rafmacalaba/datause-encoder-data huggingface dataset:
**TABLE 1: Geographic distribution of IFC equity investment compared to foreign direct investment.** This table presents IFC equity investment and global FDI inflows by the IFC regional classification. Emerging market and developing economies (EMDEs) and advanced economies are as classified by the IMF as of 2019. Brazil, China, Russian Federation, Nigeria, India and Saudi Arabia are the largest FDI destinations in their regions. The United States; United Kingdom, and Hong Kong SAR (China) are the world’s three largest FDI destinations among advanced economies. The British Virgin Islands, Cayman Islands and United Arab Emirates are the three largest EMDE FDI destinations without IFC cash deployed. Advanced economies with IFC equity investments are, in descending order of real cash deployed: the Republic of Korea, Greece, Czech Republic, Slovak Republic, Latvia, Singapore, Estonia, Slovenia, Lithuania and Taiwan (China). Regional IFC projects are grouped under their region. FDI inflows from UNCTAD up to 2018. Values are 2018 dollars, converted from nominal dollars using the US GDP deflator.

||IFC equity i|nvestment since 1956||FDI infows|since 1970||
|---|---|---|---|---|---|---|
||Billions ($)|Share<br>of<br>SUB-<br>TOTAL|Trillions ($)|Share<br>of<br>TOTAL|SUB-<br>Share of TOTAL|IFC / FDI|
|**A) Countries with IFC cash deployed**|||||||
|**Emerging Market and Developing Economies**|33.77|97.7%|11.33|85.9%|29.1%|0.30%|
|Latin America and the Caribbean|8.01|23.2%|3.45|26.2%|8.9%|0.23%|
|_Brazil_|2.23|6.50%|1.19|9.00%|3.10%|0.19%|
|East Asia and the Pacifc|6.28|18.20%|3.67|27.80%|9.40%|0.17%|
|_China_|3.22|9.30%|2.57|19.50%|6.60%|0.13%|
|Europe and Central Asia|6.37|18.40%|2.02|15.30%|5.20%|0.32%|
|_Russian Federation_|1.85|5.40%|0.61|4.60%|1.60%|0.30%|
|Sub-Saharan Africa|3.9|11.30%|0.7|5.30%|1.80%|0.56%|
|_Nigeria_|0.35|1.00%|0.15|1.10%|0.40%|0.24%|
|South Asia|4.45|12.90%|0.67|5.10%|1.70%|0.66%|
|_India_|3.31|9.60%|0.56|4.30%|1.40%|0.59%|
|Middle East and North Africa|3.22|9.30%|0.81|6.10%|2.10%|0.40%|
|_Saudi Arabia_|0.08|0.2%|0.31|2.3%|0.8%|0.03%|
|World Region|1.54|4.5%|-|-|-|-|
|**Advanced Economies**|0.78|2.3%|1.87|14.1%|4.8%|0.04%|
|Korea, Rep.|0.30|0.9%|0.29|2.2%|2.2%|0.10%|
|Greece|0.20|0.6%|0.08|0.6%|0.6%|0.25%|
|Czech Republic|0.18|0.5%|0.17|1.3%|1.3%|0.11%|
|**SUB-TOTAL**|34.56|100.0%|13.19|100.0%|33.9%|0.26%|
|**B) Countries without IFC cash deployed**|||||||
|**Emerging Market and Developing Economies**|||1.89||4.9%||
|British Virgin Islands|||0.84||2.2%||
|Cayman Islands|||0.59||1.5%||
|United Arab Emirates|||0.17||0.4%||
|**Advanced Economies**|||23.86||61.3%||
|United States|||7.02||18.0%||
|United Kingdom|||2.68||6.9%||
|Hong Kong SAR (China)|||1.68||4.3%||
|**TOTAL**|||38.95||100.0%|0.09%|

