rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5. Gorlovka and Stakhanov: comparison of sample data to official statistics**

|Demogr|aphic variable|Survey,<br>January-<br>February2002<br>(%)|Officialstatistics<br>asof01.01.2001<br>(%)|Difference(%)|
|---|---|---|---|---|
|**Gorlovka**|||||
||male|40.6|44.8|-4.2|
|gender|female|59.4|55.2|+4.2|
||18-29|12.0|21.4|-9.4|
||30-44<br>|23.9|27.7<br>|-3.8|
|age|45-59|22.8|22.6|+0.2|
||60andolder|41.3|28.3|+13.1|
|TOTAL(populat|ion18+)|1,053|236,011||
|Stakhanov|||||
||male|40.6|44.0|-3.4|
|gender|female|59.4|56.0|+3.4|
||18-29|14.1|20.6|-6.5|
||30-44|20.8|28.7|-7.9|
|age|45-59|24.5|23.7|+0.8|
||60and older|40.6|27.1|+13.5|
|TOTAL (populat|ion18+)|1,083|80,298||

144. The quality of the migration - whether temporary or permanent - is impossible to determine on the basis of this research. At the same time, the survey results (as well as the qualitative research) indicate that among those who remain, migration intentions seem to have subsided; the overwhelming majority of respondents indicated that they expect to live in the same place 5 years hence, even when the analysis was limited to the working-age population (results are the same for the two samples at 77%). A concern emerges, however, when the situation is considered not in the static view, as it is here, but rather, in the dynamic perspective. While the survey data cannot shed light on the circumstances of the out-migrants and the likelihood of their return to Gorlovka and Stakhanov, the qualitative research indicated that a new phenomenon of return migrants has been noted, primarily for negative "push" factors from the place of migration, not for positive "pull" factors of more attractive conditions in the two mining municipalities.

_145. Employment conditions._ The survey results indicate a somewhat disadvantaged employment position of the mining municipalities compared to similar sized municipalities in the rest of Ukraine. While the difference is not dramatic, it is reasonable to assert that the difference would be much greater if the observed migration had not taken place. The analysis showed that 55% of the working age adults in urban Ukraine (not including cities with populations above 500,000) compared to 47% in the mining municipalities were employed.<sup>3 3</sup> For the sake of comparison, employment in different sized settlements in Ukraine was analyzed, with the following results: the share of the employed working age population is 61% in rural areas, 55% in cities, 68% in large cities other than Kyiv, and 73% in Kyiv. Thus, of all the settlement types considered here, the mining municipalities had the lowest level of current employment. Among working age adults, there is no difference in the level of employment among women in the mining municipalities compared to women in urban Ukraine (46-47%) but the difference among

> **33** The difference is statistically significant at 5%.

42
# **Regional Integration in South Asia: Implications for Green Growth, Female Labor Force Participation, and the Gender Wage Gap**

# Muthukumara Mani<sup>1</sup>

Badri Narayanan Gopalakrishnan Deepika Wadhwa Keywords: regional trade liberalization, trade and gender, green growth, female labor force participation, South Asia, computable general equilibrium (CGE) modelling JEL codes: R23, O15, J11, J16, J31, F61, F63, F64

> 1 Corresponding author. Muthukumara Mani is with the office of the Chief Economist for South Asia. Badri Narayanan Gopalakrishnan is with the University of Washington, Seattle. Deepika Wadhwa is with Infinite Sum Modeling Inc. The authors would like to thank Hans Timmer, Martin Rama, Gladys Lopez and Yue Li for their very thoughtful comments and suggestions on an earlier version.
**_Energy demand models for policy_** **_<u>formulation</u>_**

WAES, 1977, Workshop on Alternative Energy Sources, Global Prospects 1975-2000, New York, McGrawhill.

- Wang H. and Nakata T., 2009, Analysis of the market penetration of clean coal technologies and its impacts in China's electricity sector. Energy Policy, 37, 338-351 (2009).

- Wei, Y.M., Q. M. Liang, Y. Fan, N. Okada and H. T. Tsai, 2006, A scenario analysis of energy requirements and energy intensity for China's rapidly developing society in the year 2020, Technological Forecasting and Social Change, 74(4), pp. 405-21.

- Werbos, P. J., 1990, Econometric techniques: theory versus practice, Special issue of Energy, Energy, 15, 3&4, pp. 213-36.

Westoby, R. and D. Pearce, 1984, Single equation models for the projection of energy demand in the United Kingdom, 1954-80, Scottish Journal of Political Economy, 31(3), pp. 229-254.

Wilson, C., and H. Dowlatabadi, 2007, Models of decision making and residential energy use, Ann. Rev. Envi. Res., 32, 169-203.

