rafmacalaba/datause-encoder-data huggingface dataset:
lowest education levels. This has implications for the robustness of possible brain-gain estimations. The evidence on these selectivity effects is not conclusive (see Constant and Massey, 2003 for a summary of findings) and differs by the country of interest but represents a caveat when interpreting our estimations of intentions to return to Romania.

# **IV Data and Descriptive Statistics**

The IASCI and the Soros Foundation in Romania surveyed Romanian migrants in August 2010. They interviewed nearly 3,000 migrants aged 18 years or older who were returning to Romania for a short visit at major air and land entry points and who had been economic migrants for one year or longer. The survey gathered detailed information including on employment, income, connections to Romania, networks, and education, as well as demographic information. The survey is rich but, as noted above, the methodology means that some caveats should be taken into consideration. Certain groups are likely to be underrepresented: this includes those who earn less money in their host country including the unemployed, those that live further away, those that migrated more recently, and illegal migrants who may have more difficulty re-entering their host country. The survey cannot therefore be taken to represent a cross-section of Romanian migrants. Nonetheless, the results provide a good insight into migrant labor market outcomes.

Of those surveyed, over 70 percent were in Southern Europe and 20 percent in Northern Europe. About 53 percent had a high school education compared with 70 percent of the total population. This reflects for example significant numbers of relatively unskilled migrants who sought work in the Spanish construction sector. Around 90 percent of migrants reported speaking well or fluently the language of their host country. This is partly a reflection of the linguistic similarities between Romania and the two main host countries – Spain and Italy. Despite the work restrictions in place on Romanians in many EU countries, 84 percent of migrants were legally entitled to work. Migrants tended to have either a similar or worse occupation when compared to working in Romania, consistent with previous work by Cingolani (2007). Typical examples were nurses who worked in old-age homes in Italy or skilled mechanics who become unskilled laborers in the construction sector in Spain. Networks played an important role in migration with 49 percent having received assistance from a friend or family member in gaining employment or accommodation upon migrating. Connections to Romania were still important: 64 percent of migrants reported having remitted money to Romania during the previous year. Migrants were less likely to be female than the average of the population (48 percent against 51.3 percent), likely to live in a smaller household (average size of 2.1 among migrants compared with 2.9 for the total population), and were younger than the average 11
Chart K4 THE RATIO OF BANKS' TOTAL RESERVES TO DEPOSITS <!-- Start of picture text --> Chart K3<br><!-- End of picture text --> <!-- Start of picture text --> GROWTH RATE OF REAL LOANS<br>rx \ ~~~~~~~~~~~~~~~~~~~~~~~~~1 25<br><!-- End of picture text --> The ratio of dishonored checks and bills to the total increased to 0.52 at the peak of the crisis in December 1997 from 0.17 in 1996 (an increase of 206 percent), indicating the magnitude and seriousness of the financial distress in the country.

<!-- Start of picture text --> Chart K5 Chart K6<br>MONEY MARKET RATES AND INFLATION THE RATIO OF DISHONORED BILLS TO THE TOTAL<br>.c 05_<br>.. 9 90 =.1S'M07 9095T 9)1 9)01 9)9 '909 9o 20) 99,, 9)1'01 9)o 9 ................. '00_ $1!wU1097t71707l _ t I II<br>,P<br>Inflaicn (ye-r-on-ye-r) Call rates (over-ight)<br><!-- End of picture text --> Further investigation shows that Korea experienced a **sharp increase in the risk premium on corporate debt,** as captured by the rising yield differential between corporate and government bonds (the domestic risk-free assets). Chart K7 shows that the **yield differential between corporate and government bonds** increased from about 100 basis points until October to 197 b.p. in November 1997, reached 899 b.p. in the following month at the peak of the crisis and declined relatively thereafter, though it still remained very high (678 b.p., 453 b.p., and 378 b.p. during the first three months of 1998). This observation suggests the operation of the credit crunch through the **balance sheet channel.** 21
# Table 1: Monthly Commodity Prices (2004-2010)

