rafmacalaba/data-use-mentions-extended huggingface dataset:
the simulated labor market indicators under the minimum wage policy.<sup>28</sup> The overall unemployment rate is simulated to increase by 0.4 percentage point in the short run and by 0.6 percentage point in the long run and this effect is statistically significant at the 5 percent level. Youth unemployment (aged 15-24) is expected to increase by 0.7 percentage point in the short run and by 1.1 percentage points in the long run.<sup>29</sup> The overall effect of the proposed minimum wage policy on earnings (including job losers with zero earnings) is small and positive in the short run (0.24 percent) but not significantly different from zero in the long run, suggesting that the overall increase in earnings does not compensate the loss of earnings due to job losses in the long run.

**The simulated reduction in wage inequality is small; the change in earnings due to the minimum wage increase is likely to affect only the bottom decile of the income distribution.** Figure 13 shows the simulated reduction in earnings inequality **.** We measure earnings inequality comparing average labor earnings at the 1st, 5th and 9th earnings deciles. The increase in the minimum wage reduces the 50/10 ratio from 1.75 to 1.53 (wages at the median divided by wage at the bottom decile), mostly due to the increase among wages at the bottom decile. However, if we include job losers with zero earnings, it reduces to just 1.72, which suggests that the policy has little effect on earnings inequality. A similar pattern is observed when using the 90/10 ratio.

> 27 Skilled workers are defined as workers who have professional occupations or post-secondary education.

> 28 Notice we did not model the potential impact on labor force participation, but rather focus on movements between employment and unemployment, not on movements in and out of the labor force.

> 29 As shown in Table 3
# **4 Description of Data**

The data used for this study come from the Jamaican Survey of Living Conditions (JSLC) for the years 1989 (November) to 1991. The modules consist of health, education, nutritional status, housing, distance to social services, and participation in government social programs. Grosh (1991) provides a detailed description of the JSLC. The period 1989-1991 was chosen because there was a gradual devaluation of the Jamaican dollar during this period ending in the major devaluation of 1991. In addition, during this period, there was an increase in the inflation rate and an increase in poverty indicators (see Table 1).

In estimating FOMD and SOMD, the full sample of households in 1989, 1990, and 1991 were used along with a sub-sample of households that formed a panel across these years. In order to construct the panel, dwellings were first linked using the procedure outlined in the document "Jamaica Survey of Living Conditions 1989-93: Basic Information" (page 39). However, the survey design was such that following the same dwelling over time was not synonymous with following the same household over time. In order to ensure that this was the case, it was necessary to list the household roster (for the age, sex and individual identification variables) and perfonn a manual check of individuals to ensure consistency. This led to the elimination of some households. The final panel consisted of 986 households. Summary statistics for expenditure and food stamp transfers are listed in Table 4.

15
TEEP was implemented not randomly but in the divisions identified as socially most depressed in the presidential Social Reform Agenda. Figure 3.1 shows the distribution of school districts by income category in TEEP and non-TEEP groups. School districts are concentrated in income categories 1, 4, and 5—that is, the highest income and the two lowest income rankings—for both TEEP and non-TEEP. Though we observe that more school districts are in income category 4 (and fewer in 1) in the TEEP group than in the non-TEEP group, the difference does not look significant. Further, Figure 3.2 shows the distribution of schools in the TEEP and non-TEEP groups. Our basic observation remains valid here. Therefore, it is likely that we can find (and compare) school districts that share similar socioeconomic conditions in both TEEP and non-TEEP divisions.<sup>15</sup> For TEEP implementation information, we have the Division Education Development Plan data, which was part of the TEEP completion reports. This dataset has aggregated TEEP inputs during SY 2000/01 to SY 2004/05. However, it does not identify implementation timing and inputs of different components of TEEP. Furthermore, the completeness and quality of the data substantially vary across divisions. To overcome this gap in the data, we visited 23 TEEP division offices to find the raw data on TEEP investments. The raw data we collected reveal details of different TEEP investments: textbooks, training, school-based management, school building, school innovation and improvement fund, equipment/furniture, and supplementary instructional materials. For training, we identified the starting date of teacher training and calculated the total number of man-

> 15 In general, migration occurs from TEEP provinces since poverty rate is higher in those provinces. In the case of Visayas, we observe migrations to Manila and Cebu City (non-TEEP provinces which are not included in our analysis). Though the implementation of TEEP might have created an incentive for students to move from nonTEEP to TEEP schools, this is
more productive and therefore more likely to export, the bigger Russian firms are undoubtedly responsible for most of the exports.

The North Caucasus is the most concentrated Russian region in terms of export markets. Figure 26 **Error! Reference source not found.** shows export market concentration according to the Herfindahl‐Hirschman Index. The North Caucasus relies on two export markets, those for construction and transportation (see also Table 6). The Volga region has the lowest concentration index; it is most diversified. Concentration is inevitable for some products, since most Russian services exports went to either Ukraine or the EU (2010 data).