Wilson, D. and J. Swisher, 1993, Exploring the gap: Top-Down versus bottom-up analyses of the cost of mitigating global warming, Energy Policy, pp. 249-263.

Wirl, F. and E. Szirucsek, 1990, Energy modelling – a survey of related topics, OPEC Review, Autumn, pp. 361-78.

Worrel, E., S. Ramesohl and G. Boyd, 2004, Advances in energy forecasting models based on engineering economics, Ann Rev Environ Resources, Vol. 29, 345-81 Worrel, E., S. Ramesohl, and G. Boyd, 2002, Towards increased policy relevance in energy modeling, in Worrel, E. and H.C. Park, 2002, Policy Modeling for industrial 121
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 4. Benchmarking port indicators: Dar Es Salaam as compared with largest and neighboring ports, 2006**

||Mombasa (Kenya)|Maputo<br>(Mozambique)|Sudan (Sudan)|Dar es Salaam<br>(Tanzania)|Cape Town (South<br>Africa)|Durban (South<br>Africa)|Apapa (Nigeria)|
|---|---|---|---|---|---|---|---|
|EFFICIENCY:||||||||
|Average container dwell time in terminal<br>(days)|5|22|28|7|6|4|42|
|Average truck processing time for receipt<br>and delivery of cargo (hours)|4.5|4|24|5|4.8|5|6|
|Average container crane productivity<br>(containers loaded-unloaded per crane<br>hour)|10|11|8|20|18|15|12|
|Average general cargo crane productivity<br>(tons loaded-unloaded per crane working<br>hour)|20.82|11|8|20|15|25|9|
|TARIFFS||||||||
|Average general cargo handling charge,<br>ship to gate (USD/ton)|6.5|6.0|10|13.5|1.48|17.4|8|
|Average dry bulk handling charge, ship to<br>gate or rail (USD/ton)|5|2.0|3|4.5|6.5|1.48|n.a.|
|Average liquid bulk handling charge<br>(USD/ton)|n.a.|0.5|1|3.5|2.68|n.a.|1|

_Source:_ Mundy and Penfold 2009. Derived from AICD ports database, http://www.infrastructureafrica.org/aicd/tools/data. n.a. = Not available Dar es Salaam’s performance indicators compare well to those of other eastern and southern Africa ports. It has a low container dwell time of seven days, low truck processing time of 5 hours, and high crane productivity of 20 containers or tons per crane hour. This strong performance can be explained by Dar es Salaam’s sizable terminal operations, specialized container handling equipment, and adoption of a container terminal concession to incorporate private management of operations. As a result, the port leads Sub-Saharan Africa in container handling productivity and ranks among the top in general cargo handling.

# **Challenges**

Despite its achievements in productivity, Dar es Salaam suffers from significant capacity constraints and congestion following double digit growth in the container sector during the 2000s. Its demand to capacity ratio is 140 percent in the container sector (demand of 350,000 TEU/year and capacity of 250,000 TEU/year) and 93 percent in the general cargo sector (demand of 3.8 million tons/year and capacity of 4.1 million tons/year). These are the highest ratios in Africa after Mombasa. In fact, Dar es Salaam took responsibility for transshipments that Mombasa could not handle due to severe capacity constraints and operational inefficiencies. As a point of reference, once a port’s capacity ratio surpasses the 80 percent mark, congestion becomes a serious issue that reduces the effectiveness of the port, and Dar es Salaam is already well past this level. New capacity needs to be introduced to solve this problem, 14
# WIGURE 14. INFANT MORTALITY TRENDS BY MOTHER'S EDUCATION IN COTE D'IVOIRE, 1970-86 (LSMS 1985/1986)

<!-- Start of picture text --> No schooling<br>90 s~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0<br>a.. ~ .0b . , ~~~~~~~~~~~~~Primary education<br>f / ~~~~~~~~~~~~~~~~and above<br>1970 29"<br>Year (1)<br><!-- End of picture text --> NOTE: (1) IMR 5 -Year Moving Average
In the context of India, Ravallion and Datt (1996; 2002) and Datt and Ravallion (2002) report results indicating that the sectoral and geographic composition of growth has mattered to aggregate poverty reduction. Rural economic growth has had more impact on poverty in India than urban economic growth, and growth in the tertiary (mainly services) sector has had more impact than the primary (mainly agriculture) sector, while the secondary (mainly manufacturing) sector appears to have brought little direct gain to India’s poor. Empirical support for the PGH has also come from cross-country evidence suggesting that more labor-intensive growth processes have greater impact on poverty, as found by Loayza and Raddatz (2009).