## **<u>1. Autocorrelations (1 month lag)</u>**

||Banjarmasin||Lampung||Makassar||Mataram||
|---|---|---|---|---|---|---|---|---|
||Pre|Post|Pre|Post|Pre|Post|Pre|Post|
|Rice|0.844196|0.742783|0.623171|0.736379|0.650719|0.157271|0.444033|0.397506|
|Maize|0.821649|0.827385|0.116147|0.843246|0.8267|0.724469|0.557993|0.473394|
|Cassava|0.864643|0.76801|0.471296|0.476669|0.671444|0.76885|0.7887|0.807267|
|Estate|0.713411|0.731777|0.734365|0.792293|0.821507|0.850851|0.510722|0.585104|
|Horticulture|0.652521|0.510954|0.604389|0.741782|0.729974|0.25017|0.362384|0.547258|
|Average|0.779284|0.716182|0.509874|0.718074|0.740069|0.550322|0.532767|0.562106|
||Menado||Semarang||Surabaya||All||
||Pre|Post|Pre|Post|Pre|Post|Pre|Post|
|Rice|0.675637|0.584909|0.823902|0.349811|0.654806|0.525774|0.673781|0.499205|
|Maize|0.703467|0.612008|0.788834|0.702844|0.763673|0.850674|0.654066|0.719146|
|Cassava|0.772064|0.857352|0.847457|0.842138|0.75803|0.802342|0.739091|0.760375|
|Estate|0.712543|0.805274|0.756301|0.873499|0.841585|0.821223|0.727205|0.780003|
|Horticulture|0.607716|0.572798|0.675657|0.554181|0.687848|0.537905|0.617213|0.530721|
|Average<br>**2. Standard de**|0.694286<br>**viations**|0.686468|0.77843|0.664495|0.741188|0.707583|0.682271|0.65789|
||Banjarmasin||Lampung||Makassar||Mataram||
||Pre|Post|Pre|Post|Pre|Post|Pre|Post|
|Rice|718.7351|202.1569|290.385|363.8876|244.2571|95.09454|215.8059|167.7218|
|Maize|95.49091|354.9766|217.2418|729.6438|80.84213|533.2995|224.5837|127.4998|
|Cassava|88.14651|421.188|228.0822|146.6009|68.04749|107.7264|257.8521|260.2294|
|Estate|34.98767|35.93951|108.3112|55.1585|111.552|82.88177|10.67901|5.14049|
|Horticulture|33.01025|2.011518|23.59837|4.562912|17.42777|1.982555|16.90491|6.173323|
|Average|194.0741|203.2545|173.5237|259.9707|104.4253|164.197|145.1651|113.353|
||Menado||Semarang||Surabaya||All||
||Pre|Post|Pre|Post|Pre|Post|Pre|Post|
|Rice|257.1523|197.8883|249.6365|86.9732|190.6296|178.155|309.5145|184.5539|
|Maize|147.2047|264.1175|85.06493|173.7563|147.8522|240.0603|142.6115|346.1934|
|Cassava|129.9688|579.6494|95.79269|656.522|90.74894|252.6408|136.9484|346.3653|
|Estate|152.8929|69.11196|21.53614|31.76717|45.2243|54.8653|69.31189|47.83781|
|Horticulture|28.72062|5.073268|20.00913|2.8141|22.69024|4.82508|23.19447|3.920394|
|Average|143.1879|223.1681|94.40788|190.3666|99.42906|146.1093|136.3161|185.7742|

Pre = Pre-food crisis (from 2004m1-2006m12) Post = Post-food crisis (from 2007m1-2010m12) (Note) Autocorrelations and standard deviations are computed based on the de-trended series of each crop and province using pre- and post-crisis monthly price data. We remove linear time trend to construct de-trended price series. Average is the simple average.

(Source) Bulog, BPS
prioritizing specific deals. Rwanda’s success in sequencing its reforms was in part due to a long-term vision of policy framework and development goals outlined by the government. Rwanda Vision 2020 provides a policy framework to reform that allows for consistency and reduces the inter-temporal policy risk faced by private investors.

