rafmacalaba/datause-encoder-data huggingface dataset:
The last concern relates to a possible correlation between labor regulation and store characteristics. We looked at a number of store characteristics and found these to be only weakly correlated with the _Perception_ index. For example, averaged at the state level, current employment, current sales and floor area of the shop show a correlation of -.125, .088 and -.109, respectively, with the _Perception_ index. Our results for labor regulation hardly change due to store level controls (discussed below).

# **3.  Estimation**

We use a probit specification clustering the standard errors on the state. Estimated marginal effects (at the mean value of the explanatory variables) are reported in Table 1. Our main control is the _Land_ index for reasons discussed above. Using the _Perception_ index, we find no significant effect of labor regulation on computer usage without any controls. In column 1 of Table 1 we control for the _Land_ index. Labor regulation now shows a significant effect (at less than 5%). Specifically, a 1 percentage point increase in the number of stores who find labor regulation to be a problem raises computer usage by .179 percentage points. In contrast, stricter land laws have a negative effect on computer usage (significant at less than 1% level). For overall development we use ratio of females to males ( _Sex ratio_ ) at the city level.<sup>6</sup> Other proxy measures like literacy rates, etc. are used in the robustness checks below. Column 2 of Table 1 shows that controlling for _Sex ratio_ increases the estimated coefficient of labor regulations from .179 to .189 (p-value of .004) confirming the downward bias discussed above. _Sex ratio_ has a positive effect on

> 6 The correlation between _Sex ratio_ and _Perception_ index is -.237 in the full sample and -.077 in the restricted sample (excluding West Bengal and Gujarat). We use (lagged) 1991 values of _Sex ratio_ taken from Census of India (1991). Our results do not change if we use 2001 values of sex ratio.

4
Table 3: Robustness, restricted sample

|Dep. variable|ln simp<br>(1)|le tarifs<br>(2)|ln weig<br>(3)|hted tarifs<br>(4)|
|---|---|---|---|---|
|ln rain_jt_|0.039|0.006|0.106|0.061|
||[0.092]|[0.091]|[0.113]|[0.111]|
|ln rain_jt ∗_agric_i_||0.263***<br>[0.049]||0.354***<br>[0.068]|
|Observations|32,436|32,436|32,117|32,117|
|Number of countries|51|51|51|51|
|Number of products|65|65|65|65|
|R-squared|0.579|0.582|0.415|0.418|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 Table 4: Robustness, alternative definition of agricultural goods

|Dep. variable|ln simp<br>(1)|le tarifs<br>(2)|ln weig<br>(3)|hted tarifs<br>(4)|
|---|---|---|---|---|
|ln rain_jt_|0.064|0.015|0.125|0.062|
||[0.076]|[0.074]|[0.094]|[0.092]|
|ln rain_jt ∗_agric_i_||0.235***<br>[0.051]||0.304***<br>[0.059]|
|Observations|41,043|41,043|40,606|40,606|
|Number of countries|70|70|70|70|
|Number of products|65|65|65|65|
|R-squared|0.572|0.574|0.401|0.403|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 2
59 that are specialized in the provision of old–age insurance can handle and manage the old– age credit balances in a more professional and potentially more successful way. Third, already the choice of the administrative form often defines the degree of risk coverage by another party. Table 17 shows that 75 out of 100 collective–institutions have all their risks covered by an insurance company.

**Table 18** **<u>Shift in the Choice of Administrative Form</u>**

||1994|2002|Abs. change|Rel. change|
|---|---|---|---|---|
|Collective-Institution|139|126|-13|-9%|
|# Members|1,079,964|1,152,448|72,484|7%|
|# attached employer|158,643|183,002|24,359|15%|
|# attached employerper PF|1,141|1,452|311|27%|
|Common-Institution|147|130|-17|-12%|
|# Members|562,129|615,849|53,720|10%|
|# attached employer|94,354|111,948|17,594|19%|
|# attached employerper PF|642|861|219|34%|
|Institutions of firms, holdings<br>or mother companies|1,309|804|-505|-39%|
|# Members|667,732|659,576|-8,156|-1%|
|# attached employer|6,682|4,862|-1,820|-27%|
|# attached employerper PF|5|6|1|18%|

Source: 1: Bundesamt für Statistik (1999), p.43 2: Bundesamt für Statistik (2004b), p.30, T7 To summarize, the dominant form of pension funds is registered and subject to private law. It manages funds in a centralized way for more than one employer. The degree of risk coverage displays a wide range of options, but there is an increasing tendency to reinsure certain risks, or to hand them over to an insurance company.

