rafmacalaba/datause-encoder-data huggingface dataset:
OECD. July 1994. _Employment outlook._ Paris.

Topel, Robert, Anat Levy and Martin Rama. 1992. _A labor market cross-country database._ Paper prepared for the conference on Labor Markets in Developing Countries, held at the World Bank on July 6-8, 1994. Mimeo. Washington, D.C.

World Bank. 1994. _World Development Report 1994: Infrastructure for Development._ Oxford University Press. Washington, D.C.

World Bank. 1995. _World Development Report 1995: WVorkers in an Integrating World._ Oxford University Press. Washington, D.C.

11
for the sources of income distribution data is always significant along expected lines. Finally, a reduction in macroeconomic instability (proxied by a reduction in inflation) also reduces within-country inequality.

Third, simulations suggest potentially significant endowment-related effects associated with trade liberalization. Because of remaining biases, these estimates should be interpreted very cautiously, although we would argue that the relative robustness of the endowment effects to changes in specification justifies looking beyond averages and quantifying effects on the poor.

25
_Gender Equality and Economic Development: The Role for ICTs_ 15 supply labor for the production of goods and services during a specified period. It includes both the employed and the unemployed. While national practices vary in the treatment of such groups as the armed forces and seasonal or part-time workers, the labor force generally includes the armed forces, the unemployed, and first-time job seekers, but excludes homemakers and other unpaid caregivers and workers in the informal sector. (Source: WDI)

# **Independent Variables - ICT Variables**

To determine the effects of ICT on gender inequality, five different indicators were used to proxy for the level ICT infrastructure or ICT density available within the economy. These measures are the number of computers per 1,000 persons, the number of Internet users per 1,000 persons, the number of telephones per 1,000 persons, ICT expenditure as a share of GDP and ICT expenditure per capita.

1. _Number of Computers (per 1,000 persons)_ :  Personal computers are selfcontained computers designed to be used by a single individual.  Computers per 1,000 population refers to the number of self-contained computers designed to be used by a single individual and is an indicator of personal computer penetration and use of relatively new technology for information processing.  (Source: WDI)

2. _Number of Internet Users (per 1,000 persons)_ : Internet users are people with access to the worldwide network. (Source: WDI) 3. _Number of Phones (per 1,000 persons)_ : Telephones per 1,000 persons is the sum of telephone mainlines and mobile phones and provides a better indicator of connectivity than either in isolation. Telephone mainlines are telephone lines connecting a customer's equipment to the public switched telephone network. Mobile phones refers to users of portable telephones subscribing to an automatic public mobile telephone service using cellular technology that provides access to the public switched telephone network. (Source: WDI) 4. _Information and communication technology expenditure (% of GDP)_ (Source: WDI) 5. _Information and communication technology expenditure per capita (US$)_ . Information and communications technology expenditures include external spending on information technology ("tangible" spending on information technology products purchased by businesses, households, governments, and education institutions from vendors or organizations outside the purchasing entity), internal spending on information technology ("intangible" spending on
Table N2: Effect of SNAP-benefit changes on prices, first stage

|VARIABLES|(1)|(2)|(3)<br>Log benefts|(4)<br> per populat|(5)<br>ion|(6)|
|---|---|---|---|---|---|---|
|Log synthetic benefts per population<br>Log housing price|1.376<br>(0.857)|1.832***<br>(0.490)<br>-0.556***|1.892***<br>(0.448)<br>-0.514***|1.706***<br>(0.375)<br>-0.503***|1.727***<br>(0.380)<br>-0.500***|1.692***<br>(0.377)<br>-0.491***|
|||(0.132)|(0.118)|(0.112)|(0.111)|(0.0889)|
|Log unemployment rate|||0.0911***|0.0912***|0.0879***|0.0741***|
||||(0.0199)|(0.0198)|(0.0191)|(0.0154)|
|Log population|||0.355*|0.351*|0.336*|0.335**|
||||(0.181)|(0.182)|(0.182)|(0.162)|
|Log average wage|||0.0151|0.0160|0.0128|0.0147|
||||(0.0201)|(0.0201)|(0.0203)|(0.0198)|
|Log tobacco tax||||0.0236|0.0232|0.0196|
|||||(0.0204)|(0.0205)|(0.0191)|
|Log SNAP average gross income|||||-0.101|-0.102|
||||||(0.0872)|(0.0897)|
|Log SNAP average rent|||||0.0226*|0.0220*|
||||||(0.0119)|(0.0126)|
|Observations|382560|382560|382560|382560|382560|382524|
|R-squared|0.979|0.986|0.987|0.987|0.987|0.988|
|Prob _>_ F|0.115|0.000|0.000|0.000|0.000|0.000|
|Number of units|7970|7970|7970|7970|7970|7970|
|Number of clusters|48|48|48|48|48|48|
|Energy controls||||||X|