**Figure 7:  Evolution of investment in Rwanda following the conflict and**

# **genocide**

<!-- Start of picture text --> Evolution of G ross C apital Formation<br>-5 0 5 10 15<br>Year since e nd of conflict<br>Buru ndi Rwand a Uga nda<br>Sierra Leo ne Mozambiqu e Con go, De m. Rep .<br>Cape Verde Gh ana<br>Sou rce: W D I<br>1000<br>800<br>600<br>400<br>200<br>0<br>GDP Index (constant LCU, last year of conflict = 100)<br><!-- End of picture text --> In the past 10 years, Rwanda has made significant progress in both simplifying the rules and in enforcing them effectively, as can be seen in the cross-national tables above. In Rwanda 90 percent of firms believe regulations _are_ enforced “consistently and predictably” (Figure 3), the level and variability of days to get an operating license are among the lowest (Figure 2a and 2b), and in Rwanda, less than 5% of the firms identify corruption as a constraint, according to the World Bank Enterprise Survey 2006, 58
WPS4047

# Unraveling the Worldwide Pollution Haven Effect*

Jean-Marie Grether †, Nicole A. Mathys ‡, Jaime de Melo § Abstract This paper contributes to the debate on the existence of pollution haven effects by systematically measuring the pollution content of trade (measured by the pollution content of imports (PCI)) and decomposing it into three components: a ‘deep’ component (i.e. unrelated to the environmental debate but including variables traditionally present in the gravity model) and two components (factor endowments and environmental policies) that occupy centerstage in the debate on trade and the environment. The decomposition is carried out for 1986-88 for an extensive data set covering 10 pollutants, 48 countries and 79 ISIC 4-digit sectors. Illustrative decompositions presented for 3 of the 10 pollutants in the data set indicate a significant pollution haven effect which increases the PCI of the North because of stricter environmental regulations in the North. At the same time, the factor endowment effect decreases the PCI of the North as the North is relatively wellendowed in capital and pollution-intensive activities are also capital intensive. On a global scale, because the bulk of trade is intra-regional with a high North-North share, these effects are small relative to the ‘deep’ determinants of the worldwide pollution content of trade. In sum, although the impact has been stronger on vertical (North-South) trade flows, differences in factor endowments and environmental policies have only marginally affected the pollution content of world trade during the 1986-88 period.

JEL Classification numbers: F18. Key Words: Trade and the environment, pollution haven

## World Bank Policy Research Working Paper 4047, November 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*We thank Marius Brülhart, Olivier Cadot, Matthew Cole, Céline Carrère, Robert Elliott, Jaya Krishnakumar, Carlos Ordas, David Wheeler, and participants at the ETSG meeting in Dublin (2005) for comments on an earlier draft. Financial support from the Swiss National Fund under research grant No 100012-109926 is gratefully acknowledged.

†University of Neuchâtel, Pierre-à-Mazel 7, 2000 Neuchâtel, email: jeanmarie.grether@unine.ch ‡University of Lausanne, e-mail: nicole.mathys@unil.ch §University of Geneva, CERDI, CEPR, and WBER editor: email: demelo@ecopo.unige.ch
of past military herbicide exposure and the prevalence of (reported) cancer in the Vietnamese population some 30 years after the end of the war.<sup>2</sup> When comparing communes that have been exposed with those that have not, no significant difference in reported cancer prevalence is detected. This finding could well reflect an attrition bias, since mortality due to cancer can induce a lower observed prevalence (stock), while still being consistent with a higher incidence rate (flow). A dose‐response relationship is furthermore detected; conditionally on being exposed to past military spraying, increasing exposure is associated with increasing prevalence of self‐reported cancer in 2001‐2002. Although the findings could be driven by a causal impact of Agent Orange exposure on cancer, a careful interpretation of these results is called for, given the limitations of the data and methodology used for the analysis. First, information on cancer prevalence is obtained in a survey context and therefore subject to reporting biases; no validation has been undertaken, and there is no information on the types of cancer affecting individuals. Second, available data only allow computing prevalence rates rather than incidence rates potentially leading to attrition biases, although it might be reasonable to suppose that in this instance, coefficients would be biased towards zero. Third, the patterns of military herbicide spraying were surely not a random process, and responded to local conditions that could well be contemporaneous risk factors of cancer. Although a large number of confounders can be observed and controlled for, latent heterogeneity cannot be ruled out.

