rafmacalaba/data-use-mentions-extended huggingface dataset:
over-sample large establishments. Sample sizes for enterprise surveys range from 250-1,500 firms by country.

The Enterprise Survey data provide information about innovation in developed and developing countries. It allows us to study the effect of knowledge spillovers in countries that have the most to gain from the knowledge of others. The other advantage is that it gives us the possibility to examine the existence of international knowledge spillovers on a wider base. This is in stark contrast with previous works, which explore the issue using two-country studies. In addition, the surveys collect information on more than one type of innovation, which allows us to compare the economic impact of spillovers across different dimensions of product innovation. The resulting data set is a cross-section of 17,271 firms, from 72 countries from 2003-2006.

Tables 5 and 6 show the share of firms engaged in quality and variety innovations across regions and industries. A large proportion of firms report quality and variety innovations. However, there is substantial heterogeneity across regions. The Sub-Saharan Africa and Latin America and Caribbean regions have the largest propensities to introduce new products, 52% and 56%, respectively. While the Middle East and North Africa zone has the lowest one, 16%. Europe and Central Asia and the Pacific areas have the highest share of firms innovating to improve the quality of existing products, 60%. While the Latin America and Caribbean region registers the lowest one, 33%.

Industries such as Textiles, Beverage, Auto and auto components, and Other transports show propensities to up-grade the quality of existing products that exceed 66%. While the Food and Other manufactures industries display the lowest shares, 38% and 17%, respectively. There is not much variation in the percentage of firms that perform variety-innovations across sectors. The lowest percentage is registered for the Paper industry, 30%, while the highest share is reported for the Food sector. Finally, Table 7 presents summary statistics of the main variables.

# **6 Results**

As mentioned, we report results
To obtain a generalized picture, we run OLS regression outcomes for the impact of war on the average years of education by region as identified by the victimization indicators. Table 5.4.2 compares the outcomes between the full sample, i.e. the North and the South sample. For the displaced household members and households that registered death due to war, the effect of war is negative and statistically significant only in the North. However, when we compare educational outcomes of households where members lost their jobs or reported declines in income, we find that war had a similar negative effect. Overall, the similarities and dissimilarities in these findings might have a lot to do with the subjective bias in responses and selection bias into victimization between the North and the South sub-populations. Nevertheless, we do not find any clear evidence in support of the North being the worst war-affected region in terms of education outcomes.

Table 5.4.2 Comparison of OLS estimates of war on years of education: Full sample, the North sample and the South sample

||Full<br>sample|North<br>sample|South<br>sample|
|---|---|---|---|
|Registered deaths|-0.128**|-0.420***|-0.049|
|Registered injury|-0.115*|-0.163|-0.102|
|Displaced|-0.033|-0.389***|0.081|
|Income dropped|-0.220***|-0.257**|-0.211***|
|Lost ownership|-0.125|-0.325|-0.062|
|Lostjob|-1.602***|-1.092*|-1.701***|
|Lost farm|-0.516|-0.
Policy Research Working Paper 9683

# **Abstract**

The movement of workers from the farm sector to a more productive nonfarm sector has failed to generate significant gains in labor productivity in recent decades in many developing countries. This paper offers a new perspective on the barriers to growth-enhancing structural transformation, combining structural modeling with enterprise census data from Ghana. The paper argues that subnational differences in the intersectoral productivity gap between the nonfarm informal and formal sectors constrain the productivity gain from structural transformation. In Ghana, intersectoral productivity gaps among the richer regions are on average three times larger than among the poorer regions. The disparity in regional intersectoral productivity gaps is modeled as reflecting the disparity in the regional misallocation of labor between the informal and formal sectors. Misallocation is identified as the output wedge between the informal and formal sectors. Simulations suggest that a more productive nonfarm informal sector reduces the disparity in regional intersectoral productivity gaps and, in turn, increases national productivity and the contribution of structural transformation to national productivity. For example, a 90-percent reduction in the disparity in regional intersectoral productivity gaps raises Ghana’s national aggregate productivity by 11.9 percent and the contribution of structural transformation to productivity by 19.7 percent.