However, all this sits uneasily with the observation that the country that has undoubtedly made the most impressive progress against absolute poverty over recent decades has also had one of the most sectorally and geographically unbalanced growth processes. We refer to China. While the impressive growth performance of China since the early 1980s is well known, there has been much concern in recent times that this growth process has been “unbalanced,” and in particular that growth rates in agriculture have appreciably lagged those in other sectors, notably industry and services (Kuijs and Wang, 2006; Chaudhuri and Ravallion, 2006). The primary sector’s share fell from 30% in 1980 to 15% in 2001, though not montonically. Yet China ́s record against absolute poverty has been impressive. Using their national poverty line, Ravallion and Chen (2007) found that the poverty rate (headcount index) fell from 53% in 1981 to 8% in 2001. Using decomposition methods, the same authors found that about three-quarters of this reduction in poverty nationally was due to poverty reduction solely within rural areas.

These observations motivate the main questions addressed by this paper: What role did the apparent “imbalances” of China ́s growth process play in China’s progress against poverty? Would a more balanced growth process have had a larger impact on poverty? Or could it be that the unbalanced growth actually fostered poverty reduction, by allowing a higher overall growth rate?

There is already evidence in the literature to suggest that China ́s rate of poverty reduction would have been even higher if not for the pattern of growth. Using aggregate (national level) time series data for China, Ravallion and Chen (2007) find evidence that the sectoral composition of growth (how much comes from agriculture versus manufacturing versus services) matters to both poverty and inequality independently of the rate of growth. If the same rate of growth had been possible without the sectoral imbalances observed then 3
# **Supplemental tables**

**Table S1: Average price distances by commodity and year**

|**Commodity**|**1995**|**1997**|**2001**|
|---|---|---|---|
|<br>beans|0.333|0.226|0.149|
|beer|0.261|0.364|0.209|
|bread|0.218|0.189|0.235|
|bulgar|0.409|0.364|0.291|
|cabbage|0.643|0.268|0.229|
|carrots|0.698|0.792|0.221|
|chicken|0.087|0.207|0.051|
|cucumbers|0.480|0.485|0.303|
|fresh milk|0.262|0.288|0.408|
|ground meat|0.085|0.204|0.051|
|honey|0.356|0.355|0.341|
|lamb|0.193|0.324|0.116|
|lard|0.445|0.527|0.511|
|lentils|0.312|0.314|0.105|
|margarine|0.097|0.124|0.154|
|oranges|0.219|0.382|0.182|
|pasta|0.473|0.344|0.235|
|pears|0.128|0.387|0.105|
|pork|0.086|0.186|0.180|
|rice|0.162|0.113|0.113|
|sausages|0.082|0.215|0.238|
|soft drinks|0.837|0.408|0.552|
|sweet peas|0.288|0.302|0.374|
|tomatoes|0.499|0.428|0.124|
|veal and beef|0.204|0.148|0.260|
|vegetable oil|0.182|0.384|0.112|
|white cheese|0.212|0.182|0.131|
|Total|0.321|0.323|0.233|

Source: Bulgaria Integrated Household Surveys and authors’ calculations.

**Table S2: Selected OLS estimation results and test-scores.**

|**Estimated time effects**|**Parameter**|**Sample**<br>**t-score**|**Bootstrap**<br>**z-score**|
|---|---|---|---|
| 1997|0.010|0.56|0.40|
| 2001|-0.095<sup>a</sup>|-5.36|-4.01|
|**Tests**||||
|**Time effects are equal**||||
| 1997 = 2001|0.105<sup>a</sup>|6.04|5.37|

a denotes significance at the 1 percent level.

26
# **Table 4.2. Macro indicators in 2012 and by simulation in 2030 or averages for 2012-2030 (Percent of nominal GDP)**

