rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2 Results for Full Liberalization. Central case. Changes compared to baseline.**

||**Real inc**<br>|**ome(%)**<br>|**Terms of t**<br>|**rade(%)**<br>|**Export Vo**<br>|**lume(%)**<br>|
|---|---|---|---|---|---|---|
|**World total**|_Trade_<br>_Weighted_<br>_Aggregator_<br>**0.50**|_Optimal_<br>_Aggregator_<br>**0.88**|_Trade_<br>_Weighted_<br>_Aggregator_<br>**0.00**|_Optimal_<br>_Aggregator_<br>**0.00**|_Trade_<br>_Weighted_<br>_Aggregator_<br>**7.1**|_Optimal_<br>_Aggregator_<br>**8.3**|
|**_Low and middle income_**<br>|**0.58**|**1.30**|**_-0.28_**|**_-0.08_**|**_14.8_**|**_16.6_**|
|**_countries_**|||||||
|Bangladesh|-1.44|-1.03|-2.26|-2.12|56.6|61.2|
|Brazil|1.39|3.18|1.01|2.52|16.0|17.5|
|Chile|0.30|0.06|0.13|-0.08|0.1|-0.3|
|China|-0.06|0.17|0.07|0.42|14.7|16.2|
|Egypt|0.24|5.01|-0.76|-0.27|14.8|21.5|
|India|0.60|1.24|-0.52|-0.28|46.9|51.3|
|Indonesia|0.49|0.69|0.19|0.18|11.3|12.4|
|Pakistan|0.34|-0.34|0.41|-0.49|36.8|39.0|
|Thailand|2.48|3.49|0.02|0.09|17.1|18.7|
|Mexico|-0.02|0.35|-0.61|-0.55|7.8|8.8|
|Nigeria|4.68|6.30|-1.95|-1.96|14.1|14.3|
|Turkey|1.03|2.21|0.22|0.76|9.3|13.8|
|Rest of Asia|3.40|4.68|-0.67|-0.63|14.4|15.0|
|Rest of LAC|0.38|0.44|-0.16|-0.18|18.2|19.3|
|Morocco & Tunisia|2.77|5.82|-2.90|-1.79|60.1|67.2|
|SACU|0.96|2.25|0.04|0.00|14.5|19.0|
|R. of Sub Saharan Africa|-0.44|-0.55|-1.75|-2.13|23.8|23.7|
|Rest of the World|0.45|1.54|-0.62|-0.72|9.2|11.4|
|**_High income countries_**|**0.48**|**0.76**|**_0.08_**|**_0.02_**|**_3.9_**|**_4.9_**|
|Australia & New Zealand|0.46|0.59|0.37|0.40|9.0|9.9|
|Rest of Europe|1.53|3.56|0.42|0.88|4.7|7.3|
|EU 27|0.44|0.96|-0.04|-0.18|2.1|3.1|
|United States|0.10|0.13|0.05|0.03|6.1|6.9|
|Canada|0.05|0.24|-0.30|-0.30|3.0|4.1|
|Japan|0.94|0.94|0.35|0.34|6.4|7.4|
|Korea & Taiwan, China|2.99|3.95|0.27|0.14|11.9|14.1|
|H. Kong & Singapore|1.12|1.28|1.10|1.22|-1.5|-1.8|

_Source: Authors’ computation. LINKAGE Model._ In Table 2, the average increase in the estimated welfare impacts of global trade liberalization is 76 percent at the global level, with the estimated income gains from reform rising from 0.5 percent of initial GDP to 0.88 percent. This suggests that the use of appropriate aggregation techniques is likely to be very important in real-world analysis of trade reforms. However, the effect of using optimal aggregation techniques varies a great deal from one country or region to another. The gains are much larger for developing countries than for the industrial countries. For the industrial countries, the average increase in the measured welfare impact is just 18
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- Marchionni, M., Gluzmann, P., Serrano, J. and Bustelo, M. (2019). “Participación laboral femenina: ¿qué explica las brechas entre países?” Inter-American Development Bank.