Disaggregated, the Moscow region is the most diversified and the Arkhangelsk and Pskov regions the least. **Error! Reference source not found.** shows the five most diversified smaller regions (panel A) and the five most concentrated (panel B). The Nizhni Novgorod region is highly diversified—not surprising, because it adjoins the Moscow region. Yet regions more distant from the capital, such as Omsk and Voronezh, also have low concentration levels.

Among the most diversified regions, each has its own comparative specialization due to geographical endowments and development patterns. **Error! Reference source not found.** shows the Location Quotient index, which uses exporter revenue data. The index measures the extent to which each region has relatively high productivity compared to the national average. The pattern of specialization clearly differs by region. For instance, the Northwest has an obvious comparative advantage in administrative support activities and Siberia in financial and insurance services, followed by transportation. The Central region has high relative productivity in health, distribution, finance, and real estate. The Ural region scores high in information and communication services. The fact that each region has its own specialization suggests that services policies will affect each region to a different degree.

Russian regions also profit from foreign direct investment (FDI) in services, which makes services firms that provide inputs to goods manufactures more productive.
Summarizing the results from the analysis at the district and firm levels, the variation in the mining employment shock across districts corresponds to employment growth particularly in heavy manufacturing sectors in the south between 2003 and 2013. This process of structural transformation was facilitated by the entry of new firms in the heavy manufacturing sector in the mining districts as well as by an increase in the average size of employment in heavy manufacturing firms in the mining districts over time.

# **5. Conclusion**

Evidence based on regional I-O tables and census data for firms supports the occurrence of a mining shock in the south of Ghana. We find evidence that the mining productivity shock drove the differentiation of production networks and the structural transformation process in the north and the south of the country. We interpret the regional difference in the sectoral composition of economic activities to be possibly due to weak interregional linkages which prevent the propagation of the local sectoral productivity shock from the south to the north.

Regional differences in intersectoral linkages and the pattern of structural transformation have several implications for aggregate productivity growth in Ghana. First, a large mining output shock in the south creates scope for policy interventions to sustain productivity growth by maintaining a strong production network in the region. Industrial policies aimed at fostering stronger linkages between mining and other sectors can help not only to secure greater economic gains from mining in the south but also to redress the lack of access to modern energy services in other sectors, particularly in manufacturing, as suggested by Aryeetey and Ackah (2018). Second, allocating resources to strengthen intersectoral linkages in the north also appears crucial as geological preconditions for oil and gas deposits have been found in the Voltaic basin located in the north of the country (Skaten 2018).

Third, divergent patterns of structural transformation across regions promote regional specialization (for example, clothing industry in the north, heavy manufacturing in the south), which, with lower trade costs, could enhance aggregate productivity growth through stronger interregional linkages (Caliendo et al. 2018). Finally,
[0.113]|[0.116]|[0.089]|[0.089]|
|Observations|1.271|1.271|1.164|1.164|
|Adjusted R-squared|0.081|0.086|0.097|0.107|

_Source:_ Author using _Ser Maestro_ 2016 and _Ser Bachiller_ 2017/2018 datasets. _Note:_ *** = P ≤ 0.001. Standard errors in brackets.

# _Improving Program Targeting_

In-service training opportunities in Ecuador under the SIPROFE program are made available to teachers on a first-come, first-served basis. Once these training opportunities become available, program administrators contact teachers by e-mail or through local campaigns organized by the MINEDUC in coordination with school district authorities. Teachers who want to participate in the available programs sign up until all slots are filled.

The combined information from the _Ser Bachiller_ and the _Ser Maestro_ assessments constitutes a powerful tool to target interventions in schools (and school districts) with the largest needs. Figure 4 plots school-level overall results of the _Ser Maestro_ (y-axis) and _Ser Bachiller_ (xaxis) assessments. The dotted lines in the figure represent test scores that are one standard deviation above and below the average scores. Each dot in the figure represents a school. The color of the dots represents the poverty rates of the parishes (the smallest administrative territorial disaggregation) where schools are located. The blue dots represent the schools located in areas with the highest poverty rates, and the black dots represent the schools located in areas with the lowest poverty rates.<sup>7</sup> Figure 4 illustrates that schools with the worst-performing teachers also have the worstperforming students (bottom left panel of the chart). While this relationship does not necessarily imply a causality, it contributes to identifying the worst-performing institutions, presumably those

> 7 Poverty is measured by the (census-based) Unsatisfied Basic Needs Index produced in 2010 by Ecuador’s National Office of Census and Statistics.

16
. “Implications of Alternate Policies on Welfare of Slum Dwellers: Evidence from Pune, India.” Journal of Urban Economics 63: 56–73.