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Store and period fixed effects are included. Energy controls refer to annual state-level total energy, electricity, LPG, and natural gas quantities consumed and prices for the residential sector.

73
- Djankov, Simeon, Caralee McLiesh and Rita Ramalho (2006), ‘Regulation and Growth’, _Economics Letters_ , 92: 395–401.

- Djankov, Simeon, Caroline Freund and Cong S. Pham (2010b), ‘Trading on Time’, _The Review of Economics and Statistics_ , 92(1): 166-173.

- Djankov, Simeon, Olivier Hart, Caralee McLiesh and Andrei Shleifer (2008b), ‘Debt Enforcement around the World’, _Journal of Political Economy_ , 75(2): 1105-1149.

- Djankov, Simeon, Rafael La Porta, Florencio Lopez-de-Silanes and Andrei Shleifer (2002), ‘The Regulation of Entry’, _The Quarterly Journal of Economics_ , 117 (1): 1-37.

- Djankov, Simeon, Rafael La Porta, Florencio Lopez-De-Silanes and Andrei Shleifer (2003), ‘Courts’, _The Quarterly Journal of Economics_ , 118(2): 453-517.

- Djankov, Simeon, Rafael La Porta, Florencio Lopez-de-Silanes and Andrei Shleifer (2008a), ‘The Law and Economics of Self-dealing’, _Journal of Financial Economics_ , 88 (3): 430–465.

- Djankov, Simeon, Tim Ganser, Caralee McLiesh, Rita Ramalho, and Andrei Shleifer (2010a), ‘The Effect of Corporate Taxes on Investment and Entrepreneurship’, _American Economic Journal: Macroeconomics_ , 2(3): 31-64.

- Dollar, David, Tatjana Kleineberg, and Aart Kraay (2013), ‘Growth Still Is Good for the Poor’, Policy Research Working Paper 6568, World Bank, Washington, DC.

- Eiffert, Benjamin (2009), ‘Do Regulatory Reforms Stimulate Investment and Growth? Evidence from the Doing Business Data, 2003-07’, Working Paper No. 159, Center for Global Development.

- Fisman, Raymond, and Virginia Sarria-Allende (2010), ‘Regulation of Entry and the Distortion of Industrial Organization’, _Journal of Applied Economics_ , 13 (1): 91–120.

- Hayo, Bernd, and Stefan Voigt (2008), ‘The Relevance of Judicial Procedure for Economic Growth’, CESifo Working Paper 2514, CESifo Group Munich, Munich. http://ssrn.com/abstract=1318292.

- Helpman, Elhanan, Marc Melitz and Yona Rubinstein (2008), ‘Estimating Trade Flows: Trading Partners and Trading Volumes’, _Quarterly Journal of Economics_ , 123 (2): 441–87.

12
In sum for the region as a whole, and for the vast majority of African economies, growth from 1975 to 2005 has been both disappointing and volatile. Growth on average has accelerated and has shown a weak tendency to become more persistent over time, but for the individual African country, past growth helps very little to predict future growth.

# **3.  Rich Country, Poor Country**

Africa’s mean GDP per capita had a slowly rising long-term trend, consisting of about 20 years of virtual stagnation with an inflexion point upward in the mid 1990s (figure 7).<sup>12</sup> Income per person in the average African economy declined into the middle of the 1990s and then recovered. Weighting by GDP (figure 7b) gives a U-shaped pattern of GDP per capita, reaching a minimum in the mid-1990s. Africa’s largest economies, measured in terms of GDP, experienced some of its greatest income declines between 1975 and 1994. By 2005, income weighted GDP per capita had not yet recovered to the levels observed in mid-1970s.

**Figure 7: GDP per capita 1975-2005**

<!-- Start of picture text --> Figure 7a: Unweighted data<br>2800<br>2400<br>100<br>2000<br>0<br>-100<br>1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>GDP per capita Trend Cycle<br><!-- End of picture text -->

> 12 We employ the Hodrick-Prescott filter in Figure 7.