This paper is a product of the Social Protection and Jobs 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 saumik.paul@newcastle.ac.uk and draju2@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
wholesale trade managers|142|0.4|0.0|0.5|0.5|0.1|Verylow|
|Mobileplant operators|834|0.0|0.5|0.8|0.5|0.3|Verylow|
|Other elementaryworkers|962|0.2|0.7|1.5|0.9|0.9|Verylow|
|Primaryschool and earlychildhood teachers|234|0.3|0.8|1.9|1.4|1.5|Verylow|
|Buildingand housekeepingsupervisors|515|0.1|0.0|0.2|0.7|0.5|Verylow|
|Domestic,hotel and office cleaners and helpers|911|0.1|0.6|2.1|1.4|0.7|Verylow|
|Secondaryeducation teachers|233|0.1|0.5|1.4|0.6|1.0|Verylow|
|Garment and related trades workers|753|0.0|9.3|1.1|0.6|2.1|Verylow|

_Source:_ Authors’ calculations based on SEAD data.

_Notes_ : The 27 occupations in bold are the ones whose skills profile is imputed from one or several similar occupations (see section 3).

59
**Figure 1. Learning loss depending on the length of school closures**

<!-- Start of picture text --> shortest (p10) short (p25) average (p50) long (p75) longest (p90)<br>0<br>-2<br>-4 full school closures (UNESCO)<br>-6<br>-8 full or partial school closures<br>(UNESCO)<br>-10<br>PISA country average closures<br>-12<br>reported by students<br>-14<br>PISA country average closures<br>-16 reported by principals<br>-18<br>-20<br>Length of school closures in weeks<br>Learning loss in mathematics<br><!-- End of picture text -->

# **Learning loss by gender, immigrant background and achievement level**

The pandemic and school closures could affect students of different backgrounds differently. For example, differences in self-regulation, motivation, home and school support, gender, immigrant background, socioeconomic status, and achievement level could lead to differences in how students learned during the pandemic. We re-estimate the main models for the overall impact of the pandemic and for the effect of the length of school closures using weeks of full closures from UNESCO. To check if students of different background were differently affected, we first interact their characteristics the indicators capturing the departure from the linear time trend in 2022 and with the measures of school closures. To exclude potential differences in pre-pandemic trends, we also estimate separate trends by these characteristics in each country, in addition to controlling for individual and country-average effects of these indicators on achievement as in the previous regressions.

While boys and immigrant students experienced a lower learning loss compared to girls and natives, nevertheless, longer school closures had a more negative effect on both groups (see Table 3). Depending on the closure length measure,
2009 2010<br>2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text -->

**Source:** Técnicas de Salud 2011, from data included in the series _La tutela y el Derecho a la Salud_ , published by the Defensoría del Pueblo between 2007 and 2011.

> 45 Data included in the series _La tutela y el Derecho a la Salud_ , published by the Defensoría del Pueblo between 2007 and 2011.

44
Service users ́ associations and civil society organizations reported that, on occasions, some EPSs of the subsidized regime refused to grant the ELISA<sup>52</sup> test to their affiliated persons—on the grounds that it was excluded from the POS-s. This practice would be illegal. Paradoxically, the available data about tutelage actions produced by the Ombudsman Office seems to suggest otherwise: that effective access to diagnostic tests is not currently the problem that it used to be a decade ago.

# **Efforts to Target Vulnerable Populations**

One of the five key objectives of the PEN focuses on preventive actions for vulnerable populations, including Men who have Sex with Men (MSM), Sex Workers (SW), members of the Armed Forces, inmates, domestic and international migrants, former guerrillas, and ethnic minorities.

As explained above (Section IV: Research Findings; Programmatic Dimension), many DTSs have not yet identified the most at risk populations within their areas—and as a consequence, those DTSs have not developed specific programs or interventions for preventing HIV infection and treating these groups more effectively. Among those DTSs that have identified their vulnerable populations, more could be done to target those groups through specific strategies and interventions.