- Muis S., M. Verlaan, H. Winsemius, J. Aerts, P. Ward (2016). A global reanalysis of storm surges and extreme sea levels. Nature Communications. Vol. 7

- Narloch, U., & Bangalore, M. (2018). The multifaceted relationship between environmental risks and poverty: New insights from Vietnam. Environment and Development Economics, 23(3), 298-327

- Patankar, A. 2015. “The Exposure, Vulnerability and Adaptive Capacity of Households to Floods in Mumbai.” Policy Research Working Paper 7481, World Bank, Washington, DC.

- Peduzzi, P., H. Dao, C. Herold, F. Mouton (2009) Natural Hazards and Earth System Sciences Assessing global exposure and vulnerability towards natural hazards: the Disaster Risk Index. Natural Hazards and Earth System Science. Vol. 9, pp. 1149–1159,

- Rentschler, J, de Vries Robbé, S, Braese, J, Huy Nguyen, D, van Ledden, M, and Pozueta Mayo, B. 2020. Resilient Shores: Safeguarding Vietnam’s Coastal Development in the Face of Disaster Risks. Washington, DC: The World Bank.

- Smith, A., C. Sampson, P. Bates (2015) Regional flood frequency analysis at the global scale. Water Resources Research. Vol.51, 539–553

- Smith, A., P. Bates, O. Wing, C. Sampson, N. Quinn, J. Neal (2019) New estimates of flood exposure in developing countries using high-resolution population data. Nature Communications Vol.10, pp. 1814

- UNDRR (2017) Global Assessment Report 2017. Geneva: United Nations Office for Disaster Risk Reduction

- World Bank and Australian AID, 2014. “’Where Are We During Flooding?’ A
br>**anel B: B**|4,956<br>**y religio**|1,108<br>**n**|10,834|6,011|2,939|6,118|
|||Ur|ban|||Ru|ral||||
||Hindu|Muslim|Christian|Sikh|Hindu|Muslim|Christian|Sikh|||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|||
|Expenditure by bride’s family|137,992|119,959|143,553|194,341|84,014|100,849|103,211|161,513|||
|Expenditure by groom’s family|90,649|76,515|66,274|132,988|55,309|57,894|48,767|107,471|||
|Diference|47,350|43,444|77,278|61,353|28,706|42,956|54,443|54,042|||
|N|11,286|2,215|514|258|22,239|2,573|862|732|||

NOTES: This table provides means of wedding expenditure in the 2005 India Human Development Survey (IHDS). The survey asks “At the time of the marriage in your community ( _jati_ ) for a family like yours, how much money is usually spent by the girl(boy)’s family?” The IHDS data set has five broad social groups: (1) Brahmin (2) OBC (3) SC (4) ST (5) Others.
indicate that the equality of the

> 29 One exception is a study by Braunstein and Brenner (2007) which compares the foreign wage premiums by gender in urban China in 1995 and 2002. Using province-level macro data and household survey data for 1995 and 2002, Braunstein and Brenner find that women received a higher foreign wage premium than men did in 1995, perhaps because FDI in export-oriented and female-dominated industries raised labor demand for women. However, after China committed to opening domestic markets in preparation for WTO accession in 2001, gender-based advantages reversed, as foreign investment in more capital- or technology-intensive sectors grew by 2002 to more greatly advantage workers in male-dominated than in female-dominated industries (Braunstein and Brenner, 2007).

15
. The dependent variable is bilateral digital services exports (DSX) using the underlying gross trade data from the TiVA database. Sector groupings in each column can be found in Annex Table A2. Robust standard errors are clustered at the country-pair level. Fixed effects for exporter and importer are applied. CB refers to the cross-border transfer of data, whereas DR refers to the domestic data processing. Both ECIPE CB and ECIPE DR variables are taken for the importer’s side and are interacted with intl as part of the C ∗intlo term. Coefficient results for the control vectors of GRAV and C can be found in Annex Table A4.

Regarding the two control variables, they show an expected outcome, which however is not always significant and at times positive. The coefficient for the DTRI CB is negative and significant only for the Digital+ grouping and only weakly significant for Telecom. On the other hand, the coefficient for DTRI DR is negative and significant for IT and information services, but positive for Digital+. In 30
the final survey year.

The paper is structured as follows. Section 2 describes the data sources, key findings from the Tanzania Household Budget Survey report, and issues surrounding the data. Section 3 presents some poverty and growth diagnostics: a decomposition of growth and inequality, a sectoral decomposition of poverty changes, and growth incidence curves. Section 4 outlines the simulation methodology—both the Datt and Walker method and the alternative approach. Section 5 presents the simulation analysis. The section investigates different assumptions regarding the data and the method of simulation. Our preferred scenario is presented in Section 5.7 after which Section 5.8 considers the main assumption underlying the Datt-Walker approach: that economic growth translates into higher consumption for all population groups. This is done by investigating whether since 1994, and for given GDP growth, inequality changed rather than that poverty dropped. Section 5.9 presents MDG projections, and Section 6 concludes.

