rafmacalaba/datause-encoder-data huggingface dataset:
INTRODUCTION XXI The estimates assign to the United States more net immigrants in 1985-90 than all other countries of the world combined. Other major destination countries are Australia, Saudi Arabia, Canada, and Cote d'lvoire. Mexico has by far the largest net emigration, and its dominance in this area is expected to increase in the 1990X

# SUMMARY OF RESULTS

A brief discussion of projection results in this section will focus, first, on the population of the world and continents; second, on the population of subcontinental geographic regions and of countries grouped by income; third, on the population in World Bank regions, which differ from the U.N. geographic regions; and, finally, on the population of particular countries or economies.

# _World and Continents_

For the world and the continents, we consider population totals and medium-term growth; long-run growth; age structure and dependency, and the fertility and mortality rates underlying the projections.

World population. The population of the world is estimated at 4.84 billion in 1985. It surpassed 5.0 billion in mid-1987, and is projected to reach 5.3 billion in 1990 (Table 4). The annual rate of growth in 1985-90 has been 1.74 percent. This rate is projected to decline to 1.66 percent in the first half of the 1990s, and to 1.54 percent toward 2000. In spite of this, the annual net increase in the number of people will be larger each year until the late 1990s, averaging Table 4 Total Population, Annual Increase, and Growth Rates for the World and Less Developed and More Developed Countries, 1985-2025

||Popul|ation<br>(mil|lions)||Annua|l<br>increase<br>(m|<br>illions)|<br>Growt|h rate (pe|rcent)|
|---|---|---|---|---|---|---|---|---|---|---|
|||Less|More|||Less|More||Less|More|
|Year|<br>Uorld<br>d|<br>evelope**d**|eveloped|Period|Uorl|d<br>developed<br>d|<br>eveloped|World<br>d|<br>evelope**d**|eveloped|
|1985|<br>4,844|<br>3,666|<br>1,179|1985-1990|<br>88.2|<br>81.7|6.5|1.74|<br>2.11|<br>0.54|
|1990|<br>5,285|<br>4,074|<br>1,211|1990-1995|<br>91.7|<br>85.9|5.8|1.66|2.00|<br>0.47|
|1995|<br>5,744|<br>4,504|<br>1,240|1995-2000|<br>92.1|<br>87.0|5.1|1.54|<br>1.84|<br>0.41|
|zooo|<br>6.204|<br>4,939|<br>1,265|2000-2005|<br>91.7|<br>87.4|4.3|1.43|<br>1.70|<br>0.34|
|2005|<br>6,663|<br>5,376|<br>1,287|2005-2010|<br>89.9|<br>86.4|3.5|1.31|<br>1.55|<br>0.27|
|2010|<br>7,112|<br>5,808|<br>1,304|2010-2015|<br>88.7|<br>85.7|2.9|1.21|1.42|<br>0.22|
|2015|<br>7,556|<br>6,237|<br>1,319|2015-2020|<br>87.5|<br>85.3|2.1|1.13|1.32|<br>0.16|
|2020|<br>7,993|<br>6,663|<br>1,330|2020-2025|<br>84.4|<br>83.0|1.4|1.03|<br>1.21|<br>0.10|
|2025|<br>8,415|<br>7,078|<br>1,336||||||||

Note: "More developed" comprises Europe, U.S.S.R., Northern America (United States and Canada), Australia, New Zealand, and Japan. "Less developed" comprises the rest of the world.
Policy Research Working Paper 6977

# **Abstract**

A central policy of the Government of Indonesia’s strategy for enhancing its country’s economic and social development is to develop infrastructure and expand service delivery. Public procurement reform is a key component of this policy. Despite the decentralization of financial responsibility and authority to relatively autonomous local level governments, procurement reform in Indonesia is a centrally-driven effort. In this paper, we examine the extent to which procurement reform is translating into improvements in sub-national performance. Data on local government procurement expenditures point to an overall decline in the volume of procurement, especially in poorer districts. This paper uses qualitative case studies of procurement reform in six local governments and finds that local government leadership is associated with the uptake of reform. There is little evidence to suggest that procurement reform has been “demand”-led, since neither the private sector nor Civil Society Organizations (CSOs) have been active in advocating for procurement reform.

