rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10446

# **Abstract**

This paper studies the effect of a local sectoral productivity shock on subnational structural transformation. The analysis is based on regional input-output tables constructed for 2004 and 2013 and available censuses of firms in 2003 and 2013 for Ghana. Based on the data, the analysis confirms the occurrence of a mining productivity shock. Between 2004 and 2013, mining grew dramatically as a share of gross domestic product. The mining shock occurred primarily in the south of Ghana with much larger increases in mining’s share in regional output, the number of mining firms, and mining employment than in the north of the country. The findings show that the mining productivity shock led to growing regional (north-south) differences in intersectoral linkages, with greater intermediate use of mining output and a larger sectoral total factor productivity ratio between mining and manufacturing in the south than in the north. Informed by international evidence of strong intersectoral linkages between mining and heavy manufacturing industries, the paper examines the performance of heavy manufacturing in response to the mining productivity shock. The elasticity of heavy manufacturing to mining employment growth is 50 percent larger in the south than in the north, generated by an increase in both average firm employment and the entry of new firms. These north-south differences are interpreted as possibly due to weak interregional production linkages.

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 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
in countries that had high energy intensity initially. Beyond this general trend, we find some Central Asian countries are notable exceptions where energy intensity had remained largely unchanged. Interestingly, intrinsic efficiency improvement turns out to have contributed to most of the reduction in manufacturing energy intensity, whereas structural change had had a less influence, and had led to higher energy intensity in some countries.

Country-level analysis indicates that rising per capita income and higher energy prices are strong predictors of more efficient energy use, both through the channel of efficiency rather than structural mix. A one percent increase in per capita GDP is on average associated with a 1.3

> 3 Manufacturing consists of all industrial activity outside of mining and construction. The detailed composition of manufacturing industry is described in section III.

> 4 Following the definition of World Bank regions, Eastern European and Central Asia includes Albania, Armenia, Azerbaijan, Belarus, Bosnia & Herzegovina, Bulgaria, Croatia, the Czech Republic, Estonia, Georgia, FYR Macedonia, Hungary, Kazakhstan, Kosovo, the Kyrgyz Republic, Latvia, Lithuania, Moldova, Montenegro, Poland, Romania, the Russian Federation, the Slovak Republic, Slovenia, Tajikistan, Turkey, Turkmenistan, Ukraine and Uzbekistan. Kosovo and Montenegro are not included in the analysis because energy consumption data are not available for these two countries.

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# **6. Scaling Considerations: Targeting, Trust, and Village Relations**

Our results demonstrate benefits to treated households and negligible impacts on control and nonparticipant households in communities with greater access to warrantage. The next step is to determine whether warrantage communautaire is scalable and, if so, to understand why we have not seen more experimentation with rural finance of this nature. In this section, we attempt to shed light on these important questions.

To effectively scale, organizations and/or governments need to be able to effectively identify interested participants in relatively localized areas in order to minimize costs of implementation. Throughout the project, we elicited interest in participating in warrantage at the time of the census, baseline, lottery, and endline. One goal was to assess whether we can predict warrantage participation and if so, at what point we are able to do so effectively. Overall, the level of expressed interest is consistently high. Yet, correlation in interest across the different stages is relatively low (correlation coefficient = 0.19); this is not terribly surprising, as inferring actual behavior using hypothetical questions is difficult. Using lottery participation (the only elicitation indicator that is incentivized), we examined the determinants of interest in warrantage in order to identify how to target sensitization efforts (Appendix Table 3). Familiarity with warrantage is an important determinant, demonstrating that sensitization activities prior to scale-up are important. Larger households with younger household heads and households with an unmarried household head are also more likely to participate. More generally, our results suggest that it is hard to predict participation based on household characteristics, implying that it is likely to be challenging to identify interested households to target sensitization efforts and reduce implementation costs.

Casaburi _et al._ (2013) identify two additional challenges for scale, particularly with respect to low take-up: alternative and preferred home storage options and trust in the institutions involved.
**Figure 16: Unemployment Rates in World Regions**

<!-- Start of picture text --> 14% Developing Europe<br>& Central Asia<br>12%<br>European Union<br>10%<br>Latin America &<br>8%<br>Caribbean<br>6%<br>Middle East & North<br>4% Africa<br>North America<br>2%<br>0%<br>Industrialized East<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Asia<br><!-- End of picture text --> Sources: International Monetary Fund, Middle East and Central Asia Regional Economic Outlook April 2011 and World Economic Outlook Database April 2011; International Labour Organization, Key Indicators of the Labour Market database.