# **2 Data Sources**

# **2.1 Household Budget Surveys**

The primary sources of poverty data for Tanzania are the Household Budget Surveys conducted in 1991/92 and 2000/01. Basic poverty statistics calculated from the survey data are shown in Table 1. In Tanzania<sup>1</sup> as a whole, in rural areas, and in urban areas other than the capital, the household survey data shows a statistically insignificant growth in consumption per adult equivalent of 6.4%, with small and insignificant drops in the fraction living in poverty, and no

> 1 When we refer in this paper to Tanzania as a whole we only refer to mainland Tanzania, i.e. the United Republic of Tanzania, excluding Zanzibar.
Table 2 presents average applied MFN tariffs for our sample of countries for three key years chosen to reveal the impacts of important institutional milestones arising across three decades: 1993, 2003, and 2013.<sup>21</sup> The table also provides information on when (if ever) the country became a member of the GATT, as well as when (if ever) the country became a member of the WTO.

For countries that were members of the GATT, the 1993 data in Table 2 reflects their applied MFN GATT tariffs _before_ they implemented any changes resulting from the Uruguay Round of negotiations that ushered in the WTO. By 2003, the countries that joined the WTO at its 1995 inception had phased in most of their Uruguay Round tariff liberalization commitments; thus a comparison of 1993 and 2003 for such countries provides a first-order assessment of the impact of the Uruguay Round on average applied MFN tariffs. For countries that acceded to the WTO somewhat later, comparisons of the tariff data can provide information on pre-WTO and post-WTO accession applied MFN tariffs.

The Table 2 evidence on changes to applied MFN tariffs for the G20 high-income economies over this extended period is mixed. Long-standing GATT/WTO members such as the European Union, Japan and the United States already had relatively low MFN applied rates at the end of the GATT period in 1993 at 7.0, 4.4, and 5.6 percent, respectively. These economies cut their average applied tariffs by another 1 to 3 percentage points as a result of the Uruguay Round; and, given their low initial applied levels, low tariff bindings, and resulting lack of overhang (see again Table 1), applied MFN tariffs for these economies were virtually unchanged between 2003 and 2013. For Australia and Canada, average applied MFN tariff cuts over the 20 year period have been larger. Korea’s average applied MFN import tariff is actually higher in 2013 than it was in 1993. Finally, Saudi Arabia’s tariff by 2013 is roughly one third of
a squeezing of wage differentials of all kinds, the scale related wage differential continues to be as important in Ghana as in other developing countries. Some detailed data exist for the manufacturing sector in Ghana due to special enterprise surveys conducted by the Research Program of Enterprise Development (RPED) sponsored by the World Bank. These surveys, three waves spanning the years from 1991 to 1994 in a number of African countries, obtained data from enterprises and their employees covering the entire spectrum of sizes, ranging from micro to large firms. This material is especially useful for studying the wage structure in the manufacturing sector in Ghana in a comparative context. A brief review of the structure of the informal sector activities, undoubtedly the largest sector of employment activity of the poor, is given to complement the more comprehensive formal sector employment. In the following Section _5_ we utilize the three waves of data collected from a sample survey of households over the years 1987-91 (called the Ghana Living Standard Survey-- GLSS) to study another important aspect of the labor market-the relationship between education and earnings. Lastly, in Section 6 we turn to the larger issues of differences in the levels and distribution of earnings in the rural and the urban sectors of Ghana, and the related issues of labor market behavior and poverty. The treatment here is historical as we are interested in following the trends over time as they have evolved with the macro-economic development of Ghana. Section 7 concludes with the general findings of this paper and suggestions for further analysis to supplement the results reported in this paper; particularly we highlight gaps which could be filled through further analysis of the Ghana Living Standard Survey (GLSS) data sets. The appendices deal with role of labor in Ghana's economic growth using growth accounting and project poverty in Ghana up to 2000 using various growth projections based on Ghana CAS for 1997.
The micro-level analysis examine the relation between an individuals education level and his/her productivity in the labor market as a wage worker or self-employed worker; or by linking the education of individuals to the outcomes of household behavior, such as fertility, child mortality etc. The macroeconomic evidence relates to the growth of national economies to prior investments in education controlling for other factors. Even though there is an enormous literature on estimating rates of return to schooling and on the job training, there are few empirical studies from sub-Saharan Africa (Ghana, Kenya). This paper build on the earlier paper on returns to education in Ghana (Glewwe, 1988). While Glewwe's (1988) work was based on GLSS 2, we look at the returns to education during the period 1987-91. This survey includes two time periods, before and after the educational reforms in Ghana, and can help us better understand whether education pays in the labor market..