This paper is a product of the Governance Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at jturkewitz@ 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
which groups of enterprises benefited most from the CIMO program by estimating the model separately for each sector and firm size, again with mixed results by size. The inclusion of several endogenous variables, such as wages, to explain labor productivity differences was also questionable. Since wages are an outcome of training and of program participation, they are correlated not only with the outcome the model is trying to explain – labor productivity – but also with the variable – CIMO participation – whose impact on productivity the study was attempting to estimate.

The second STPS study used a Cobb-Douglas production function framework to estimate the impact of CIMO on productivity levels in 1995. A production function is an input-output relationship that measures the output possible with different combinations of inputs of labor, raw and intermediate materials, and equipment. This methodology was an improvement over the previous study because it embedded the analysis in a theory-grounded framework; however, it too did not exploit the panel nature of the data, except in using lagged explanatory variables. The model estimated a cross-sectional production function for 1995, in which the logarithm of value-added was regressed on the logarithms of fixed capital assets and labor, plus the logarithm of investments in training in 1994 and in 1995, use of quality control in 1994 and in 1995, and an indicator variable for organizational change in 1995. These separate variables were hypothesized to collectively measure the impact of program participation on productivity. Separate production functions were estimated for the two groups, so the net impacts of the CIMO program on productivity were not directly estimated controlling for differences between the two groups of firms. If it had, the results would also have been negative, as in the first study.

To summarize, both STPS impact evaluation studies found statistically significant impacts of CIMO participation on intermediate outcomes—such as investments in training, capacity utilization, use of quality control systems, workplace organization, changes in production processes, and job retention—that are believed to lead to productivity improvements. But they found no evidence that participation in the CIMO program had a positive impact on post-program labor productivity or value added, which was counter-intuitive.

# **IV. A Re-examination of the Evidence**

How can this apparent contradiction between positive intermediate outcomes but negative final impact on firm performance be resolved, and the estimation methodology improved? The 8
which the individual was living, whether the individual speaks English, reason for emigrating, municipality and city of birth, address of residence after repatriation, occupation and whether the subject has any criminal records. For repatriations that took place between 1995 and 2010, this detailed interview was not in place, thus, we observe only information on the reason and date of deportation, intended place of residence after repatriation, and date and place of birth.

This data set of Salvadoran returned migrants is the only source of information on all individuals who were returned to El Salvador, shedding light on their motive to flee the country, and the situation they may face once back in the country. Since the registration and interview is mandatory for all returnees, the data set includes information on the total number of individuals returned in each municipality.

Descriptive statistics of our sample are presented in Table 2. Around 64 percent of returnees have primary or no education—88 percent of those from which we have education data— and around 80 percent of them lived less than 1 month abroad—i.e., they were likely apprehended and repatriated when entering the United States.

Figure 5 presents the geographic distribution of the intensity of repatriations as a share of the mean municipal population between 2011 and 2016. There is an important variation in the share of repatriations across municipalities. The mean repatriation share for all the municipalities is 0.05, suggesting that, on average, all Salvadoran municipalities received approximately 5 percent of their population due to forced repatriation between 2011 and 2016. There are also some municipalities that saw a repatriation share of 67 percent, suggesting that their population was almost doubled by returnees. The figure also shows that the share of returned migrants to the northwest of the country was of relatively low intensity.

3. Additional municipal controls: other municipal covariates employed in our analysis include population and crime variables. Information on crime comes from the National Civilian Police of El Salvador ( _Polic ́ıa Nacional Civil_ , PNC) and socioeconomic and demographic 11
# **3 Fiscal Deficits, Inflation and Sustainability of Public Debt in Turkey**

Throughout 1980s and 1990s, there have been many attempts to stabilize inflation in Turkey, but all of the failed until the recent successes changed the pattern. After each failed attempt, inflation in the end settled at a rate higher than prevailed before the stabilization attempt was initiated. Similarly, net public debt-to-GNP ratio has been steadily increasing in 1990s, and doubled in 1999, compared to its level in 1990 (less than 30 percent). After a small decline in 2000, the debt ratio increased by nearly 40 percent, reaching 91 percent in 2001, or more than three times its 1990 level.