# **5.2 Contributions, Old–Age Capital and Annuities**

Contributions: Table 19 shows the contributions collected by all Swiss pension funds since 1996. In 2002, the total amount of contributions paid by employers, employees and others amounted to 31.3 billions CHF, or approximately 7 percent of Swiss GDP. Almost 50 percent went to _autonomous pension funds_ and over 25 percent to _non-autonomous pension funds_ . The remaining contributions were evenly distributed among the other forms. Pension funds under public law contribute 7,117 million CHF or equivalently 23 percent of the total premia volume. The fraction paid by the employers is 63.4 percent, which is higher than the average of 57 percent, indicating more rigorously sponsored pension plans at pension funds under public law. In contrast, employers affiliated to Collective– and Common–Institutions contribute around 54 percent. In total, contributions to Collective– and Common– Institutions make up 35 percent of the overall premia paid in 2002. This share can be considered as an upper bound approximation for the contributions managed by life insurance companies.<sup>56</sup>

> 56It is an approximation since 91% of the insured in _non-autonomous pension funds_ and 72 percent of the insured in _semi-autonomous pension funds_ belong to Collective– and Common–Institutions.
24 on EU markets, an interesting question is whether CEECs have comparative advantage in parts in these markets. Thus, instead of calculating revealed comparative advantage in reference to the share of a product in world exports, we do it in respect to EU exports of manufactures. Following Balassa (1965), the revealed comparative advantage index (RCAij) for a component jof country i, is specified here as follows: RCAij = (xi/Xi) / (xLuj/XEu), where: xij is country i's exports of component j to the EU; Xi = Ej xij is country i's exports to the EU' XEU j = Ej xij is EU's total 'external' exports of a componentj; XEU = :i Ej xij is EU's total external exports of manufactures (excluding chemicals). Its interpretation is straightforward. If the index for a product j exceeds unity, a country is said to have comparative advantage in the production of good j because this sector is more important for this country than for EU exporters. If the RCA index is below one, the country is at a comparative disadvantage in EU markets for a good j.

Secotid, we supplement RCA calculations on exports with those on imports. Ng and Yeats (1999) propose using the revealed comparative advantage concept calculated on imports (rather than exports) to identify specialization pattern in terms of upstream operations. The import RCA index is defined similarly as the traditional RCA index but with imports of a country i from the EU replacing exports to the EU. RCA1<sup>jAmeasures normalized import shares with</sup> normalization in respect to imports of the EU. Thus, the RCA of country i in the assembly of productj is RCAij A = (mij/Mi) / (MEUj/MEU).

The reasoning behind this modification is simple: Except for an imported part used as replacement for a failed component in an assembled good, parts and components are exchanged for further assembly into a product. It follows that countries with above average import shares for components have a comparative advantage in the assembly operation. In other words, the value of RCA index above unity suggests that firms in country i are better endowed to carry out assembling than those in the EU.

But it does not necessarily imply that they do. RCAj A above unity simply means that country _i_ imports relatively more of a product j than the EU. These imports may be used for producing a final product sold domestically rather than shipped back to feed a global production process. If these imports are further processed, as production fragmentation would impose, and then delivered for further processing in another country, then foreign trade data should capture these transactions. Thus, at a minimum, there should be not only imports but also exports of parts within the same product category. Although without firm-specific data one can be never sure whether the import-export transaction does not involve completely different firms, i.e., firms operating in different production and distribution networks, there is at least likelihood that production sharing occurs.
by multiple factors, and the intersection of poverty, rurality, and gender decreases the likelihood of children attending school: a poor girl living in a rural area is 25 percent less likely to attend school than a boy living in an urban area (World Bank 2012a).