**Figure 17: Unemployment Rate in Crisis Economies**

<!-- Start of picture text --> 25%<br>Spain<br>20%<br>Portugal<br>15%<br>Ireland<br>10%<br>Greece<br>5%<br>United<br>0% States<br>Q1 2000 Q1 2002 Q1 2004 Q1 2006 Q1 2008 Q1 2010<br><!-- End of picture text --> Source: International Monetary Fund, International Financial Statistics.

_26._ The global slowdown in growth heightened vulnerabilities that had already been in place before the crisis. Notably, countries that had their own housing booms, like Ireland and Iceland, or had high fiscal deficits before the crisis, like Greece and Portugal, now teetered on the brink of a sovereign debt crisis and required support from the European Central Bank (ECB) and International Monetary Fund. At present, the fiscal crisis in peripheral Euro countries has already turned into a sovereign debt crisis in the Euro zone, with potentially significant consequences for the world economy as a
, subsidies and in-kind transfers<br>A. 0verall tax burden/benefit by decile B. 0verall tax burden/benefit (% of market<br>(KHR/month) income)<br>40000 15.00<br>20000<br>10.00<br>0<br>-20000 5.00<br>-40000 0.00<br>-60000<br>-5.00<br>-80000<br>-100000 -10.00<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Income decile Income decile<br>Education transfers Health transfers Total transfers - cash Education transfers Health transfers Total transfers - cash<br>Electricity subsidy Total direct taxes Total indirect taxes Electricity subsidy Total direct taxes Total indirect taxes<br>Total Total<br>KHR / month<br>Proportion of income (%)<br>KHR / month<br>Proportion of income (%)<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

Welfare transfers have limited poverty-reducing and redistributive effects, however, because they provide low coverage, are small, and are poorly targeted. In 2019, about 10 percent of Cambodian households possessed an IDPoor “equity” card and only 2 percent received conditional cash transfers 25
first round of the GHS, there are no statistically significant differences in the poverty status, level of consumption, and the CSI between households that had experienced conflict events between 2010 and 2017 and households that had not. We find only a couple of statistically significant differences in household characteristics across these samples. Larger households and polygamous households were more likely to have experienced conflict events. This is unsurprising because our conflict indicators include variables such as a household member being robbed or a household member being injured due to an attack. Larger households (a variable that correlated strongly with polygamy)

> 20 The summary statistics and the description of the geographical variables are presented in appendix A, tables A.1 and A.2, respectively.

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# **Executive Summary**

Poor households in Bangladesh depend heavily on wood, dung and other biomass fuels for cooking. This paper provides a detailed analysis of the implications for indoor air pollution, drawing on new monitoring data for respirable airborne particulates (PM10) in a large number of Bangladeshi households. Concentrations of 300 ug/m<sup>3</sup> or greater are common in our sample, implying widespread exposure to a serious health hazard. For comparison, Galassi, Ostro, et al. (2000) find substantial health benefits for PM10 reduction in eight Italian cities whose annual concentrations are far lower: 45-55 ug/m<sup>3</sup> .

As expected, our econometric results indicate that fuel choice significantly affects indoor pollution levels: Natural gas and kerosene are significantly cleaner than biomass fuels. However, household-specific factors apparently matter more than fuel choice in determining PM10 concentrations. In some biomass-burning households, concentrations are scarcely higher than in households that use natural gas. Our results suggest that cross-household variation is strongly affected by structural arrangements: cooking locations, construction materials, and ventilation practices.

To assess the broader implications for poor Bangladeshi households, we extrapolate our regression results to representative household samples from rural, peri-urban and urban areas in six regions: Rangpur in the Northwest, Sylhet in the Northeast, Rajshahi and Jessore in the West, Faridpur in the Center, and Cox’s Bazar in the Southeast. Our results indicate great geographic variation, even for households in the same per capita income group. This variation reflects local differences in fuel use and, more significantly, construction practices that affect ventilation. For households with per capita incomes less than $1.00/day, rural PM10 concentrations vary from 410 ug/m<sup>3</sup> in Cox’s Bazar to 202 ug/m<sup>3</sup> in Faridpur. In urban areas, concentrations for such households differ by almost 100 ug/m<sup>
poverty status.<sup>14</sup> Within each public/private, region, and poverty status strata, a random sample totaling 46 districts was drawn.<sup>15</sup> Five schools were randomly selected within each district.<sup>16</sup> A total of 214 schools were sampled.<sup>17</sup> Data from 213 schools are included in this study.<sup>18</sup> Data were collected for up to three KG1 and three KG2 classes per school (randomly selected if more than three). There were 638 classrooms with child and teacher data completed. A random sample of four children per classroom was selected. The sample of children whose data were successfully collected was 2,455 observations.<sup>19</sup> The data collection firm tried up to three times to reach parents, based on phone numbers provided by the school. For the parent data, there was substantial non-response (primarily that parents did not pick up, but some refusal when reached) such that only 1,437 parents were reached and consented.<sup>20</sup> We focus on the sub-sample with parental data in order to be able to investigate home environments and inequality.