# **Figure 1.a. Rate of inflation (80- 05)**

# **b. Public debt to GDP ratio (1990-2005)**

<!-- Start of picture text --> 100<br>140 Public Domestic<br>90<br>Debt/GNP<br>120<br>80 Public External Debt/GNP<br>100 70<br>80 60<br>50<br>60<br>40<br>40<br>30<br>20 20<br>0 10<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Jan-80Jul-81Jan-83Jul-84Jan-86Jul-87Jan-89Jul-90Jan-92Jul-93Jan-95Jul-96Jan-98Jul-99Jan-01Jul-02Jan-04Jul-05<br><!-- End of picture text --> _Note: Rate of change of the CPI index 12 month ahead. IMF projections were used to calculate the 12 month ahead inflation after 2004/8._ But the devastating financial crisis in 2001 seems to have triggered a break in the cycle of high inflation, failed stabilization attempts, rising debt burden and higher inflation. Following the 2001 financial crisis, Turkey has adopted economic reforms to address the underlying economic vulnerabilities, regain macroeconomic stability, and to signal a clear break with the past.

Key elements of these reforms were maintaining a floating exchange rate, government’s commitment to high and sustained primary fiscal surpluses, bank recapitalization and restructuring to reduce quasi-fiscal activities, as well as other structural reforms, ranging from privatization to governance reform. As a result of these reforms, and driven mainly by revenue increases, the average primary fiscal surplus during the past four years was increased to nearly 6 percent of GDP.

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On Ww) Yh, Yn = D1 9ni wit Yr Py Png Onis Pr = PrOn¢ + Png (1 — On) Ong dw, D, = {Wie oo Wwe} = {f (Yi. Prt) f Une Pye) Oni Wi Pp Png D, = {W, oe We} = {f (Pie Pic) wf (Yue Pye)
<!-- Start of picture text --> SSA - Income Distribution - GIDD Projection for 2030<br>ed<br>v<br>ae)<br>N<br>oO<br>wo wo Oo fo) oO<br>N N Oo ° ad<br>oO 7 N fo) oO<br>~ =<br>Income per capita per day in PPP<br>— 2007 9 —m— Skill (hi) M——— Sill (hic)<br><!-- End of picture text -->
- Timilsina, Govinda R., J. Pang and X. Yang (2019). Linking Top-Down and Bottom-UP models for Climate Policy Analysis: The Case of China, World Bank Policy Research Working Paper. Forthcoming.

- Xu, S. and W. Chen. 2006. The reform of electric power sector in the PR of China. Energy Policy, Vol. 34, pp. 2455–2465.

- Yeh, T.E. and J.I. Lewis (2004). State Power and Logic of Reform in China’s Electricity Sector. Pacific Affairs, Vol. 77, No. 3, pp. 437-465.

Yuan, J., X. Guo, W. Zhang, S. Chen, Y. Ai, and C. Zhao, “Deregulation of power generation planning and elimination of coal power subsidy in China,” Util. Policy, vol. 57, pp. 1– 15, Apr. 2019.

- Zou, P., Q. Chen, Q. Xia, C. Kang, G. He, X. Chen. Modeling and algorithm to find the economic equilibrium for pool-based electricity market with the changing generation mix. 2015 IEEE Power & Energy Society General Meeting. DOI: 10.1109/PESGM.2015.7285840.

- Geng, Z., Q. Chen, X. Chen, Q. Xia, J. Li, Y. Wang, Y. Chen. Environmental economic dispatch towards multiple emissions control coordination considering a variety of clean generation technologies. 2015 IEEE Power & Energy Society General Meeting. DOI: 10.1109/PESGM.2015.7286311.