**Figure 11. School enrollment rates, %, gross**

<!-- Start of picture text --> 100.0<br>77.9 79.2<br>80.0 74.4<br>67.6<br>64.7<br>60.0 54.7<br>47.0 46.3<br>40.3<br>36.2<br>40.0 38.1<br>31.0<br>17.1 16.8<br>20.0 15.9 14.6<br>9.0 7.8<br>0.0<br>Female Male Female Male Female Male<br>Primary Secondary Tertiary<br>2001 2008 latest available<br><!-- End of picture text --> _Source:_ WDI data retrieved May 2020.

_Note:_ Latest available year is 2017 for primary and secondary enrollment and 2015 for tertiary enrollment rates.

Early marriage is one of the main reasons girls (4–24 years) drop out of school. According to MICS 2014, early marriage was the third most common reason for girls’ dropout but was not a factor for boys, echoing broader analysis from the region showing that child marriage is one of the main reasons that girls leave school early (Wodon et al. 2017). MICS 2014 data indicate that 21 percent of girls in Sudan were married by the age of 15, but this rate rises to 26 percent if only girls in rural areas are considered. Though national child protection legislation was introduced in 2010, it does not cover protection for girls against early or forced marriage.<sup>10</sup> Early marriage has negative effects on educational attainment for girls and is strongly correlated with poverty and low education of the mother (World Bank 2018a). Early marriage can also have cyclical effects, as it often leads to early pregnancies that prevent a girl from going to school. In line with this trend, MICS 2014 data show that fertility rates are nearly twice as high for adolescent girls who live in rural areas than for those in urban areas, where marriage rates are lower (World Bank 2018a). It should be noted that the Government of Sudan’s decision in November 2020 to fully adopt the African Charter on the Rights and Welfare of the Child (ACRWC), including the prohibition against child marriage, has the potential to have a positive impact on girls’ educational attainment moving forward. However, the implementation of the legislation would need to be coupled with a change in cultural norms, a topic that is discussed in subsequent sections of this report.

> 10 More about the legal framework and the reasons behind early marriage can be found in the sections related to informal and formal institutions below (specifically in ‘Customs and norms impact women’s roles in society’ and ‘A legal and institutional framework with direct implications for women’s economic participation’).

17
Policy Research Working Paper 7672

# **Abstract**

Inadequacies in Tanzania’s education and training systems compromise the quality of workforce skills, giving rise to skill shortages, and constraining the operations and growth of formal sector firms in the country. This study addressed these concerns using data from a unique Enterprise Skills Survey that asked Tanzanian employers about the education, training, and occupational mix of their workforce, the skill gaps in cognitive, noncognitive, and job-specific competencies affecting their operations, and the strategies they are using to overcome these skill gaps. The study investigates the consequences for firm productivity of employers’ choices about their optimal skills mix, and their strategies to mitigate shortfalls in skills supply. Compared with noninnovators and firms primarily serving the domestic market, exporters and innovators face greater skill demand and suffer from skill shortages that are more likely to constrain their operations in such areas as quality assurance, use of new technology, and introducing new products and services. In analyzing firm performance and its relation to skill mix, the study found that firms with higher shares of tertiary-educated workers are more productive; it found no impact, however, from secondary education and technical vocational education and training qualifications, possibly reflecting the universally acknowledged poor quality of secondary education in Tanzania. Employers use a range of strategies to address skill deficiencies, from hiring new workers, to training current workers in-house or externally, using high-skill expatriate workers, or outsourcing professional services. Almost all were associated with higher labor productivity. The exception, employer provided inhouse training, had no measurable impact on productivity.

This paper is a product of the Education Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sbashir@ worldbank.org and ntanaka1@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
_5_ FCSK wem much more adminiive units of the state than their coun parts in Hungary and Poland. Even so, SOEs in all CEE-5 economics operated in an administrative environment devoid of competition, market clearing prices, freedom of entry, and a final penalty fa: poor performance, i.e., bankruptcy. For all, foreign trade equalization schemes provided a buffer between domestic producers and world prices.

