rafmacalaba/data-use-mentions-extended huggingface dataset:
quadratic term respectively, 0 is an intercept, 1 are the respective slope estimates, refers to a normally distributed error term. nn bb bb . . . bb εε <u>Dependent variable</u> For the dependent variable, we use the logarithm of the average monthly wage within the past year from a person’s primary job (variable in the RLMS data set). If a person had an additional job, the maximum wage value among the two (variables and ) was selected for the analysis. In the waves from 1994 to 1996, the question mentioned above was absent; for those JJ13.2 waves, we exploited a variable about the average amount of money earned by a respondent within JJ13.2 JJ40 the past 30 days (variable ) as a reasonable approximation. <u>Independent variables</u> JJ10

# <u>Independent variables</u>

The present research uses both metric (measured in years) and categorical education variables. The metric version was created by assigning the average expected number of years corresponding to each attained education level. For the categorical version (EDUC), we distinguished three categories: _(1) secondary, (2) vocational_ and _(3) higher_ . Incomplete levels were incorporated into the respective upper categories (e.g., incomplete higher into higher). Vocational education here includes the International Standard Classification of Education (ISCED) levels for vocational education: 35, 45 and 55.<sup>2</sup> We are interested in exploring returns to education in general, and vocational and higher education. Estimations of premiums to primary and secondary schooling levels are technically not possible since there is a minuscule proportion of people with only primary education or lower. The experience variable was calculated as a potential experience, subtracting from the current age the years of education minus 6 (the typical school starting age). Regression (1) was estimated separately for each year for the entire sample and separately for males
of trade costs and rents, though increasingly with a better basis in firm surveys and econometrics.

# **4 Implementation Issues**

## **4.1 Quantifying policies**

The need to consider trade, FDI, and cross-border movement of people (suppliers, consumers) has meant that efforts in the research community to characterize and quantify barriers to market access in services (a basic requirement if we want to gauge liberalization in a CGE model) have been problematic. The initial approach (see Hoekman, 1996) was to establish inventory measures of policies, distinguishing between different modes of supply. This involves simply counting the instances in which certain types of measures and policies are observed for – various services sectors and modes of supply cross-boder trade, FDI, etc.<sup>14</sup> As an alternative approach, there has also been a branch of research based on priceand cost comparisons by industries across countries (see Kalirajan et al., 2000; Nguyen-Hong, 2000; OECD, 2009). More recently, there is an increasing emphasis on integrating econometric estimates of trade costs from gravity models with firmlevel, survey-based information on access to and quality of services inputs (such as energy, finance, etc.) that can be used to determine what share of observed price margins can be attributed to the effects of policy, which in turn can be used when defining services policy reform experiments in CGE models. Such estimates are admittedly crude, but may be the best immediate way forward for the purposes of simulating the effects of liberalization.

A range of gravity and price- and cost-based estimates of trade costs in services, much of which is surveyed in Francois and Hoekman (2010), concludes that the extent of barriers to trade in services may be very substantial, and potentially larger than the barriers presented by conventional trade measures such as tariffs and subsidies (see, e.g., Deardorff and Stern, 2008; Dee, Hanslow and Phamduc, 2003; Dee, 2005;
Policy Research Working Paper 9727

# **Abstract**

Transport infrastructure is an important driving force for economic growth. Africa has been lagging behind in the global manufacturing market. Among others, infrastructure is often an important constraint. The paper reexamines the impacts of transport infrastructure on firm productivity in Mozambique. A conventional cost function is estimated with two rounds of firm data collected in 2007 and 2018. It is found that improved transport connectivity, particularly access to a port, has a positive impact on firm production.

Firm inventory is also an important determinant of firm productivity. Over the long term, agglomeration economies are also found to be significant and associated with port accessibility. These findings are consistent with the fact that the Government of Mozambique heavily invested in the primary road network during the first half of the 2010s. The improved transport network is considered to have contributed to the country’s robust economic growth in the industrial and service sectors until the mid-2010s.

This paper is a product of the Transport 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 author may be contacted at aiimi@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
combining the IMF’s International Financial Statistics (IFS) data with information from the IMF’s Currency Composition of Official Foreign Exchange Reserves (COFER) database.