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# **B. Managerial autonomy:**

Managerial autonomy involves the freedom of the agency to determine the use of its budget and the organization of its resources. It includes aspects such as the ability of the agency to determine its organizational structure, the freedom to make its own decisions on personnel, the financial autonomy to determine its own expenses, and the type of legal regime that applies to its employees (private law, civil service law, or both). It also includes other aspects related to tools that contribute to improving its management, such as the existence of its own structure of posts and salaries and of performance-based payments for its employees.

**Figure 9: Managerial Autonomy Index**

<!-- Start of picture text --> Managerial Autonomy Index<br>Source: LAC Electricity Regulatory Governance Database, The World Bank, 2007.<br>1<br>.8<br>.6<br>Index (0-1)<br>.4<br>.2<br>0<br>JAM GUA BRA ARG T&T PER BAR R D PAN NIC ESV ECU C R BOL T1 URU MEX T2 HON CHI COL<br><!-- End of picture text --> Countries’ rankings in managerial autonomy are reflected in the Managerial Autonomy Index. Jamaica, Guatemala, Brazil, Argentina, Trinidad and Tobago, Peru, and Barbados are among those countries with desirable conditions to manage its resources. These agencies show the existence of adequate mechanisms and procedures to guarantee an autonomous administration of the agency by its authorities. On the contrary, Colombia, Chile, and Honduras, in that order, are the countries with less managerial freedom and where the space of the agency to decide its organizational structure and the use of its resources is limited.

Results in this section are not an indication of the effectiveness of the agency’s management, but of powers aimed at allowing the agency an autonomous administration. This is particularly the case of Chile, whose public sector bureaucracy is, according to several governance indicators<sup>6</sup> , well-trained and based on meritocracy Countries at the top of the index have full powers in all the aspects mentioned in the first paragraph. Brazil is among the leading countries in managerial autonomy, emerging from the middle of the Autonomy Index with Jamaica, Guatemala, and Argentina. In contrast, agencies at the bottom of the index lack the autonomy to determine their organizational structures and do not have the power to

> 6 According to World Bank’s Governance Effectiveness indicator, Chile heads the LAC region in terms of the quality of its policy-making. The Governance Effectiveness index measures the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. It is the result of a large number of individual data sources that provide information on perceptions on governance (Kauffman, 2006).

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children (see tables for details). Our results are qualitatively robust to controlling for a variety of other covariates, but given the relatively small sample sizes, we cannot include a huge number of control variables at the same time.

# **5. Impacts on Household Level Outcomes**

We now turn to estimating the impact of emigration on remaining household members. A limitation of this analysis is that our surveys do not provide detail on how resources are allocated within households. While this means some caution must be had in interpreting the results, it is entirely in keeping with the existing literature, which has also not been able to look at within household allocation.<sup>19</sup> Moreover, the comparison of experimental and nonexperimental methods will not depend on this limitation.

# **5.1. Household Size and Composition**

We begin by examining the impact of emigration on household size and composition, since one immediate effect is that there are “fewer mouths to feed”. The impact of having some household members migrate to New Zealand on household size and composition is shown in Table 4. Emigration leads to a significant reduction in household size. The mean household has 6.7 people, and emigration is estimated to reduce this by 2.2 people. Emigration leads to households having, on average, 1.5 fewer prime-age adults and 0.7-0.8 fewer children. There is no change in the number of older adults (>45 years), which is reassuring since they are not eligible to move as Secondary Applicants.

Table 2 showed that whole households which move are smaller than stayer households. Failure to remove “all move” ballot losers would therefore cause us to understate the fall in household size from migration.20 Similarly, households which apply to migrate are larger than those which do not. Thus, panel 3 shows that all the non-experimental estimates, which ignore this selectivity, therefore understate the fall in household size arising from migration. Instead of correctly estimating a fall of 2.2 people, we now find that household size decreased by only 0.6 to 0.8 people, and is insignificant for two of the three non-

> 19 The only paper we are aware of which collects data on within household allocation and migration is de Vreyer et al. (2008) who consider large polygamous households in Senegal and look at allocation within subunits of the household.