# **_3.4 Outcomes_**

We examine three main categories of outcomes: early childhood development (collected through direct assessments and teacher reports), pre-primary quality (collected through observations), and stimulation at home (collected through parent reports). We summarize a large number of variables (as detailed in the appendix) into factors using confirmatory factor analysis.<sup>21</sup> The only selection criteria was that the first factor has an eigenvalue of at least one. We kept even items with low loadings in making the index, but since the loadings were small, they have a small role in determining the value of the factor.

For ECD, we examine:

- Literacy skills

- From
observe that the estimated marginal effect for the first quarter of 2020 compared to the corresponding quarter of 2017 is negative although only marginally significant. These results suggest that the slackness of the labor market in the first quarter of 2020 may well have contributed to slowing down the employment prospects of jobless individuals.

**<u>Table 8. Employment entry during the onset of COVID pandemic</u>**

|||2019|-2020|2017-|2020|
|---|---|---|---|---|---|
|Year start work||[1]|[2]|[3]|[4]|
||2020|-.045***<br>(.010)|-.046***<br>(.010)|-.017*<br>(.009)|-.016<br>(.009)|
||2019|-|-|.028***|.029***|
|||||(.010)|(.010)|
||2018|-|-|.001|.001|
|||||(.009)|(.009)|
|Fixed effects||-|Yes|-|Yes|
|Observations||3|802|79|42|

Source: Hellenic Statistical Authority (EL.STAT.), Quarterly Labor Force Survey-LFS (2017 – 2020, 1<sup>st</sup> quarter).

Notes: Reported estimates are average marginal effects drawn from a probit model (the first year serves as the reference category, i.e., 2019 and 2017 in columns 1-2 and 3-4, respectively). Based on the ILO definition of employment, the dependent variable takes the value of 1 if the individual has start working in the current employer during the last
In their study of power generation and the US SO2 program, Considine and Larson (2006) considered the use of the atmosphere for the disposal of emissions as a factor of production, priced by tradable emission permits, and derived related input demand schedules in a cost-function framework. The authors applied a similar approach in their paper on the European Union’s program for greenhouse gases (Considine and Larson 2009).

Several studies estimated the abatement cost function by separating cost of abatement from the cost of production. Using data from the U.S. Census Bureau, Hartman, Wheeler, and Singh (1994) estimated air pollution abatement costs by industry sectors. Assuming that the abatement cost function was separable from the firm’s production cost function, they estimated abatement costs as a quadratic function of emissions abatement. Hamaide and Boland (2000) defined abatement costs as a second-order polynomial function of abatement alone. While estimating the cost of abating agricultural nitrogen pollution in wetlands, Bystrom (1998) estimated linear, quadratic, and log-log specifications of a cost function.

For the projects that generate CERs only, total project costs are synonymous with abatement costs. However, in some cases, project investments increase power generations as well as mitigate greenhouse gas emissions. In this paper, our initial focus will be on a separable cost function; consequently we calculate a net cost of abatement by subtracting out net revenues from the expected sales of electricity, a “by-product” of abatement. Later, we repeat the analysis using total project costs, but include expected power increases as a control.

# **2.1 The Conceptual Model**

As a starting point for the derivation of our applied model, consider the expected project value function, where the value of the investment, V0, is determined by the discounted value of two streams of profit from a project initiated in year 0 and expected to last _n_ years<sup>7</sup> :

> 7 See Timilsina and Lefevre (1999) for a related discussion
All variables, except for the inequality measures and dummies, are averages by subcounty _i_ : _g_ is the annual expenditure growth rate between 1992 and 1999; _y_ is the logarithm of per capita expenditure; _I_<sup>exp</sup> is the Gini coefficient for per capita household expenditure; _I_<sup>edu</sup> is the Gini coefficient for the number of years of formal education of the household head. **X** is a matrix of other covariates consisting of human capital (number of years of formal education entered separately for household heads and for spouses), household head age, gender of the household head, adult equivalent household size, and the fraction of own children in the household. Given our approach, we are limited in our choice of covariates in **X** to what the census has to offer. We use district fixed effects, represented by α d, to control for unobserved spatial heterogeneity; _ui_ is an error term. The specification includes most variables that are common in (cross-country) inequality-growth studies. We add demographics to account for differences in production technology and fertility, as many theoretical models require. We do not have a measure of ‘market distortions’, but do not expect the value of this variable to vary much between sub-counties.