On the other hand, the Study Team did find a number of examples of good practice. In particular, the health secretariat at the local council of Bogotá (Secretaría de Salud del Distrito Bogotá) reported: (i) regular monitoring studies of their most at risk populations; (ii) preventive and formative activities with those groups (including visiting places where MSM and SW concentrate and domestic migrants reside); and (iii) distribution of condoms among those groups.

In addition, prevention and control of HIV/AIDS among one of the groups that is most at risk—the Armed Forces and Police—is done through a special Committee called COPRECOS, supported by health information systems that allow for a continuous epidemiological vigilance for HIV/AIDS.

> 52 Enzyme-linked immunosorbent assay (
an open question. We estimate the impacts of an innovative model of providing affordable access to childcare by trained providers.<sup>2</sup> Several studies have found null or negative impacts of center-based

> 1In addition to disrupting operation of the childcare centers, the pandemic also reduced employment opportunities. Analyses from the first round of a nationally representative High Frequency Phone Survey (HFPS) of households conducted in June 2020 by the Burkina Faso National Institute of Statistics and Demography with technical and financial assistance of the World Bank found that around 10% of respondents who used to work before the COVID19 outbreak were not working at the time of the survey, and that the COVID-19 related economic slowdown has translated in a reduction of income for most employees, non-farm business owners as well as farmers (Nkengne et al., 2020).

> 2Similar mobile childcare interventions exist in other countries, notably the Mobile Creches NGO in India has been operating mobile childcare centers alongside construction sites in urban areas for the past 50 years, but the best existing quantitative evidence on effects of access to these facilities comes from a comparison of children who attended childcare centers for less than a month to those who attended for a full six months, in the same public works site (Creches, 2012). Without an explicitly-designed comparison group, it is difficult to know whether differences in observed outcomes truly reflect the causal effects of access to childcare centers or partly result from preexisting

3
# I. **Introduction**

A substantial literature exists on limited dependent models in a panel context (see or Maddala 1983 or Baltagi, 1996 for overviews) and continuous dependent variables in an incomplete or rotating panel context (Bjom and Jansen 1983, Nijman, Hsiao 1986, Verbeek and van Soest 1991). This paper derives a methodology for estimating logit models in a rotating panel. It then uses the technique to examine an unresolved problem in development economics: the role of self-employed workers unprotected by labor legislation in the LDC labor force. In particular, we are interested in the determinants of the decision to leave protected (formal) work to enter self-employment. An alternative theoretical model to the dualistic view generally accepted is offered. Both views are tested using rotating panel data set from Mexico and the alternate view supported.

## **II. An Alternate View of Informal Self-employment**

Much of the literature on the informal self-employed sector in LDCs beginning with Harris and Todaro(1970) has seen self-employed workers unprotected by labor legislation as those rationed out of protected or "formal" salaried jobs sector jobs by above market clearing remuneration in the protected sector. Transitions should be largely unidirectional, from the informal and presumably very low capitalized micro-enterprises, to the formal sector except in the event of downturns in which case laid off workers will be thrown back on the informal safety net.

However, there is little reason to suppose that the expanding literature on selfemployment in the industrialized world that views self-employment as a desirable and I
**approach. This problem is especially apparent within the literature using expenditure-based approaches.** Often, even if researchers use the same metric, approaches on how to construct this metric deviate; this problem is especially apparent within the literature relying on expenditure data focusing on the affordability aspect of energy poverty. We identify the following inconsistencies in the literature:

- **On scaling energy expenditure and measuring energy costs:** While some scholars divide energy expenditure by adult equivalents, others scale it by the number of household members (Schulte and Heidl, 2016) or the OECD equivalence scale (Hills, 2012). A third possibility is to use absolute values altogether (Thema and Vondung, 2020). The rationale behind this debate is that residential energy use is subject to important economies of scale (Schulte and Heidl, 2016). There is no consensus in the literature on how to address this point. Moreover, different researchers consider different components of energy costs. For example, one could include transport-related energy costs or abstract from them. Finally, there is debate on whether theoretical energy needs for a household with certain characteristics at current prices or actual energy expenditures reported by households should be used.