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- World Bank (2021). World Development Indicators. Retrieved April 2021. <u>https://datatopics.worldbank.org/world-development-indicators/</u>

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# **5. Benchmarks and capital flows**

In this section, we explore the consequences of our findings that benchmark weights affect mutual fund weights for the capital flows of these funds, and we study the various channels through which the benchmark effect impacts country flows.<sup>31</sup> To capture the relation between benchmark weights and capital flows, we start from the following identity: where is the net flow (in dollars) from fund _i_ in country _c_ at time _t_ . is the portfolio weight the fund decides to have in that country at time _t_ , ̃ is the value of the fund’s assets at the beginning of time _t_ , and is the fund’s buy-and-hold weight in that country resulting from movements in total and relative returns. is the net flow (in dollars) to fund _i_ at time _t_ , also known as injections or redemptions.

The two terms in the equation above relate to the two forces driving a fund’s flows to a country: net inflows and reallocation. Net inflows to countries occur as net flows to the fund ( ) are allocated across countries in proportion to the fund’s desired country weight at that moment ( ).<sup>32</sup> The flows due to reallocation of existing assets, ̃ ( ), arise from the difference between a fund’s desired country weight and the buy-and-hold weight that mechanically results from the fund’s previous allocation and movements in relative returns. Equation (9) shows a direct connection between weights and country flows. Fund managers’ decisions about country weights have a direct impact on country flows. For instance, an increase in the desired weight in a given country induces both a reallocation of existing assets to that country and more inflows to that country when the fund itself has injections.

To describe and quantify the various mechanisms through which the benchmark effect operates on flows, it is useful to normalize Equation (9) by lagged fund assets ( ), obtaining:

> 31 By capital flows we mean the flows of the funds we analyze into countries in which they invest. Because we do not know who is on the other side of the transactions, we cannot determine to what extent these flows are reflected in the balance of payments statistics at the country level. However, according to some estimates, the EPFR funds alone account for around 25 percent of total foreign portfolio investments (from all sources) at the country level (Puy, 2013) and there is a significant correlation between the EPFR flows and those obtained from the balance of payments (Fratzscher, 2012; Miao and Pant, 2012).

> 32 We use the term “desired country weight” to refer to the weight the fund decides to have in that country considering all the possible constraints it faces. It does not mean to imply that it is the optimal weight that the fund would choose in an unconstrained or partially constrained scenario. For example, if the fund cannot change positions in a country to move to the portfolio suggested by its view of the country fundamentals because doing so would be too costly, we consider the desired outcome of this trade-off. Thus, this is a constrained optimal decision of the portfolio manager.

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# **Are Skills Rewarded in Sub-Saharan Africa? Determinants of Wages and Productivity in the Manufacturing Sector**<sup>*****</sup>

Louise Fox and Ana Maria Oviedo<sup>†</sup> _The World Bank_ _JEL classification codes:_ J21, J24, J31, O15, O49.

_Keywords:_ Sub-Saharan Africa, employer-employee data, wage determination, unions, productivity, human capital.

> * We are grateful to Vijaya Ramachandran, Manju Shah, John Haltiwanger, Alan Gelb, Benn Eifert, Rita Almeida, John Giles, Fabiano Bastos, Melissa Gaal, Mary Hallward-Driemeier, and participants at the Labor Market Group seminar for their helpful comments, suggestions, and data. The views expressed herein are those of the authors and do not necessarily represent those of the World Bank.

> † E-mail: lfox@worldbank.org, aoviedo@worldbank.org.