32
# **Table A3: Panel A – Multinomial Regressions, 2005**

Log Odds ratio between Renters and Mortgage holders (Marginal Effects)

||||||||||**Hausman F**|**-**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**parttime**|**unemp**|**retired**|**urban**|**lage*secedu**|**lage*teredu**|**inc*secedu**|**inc*teredu**|**Statistic**|**Pseudo r**<sup>**2**</sup>|
|_New EU Countries_|||||||||||
|Czech Republic|0.0066|-0.0223|0.0685|0.0996 ***||-0.0583||0.1067 **|1.05|0.24|
|Estonia|-0.0230|-0.0304|-0.0214|-0.0324 **||-0.0518||0.0112|1.70 **|0.21|
|Hungary|0.0484|0.0251|0.0508 **|0.0428 ***|0.0698|-0.0298|0.0049|-0.0125|2.29 ***|0.12|
|Latvia|-0.0777 *|-0.0673 *|-0.0789 *|-0.0260|0.0324|0.0810|-0.0307|-0.0497|0.95|0.09|
|Lithuania||-0.0212||-0.0137||0.0190||0.0070|0.74|0.22|
|Poland|-0.0216|0.0647 *|0.0048|0.1871 ***|0.0632|0.0883|0.0149|-0.0056|4.37 ***|0.34|
|Slovak Republic|-0.0256|-0.0953 ***|-0.0006|0.0602 ***|0.2157 ***||-0.0886 ***|-0.0328|4.57 ***|0.17|
|Slovenia|0.2351 **|0.1258 *|0.0729||-0.0947 **|-0.1347 **|-0.0542 ***|-0.0533 **|1.81 **|0.15|
|_EU-15 and Cyprus_|||||||||||
|Austria|0.0368|0.1555 **|0.0830|0.1475 ***||-0.1346 *||-0.0084|2.21 ***|0.25|
|Belgium|-0.1247 ***|-0.0623|-0.0243|0.0428 **|-0.1451|0.0738|-0.0810|0.0008|1.67 **|0.35|
|Cyprus|-0.1187 **|0.1033|0.0181|0.0703 ***|-0.0995|-0.1612 *|-0.0202|-0.0149|1.29|0.16|
|Denmark|0.1360 ***|-0.0244|0.1517 ***|-0.0313|-1.0301|-0.9949|-0.4199|-0.3710||0.35|
|Finland|-0.1296 ***|0.0066|-0.0305|0.0562 ***|-0.1245|-0.2312|-0.0148|-0.0001|2.62 ***|0.31|
|France|-0.0882 *|0.0607|-0.0760 *|0.0024|-0.3185 ***|-0.3562 ***|-0.0953 **|-0.0931 *|2.03 ***|0.32|
|Greece|0.0865|0.0806|0.0028|0.0718 ***|-0.0311|0.0097|-0.0892 ***|-0.0619 **|1.09|0.13|
|Ireland|-0.0851 ***|-0.0133|-0.0344|0.1004 ***|-0.0099|-0.0435|-0.0964 ***|-0.0432|3.32 ***|0.28|
|Italy|0.0761 ***|0.0748 **|-0.0321 **|0.0507 ***|0.0709 *|0.0351|-0.0284 *|-0.0352|4.02 ***|0.12|
|Luxembourg|-0.0455|0.1971|-0.0586|-0.0315|-0.1852|-0.0306|-0.0547|0.0585|0.68|0.35|
|Netherlands|0.1462 ***|-0.0591|0.0755 ***||-0.2294 ***|-0.1074|-0.0162|0.0852||0.29|
|Portugal|-0.0829|-0.0677|-0.0046|0.1860 ***|0.0369|0.1034|-0.0071|-0.0612|1.47 *|0.19|
|Spain|0.0164|0.0283|-0.0068|-0.0051|0.0211|0.0616|0.0123|-0.0147|2.73 ***|0.15|
|Sweden|-0.1522 ***|0.0279|-0.0593|-0.0236|0.1607|0.2078|0.0101|-0.0399|2.16 ***|0.31|
|United Kingdom|-0.2447 ***|0.0225|-0.0166|-0.0150|0.2805 ***|||-0.0355|4.76 ***|0.32|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