A non-standard econometric issue lies in the fact that some of our variables are not observed but imputed as described in the previous section. The imputed variables, expenditure, growth and expenditure inequality, are denoted using tildes. See Table 2 for definitions and summary statistics and Section 4 for a discussion of the estimation issues involved.

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_Ingo Borchert and Mattia Di Ubaldo_ of GVC activity, which allow us to provide novel evidence on the redirection of VA trade from non-PTA to PTA members as a consequence of entering a services PTA.

Finally, it of relevance to acknowledge a strand of literature analysing the determinants of services PTA formation, which we are aware of in specifying our empirical models. From the seminal work of Baier and Bergstrand (2004), the literature on PTA formation has generally found that countries are more likely to enter goods PTA if potential welfare gains are larger. This is the case when trade partners are geographically closer, more remote from the rest of the work, economically more similar, with greater difference in relative factor endowments. Determinant of services PTA have been analysed recently by (Egger and Shingal, 2020). In addition to ‘standard’ determinants such as countries’ size and proximity, they find that countries with more similar unilateral services regulations, as measured in the World Bank Services Trade Restrictiveness Index (Borchert et al., 2014)), are more likely to enter a services PTA.

# **3 Data**

## **3.1 Services Policy and PTA Data**

The findings in this paper are made possible by exploiting novel information from the World Bank Deep Trade Agreements 2.0 (WB-DTA) dataset. These data are publicly available and documented in detail in Mattoo, Rocha and Ruta (2020). The full WBDTA dataset provides detailed information on the design and depth of 279 PTAs notified to the WTO from 1962 to 2017, across 18 subject areas. In this paper, we focus on the ‘Services’ subject area of the WB-DTA, which contains information from 143 PTAs, i.e.

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exclude personnel in paramilitary units, i.e. Border Guards (16,000), under the authority of the Ministry of the Interior, the Maritime Border Guard and the Prevention Units of the Police.

Wages and salaries of Consolidated Central Government are from IMF Government Finance Statistics Yearbook 1995 and are for 1994. Wages and Salaries as percent of GDP and Average government wages are taken from Barbara Nunberg's report of September 1995 (draft) Administrative Reform in Hungary: The Advanced Transition experience, prepared in conjunction with EMTPM's Seminar on State Transformation in the Transition (Salzburg, November 1995).

# Romania

Number of unemployed is drawn from Economist Intelligence Unit 1995-96 (p. 19) and reflects the situation in April 1993. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1993.

Central Government employment figures are drawn from State Transformation in the Transition - Barbara Nunberg (p. 22). Education data are from EC1/2HR draft paper on health and education in the ECA and are for the year 1994. Data on military employment includes conscripts (104,700), but excludes personnel in paramilitary units, i.e., Border Guards ( 22,300), the Gendarmerie (10,000) and the Security Guards (46,800), all under the authority of the Ministry of Interior.

GDP and wages and salaries are taken from IMF Government Finance Statistics Yearbook 1995 and are for 1993. Data on wages in manufacturing are taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1993.

# Slovakia

Number of unemployed is drawn from Economist Intelligence Unit 1995-96 (p. 41) and reflects the situation in December 1994. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994. Central Government, education and health employment are taken from IMF Report No. SM 95/206 of August 18
**||||
|Less than two|83,138|983,782|1,102,833|
|Between two and five|569,144|(1,550,261)|596,770|
|More than five|(33,439)|(486,374)|(701,495)|
|No monetary income|(86,976)|(37,442)|148,427|
|Undeclared|428,173|567,880|161,016|

**Source: Own calculations using data from ENOE.**

The discussion above describes employment flows during the crisis and the recovery. What are the characteristics of unemployment flows? Interestingly, in 2008, the year before the crisis, as many women as men lost their job or couldn’t find one. In 2009, however, the relation was four-to-one against men. More than 600,000 men became unemployed, while only 150,000 women did. During the recovery, the roles changed. A meager 7,000 men became unemployed while another 113,000 women lost their jobs (see table 3.5). This indicates that the crisis hit men, but the recovery is relatively creating more female unemployment.

Regarding levels of education, nearly half of the new unemployed during the recession had completed secondary education but not high school (which in Mexico represents between 9 and 12 years of education). This group is not the usual target of payroll cutbacks or job-searchers. The figures indicate, as usual but with larger magnitudes, that unemployment concentrates among 18