1
**Figure 19: Public ownership and state involvement in business operation**

a. Public ownership b. State involvement in business operations <!-- Start of picture text --> 4.5 4.1 4.2 4.2 4.5<br>4.0 3.5 3.8 4.0<br>3.5 3.0 3.5<br>3.02.5 1.9 2.4 2.4 2.5 2.6 3.0 2.6 2.6 2.8 2.8<br>2.0 2.5 2.2 2.2<br>1.5 2.0 1.7 1.9 1.9 2.0<br>1.0<br>0.5 1.5<br>0.0 1.0 0.8<br>0.5<br>0.0<br>Slovak Republic 2003 Albania 2007 OECD 2003 MICs 2003 EU15 2003 Czech Rep. 2003 Bulgaria 2006 Hungary 2003 Romania 2006 Croatia 2007 Poland 2003 Slovak Republic 2003 OECD 2003 EU15 2003 Czech Rep. 2003 Albania 2007 Romania 2006 MICs 2003 Hungary 2003 Croatia 2007 Bulgaria 2006 Poland 2003<br><!-- End of picture text --> _Note: Other MICs are Brazil, Mexico, Turkey. Values refer to 2007 for Croatia and Albania, 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see Annex I._

# **_4.2.1 Public ownership_**

**The presence of the Croatian state in the economy is pervasive, in terms of its size, of the number of sectors it is involved in, and on the nature of control it exerts over state-owned enterprises.** The aggregate indicator for public ownership covers size and scope of public enterprise sector, as well as direct control over business enterprises.  Given that there is much variation among EU15 or OECD countries for these indicators, we show the full set of comparators (Figure 20).  The figure suggests that whereas the size of the public enterprise sector in Croatia is not exceptional (although within the upper tier), the scope of state involvement (number of sectors) and the form it takes is among the heaviest in the sample.

21
Asia average, and in 2001-02, net-enrollment was 51 percent compared to 83 percent for India, 90 percent for Sri-Lanka and 70 percent for Nepal. For the country’s level of income, the forecasted net enrollment rate (based on a regression of primary net enrollment on log per-capita income and the square of log per-capital income for 138 countries) is 77 percent: Pakistan’s net enrollment is thus far below what one would expect for its level of income.

The problem of low overall educational performance is further compounded by large gender, income and geographical disparities. There is a 20 percentage point difference in gross enrollment rates at the primary level between boys and girls and in some provinces, notably NWFP and Balochistan, this difference increases to 40 percentage points (PIHS 2001-02). Wealth also matters: at the primary level gross enrollment rates for the top expenditure-decile is twice as high as for the lowest decile. Finally, the rural-urban divide is large with a net-enrollment ratio of 45 percent in rural areas compared to 66 percent for the urban areas. Net-enrollment ratios for rural females, at 36 percent, are therefore the lowest for _any_ sub-group of the population.

Private education in Pakistan has a long history dating back to before independence in 1947. Limited data suggest that private schools catered to a niche market restricted to big cities from 1947-1972, dominated by missionary-run schools or local schools imitating the missionary model, and were mainly used by the elite. In 1972 private schools were nationalized amidst a government program of nationalization of all industry. The policy was reversed in 1979 through a process of denationalization. Private schools were allowed to open and the schools taken over by the government were gradually returned to the original owners. However, government policy towards private schools was and still is one of _laissez faire_ —there are no subsidies in the form of grants to parents or schools (as in Bangladesh, the Philippines or India) so that private schools arise and survive purely as a market based phenomenon.

# **The Data**

We employ four primary government data sources and data from a comprehensive education survey on Learning and Educational Achievement in Punjab Schools (henceforth LEAPS), collected by the authors. Our government sources are the 1998 Population Census, the Punjab Educational Management Information Systems, the Census of Private Educational Institutions in Pakistan (PEIP), and the Pakistan Integrated Household Surveys (PIHS). In brief, the Population Census is the decennial census conducted by the Population Census Organization provides information on village level attributes for every village in Pakistan. The census of private educational institutions was conducted by the Federal Bureau of Statistics in 2000 and provides 6
established but only for the case of developed countries. The present paper makes a first attempt at extending this literature to a developing country. Second, our findings are important for the appropriate design and targeting of competition policy. As economic development spreads to the relatively poorer countries, household structure in these countries is likely to change as it is happening in India. Additional policy measures that factor in the structure of households could play an important role in ensuring that markets remain truly competitive. Tying of competition policy to specific consumer attributes (different from ours) is also recommended by Giulietti et al based on their finding that pensioner households, low-income households, rural dwellers and consumers with disabilities benefited differently from the recent deregulation of the U.K. energy markets.

