rafmacalaba/datause-encoder-data huggingface dataset:
A subset of 47 of the 52 countries in our sample, which represent roughly 74% of the households in the full sample, contain occupational information. We use this information to examine the relationship between household wealth and likelihood of climatic exposure at work.<sup>21</sup> DHS occupational information includes occupation codes for the survey respondent as well as for the partner for married households, sometimes down to very specific categories: e.g. “Okada rider”, “Poultry farmer”, or “Dock Worker”.

While the degree of specificity in occupations varies by country, DHS provides 9 aggregated parent categories for all 47 countries in this subsample. The aggregated occupation categories are: “professional/technical/managerial”, “clerical”, “sales”, “agricultural”, “household and domestic”, “services”, “skilled manual”, “unskilled manual”, and “army”.

This leaves us with 510,600 households for whom we have information on temperature, wealth, and occupational characteristics, as well as the demographic controls listed above.

# _Temperature Data (CRU)_

We match households to local weather variables by using the cluster level geographic coordinates provided in the DHS surveys (all households within each cluster have the same coordinates; there are about 25 households per cluster). Average monthly temperature and precipitation data is taken from the Climatic Research Unit (CRU) at the University of East Anglia. CRU provides time‐series reanalysis data on month‐to‐month weather at a high spatial resolution (0.5 degree x 0.5 degree grid cells), which is averaged over the period 1950‐2013 at the grid cell level. For each coordinate location, we extract the climate data from CRU on temperature and precipitation.

For the econometric analysis, we use climate data for the year 2013, the year in which most of the DHS surveys were conducted. For the poverty exposure bias analysis, we use the range of climate data from 1960‐2013.

Our preferred measure of heat exposure is average temperature of the hottest month. The primary results reported here – namely a strong negative correlation between household wealth and warmer temperature in hot countries, as well as a strong positive correlation between household wealth and warmer temperatures in cold countries – are robust to alternative classifications of temperature, including monthly high temperatures, coldest month temperatures, as well as average annual temperatures. Our preferred specification controls for average monthly precipitation and elevation.

> 21 We were unable to obtain complete occupational data for Cambodia, Angola, Uganda, Mali, or Bangladesh.

14
in 2008, 23.7 cents in 2009 and 31.6 cents in 2010.<sup>24</sup> Increase in dividend payments are 1.7 cents in 2008, 2.8 cents in 2009 and 4.1 cents in 2010. Share repurchases increase by 5.5 cents in 2008, decrease by 2.3 cents in 2009 and there is an insignificant increase of 1.7 cents in 2010.

We also find that firms facing positive cash flow shocks use their cash flows to reduce their short-term debt and other financing while raising equity. In 2008, increases in cash flow are associated with a decrease in short-term debt and other financing of 4 cents and 1.5 cents, respectively and an increase in long-term debt and equity of 5 cents and 9.7 cents, respectively. In 2009, increases in cash flow are associated with a decrease in short-term debt of 4 cents and an increase in equity issuances of 8 cents, respectively. Long-term debt and other financing decrease by insignificant amounts in 2009. In 2010, increases in cash flow are associated with a decrease in short-term debt and other financing of 2.3 cents and 1.3 cents, respectively and an increase in equity issuance by 10.8 cents. Note that not all equity issuances are issuances of new stock since the item includes conversion of special stock and/or debt into common stock, exercise of stock options and/or warrants and any stock splits/reverse stock splits associated with merger and acquisition activity.

Panel B presents cash flow sensitivities for the firms facing adverse cash flow shocks. Note that the interpretation of the signs of the coefficients is switched here so that a negative coefficient means an increase in the dependent variable and a positive coefficient implies a decrease in the dependent variable. Overall, we find that firms facing adverse cash flow add to their cash balances during crisis and recovery periods, increase their capital expenditures and other investments, borrow long-term debt and issue equity while reducing their other financing. Specifically, when firms in our sample face a $1 shortfall in cash flow, they add 18.5 cents to their cash balances in 2006-07 (pre-crisis), 20 cents in 2008 and 23.6 cents in 2009 and 23.3 cents in 2010. Increases in capital expenditure are 1.8 cents in 2006-07 and 2008, 2.2 cents in