Source: EU Statistics on Income and Living Conditions (SILC) database, 2005 28
this variable, 1.27% of the population living in the treated locations resided in Mexico City before. Around 971 thousand people were living in this area before the shock; this means that approximately 12,500 people moved from Mexico City to the treated municipalities. Then, I can estimate a lower-bound effect under the extreme assumption that all the migrants were formal. In particular, removing these people from the specification yields a lower bound of the impact. Figure 5 plots the results of this lower bound for different values of the population that moved and decided to live in the treated areas after. For the value of 1.27%, there is a lower bound of 4.6%. Then, even under this extreme scenario, the ratio of formal to informal employment increases in the locations that experience the transit shock.

# **4 Model**

In this section, I present a quantitative model to assess first-order aggregate welfare effects of transit infrastructure on allocation. The model is based on recent work by Tsivanidis (2019), Monte et al. (2018), Heblich et al. (2018), and Ahlfeldt et al. (2015). My model extends this framework by adding intersectoral wedges and resource misallocation.

The main theoretical result is a formula from a first-order approximation that decomposes the total change in welfare after a transit shock into three different components: the first is a “direct” effect term, and the second is an allocation term that can be decomposed into a resource misallocation term and an agglomeration externality term. This formula is similar to the general case from Baqaee and Farhi (2020) of GE models on changes in productivity.

In the model, I assume that there are three groups of agents in the economy: workers denoted by _L_ , house owners denoted by _H_ , and commercial floor space owners denoted by _Z_ .<sup>27</sup>

## **4.1 Preferences**

There is a mass of _N_ locations in the economy that are indexed by _n_ and _i_ . There is a mass of _LL_ workers that operate in 2 sectors indexed by _s ∈ I, F_ , where _I_ and _F_ represent the informal and formal sectors respectively. The utility function takes a standard Cobb-Douglass form. Consumers obtain utility from a composite consumption good and housing. The utility function of worker _ω_ is: where _C_ is consumption, _H_ is housing, the parameter _α_ is the expenditure share on the consumption good, _dni_ is an iceberg commuting cost to move from location _n_ to _i_ , and _ε_ is an idiosyncratic shock

> 27The focus of the paper is efficiency. In the Appendix, I generalize the results to consider different group of workers such as high- and low-skilled workers. Intuitively, the results are isomorphic if preferences for the formal and informal sector come from the scale parameters of Fréchet shocks, or if the commuting and labor supply elasticities differ between the two groups, and low-skilled workers prefer to work in the informal sector.

16
# **2.6 The direction of European service trade**

The nature of modern service exports has affected the direction of global trade and the global centers of growth. This raises the question to what extent Central and Eastern Europe can be an effective competitor to Asia. For example, geographic proximity is important for construction services but is negligible for computer-related services (Kandilov and Grennes, 2010). We are here interested in looking at the direction of commercial services exports in modern and traditional activities. Given the limitation of such data, we compare modern and traditional service exports to EU-15 from India, EU-15 itself and NMS, in 2002 and 2004. As seen in Figure 8, modern services exports from India to EU-15 grew rapidly whereas the growth in traditional and construction services shrunk. In the case of exports from NMS to EU-15 both modern and traditional service exports have increased, but traditional more so. No hard conclusions should be made as we do not have recent enough data, but at least in the beginning of the 2000s, India seems to have made a more radical change in their exports to EU-15 following the new opportunities in modern service exports. Given NMS‘s geographical proximity, it most likely made sense to first reap the benefits from specializing in traditional service exports to EU-15, but future services growth will more likely lie in further developing modern service exports.