||**2012**|**base**|**min+**|**inf+**|**hd+**|**geff+**|
|---|---|---|---|---|---|---|
|**Absorption**|184.2|126.6|118.6|125.7|126.4|126.2|
|**Consumption - household**|83.9|74.5|69.0|74.1|74.4|74.2|
|**Consumption - UNMIL**|25.4|2.0|2.0|2.0|2.0|2.0|
|**Consumption - government**|21.2|24.8|23.8|22.2|24.3|24.0|
|**Fixed investment - private**|38.8|18.3|15.6|18.2|18.3|18.2|
|**Fixed investment - government**|14.9|7.0|8.1|9.1|7.3|7.8|
|**Exports**|37.8|65.7|70.8|68.6|66.6|68.3|
|**Imports**|-121.9|-92.3|-89.3|-94.3|-93.0|-94.5|
|**GDP at market prices**|100.0|100.0|100.0|100.0|100.0|100.0|
|**Net indirect taxes**|11.3|8.7|8.1|8.9|8.7|8.9|
|**GDP at factor cost**|88.7|91.3|91.9|91.1|91.3|91.1|
|**GNI**|91.2|89.5|87.9|89.7|89.5|89.5|
|**GNDI**|151.9|116.8|111.2|116.1|116.6|116.5|
|**Foreign savings**|32.2|9.8|7.4|9.6|9.7|9.7|
|**Gross national savings**|21.4|15.5|16.3|17.7|15.9|16.3|
|**Gross domestic savings**|-30.5|-1.3|5.2|1.6|-0.8|-0.2|
|**Foreign government debt**|8.6|20.9|15.4|18.9|20.6|20.0|
|**Domesticgovernment debt**|15.1|18.4|16.0|17.2|18.2|17.8|

**Note:**

**Columns other than 2012 show level in 2030 (for debt indicators) or averages for 2013-2030 (all non-debt indicators). Gross domestic savings equals gross national savings minus the sum of net transfers and net factor income from abroad.**

25
Policy ReseaRch WoRking PaPeR 4575

# **Abstract**

Using firm-level data on manufacturing sectors in Africa, this paper addresses how domestic supply constraints and other firm characteristics explain the geographical orientation of firms’ exports and the overall market diversification of African manufacturing exports. The degree of market diversification, measured by the number of export destinations, is highly correlated with export intensity at the firm level, and both embody strong scale effects. Technological factors, such as new vintage capital and Internet access, which improve production efficiency and lower export costs, show strong effects on the firmlevel export intensity. Some qualitative differences exist between Africa’s regional exports and exports to the global markets. Foreign ownership is a significant factor in characterizing the intensity of global exports but not regional exports. The technological factors are significant in both cases, but more so in global exports. Public infrastructure constraints, such as inferior power services and customs delays, seem to have more immediate impacts on regional exports in general, implying the relevance of addressing behind-the-border constraints in fostering regional integration in Africa. Customs efficiency does matter for textile exports to the global markets, underscoring the importance of improving trade facilitation in Africa for competitive participation of African producers in global supply chain industries.

This paper—a product of Poverty Reduction and Economic Management, Africa Region—is part of a larger effort in the department to improve understanding of the factors that contribute to successful export diversification and export growth in Sub-Saharan Africa. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at yyoshino@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
- Bank branch regulations

- Collateral and lending

- Transparency and consumer protection

- Policies promoting of access to finance.

In this part of the survey, we ask regulators to respond with answers that reflect the rules and regulations that commercial banks must follow if there is any discrepancy in how the regulations apply across institutional categories.

# **_2.2 Implementation of the Data Collection_**

The questionnaires were sent directly to the head office of the main commercial bank regulator, usually the Governors’ offices of Central Banks or Banking Supervisory Agencies. Depending on the country and the structure of the Central Bank, the questionnaires were filled out by one or several of the following departments: research, statistics, supervision, and/or foreign relations.

Out of 144 questionnaires sent, 129 countries responded; Eastern Caribbean Central Bank responded with seven additional unsolicited questionnaires which are included in the dataset.<sup>11</sup> Central Bank of West African States (BCEAO), which accounts for eight countries, did not provide a response. Also, Egypt, Saudi Arabia, United Arab Emirates, Sudan, Liberia, Nigeria and Sierra Leone did not return the surveys. We discuss response rate and data availability in greater detail in Section 3 below.

To verify the data we conducted multiple phone and email follow-up sessions with any country for which clarification was needed. We also conducted a full set of internal consistency checks and verified the data against external data sources where available including the IMF-IFS statistics, the MIX and data from World Bank (2008) and Beck, Demirgüç-Kunt, and Martinez-

> 11 Additional surveys were received from Anguilla, St. Kitts and Nevis, St. Vincent and the Grenadines, St. Lucia, Dominica, Grenada and Montserrat.

9
Two main features of the DR trade with the U.S can be highlighted. First, the share of exports to the U.S. has declined since 2002 as a result of market diversification and a slowdown in the exports to this market.<sup>10</sup> Between 2002 and 2008 exports to other countries in the Caribbean (e.g. Haiti and Jamaica) as well as to other developed countries (e.g. Spain and Belgium) experienced a rapid expansion, and grew by some 150%, while exports to the U.S. expanded by a modest 10%. Second, exports to the U.S. are relatively volatile. They exhibit alternating periods of modest growth and decline. Figure 2 shows that following a decrease in 2004, exports picked up, before falling again in 2007 and in 2009. As a result the years post-DR-CAFTA have been characterized by a slow export growth. In 2009, the demand for DR exports fell across all regions (except for the exports to the OECD countries) and the exports to the U.S. were almost at the same level as in 2002. However, this does not necessarily reflect the effectiveness of the agreement but rather the difficult business conditions in the U.S. market during this period. In a recent study, Swiston (2010) shows that the U.S. 2008-recession reduced growth by 4 to 5 percent in Central America and that the transmission channels were mainly the financial conditions and the fluctuations in export demand.

