in employment who are seeking a change of job or extra work and are therefore also
a consequence, the findings from the existing literature are mixed. In addition, despite the increasing utilization of NTMs, the literature on their impact on exporters in developing countries is limited (Grundke and Moser, 2019).

In this study, we examine the impact of NTMs, specifically standards and technical regulations, on exports from Africa. To do so, we combine dis-aggregated firm-product level customs transactions panel data in three African countries (i.e., Burkina Faso, Malawi, and Senegal) and panel data on NTMs within a reduced-form gravity model. Of the different NTM types, we focus on standards and technical regulations, which are mainly sanitary and phytosanitary (SPS) and technical barriers to trade (TBT) measures.<sup>1</sup> While they are _prima facie_ introduced to address market imperfections such as genuine health and safety concerns, they can also disguise protectionist intents including protecting domestic producers from foreign competition. For instance, the introduction of standards and technical regulations could increase both fixed (e.g. costs related to efforts devoted by firms to show compliance with the standards) and variable (e.g. costs related to substituting low-quality intermediate inputs with high-quality ones) trade costs (Ing et al., 2016; De Melo and Shepherd, 2018). In addition, standards and technical regulations could enhance trade by reducing information asymmetry between producers and consumers in terms of product safety and other product

> 1NTMs can be either technical or non-technical. Non-technical measures are only applied at the border (e.g., quantitative restrictions, import licenses, competition measures) and thus only affect third-country producers. Given their discriminatory nature, the trade effects of border NTMs are often obvious, e.g., import (export) bans will reduce imports (exports). The technical measures are behind-the-border measures that affect both domestic and foreign producers. These are mainly dominated by SPS and TBT measures.

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| POLICY RESEARCH WORKING PAPER 2389

# Summary findings

Since Vietnam's adoption of the _doi moi_ or renovation policy in 1986, the country has been undergoing the transition from central planning to a socialist marketoriented economy. This has translated into strong economic growth, led by the industrial sector, which expanded more than 13 percent a year from 1993 to 1997. Vietnamese policymakers are concerned, however, that employment growth has lagged.

To address this concern, Belser compares new employment data from the Vietnam Living Standards Survey (VLSS 2), completed in 1997-98, with data from the first household survey undertaken in 1992-93.

He shows that in 1993-97, industrial employment grew an average of about 4 percent a year, which is low compared with industrial GDP growth. This slower growth was attributable to the capital-intensive, importsubstituting nature of the state sector and foreign investment, which dominate industry.

The more labor-intensive, export-oriented domestic private sector is still small, although growing quickly.

In the future, growth promises to become more laborintensive. Before the Asian crisis there were signs of an emerging export-oriented sector. Using previous statistical analysis (Wood and Mayer 1998) as well as factor content calculations, Belser estimates that given Vietnam's endowment of natural and human resources, Vietnam could triple its manufacturing exports and create about 1.6 million manufacturing jobs in export sectors in the near future.

After examining Vietnam's labor regulations, Belser concludes that there is no need for basic reform of the labor market. At current levels, minimum wages and nonwage regulations (even if better enforced) are unlikely to inhibit development of the private sector or hurt export competitiveness.

But a restrictive interpretation of the Labor Code's provisions on terminating employment could hurt foreign investment, reduce the speed of reform in the state sector, and slow the reallocation of resources to the domestic private sector.

- - This paper a product of the Vietnam Country Office, East Asia and Pacific Region was prepared as a background paper for the Vietnam Development Report 2000, "