The major lesson that can be drawn from these attempted reforms of fcreign trade regimes under central plarning is that their impact on export performance and competitiveness was limited while an administrative economic system remained. With the colLpse of the communist regimes it became politically possible to dismantle cntral planning (or whatever was left of it) and establish an economic system based on market-clearing prices and competition among autonomous economic units. However, the results of the collapse of communism varied with the pace of 'system replacement" pursued in different CEE-5 countries. Hungary followed a 'shock minimization' approach, Poland and the FCSK adopted radical programs in January 1990 and 1991 respectively, and two latecomers, Bulgaria and Romania, began introducing reform measures throughout 1991 and early 1992. As we shaU see, the (Ufferent paces reflected to some degree earlier expressed attitudes towards reform.

# 1a. CEE-S EXPORTS TO THE OECD IN THE 1980s: THE INITJAL BREAKDOW'N AND GROWING MARGINAUZATION

This section provides background for the assessment of export perforn_... of the CEE-5 economies following the collapse of communism. Since the objective is to identify major trends in their ompetitive position between 1980 and 1989, aud to assess the degree to which their export performance in 1990-91 represented a break with the past, the analysis in this section is limited to the following broad commodity categories: foods and feeds (..ITC Rev.2. 0+1+22+4); raw materials (SITC Rev.2. 2-22-27-28); mineral fuels (SlTC Rev.2. 3); ores and metals (SlTC Rev.2. 27+28+68); and manufactures (SITC. Rev. 2. 5+6+7+8-68). In order to minimize wellknown problems conceniing the qua,ity or statistical information from this region, the analysis of CEE-¶ expore performance in OECD markets is based on import data of OECD countries. The analysis covers European and North American OECD members and Japan.'

## A. An Overviow of Major Tendencies in Exort Performance

No matter how vigorously the various communist governments pursued reform policies the foreign trade performance of individual countries revealed disturbing similarities. First, despite govemments' efforts to reverse declining competitiveness evident in the 1970s (Pozmaski, 1988), the competitive position of all CEE-5 countris in OECD markets dropped significantly further in the 1980s. The competitive position of all CEE-5 economies, 5 The analysis does not cover all OECD members. It includes ten members of the European Communities (i.e., excluding Greece and Portugal), all members of the European Free Trade Association (Austria, Finland, Iceland, Norway, Sweden and Switzerland), North America (USA and Canada), and Japan.
- (iii) the effects of changes in policy variables on household decisions concerning, for example, market labor supply, domestic production, consumption and saving.

The central thesis of the paper is that the analysis of these issues requires a modeling approach that fully integrates the economics of household production. This requires time use data.

The paper is organized as follows. Section 2 begins with an overview of basic approaches to modeling the household. The section presents a discussion of the theoretical framework underpinning empirical work in developed economies, followed by an outline of the approach of the development literature, with its emphasis on agricultural production and health and human capital outcomes. The aim is to show that while there are important differences between the modeling approaches of the two literatures, these largely reflect specific interests arising from differences in the development of outside markets. Their treatment of domestic production is essentially the same. Both fail to integrate fully the theory of household production in analyzing the work and leisure decisions of household members.

Section 3 turns to models of the multi-person household, summarizing first the household utility function approach adopted to deal with the fact that most individuals are members of families in which there are two or more adults present. The section then reviews the standard household model, identifying its implicit assumptions on household production as its major limitation. This is followed by an overview of more recent developments that focus on modeling the decisions of individual family members and the intrahousehold allocation of resources. A clear distinction is made between models that mistakenly interpret domestic work as leisure, and therefore omit household production altogether, and those that do not. The exposition emphasizes the importance of modeling the multi-person household as a small economy, engaged in production and exchange within the household, and of analyzing issues of gender and the effects of policy within such an approach. Section 4 contains a more technical presentation of the kind of 2
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