Figure 2: Exports to the U.S.

<!-- Start of picture text --> Exports to the U.S.<br>2002 2003 2004 2005 2006 2007 2008 2009<br>year<br>3000<br>2500<br>2000<br>in US $ millions<br>1500<br>1000<br><!-- End of picture text --> On the other hand, exports to the other CAFTA members (i.e. El Salvador, Honduras, Guatemala, and Nicaragua) not only significantly expanded since 2005, but accelerated in 2007 before declining in 2009. Figure 3a shows that exports to the CAFTA area went from US$ 21.5 million in 2005 to US$ 57.7 million in 2008. This remarkable performance is mainly due to the surge in exports to Guatemala and Honduras (Figure 3b). Between 2005 and 2008, the value of exports to Guatemala doubled to US$ 11.8 million, while exports to Honduras trebled from US$ 10.9 million to US$ 32.8 million (Appendix 2). Exports to El Salvador and Nicaragua also exhibited strong growth: the value of their exports more than doubled from 2005 to 2008.<sup>11</sup> This suggests that the tariff preferences were effective in boosting the exports to the CAFTA members. Moreover, Dominican exporters seem to have anticipated the agreement and start expanding their exports to the CAFTA area already in 2006.

> 10 In 2009, exports to the U.S. accounted for 54.6 % of the total exports, compared to 73% in 2002.

> 11 It is worth mentioning that exports to Costa Rica also experienced an important expansion since 2005. They went from US$ 6 million to US$ 31 million. This could suggest that Costa Rican exporters may have anticipated the ratification of the DR-CAFTA.

7
the World Bank’s World Development Indicators (WDI). The income and population data for all countries come from the 2018 version of the Maddison Project Database, which provides historical income statistics for 169 countries through 2016 (Bolt, Robert Inklaar & van Zanden 2018). The income figures are at constant 2011 US dollars. Our main analysis sample is an unbalanced panel of 41 sub-Saharan African countries over the period 1960 through 2016.

We also contrast our main results with the analysis of the evolution of manufacturing in non-oil GDP series for a smaller subset of countries. For 38 countries in our main sample (excluding Equitorial Guinea, Sudan, and Swaziland), we collect data on real growth rates of non-oil GDP, oil revenues as a percent of GDP, and real effective exchange rates from the International Monetary Fund (IMF)’s Africa Regional Economic Outlook (AFRREO) Fall 2018 database for the period 2004 through 2016. The AFRREO Fall 2018 database is in turn based on the statistical appendix of the Regional Economic Outlook (Sub-Saharan Africa) of the Fall 2018 World Economic Outlook (WEO).<sup>4</sup> We utilized the real growth rates of non-oil GDP data series to recover estimates of the manufacturing output shares in non-oil GDP. Finally, for the purpose of testing the implications of the Dutch Disease for countries with significant oil-exporting and agricultural sectors, we collect data on the monthly minimum wages from the International Labor Organization’s ILOSTAT Database for Cameroon, Ghana, Nigeria, and South Africa.

Three measures of industrialization are commonly employed in the literature, namely the share of manufacturing valued added in GDP at constant prices (RMVA), the share of manufacturing valued added in GDP at current prices (CMVA), and the share of manufacturing employment in economy-wide employment (EMP). We focus on RMVA and EMP for our substantive statistical analysis since the trend in the share of manufacturing valued added in GDP at current prices derives from movements in both price and quantity that are indistinguishable to the researcher. While the manufacturing share at current prices conflates price and quantity, the real manufacturing output share adjusts for general movements in prices over time, and thus, is more reliable for understanding structural changes in industrialization and the economy. The real manufacturing output shares are at constant 2005 national prices. Also, since WDI does not include data on manufacturing employment, our employment analysis is limited to the aforementioned eleven countries in the GGDC’s Africa Sector Database. To account for possible natural resource boom, we also examine the trends and patterns of the share of manufacturing value added in non-oil GDP (RMVA-NOIL). This could be important in separating the

> 4As of January 2019, the AFRREO Fall 2018 Database is accessible at `https://www.imf. org/external/datamapper/datasets/AFRREO` .

4