**Figure 8: Share of commercial modern and traditional service exports to EU-15, by origin, in 2002 and 2004 [LHS], and Growth from 2002-04 (%) [RHS].**

<!-- Start of picture text --> 100% 100<br>90%<br>80% 80<br>70%<br>60% 60<br>50%<br>40<br>40%<br>30% 20<br>20%<br>10% 0<br>0% India to EU15 within EU15 NMS to EU15<br>2002 2004 2002 2004 2002 2004 -20<br>India to EU15 EU15 to EU15 NMS to EU15<br>-40<br>Construction Computer and business services<br>modern traditional<br><!-- End of picture text --> _Source:_ EuroStat, Direction of Trade Statistics. 2011.

In summary, the stylized facts show that services dominate growth in EU-15 countries and to a lesser extent in NMS and ACC. Service productivity is many times higher in EU-15 than in the other two groups but at least NMS are starting to catch up. Highest growth in ―service tradability‖ is recorded for NMS followed by EU-15 while ACC are falling behind. Both EU15 and NMS have revealed comparative advantage in services, but this is not the case for ACC. Moreover, growth in modern services outweigh growth in traditional services in EU-15 and NMS, while modern services are even shrinking in ACC. Hence, EU-15 and NMS do seem to change the nature of their services while for ACC - even though the contribution of services to growth is strong in ACC - it seems to be less due to an improved quality of services. We next turn to the sophistication of services. After a short description of the methodology, we will present some descriptive statistics for Europe that may help us further understand the patterns in Europe that we have just observed.

10
<!-- Start of picture text --> o;<br>=o<br>£ ,<br>:<br>Be:<br>i<br>=<br>otA5<br>ad<br>3%<br>2<br>3BO::<br>i<br>,<br>-100 -50 Ipotysmoothing0 ; rid; 50 100<br><!-- End of picture text --> <!-- Start of picture text --> =<br>32o<br>i}o<br>:<br>2:<br>&<br>s:é<br>oN<br>E:<br>5<br>SssO°<br>°<br>-100 -50 Ipoly smoothing0 grid * 0<br><!-- End of picture text -->
For those LDCs that are eligible for clothing benefits a substantial proportion of exports to the United States request preferences, about two-thirds in 2002, and most of those preferences are for clothing products under AGOA. In fact 90 percent of products from these countries requesting preferential access to the United States in 2002 were clothing products. It is worth remembering that all of these countries are able to export clothing to the United States with the liberal rule of origin that allows for the global sourcing of fabrics. Products covered by the GSP are not important in the exports of these countries whilst 30 percent of the exports of these countries to the U.S. are products for which the MFN duty is zero.

|**Table 4b: Value of Non-LDCs Exports to the U.**|**S. under AGOA in**<br>Non-LDCs with<br>benefi|**2002 ($mill)**<br>out apparel<br>ts|Non-LDCs with|apparel benefits|
|---|---|---|---|---|
||ALL|Non-Oil|Special Rules of<br>Origin|Restrictive Rule<br>of origin|
|Total Value of Exports|8050|627|666|4426|
|Value for Exports RequestingPreferences|6712|156|368|1465|
|_GSP_|_26_|25|_25_|_561_|
|_Total AGOA_|_6686_|_131_|_342_|_896_|
|_AGOA Apparel_|_0_|_0_|_201_|_192_|
|Exports for which MFN rate is zero|519|344|233|2532|
|Exports ofproducts excluded from Preferences|1|0|2|102|
|Exports Eligible for Preferences but Preferences<br>not Requested|819|127|63|326|
|Countries|Rep. Congo, Cote<br>d'Ivoire, Eritrea,<br>Gabon, Nigeria,<br>Seychelles|Rep. Congo,<br>Cote d'Ivoire,<br>Eritrea,<br>Seychelles|<br>Botswana,<br>Cameroon, Ghana,<br>Kenya, Namibia,<br>Senegal,<br>Swaziland|<br>South Africa,<br>Mauritius|

Data source: USITC dataweb Note: Since 2002 Eritrea has been removed from the list of AGOA beneficiaries and Cote d’Ivore has become eligible for apparel benefits.