The IFS data on total holdings of international reserves (excluding gold) are available annually at the country level, without a breakdown across counterparty countries. The COFER database contains the currency breakdown of international reserves held in aggregate by advanced and emerging regions for 2001–14.<sup>32</sup> We use the COFER database to calculate the share of international reserves that North and South countries allocate in North and South currencies each year, assuming that this allocation in global currencies holds uniformly across countries within each region. That is, we assume that each country in the North (South) holds international reserves in North and South currencies in the same proportion as the region as a whole.

Using this methodology, we can construct country-to-region but not country-to-country data. For example, the data reveal the amount of international reserves that countries hold in euros, but not the amount of reserves held in assets of each euro area country. Similarly, COFER includes the share of international reserves in other currencies. We understand that this share corresponds to South currencies, but we cannot assign it to specific countries in this region.

The yearly value of international reserves that each country holds in the North and South results from multiplying the country-level IFS data by the corresponding share of international reserves in North and South currencies for each source country.<sup>33</sup> Implicit in these calculations is the assumption that holding reserves in a North (South) currency is equivalent to holding reserves in a North (South) country. For example, for a North country the value of international reserves held in

> 32 The IMF’s advanced and emerging regions are not identical to our definitions of North and South, but we explain below how we harmonize the two.

> 33 Because the IMF does not publish the currency breakdown for 2015–18 for confidentiality reasons
The estimation strategy addresses these issues as follows. First, in addition to reporting coefficients, I calculate the marginal effects on the bottom four steps for each of the independent variables, where variables are evaluated at the mean. ‘® Second, when comparing the effect of a covariate on welfare and power, I examine significance levels across the two regressions. Finally, assuming that ladder steps represent cardinal (rather than ordinal) rankings, I run OLS regressions on welfare and power. The estimated threshold values of the ordered probit model give an indication whether the rungs are equally spaced. OLS estimation allows me to explicitly test equality across the coefficients from welfare and power regressions. ‘ 4.2 Covariates This section explains the choice of the (2*, xe, x*, x°) vectors. The empirical analysis is obviously limited to factors that are measured in the TLSS and the village census. These surveys covered economic variables more extensively than measures of social capital. Nevertheless, it includes a number of proxies of empowerment. Since such measures are not typically covered in standard economic analysis, it is useful to review them briefly. First, important elements of empowerment are inclusion in decision making. !° Language is a key vehicle for this dimension. Timor-Leste has an ethnolinguistically diverse population, with more than 30 languages or dialects in use. Timor-Leste adopted Tetun and Portuguese as official languages. While four in five individuals speak Tetun, it is the mother tongue of just over one in ten persons. As a local indicator of social inclusion, I will include a binary variable for having the same mother tongue as the most common mother tongue spoken in the sub-district.

Other indicactors are likely to correlate with social inclusion. Speaking Porand can therefore not account both parts of Hypothesis 3 at the same time. See also Footnote 17. 16 Tn the case of binary and categorical variables, the marginal effects respond, unless otherwise indicated, to a discrete change from 0 to 1. 17 An alternative approach would be to restate
dropping by about 73, 37 and 23 percent respectively.<sup>45</sup> We find that Indian merchandise export growth has decelerated both in value and volume terms.<sup>46</sup> While the deceleration in Indian export values is significantly sharper than the volume of exports, volume growth turned negative in 2009, and again in 2016. The decline in India’s trade volume is larger in comparison to the global decline in trade volume, resulting in India’s reduced share in global exports. This is indicative of the role of India-specific factors in determining the export slowdown, or deteriorating external conditions specifically for India’s export basket.

**Figure 25: Export Growth: India and Global, in Value and Volumes**

25 A: Value v/s Volume for World - Growth 25 B: India’s Share in World Exports <!-- Start of picture text --> 2<br>Value Volume<br>30 1.8<br>Value<br>1.6<br>20<br>1.4 Volume<br>10 1.2<br>1<br>0<br>0.8<br>-10 0.6<br>0.4<br>-20<br>0.2<br>0<br>-30<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Note: Data is from WDI indicators on export values and from UNCTAD on export volumes. Years are calendar years. The global volume index is calculated as the weighted average of countries’ volume indices with weights equivalent to countries’ (value-based) share in global merchandise exports.