The rest of the paper is as follows. In section 2 we describe the data and our main variables. In section 3 we present our main empirical findings. Robustness checks are reported in section 4 while in section 5 we focus on the income-competition relationship. A summary of the main findings is provided in the concluding section.

# **2. Data and Main Variables**

A formal definition of all the variables used in the regressions is provided in Table 1. Summary statistics for the main variables are reported in Table 2. Correlation coefficients for the main variables are provided in Table 3 and discussed in detail below. We use store level data for India’s retail sector collected by the World Bank’s Enterprise Surveys in 4
# **5.   Productivity Performance and the Effects of Economic Reform**

# _Estimates of total factor productivity_

The estimate of capital's share obtained above, α = 0.43 was used to find a direct measure of change in total factor productivity for the periods 1961-2003, 1980-1989, and 1995-2003. Table 7 reports the decomposition of output growth into the separate contributions of capital, labor, and total factor productivity (TFP).

Table 7 Decomposition of Real GDP Growth in Rwanda, α = 0.43

|||Contributi|on of:||
|---|---|---|---|---|
||Real GDP<br>Growth|Physical<br>Capital|Labor|TFP|
|1961-|||||
|03|3.6|1.66|1.57|0.37|
|1980-|||||
|89|3.22|3.73|1.86|-2.37|
|1995-|||||
|03|11.5|0.45|3.27|7.78|

Source: World Bank database and computed by authors.

The findings show some rather stark contrasts evident between periods. In particular, the residual calculation of TFP points to the possibility of a major shift in economic structure beginning in 1995. The year 1995 was selected because of the cataclysmic events of 1994, which reflected the culmination of several years of economic and political instability in Rwanda. The contribution of TFP may appear to have been slightly positive for the entire period, but that was likely due to the extraordinary leap in TFP that took place since 1995. In the post-1995 period TFP accounted for about two16
6

# Table 1: COMHODITY MARKET STRUCTURE AND PERFORMANCE IN KOREAN MANUFACTURING

# (la) Comodity **Market Stzucturea** (1982)

||Monopoly|Duopoly|Oligopoly|<br>Competitiv|e Total|
|---|---|---|---|---|---|
|Numbes of|533|251|1.071|405|2260|
|Commoditiesa|(23.6)|(11.1)|(47.4)|(17.9)|(100)|
|Sales|5,649|3,275|24,967|15,481|49,372|
|(billion won)a|(11.4)|(6.6)|(50.6)|(31.4)|(100)|

# **(lb)** Performance of Different Market **Structures** (average of 1978 and 1983)

||Monopoly/<br>Oligopoly|<br>Competitive|Protected|Less<br>Protected|High<br>Export<br>Share|Low<br> <br>Export<br>Share|
|---|---|---|---|---|---|---|
|Price|||||||
|Cost<br>Harginb|0.29|0.26|0.|0.24|0.25|0.29|
|(Mean)|||||||

Note: Monopoly if CR1 > 80 percent, S1/S2 < 10.

Duopoly if CR2 > 80 percent, S1IS2 < 5, S3 < 5 percent.

Oligopoly if CR3 > 60 percent (monopoly and duopoly excluded).

Competitive if CR3 > 60 percent.

Where CRi indicates i-firm concentration ratio, and Si indicates area of largest ith firm.

a. Numbers in parentheses are percentaget totals sum to 100.

b. Percent * PCM is calculated as value of sales less labor costs divided by value of sales.

Source: Lee, Urata, and Choi (1988), Tables 3 and 8.
7a). While consumption poverty declined faster during 2005‐2012 from the previous decade, no such acceleration is seen for non‐monetary indicators (Figure 7b).