Table 4b for the non-LDCs again distinguishes between those countries that are eligible to export clothing under preferences and from those that are not. The table first shows that for those countries not eligible for clothing benefits, AGOA preferences still affect a substantial proportion of trade – $6.6 billion out of total exports to the U.S. of $8 billion. However, the table then shows that almost all of this amount is accounted for by two oil exporting countries, Gabon and especially Nigeria. The next column of the table shows that for the non-oil exporters of this group, products exported to the U.S. under AGOA 14
# Adapting to Heat:

# Evidence from the Texas Criminal Justice System<sup>_∗_</sup>

## A. Patrick Behrer<sup>1</sup><sup>_,_2</sup> and Valentin Bolotnyy<sup>3</sup>

> 1Stanford University, Center for Food Security and the Environment

> 2The World Bank

> 3Stanford University, Hoover Institution

**Keywords:** Climate change, adaptation, extreme heat, criminal justice **JEL Codes:** Q5, H75, K42, D91.

Originally published in the Policy Research Working Paper Series on _January 2022._ This version is updated on _October 2023._ To obtain the originally published version, please email prwp@worldbank.org.

> _∗_ We thank Elise Breshears, Alex Bunin, Marshall Burke, Natalia Emanuel, Rebecca Goldstein, Adrienne Harrold, Peter Hong, Wallis Nader, Ishan Nath, and Jisung Park, as well as members of ECHOLab at Stanford, participants in the LAGV conference, the N. American Urban Economics Association meeting, the Midwest Economics Association meeting, and UCLA’s Climate Adaptation Research Symposium for their assistance and suggestions. We are also grateful to Robin Robinson for her excellent research assistance. The study was approved by Stanford University’s Institutional Review Board as Protocol 56777. An earlier version of this study was circulated as a working paper titled “Heat, Crime, and Punishment.” The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. The data used for the project are proprietary and cannot be freely posted online. Code used to analyze the data will be posted in the World Bank’s replication database. We can facilitate other researchers’ potential access to the data. Other researchers can gain access to these data for replication purposes by contacting the Texas Department of Public Safety (TDPS). Neither Behrer nor Bolotnyy have anything to disclose in line with JHR’s disclosure policy.
in these statistics can be mapped into the within and between components as shown in the table below.

|||**TFP M**<br>|**EDIAN**<br>|
|---|---|---|---|
|||UP|DOWN|
|**PERSION**|UP|• Upgrading firms’ capabilities<br>• Allocative inefficiencies/Distortions<br>• Adjustment costs in capital<br>• Uncertainty and volatility in sales<br>(+within) (-between)|• Lack of upgrading of firms’ capabilities<br>• Allocative inefficiencies/Misallocation of<br>capital<br>• Adjustment costs in capital<br>• Uncertainty & volatility in sales<br>(-within) (-between)|
|**TFP DIS**|DOWN|• Upgrading firm’s capabilities<br>• Allocative efficiencies<br>• Reduced uncertainty and volatility in sales<br>• Higher flexibility to adjust capital<br>(+within) (+ between)|• Lack of upgrading firm capabilities<br>• Allocative efficiencies<br>• Reduced uncertainty and volatility in sales<br>• Higher flexibility to adjust capital<br>(-within) (+between)|

32
additional factor is analogous to the risk factors in (3), exclusive of the complete pricing risk, rZ. This prediction is a basis for our empirical analysis based on the country fund index.