10
# **1. Introduction**

International evidence shows that investing in high-quality early childhood programs can have large economic returns, especially for children from socially disadvantaged groups (Barnett 2011, Engle et al. 2011, Walker et al. 2011, Yoshikawa et al. 2013). In response, developing countries are looking to increase public investments in early education programs (World Bank 2016a). One of the challenges faced by policy makers is deciding what to fund given the wide range of programs that exist in local settings. In order to invest smartly in early education programs, there is considerable interest in understanding how children’s various early education experiences predict their success as they transition into primary school.<sup>1</sup> This understanding is particularly pertinent to on-going policy discussions in Indonesia regarding how best to prioritize investments in pre-primary education to meet the Sustainable Development Goals.

Much of the existing literature on early education in developing countries compares children who attended preschool to those who did not, or compares children who attended preschools with improved quality to those who attended non-improved preschools (Engle et al. 2011, Nores and Barnett 2010, and the references cited therein). While these are valid comparisons in understanding whether a specific type of preschool is effective, such comparisons may not necessarily capture the reality of children’s early educational experiences, which are often not as binary. We use rural Indonesia as an example to show that there is considerable heterogeneity in the _sequence, timing,_ and _duration_ of early childhood education participation among children and argue that these factors matter in predicting children’s success in primary school.

We use a uniquely rich data set from Indonesia that allows us to link children’s primary school test scores with their entire enrollment histories in early education programs. The data were collected in 2013 and sampled children in 310 poor villages in Indonesia as part of an evaluation of the Indonesia Early Childhood Education and Development project. We focus on test scores in language, mathematics and general cognitive skills of nearly 13,000 children ages 6 to 9. First, we explore the extent to which families self-select into different sequences, timing, and duration of early education programs. Second, we analyze how these different early education pathways correlate with primary school test scores. We show that children enrolled in play-based programs

> 1 In this paper, “early education” and “early childhood education and development (ECED)” are used interchangeably and refer to the broad range of site-based early learning programs from age 3 until entrance into primary school.

2
# **_Table A2.2 Data sources and availability of_** **_<u>project level staffing variables</u>_**

||**Full Data Set**|**Analytical Data Set**|
|---|---|---|
||(N = 4,348 Projects)|(N = 2,845 Projects)|
|**Panel A - Data Sources**|Frequency<br>Percent|Frequency<br>Percent|
|**For Preparation Task Team Leader (At Approval)**|||
|Data Scraping from PADs|1,740<br>52|1,696<br>60|
|Manually Collected|929<br>28|538<br>19|
|TTL as Listed on 1st ISR|610<br>18|541<br>19|
|Staff time charge code data|76<br>2|65<br>2|
|**For Mid-Point Supervision Task Team Leader (Mid-**<br>**point ISR)**|||
|ISR Reports (stored on internal database)|3,649<br>100|2,833<br>100|
|**For Closing Supervision Task Team Leader (Last ISR)**<br>ISR Reports (stored on internal database)|3,819<br>100|2,838<br>100|
|**For Practice Manager (At Approval)**|||
|Manual Collection cross referenced with HR Reports|3,123<br>99|2,842<br>99|
|Data Scrapingfrom PADs|31<br>1|17<br>1|
|**For Country Director (At Approval)**|||
|"Ask an Archivist" Service|4,342<br>100|2,842<br>100|
|**Panel B - Availability of Staffing Characteristics**|Frequency|Frequency|
|**For Preparation Task Team Leader (At Approval)**|||
|Age|2,916|2,793|
|Education|2,929|2,704|
|Experience (# of WB projects as TTL)|3,355|2,840|
|**For Mid-Point Supervision Task Team Leader (Mid-**<br>**point ISR)**|||
|Age|3,250|2,621|
|Education|3,128|2,516|
|Experience (# of WBprojects as TTL)|3,649|2,833|
|**For Closing Supervision Task Team Leader (Last ISR)**|||
|Age|3,817|2,838|
|Education|3,598|2,674|
|Experience (# of WBprojects as TTL)|3,819|2,827|
|**For Practice Manager (At Approval)**|||
|Age|1,477|1,344|
|Education|1,217|1,119|
|Experience (# of WBprojects approved as PM)|2,211|1,862|
|**For Country Director (At Approval)**|||
|Age|-|-|
|Education|-|-|
|Experience (# of WB projects approved CD)|4,342|2,842|

42
Figure 1: Trade Links between China, Asia, and the World Source: IMF Direction of Trade Statistics; Authors’ calculation.