This paper belongs to the literature on the legacy of wars (for a review, see Blattman and Miguel, 2009). While the direct costs of wars in terms of human casualties and physical destruction cannot be over‐emphasized, attention has also been given to the analysis of post‐ war economic recovery and long‐term development. Organski and Kugler (1977) and more recently Chen, Loayza and Reynal‐Querol (2008) find evidence of countries returning to pre‐war economic development levels once peace has been restored. Evidence of long‐term

> 2 The US military, through the National Archives and Records Administration, has made available detailed information on wartime military operations. Kalyvas and Kocher (2009) analyze look at the determinants of violence using data from the Hamlet Evaluation System, a database of violence against civilians across hamlets in Vietnam. Closer to this paper in its methodology and data sources, Miguel and Roland (2006) instead use information on bombing activities to investigate their long‐term impacts on economic development.

3
Figure 4: Changes in poverty rates and migration <!-- Start of picture text --> 0 .1 .2 .3 .4<br>Change in migration rates to Gulf and Malaysia<br>Regression fit<br>-.2 -.1 0 .1 .2 .3<br>Change in migration rates to India<br>Regression fit<br>.5<br>0<br>-.5<br>Change in poverty rates<br>-1<br>.5<br>0<br>-.5<br>Change in poverty rates<br>-1<br><!-- End of picture text --> Source: Author’s calculations from the 2001 and 2011 Census and Small Area Estimates of poverty. Note: This figure shows how changes in _illaka_ level migration rates is associated with changes in _illaka_ level poverty rates. The top panel plots the changes in Gulf-Malaysia migration rates and the bottom panel plots changes in India migration rates in the horizontal axes. Each point represents an _illaka_ with the area proportional to the population in 2001. The line shows linear regression estimate of the relationship.

21
completion rates are capped at the highest levels observed in advanced economies in 2019, the latest available data point. Life expectancy is assumed to rise in each country by the largest increase in any ten-year period during 2000-21, but not above the median advanced-economy life expectancy in 2019.

For each age group in each country, the female labor force participation rate is assumed to rise by the largest increase over any ten-year period during 2000-2021, but not to exceed the male labor force participation rate in the same age group. Separately, a social benefit reform with labor market implications is modelled. For each gender and each country, labor force participation rates for workers in age groups 55-59, 60-64, and 65 years or older are assumed to rise to the participation rates of age groups that are five years younger, i.e., those of age groups 50-54, 55-59, and 60-64 years. The increase is assumed to occur gradually over 20 years for each gender in each country.

# IV.2. Raising the growth rate and efficiency of physical capital

Scaled-up fixed investment can raise potential output growth both directly through the contribution of capital accumulation and indirectly by boosting TFP growth, since TFP-enhancing technological progress tends to be embodied in new investment. Potential output can also be raised through more efficient investment spending.

# Scaling up investment

To achieve the sustainable development goals, it has been estimated that global investment needs to be raised by up to 3 percent of global GDP (UNCTAD 2014). All EMDEs and EMDE regions have sizable investment needs (Stamm and Vorisek 2023). These could be filled through either public or private investment or combinations of both, including in public-private partnerships. Increasing public investment and promoting private investment can be effective policies to support aggregate demand and activity in the short term as well as to raise potential output growth in the longer term (Calderón and Servén 2010a, 2010b, 2014; World Bank 2017b).

Although the rapid increase in public debt over the past decade has constrained fiscal space in most EMDEs, there generally remains scope to shift government expenditures toward productive, growth-promoting public investment and away from less productive spending such as subsidies (World Bank 2017c). In many EMDEs, government revenue ratios relative to GDP remain low, indicating that they could be raised, including by expanding tax bases and improving the quality of tax administration (World Bank 2015).