# IV. Extensions

The preceding analysis has focused primarily on the risk dimension and incomplete risk sharing in an international environment characterized with investor restrictions. However, there are significant cases where country funds trade at a discount even when they do not originate from countries with limited capital and inflow restrictions. As a starter, this observation is consistent with long-standing anomaly that closed-end funds trade typically at a discount even when they trade in the domestic market (e.g., closed-end funds traded in the United States). In this section, we wish to catalogue additional factors that have a bearing on the pricing of country funds relative to their net asset values. These additional factors of interest will be used in a sequel empirical paper in explaining cross-sectional variations in country fund returns and premia or discounts.

A. Interest Rate Differential. The preceding analysis assumed that investors faced the same real rate of interest across national boundaries. This may not hold between pairs of countries with differential creditworthiness such that the induced interest rate differential between the core market and the originating emerging country may deliver discount or premium on the country fund originating from the latter country. Suppose that the core country 29
Over time, the returns to university education have increased consistently (Figure 1). The returns to incomplete university fluctuated over time, but overall have shown a tremendous increase from 1992. The returns to primary were very flat for most of the 1990s, but have increased sharply in the early years of the 21<sup>st</sup> century.

<!-- Start of picture text --> Figure 1: Returns to schooling over time (percent)<br>15<br>10<br>5<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004<br>Primary vs none Sec incomplete vs Prim<br>Sec vs Primary Univ incomplete vs sec<br>University vs secondary<br><!-- End of picture text --> What accounts for the overall increasing returns to schooling in Argentina? Clearly, much of the overall rate of return to schooling has been driven by complete and incomplete university education. One test for credentialism (or screening) would be to look at the returns to schooling for people with a complete education versus those who dropped out (Layard and Psacharopoulos 1974). In order to control for the fact that those with incomplete levels of schooling have attained various years of schooling, we also control for both levels and years. A third test accounts for the timing of the dropout decision by examining year-to-year returns and looking for discontinuities in the estimates at years corresponding to the termination of levels (Hungerford and Solon 1987; Griffin and Cox Edwards 1993; Patrinos 1996; Jaeger and Page 1996; Arabsheibani and Manfor 2001; Mora 2003). Finally, we test what has come to be known as the “weak versus the strong version of the screening hypothesis.” This hypothesis draws a distinction between the weak and the strong version of the screening hypothesis, depending on

- 3 -
It is not difficult to picture an ideal study of productivity spillovers in host countries. To examiine how the development of technology and productivity in individual local firms is related to the presence of foreign MNC in the local market, the study would require detailed micro data, both quantitative and qualitative. The study would have to cover several years, to take into account the fact that spillovers are not instantaneous. It should also include a large number of firms and industries, so that inter-industry spillovers could be observed, and so that it would be possible to draw statistically significant conclusions. With this kind of detailed information, it would also be possible to study productivity spillovers in the home countries of MNCs, and examiine the presence of market access spillovers. However, to the best of our knowledge, no comprehensive analyses of this character have ever been made - one reason, of course, is the externe data requirements. Additional empirical evidence on spillovers must therefore be drawn from two other sources.

Firstly, in addition to the few case studies focusing directly on spillovers, there is a large number of detailed case studies discussing other aspects of FDI in different countries and industries, and these studies often contain valuable "circumstantial evidence" of spillovers. For instance, many analyses of the linkages between MNCs and their local suppliers and subcontractors have documented learning and technology transfers that may make up a basis for productivity spillovers or market access spillovers. These studies seldom reveal whether the MNCs are able to extract all the benefits that the new technologies or information generate amonlg their supplier firms, so there is no clear proof of spillovers, but it is reasonable to assume that spillovers are positively related to the extent of linkages. Similarly, there is much written on the relation between MNC entry and presence and market structure in host countries, and this is closely related to the possible effects of FDI on competition in the local markets. There are also studies of demonstration effects, technology diffusion, and labor training in foreign MNCs that are relevant for our purposes.

Secondly, there are a few statistical studies examining the relation between foreign presence in a host country industry and productivity (or productivity growth) in the locallyowned share of the industry or in individual locally-owned firms. These studies typically 11