- India’s export basket is broad based.
incentives for collecting the bribes and the higher the mismatch and distortion found in the labor market. To capture the institutional characteristics of the labor market, we use the **(Worker’s right)** index from the Human Rights Dataset (Cingranelli and Richards, 2010). This index is equal to 0 if workers’ rights are not protected and 1 if a full or fair protection is given to workers once hired.

Several papers have investigated the economic consequences of corruption on unemployment and the labor market in general through the channel of foreign direct investment (FDI). The theoretical view is that in a highly corrupt environment, foreign actors are more reluctant to invest and to create job opportunities in the local market (corruption increases the cost of doing business (often referred to as the corruption tax). This view has received empirical support from recent research. The perception among foreign actors that due to corruption, the allocation of domestic human capital toward the most productive economic sectors is inefficient constitutes an obstacle to the inflow of foreign capital in the economy. We use the variable **(FDI/GDP)** to analyze this hypothesis of the impact of capital inflows on the development of labor market dynamics in the presence of corruption practices.

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find that, while public school teachers tend to be better paid and have higher levels of education, training, and teaching experience than their private school counterparts, the teacher absenteeism rate in public schools is almost double that in private schools (15% vs. 8%). They also find that, while private school teacher salaries are increasing in teacher _and_ student test scores and decreasing in teacher absenteeism, public school teacher salaries are largely unresponsive to these variables and are mainly a function of teacher credentials (education, training, and experience). The greater accountability in the private sector is likely due to market competitive forces which the public sector is not subject to, at least directly.

Three: in the wake of the public sector failure to adequately address these issues, the private sector has emerged as a major alternate provider of education, growing dramatically in size and reach. In particular, the rapid growth of a private sector that offers schooling opportunities to low-income households has created a major policy opportunity. Using data from 2000, Andrabi et al. (2006) find that there was an exponential increase in the number of private schools over the 1990s, with over 50% of existing private schools established on or after 1996. They also find that the birth rate of private schools in the recent period is higher in rural areas. These changes in the patterns of growth in private schools are also reflected in changes in the patterns of participation growth among students. Data from 2004/05 show that 18.7% of children ages 6–15 years were enrolled in private schools in Punjab, a 36% increase from 1998/99, with this increase largely attributable to private school participation increases among children from rural and poor households. The demand for private schooling is likely driven by the fact that fees in private schools tend to be low, accounting for a small percentage of mean annual household expenditure (Andrabi et al. 2008). It is also likely driven by the perceived higher quality of private schooling—
per capita _Yit_ = ( _WL_ )<sup>_pc_</sup> _i,t_<sup>(equivalent to the wage bill), which is available</sup> for the whole sample, or quarterly GDP per capita _Yit_ = _GDP_<sup>_pc_</sup> _i,t_<sup>from2005.13</sup> All variables are deated by the national quarterly Personal Consumption

> 12For example, a specication of GDP growth rates regressed on the change in expenditure as share of GDP (or similar) is used in Barro and Redlick (2011), Nakamura and Steinsson (2014), Miyamoto, Nguyen, and Sergeyev (2018), Kraay (2014), among others. ∆ _tri,t/Yi,t−_ 1 is equivalent to the change in transfers per capita as a share of income per capita.

> 13Labor income data come from the Bureau of Economic Analysis (BEA) under the ocial title Earnings by place of work and mostly consist of wage and salary disbursements (70%). Labor income excludes income from transfers and is before income taxes. I focus on these dependent variables as other potential outcome variables are missing at the state level (consumption), are not available quarterly (GDP before 2005), are not seasonally adjusted, or are not available for both temporary and permanent transfer samples.

12
According to equation (12), per capita income growth is the sum of the labor productivity growth rate, growth in the labor force participation rate, and growth in the fraction of the population of working age. Substituting equation (12) in equation (10) yields the growth rate of per capita income represented by equation (13) below: (13) Equation (13) provides the basis for inferring the relative contribution of women to economic growth from the growth accounting framework. The initial parameter values in the baseline scenario are selected to be consistent with the production structure of most African economies. For instance the elasticity of output with respect to the male-owned land input is defined such that the share of agricultural value added expressed in GDP. Similarly, this choice reflects the fact that the distribution of land continues to be biased against women. The base case scenario assumes that and that the contribution of capital is gender neutral.