- **On using household income or expenditure:** In addition, there is variation in the literature on defining the denominator of the energy spending share. Several papers relate energy expenditure to household income (e.g., Thema and Vondung (2020); Hills (2012); Saghir (2005)) while others relate it to household expenditure (e.g., Freund and Wallich (1995); Schulte and Heindl (2012); Laderchi et al. (2013)). At the same time, several measures abstract from scaling household energy spending altogether and use absolute energy costs (Thema and Vondung, 2020). When using income as the denominator, there is again an inconsistency with respect to whether to consider disposable income before or after housing costs (Herrero, 2017)
Figure 1. Distribution of changes in occupational wage inequality ( _dginioww_ ) (in percentage points, 1983-1999) <!-- Start of picture text -->
- 2 0 - 1 0 0 1 0 2 0<br>x<br>.4<br>.3<br>.2<br>kdensity dginioww<br>.1<br>0<br><!-- End of picture text -->

Note: There are 532 _dginioww_ observations. Changes are expressed in Gini points.

The second large database of inter-industrial wage differences was created by James Galbraith and associates and is known as the University of Texas Inequality Project (UTIP) database (see Galbraith and Kum, 2003).The original data come from UNIDO statistics. The UNIDO statistics provide average manufacturing pay by industry. The number of industries (which provide their mean wages) varies between countries and years. On average, there are 24 industries per country/year (with the standard deviation of about 7). From these average industrial wages for a given country/year, Galbraith and his associates calculate the Theil index of inequality (variable _theil_ ). The UTIP database covers on average about 90 countries annually over the period 1975-99.<sup>24</sup> In total, we use

> 24 The data are available at <u>http://utip.gov.utexas.edu/. More recently, the database has expanded to the</u> years prior to 1975. As of January 2004, UTIP data base has almost 3200 country/year Theils, and covers more than 150 countries.

26
# **References**

Basu, Sudip, Hiroaki Kuwahara and Fabien Dumesnil (2012), “Evolution of Non-Tariff Measures: Emerging Cases From Selected Developing Countries,” UNCTAD Policy Issues in International Trade and Commodities, Study Series No. 52. New York and Geneva: United Nations.

Blockmans, Steven, Hrant Kostanyan and Ievgen Vorobiov (2012), “Towards a Eurasian Economic Union: The Challenge Of Integration and Unity,” CEPSR Special Report No. 75 (December 14). Brussels: Centre for European Policy Studies.

Bond, Ian (2014), “The EU and Russia: Uncommon Spaces.” London: Centre for European Reform.

Breaux, Michele, Yasnanhia Cabral, Michael J. Ferrantino and José Signoret (2014), “Quality-Adjusted Estimates of NTM Price Gaps,” USITC Office of Economics Working Paper No. 14-08-B, August.

European Commission, Market Access Database. Found at <u>http://madb.europa.eu/madb/indexPubli.htm</u> .

International Organization for Standards (ISO), “Standards and ‘Private Standards’ “, Geneva: 1SO, 2010, found at <u>http://www.iso.org/iso/private_standards.pdf .</u> United States Trade Representative, Office of the (2014) 2014 Report on Technical Barriers to Trade, p. 93. http://www.ustr.gov/sites/default/files/2014%20TBT%20Report.pdf; April 2014.

World Bank (2007), _Food Safety and Agricultural Health Management in CIS Countries: Completing the Transition_ , Washington DC: The World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 23
Table A.1: Composition of dowries in rural India, 2005 IHDS

|**Item**|**% of households that respond “Usually given”**|
|---|---|
|Utensils|80.42|
|Bedding/mattress|70.94|
|Gold|70.78|
|Silver|63.85|
|Watch|62.41|
|Furniture|48.89|
|Cash|41.03|
|Pressure cooker|35.12|
|Sewing machine|21.71|
|TV|20.42|
|Bicycle|16.60|
|Mixer or grinder|15.70|
|Fridge|9.81|
|Livestock|8.78|
|Scooter or motorcycle|7.49|
|Land|1.04|
|Car|0.44|
|Tractor|0.32|

NOTES: This table shows the percentage of rural households that respond “Usually” to the following question: _Generally in your community for a family like yours, what are the kind of things that are given as gifts at the time of the daughter’s marriage?_ in 2005 IHDS.