In the left panel of Figure 1, we see how the share of exports of Asian countries to China evolved over the past several decades. We observe a significant jump since 2001: particularly, exports of Australia, Japan and the Republic of Korea to China have expanded more than three-fold. Likewise, the Asian countries’ imports from China have been rising considerably in recent years (see the right panel of Figure 1). In particular, Australia, Japan, the Republic of Korea, Malaysia and the Philippines saw the largest rise in their imports from China.

China’s share of world trade rose from about 4 percent in 2000 to around 11 percent last year. Figure 2 shows the evolution of trade shares of each country in 1985, 1995, 2005, and 2013. While the United States, Japan and Eurozone countries have seen decline in their share of trade in the global economy, China’s trade share rose in recent years. China is now the largest trading nation in the world after surpassing the US, and is the largest trading partner for 35 countries across the globe. In our sample of nine Asian countries, China is the largest trading partner of seven countries (Australia, India, Japan, the Republic of Korea, Malaysia, New Zealand and Thailand), the second largest trading partner of Indonesia and third largest of the Philippines.

In the next section, we explain the GVAR methodology. As we just reviewed above, China’s trade linkages drastically changed shortly after China joined the WTO in December 2001. Thus, we expect that the magnitudes of shock propagations from China to the 6
34

**Table 8. Wage Premiums Regressions, Cambodia**

||1996|1999|2004|2007|2008|2009|2010|2011|
|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|
|Female dummy|-0.314***|-0.203***|-0.183***|-0.216***|-0.183*|-0.142***|-0.303***|-0.216***|
||(0.043)|(0.043)|(0.035)|(0.041)|(0.102)|(0.034)|(0.037)|(0.027)|
|Hours|0.010***|0.011***|0.016***|0.015***|0.020***|0.018***|0.015***|0.016***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|
|Age|0.059***|0.049***|0.041***|0.062***|0.058***|0.052***|0.082***|0.054***|
||(0.012)|(0.008)|(0.008)|(0.009)|(0.020)|(0.009)|(0.011)|(0.007)|
|Age squared|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Education|0.052***|0.043***|0.011***|0.074***|0.061***|0.047***|0.063***|0.048***|
||(0.005)|(0.011)|(0.002)|(0.010)|(0.014)|(0.004)|(0.005)|(0.004)|
|T&G dummy|-0.362***|0.543***|0.288***|0.296***|0.300**|0.102**|0.070|-0.024|
||(0.134)|(0.067)|(0.049)|(0.072)|(0.123)|(0.051)|(0.051)|(0.044)|
|Hazard ratio|0.008|0.140|-0.035|0.549***|0.136|0.054|0.594***|0.100|
||(0.131)|(0.124)|(0.102)|(0.158)|(0.297)|(0.109)|(0.131)|(0.091)|
|Constant|11.037***|10.017***|10.708***|9.540***|9.639***|10.076***|9.315***|10.335***|
||(0.514)|(0.308)|(0.248)|(0.349)|(0.717)|(0.287)|(0.337)|(0.233)|
|Observations|4,723|3,448|8,358|2,553|1,402|4,488|3,276|3,520|

_Source:_ Calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011.

_Note:_ Standard errors are in parentheses. Standard errors are clustered at the industry level. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively. The grand mean effects of the industries are calculated; additional controls include, industry, and occupation dummies. Occupation dummies are according to 1-digit ISCO-08; the omitted category is managers. Industry dummies include 15 categories: Agriculture and Forestry; Mining; Food, Beverage, and Tobacco; Textile and Apparel; Wood; Other Manufacturing; Utilities; Construction; Sales; Transport; Financial, Insurance, and Real Estate; Public Administration; Social Services; and Other Services. Log wages are measured as the logarithm of real monthly individual labor income. Log wages are measured as the logarithm of real monthly individual labor income in 2005 prices.
6 the reforms. The marketing for this choice was based on now common but then innovative theoretical arguments. The regime, it was argued, would provide high powered incentives for securing efficiency gains, at least between tariff reviews and the regime was low maintenance in the sense that it did not require, at least between tariff reviews, large amounts of informationabout firm-operation- levels. The fact that it induced a higher cost of capital because they tended to pass on to the operators a larger share of the project risks was very seldom considered. Also the fact that the regime was associated with a risk of under-investment (which has happened) was surprisingly seldom addressed in a region in which one of the main reasons to try to reform and privatize was to attract private investment to compensate for a reduction in public investment. At the time also, the fact that, in practice, both regimes tend to converge in terms of process with the level of convergence depending on the frequency of tariff reviews, was also largely ignored.".