In addition, policies can support growth-enhancing private investment. Innovationpromoting investment tends to be low in EMDE firms, partly because of limited availability of complementary inputs such as trained engineers and effective organization techniques (Cirera and Maloney 2017). Policies to expand the supply of complementary inputs and improve management skills could therefore promote private investment, as could improved protection of intellectual property rights.
# **Figure 9: Skilled and unskilled labor content of exports across sectors, South Africa 2011**

<!-- Start of picture text --> Minerals nec<br>Metals nec<br>Machinery and Equipment nec<br>Ferrous Metals<br>Transport Equipment<br>Chemical, Rubber, Plastic Products<br>Trade and Transport Services<br>Other Private Services<br>Agr, Forestry, Fisheries<br>Processed Foods<br>PubAdmin/Defence/Health/Educat<br>Energy Extraction<br>Paper Products, Publishing<br>Metal Products<br>Beverages and Tobacco Products<br>Manufactures nec<br>Wood Products<br>Textiles<br>Mineral Products nec<br>Wearing Apparel<br>Electricity, Gas, Water<br>Leather Products<br>Construction<br>Dwellings<br>0 2,000 4,000 6,000<br>US$ mil.<br>Total LVAX Unskill ed Total LVAX Skilled<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

Manufacturing matters for jobs, but its importance comes mainly through its backward linkages to the domestic economy. Replacing the underlying labor value added share in production (from national accounts contained in GTAP) with the number of jobs contained in production (from ILO), we can isolate the number of direct and indirect jobs contained in exports. Figure 10 shows the direct and indirect jobs in exports for 2011. Due to differences in sectoral aggregations across data sources, we can only group the sectors into 11 macro sectors. Manufacturing dominates the ranking, responsible for almost two-thirds of all jobs contained in exports. In 2011, manufacturing exports contributed 1.9 million jobs. The vast majority of these jobs are created in sectors providing inputs to manufacturing (1.5 million indirectly and 0.4 million directly): the indirect employment impact of manufacturing exports (jobs in industries providing inputs into manufacturing exports) is nearly 4.5 times greater than the direct manufacturing employment.

17
_Figure 8: Health Sector Expenditures Per Capita-2002 Distribution_ _(states are arranged with the highest poverty rates on the left and lowest on the right, according to the CONAPO index of marginality)._ <!-- Start of picture text --> 3,000<br>2,500<br>2,000<br>PEMEX<br>ISSSTE<br>IMSS<br>IMSS-Oportunidades<br>1,500<br>Oportunidades<br>INI<br>FASSA<br>PAC<br>1,000<br>500<br>0<br>MxP per capita<br>Chiapas Guerrero Oaxaca Veracruz-llave Hidalgo San Luis Potosí Puebla Campeche Tabasco Michoacán de Ocampo Yucatán Zacatecas Guanajuato Nayarit Sinaloa Querétaro de Arteaga Durango Tlaxcala Morelos Quintana Roo México Colima Tamaulipas Sonora Jalisco Chihuahua Baja California Sur Aguascalientes Coahuila de Zaragoza Baja California Nuevo León Distrito Federal Weighted Average<br><!-- End of picture text --> _Source:_ SHCP and Bank staff calculations.

Considering all of the resources of the health sector, the federal transfers to the sector through FASSA help increase equality (See Figure 8), in the sense that its inclusion reduces the variance in the allocation of resources per capita. (The figure excludes the high outliers of the Federal District and Baja California Sur.) Still, better design of formulas could reduce inequality further.

**_PAFEF._** Since 1998, Congress has been incorporating in the _Presupuesto de Egresos_ the _Programa de Apoyo para el Fortalecimiento de las Entidades Federativas_ (PAFEF) without any clear mechanism in the allocation of these resources across states.  The initial purpose of PAFEF was to strength the education infrastructure in those states in which the state governments were not allocating enough resources.  Therefore, these resources were earmarked for education infrastructure if the states did not allocate adequate resources for this; otherwise, they could use it for other type of infrastructure. In 2000, the rules changed, in such a way that a state could use it for infrastructure investment or for any problem the state had in their pension system.  In 2002, Congress formalized this program, which now will be disbursed under _Ramo 39_ , and modified its rules in such a way that states can use these resources for infrastructure and debt relief.  The size of the program is relatively small, about 1.5 percent of _Recaudación General Participable_ .  One of the problems of this program is the lack of transparency or equality per capita in the allocation

16