45
In developing countries, implicit stratification is often introduced in household surveys by sorting clusters in a certain geographical order within each explicit stratum, since frame lists of EAs may not contain other variables correlated with the phenomenon under investigation. In this way, the classical construction of a _serpentine_ of clusters, is the most effective method that has been implemented so far for the preparation of the list frame of clusters from which the systematic sampling is conducted. In the present paper we aim to further improve the implicit stratification of a geographic serpentine, by ordering the clusters according to another socio-economic characteristic: a specific description of the living conditions of the clusters included in the so-called _poverty mapping_ . This approach will be further described in Section 4.

However, it is well-known in the literature that this design of systematic selection of clusters in the first stage makes it impossible to find the exact formula for an unbiased estimator of the variance in (2), which could be renamed as V s �, referring to systematic sampling. Wolter (1985) and Cochrane (1977) proposed ss VV�YY<sup>�</sup> several methods of approximate estimators of V s �, while in the last two decades, replication methods have reached more precise and unbiased estimators, ss VV�YY<sup>�</sup> which include Taylor Linearization (TL), Jack-knife Repeated Replications (JRR), Balanced Repeated Replications (BRR), and Bootstrap and Random Groups (RG). Recently, Verma and Betti (2011) have proposed a developed version of JRR, while Arnab _et al._ (2015) have proposed some more refined methods of variance estimation in the case of systematic sampling in the first stage. They show that such proposed methods and JRR perform better than other methods that are usually adopted by National Statistical Offices in developing countries.

In light of the above, the variance formulas (2) and (3) are not applicable when estimating complex measures from complex sample designs, but they are
previous section, a plot of county-by-county trade flows in Figure 3 highlights the primacy of Nairobi and Mombasa. The width of each segment on the left reflects the county’s total sales within the network, while segments on the right are proportional to total purchases. The color of the trade flows aligns with the county of origin.

Importantly, visualizing trade flows also reveals that Nairobi not only has more outlinks to other counties but also is the most important buyer of goods and services by firms from other counties. Given the high concentration of manufacturing firms and their upstream position in the network, 12
There is now robust evidence that higher levels of GDP per capita is associated with lower corruption (La Porta et al., 1999; Ades and Di Tella, 1999; Svensson, 2005; Fan et al., 2009). If the level of income also varies systematically with country size, our main results could suffer from the omitted variable bias problem. Thus, we control for (log of) GDP per capita (PPP adjusted and at constant 2011 international dollars) taken from WDI. We complement the GDP per capita control with the annual growth rate of GDP per capita to capture any possible impact of recent changes in economic activity on the level of corruption. The data source for the variable is WDI.

There is some work on the impact of education attainment on civil participation and punishing the corrupt officials. Most studies in the area suggest that education lowers corruption. That is, education leads to a more vigilant political participation and “elite-challenging” behavior that identifies and punishes corrupt behavior (see for example OECD, 2007; Eicher et al., 2009). Empirical studies that confirm this prediction include for example, Galston (2001), Carpini and Keeter (1996), Popkin and Dimock (1999), Glaeser and Saks (2006), Goel and Nelson (2011), Nie et al. (1996).<sup>7</sup> Thus, our main results could be biased if the level of education attainment also varies systematically with the country size. To guard against this possibility, we control for education attainment using the gross enrollment rate in primary education (Primary Education). Due to missing data, we use average values of the variable over the last two years (prior to the date of ES) for which data are available. The data source for the variable is WDI.

The degree of urbanization might also play a role in the level of corruption in a country. Meier and Holbook (1992) argue that urbanization fosters the necessary conditions for corruption by