# **IV Data and empirical results**

Except in a few cases (annual hours worked ( ) and capital stock ( , most data are obtained from the World Bank’s World Development Indicators database. Data on annual hours worked are very difficult to obtain even for OECD countries, and are not available for countries in SSA. For all practical purposes, we assume that the hours worked variable is time-invariant and genderneutral , even though women tend to put in longer hours than men in most countries and societies [Boserup (1970)]. Understandably this hypothesis imposed by data limitations is likely to result in an underestimation of the relative contribution of women to growth.<sup>21</sup> National product and income accounts generally do not report a series for the capital stock ( . This variable is constructed using a perpetual inventory method as follows:

# ,

where is the constant depreciation rate of capital and is real investment. The perpetual inventory method requires data on real investment, a value of and a value for the initial stock of capital . In the absence of
mainly laborers, while the opposite is true for extractive sectors, which do not have much impact on wages. There has also been an attempt to estimate the sectoral labor effects of the trade contraction following the 2008 global crisis using social accounting matrices in a Leontief multiplier model, specifically in South Africa and India (Kucera et al. 2012). The industry-level changes in exports on industry-level changes in employment was mediated by its indirect and income-induced effects as well as differences in laborintensity of production across sectors. Los et al. (2015) also use input-output analysis to show that the composition of export growth mattered for job growth and ultimately structural transformation in China.

The remainder of this paper proceeds as follows. Section 2 briefly describes the World Bank database on the labor content of exports and the methodology to measure the wages and jobs content. Section 3 presents the findings of labor and jobs content of aggregate production and exports in South Africa relative to peer countries. To control for compositional effects, Section 4 presents these results at the sectoral level first in South Africa and second relative to peer countries. Section 5 narrows the focus even further to GVCoriented sectors to understand how changes in labor content relate to changes in GVC-participation in South Africa. Section 6 concludes with policy implications.

# **2. Data**

The main data we use in the analysis is the newly developed World Bank Labor Content of Exports (LACEX) database. The database encompasses information on the contribution of labor to a given country’s domestic production and exports, measured as employees’ compensation (wages) or the number of jobs. The LACEX database has been computed by Calì et al. (2016) on the basis of a panel of global input-output tables and other aggregate data spanning intermittent years from 1995 to 2011 from GTAP and employment data from ILO.<sup>6</sup> The input-output tables in the GTAP data set allow one to exploit a form of social accounting data—a variation on the social accounting matrix (
# **6 The Strength of the Male Breadwinner Model**

Previous sections established that (1) women face a disproportionate burden of unpaid household labor, yet married men and women have divergent perceptions about their respective contributions, with men more likely to claim tasks are undertaken equally, and (2) the female household share of labor is uncorrelated with respondents’ attitudes toward gender roles within the home, yet does correlate with respondents’ perceptions of men’s attitudes: respondents living in households with a higher share of female labor perceive that men hold more conservative attitudes. However, providing information about men spending more time on household tasks did not shift expectations about the gender roles Moroccans will value in the future.

This section further probes to what extent Moroccans are open, in principle, to a more equal distribution of household labor, using a conjoint survey experiment to jointly assess preferences over multiple dimensions of household organization. Conjoints are a survey technique in which the researcher simultaneously randomly varies multiple components of a set of profiles about which respondents then answer questions. The design allows researchers to manipulate multiple independent variables while estimating the causal effect of changing one variable’s value on the likelihood that a respondent will favor a profile. In a typical design, respondents make a binary forced choice about which of two alternative profiles they prefer, for example about hypothetical immigrants or political candidates (Abramson, Kocak, and Magazinnik 2022; Hainmueller, Hopkins, and Yamamoto 2014). Recent work has used this approach to measure constraints on women’s employment in the MENA region (Barnett, A. Jamal, and Monroe 2021; Blaydes, Gengler, and Lari 2021) and couples’ attitudes toward the division of household labor in the United States (Hutchinson, Khan, and Matfess 2022). Conjoints have been shown to have good validity in relationship to real-world behavior (Hainmueller, Hangartner, and Yamamoto 2015), and the design may mitigate social desirability bias in survey responses (Horiuchi, Markovich, and Yamamoto 2022).