**Table 2: Incidence of Renegotiated Concession Contracts According to Sectors and Characteristics**

||**Allinfrastructure**<br>**sectors**|**Transport**|**Water**|
|---|---|---|---|
|%**ofContract**|30%<br>**sRenegotiationswithinregu**|54.7%<br>l<br>**latoryregimes**|_74.4_<br>__|
|**With PriceCap**|38.1%|55.1%<br>||88.%|
|**WithRate ofReturnCap**|12.8%|38.1%<br>t|14.3%|
|**WithHybridRegime***|24.4%|46.2%<br>j|39.6%|

***** Hybrid regimes are defined when, under a repime of price caps, a large number of costs components are allowed automatic pass through into tariff adjustments.

Source: Guasch (2003) Table 2 shows that 1 in 3 contracts were renegotiated in Latin America and that the rate goes as high as 1 in 2 for transport and 3 in 4 for the water sector. These renegotiations took

> 1 'See Green and Rodriguez-Pardina (I999)

> 12 The numbers for the hybrid regimes should be taken with some care, since there is some subjectivity in that classification and also incomplete information in determining the classification.
Figure 9: Projected change in hydropower affected by drought 2020-2059 relative to 2000-2019, under different climate scenarios <!-- Start of picture text --> A. Latin America and Caribbean B. Central America and Mexico<br>100 100<br>50 50<br>0 0<br>−50 −50<br>−100 −100<br>C. Southern South America D. Andean Region<br>100 100<br>50 50<br>0 0<br>−50 −50<br>−100 −100<br>E. Rest of South America F. Caribbean<br>100 100<br>50 50<br>0 0<br>−50 −50<br>−100 −100<br>2020−2059 2020−2059<br>Shared Socioeconomic Pathway SSP1−2.6 SSP2−4.5 SSP3−7.0<br><!-- End of picture text --> _Notes:_ Computations based on 22 climate models from the CMIP 6. The spread of the box plots is determined by the distribution of projections given by the 22 climate models. Triangles represent the ensemble mean from the 22 models. Dots represent outliers. The overall sample is: A. Latin America and the Caribbean. The IEA sub-regions are: B. Central America and Mexico, C. Southern South America (Argentina, Bolivia, and Chile), D. Andean Region (Colombia, Ecuador, and Peru), E. Rest of South America (Brazil, Venezuela, Paraguay, and Uruguay), and F. Caribbean.

24
Policy Research Working Paper 10332

# **Abstract**

This study provides new evidence on the local labor market impacts of trade, differentiating between the employment, income, migration, and informality channels. It uses a unique dataset matching information on exports and imports from customs with indicators on employment and labor incomes for around 2,000 Mexican municipalities over 2004–14. The analysis uses an instrumental variable approach that combines the initial structure of trade across municipalities with global trends in trade between low- and middle-income countries (excluding Mexico) and the United States by sector. First, the study finds that expanding exports per worker in Mexico’s municipalities increased labor force participation but not employment rates. Exports also raised total labor incomes but not average labor incomes, implying a growing labor supply. The results also find that export and import expansion increased immigration and lowered the rate of informal workers. Second, the analysis examines differences by geography and sectors. It finds that trade affected labor markets in the North through the income and migration channels and in the South through the employment and informality channels. Exports benefitted the total incomes of workers in both the manufacturing and service sectors but reduced informality only in manufacturing. Third, the study suggests a more favorable role of intermediate relative to final imports, driven by manufacturing imports. It also finds evidence for positive spillovers from global value chain participation through the employment and income channels. Finally, it examines how local policy mediates the labor market effects from trade, focusing on connectivity, labor market flexibility, and education spending.

This paper is a product of the Macroeconomics, Trade and Investment 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 evazquez@cedlas.org and dwinkler2@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