Education attainment among adults has risen appreciably since the mid‐2000s, mainly due to increase in the share of adults who have completed at least secondary school. The decline in illiteracy among adults – from 38 percent in 2005 to 30 percent in 2012 – is matched by a 9 percent increase in the share of adults who have completed secondary education or more during this period (Figure 8). This indicates a positive trend of skilling of the Indian labor force as secondary school enrollments and completion have increased over the years. This trend could also have an impact in terms of reducing vulnerability, given that secondary education among household heads is associated with higher chances of upward mobility and lower chances of downward mobility for the household, as mentioned earlier.

**Figure 8: Improving literacy and education, mainly due to rising secondary school completion**

<!-- Start of picture text --> (a) Proportion of adults (%), 2005 (b) Proportion of adults (%), 2012<br>Secondary<br>and above, Secondary<br>23% and above Illiterate<br>Illiterate,<br>32% 30%<br>38%<br>Middle<br>completed, Literate or<br>16% Literate or primary Middle primary school<br>completed, school completed17% completed21%<br>23%<br><!-- End of picture text --> _Source:_ Employment, unemployment surveys conducted by the National Sample Survey Office (NSSO), 2005 & 2012; ‘adult’ defined as ages 15 and older.

# 4. Spatial pattern of poverty reduction<sup>16</sup>

Poverty rates declined between 1994 and 2012 across all major states in India. Furthermore, as Figure 9a shows, the acceleration in pace of poverty decline since 2005 is evident in nearly every state, including states classified as “low‐income states” (LIS).<sup>17</sup> Gains in shared prosperity have also been widespread as annual consumption growth of bottom 40% was higher during 2005‐2012 than during 1994‐2005 in all large states with the exception of four, two of which are LIS (Figure 9b).

16 This section draws extensively from Nayyar (2014), which also includes detailed analysis of growth convergence across Indian states.

17 Following World Bank (2013), LIS refers to a group consisting of the following states: Assam (AS), Bihar (BH), Chhattisgarh (CT), Jharkhand (JK), Madhya Pradesh (MP), Uttar Pradesh (UP), Orissa (OR) and Rajasthan (RJ). The other states (out of the 21 larger states considered here for the analysis) are Andhra Pradesh (AP), Delhi (DL), Haryana (HY), Gujarat (GJ), Himachal Pradesh (HP), Jammu & Kashmir (JK), Karnataka (KK), Kerala (KL), Maharashtra (MH), Punjab (PJ), Tamil Nadu (TN), Uttarakhand (UK) and West Bengal (WB).

11
**Figure 4: Number of Days/Documents for Import/Export in the Case of APEC**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Nbr. of Documents for Exports Nbr. of Documents for Imports<br>Nbr. of Days for Exports Nbr. of Days for Imports<br>AUSBRNCANCHLCHNHKG IDNJPNKORMEXMYS NZLPERPHLPNGRUSSGPTHATWNUSAVNMLow Middle High<br>Nbr. of Documents/Days<br><!-- End of picture text --> The LPI also contains two variables which are worthwhile studying in the context of simplification, namely the number of border agencies involved in imports or exports. We expect that fewer agencies will be associated with firms spending less time—and therefore money—on dealing with administrators and ensuring compliance with the separate requirements of each agency. The majority of APEC economies demonstrate a strong performance in this respect (results are not reported). Compared to the three different world averages, they have less border agencies involved in imports than the respective average. Especially Singapore appears to possess a highly efficient structure of customs.

Finally, as mentioned above, the GCR measures the extent to which unofficial payments in imports and exports play a role in an economy. Being obliged to make unofficial payments imposes an extra dimension of costs on exporters and importers. An example is the case when a bribe is required in order to “facilitate” access to the national market, even after payment of official duties and taxes.<sup>12</sup> Looking at the APEC economies (results are not reported), one finds that all high-income economies do better than the

> 12 We are aware, however, that the mechanism will not always work in this way. If a bribe is paid in order to avoid official duties, then by assumption it should result in lower nominal trade costs. Nonetheless, the importer or exporter will still need to deal with an added “layer” of costs, in the sense of having to deal with customs agents in order to “negotiate” an acceptable deal.

17