> 20 For household level impacts, the first set of non-experimental estimates do not include any controls. They are thus directly comparable to the estimates in Panel A in each Table examining household impacts. The reason for not including controls in these regressions is that the household level controls are defined in terms of stayer characteristics, and are thus not defined for all move households.

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# Terms of Trade (1990=1)

<!-- Start of picture text --> 2.0 2.0 1.2 2.5<br>1.0 2.0<br>1.5 1.5<br>0.8 1.5<br>1.0 1.0 0.6<br>0.4 1.0<br>0.5 0.5 0.2 0.5<br>00.0 .0 .b..0.0 7......7....... 2..0.0 ......... 0.0 .s .... ... ..<br>70. -75...eb 8S 90 9.;.7.7S.8000 8S 90 7S....eV7 90.9.i 00 it 80 8S 90 9;<br>I-BRAZIL -CHILE -CHINA -COLOMBIA<br>2.0 1.2 _ 1.2 1.6<br>1.0 1.0<br>1.5 1.2<br>0.8 0.8<br>1.0 0.6 0.6 0.8<br>0.4 0.4<br>0.5 0.4<br>0.2 0.2<br>0.0 ___________________ 0.0 ____________________________b___9__ 6__________________b___<br>I-INDIA -KOREA -MEXICO -MALAYSIAI<br>2.0 2.0 2.5 1.2<br>1.5 1.5 2.0 1.0<br>1.5 ~~~~~~~~~0.8<br>1.0 1.0 0.6<br>1.004<br>0 0.5 0.5 0.4<br>7 ~~~7<br>I-PERU I-PHILIPPINES g I-THAILAND F -VENEZUELA-<br><!-- End of picture text --> 35
In the previous equations, represents the logarithm of the monthly wage;<sup>15</sup> is the age in years of the individual; is the working experience of the person calculated EEOO(WWCCllEE) CCllEE following Hara (2018); _locality_ is a binary variable that takes the value of 1 if the individual is EEeeE EEOOEEOOCCEE from an urban area; is an indicator function used to represent dummy variables of _education level_ and _governorate_ where the individual resides; and stands for gender because the II coefficients are calculated independently for male and female. ll Applying the Blinder-Oaxaca decomposition to equation (4) produces the endowment-coefficients decomposition across industries. By contrast, applying the Blinder-Oaxaca decomposition to equation (5) produces the endowment-coefficients decomposition within industries. This difference may reveal whether discrimination occurs across industries or within industries.

Complementing the previous methodology, recent studies such as Ahmed and McGillivray (2015) suggest that the calibration of equations (4) and (5) may contain an implicit selection bias. The fact that individuals are employed reveals that they have some special unobservable attributes that allow them to be hired. To correct for this bias, a Heckman correction model was implemented in which the first stage estimates the probability of being employed conditional on (1) the covariates used in equation (4), (2) being head of household, and (3) dummies for marital status. The predicted score is used to calculate the inverse Mills ratio, and this variable is added as an additional covariate to equations (4) and (5). This model, called bias-corrected (BC), adjusts the Blinder-Oaxaca decomposition to incorporate those unobservable skills that guarantee the employment of the individuals in the sample. Finally, based on Hara (2018) a recentered influence function (RIF) regression method was used as proposed by Firpo, Fortin, and Lemieux (2018).<sup>16</sup> This technique allowed re-creation of the Blinder-Oaxaca decomposition at different wage levels. Figures 5 and 6 summarize the results graphically.

# **Figure 5 Coefficient effect of gender wage gap by industry**

> 15 While the TLMPS includes information on number of hours worked, converting monthly wages to hourly wages adds more noise to the data. Therefore, the results are presented using monthly wage data.

> 16 The results of the RIF regressions were calculated for every five percentiles between 2 and 95. To avoid noise from specific outliers, the results presented take those calculations and filter them using a locally weighted regression. By doing so, the trends displayed are more robust to variations in individual values.

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