> 24 As described in the Appendix, the Other Investments variable is computed from Compustat (for firms following cash flow format 7) as Acquisitions + (Increase in Investments – Sale of Investments) – Changes in Short-term Investments – Other investing Activities. Thus it includes the cash flow associated with mergers and acquisition activity. When we re-estimate the system by stripping the Other Investments variable of Acquisitions and including it as an independent variable, we find that over 50% of the Other Investments/Cash Flow sensitivity is due to Acquisitions. We don’t use this in all our regressions since we are unable to separate out acquisitions in the Bloomberg sample on emerging market sample.

28
18 DASGUPTA AND GROVER Table 1: Port access and economic activity

||∆Ou|tput|∆Empl|oyment|∆Pl|ant|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Secondary|-0.10|-0.01|-0.19|-0.10|-0.21|-0.13|
||(0.24)|(0.23)|(0.19)|(0.20)|(0.18)|(0.18)|
|Interior|-0.00|-0.07|-0.13|-0.08|-0.34<sup>_∗_</sup>|-0.36|
||(0.31)|(0.46)|(0.22)|(0.37)|(0.18)|(0.30)|
|1994-00_×_Secondary|0.59<sup>_∗_</sup>|0.63<sup>_∗_</sup>|0.62<sup>_∗∗_</sup>|0.60<sup>_∗∗_</sup>|0.72<sup>_∗∗∗_</sup>|0.74<sup>_∗∗_</sup>|
||(0.33)|(0.37)|(0.26)|(0.30)|(0.26)|(0.31)|
|1994-00_×_Interior|0.01|0.37|0.10|0.20|0.50<sup>_∗_</sup>|0.67|
||(0.39)|(0.62)|(0.29)|(0.47)|(0.26)|(0.45)|
|2000-09_×_Secondary|-0.43|-0.72<sup>_∗∗_</sup>|-0.25|-0.52<sup>_∗_</sup>|-0.17|-0.40|
||(0.30)|(0.31)|(0.27)|(0.29)|(0.24)|(0.24)|
|2000-09_×_Interior|-0.67<sup>_∗_</sup>|-0.83|-0.38|-0.58|0.11|-0.01|
||(0.40)|(0.54)|(0.32)|(0.46)|(0.27)|(0.42)|
|State-period FE|No|Yes|No|Yes|No|Yes|
|Observations|930|930|930|930|930|930|
|_R_<sup>2</sup>|0.05|0.08|0.06|0.09|0.06|0.08|

The dependent variable is cumulative growth rate of output, employment and plants at the district level, weighted by district-level population in 1991. The omitted category is the primary region during the period 1989-94.<sup>_∗_</sup> ,<sup>_∗∗_</sup> and<sup>_∗∗∗_</sup> correspond to significance level at 10, 5 and 1 percent respectively. Standard errors are clustered at the district-level.

from the primary towards the secondary region, during the period immediately following trade liberalization during 1994-2000, but these patterns reversed in the following period 2000-09, when manufacturing activity reallocated back to districts in the primary region at the expense of those in the secondary region.

The result that following trade liberalization manufacturing activity dispersed away from primary to secondary regions is in sharp contrast to the prevailing literature which finds a relative increase in activity, following a decline in external trade costs, in the regions connected with international markets (Hanson, 1996; Fan et al., 2011; McCaig and Pavcnik, 2013; Cos ̧ar bonuses, contributions to social security and welfare funds.
Chart 14 Contributions of Shocks to Interest Rate Variation (Average across G5 economics, percent of total variation) <!-- Start of picture text --> By global and domestic shocks<br><!-- End of picture text --> <!-- Start of picture text --> Global Domestic<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br>By structural shocks<br>Oil GS GD GMP DS DD DMP<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br>By types of structural shocks<br>Oil and supply Demand MP<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br><!-- End of picture text --> Sources: Authors’ calculations, based on the G5 economies (Canada, the euro area, Japan, the United Kingdom, and the United States). Notes: Forecast error variance decompositions of domestic policy interest rates based on the FAVAR model that consists of four global variables (global output growth, inflation, interest rates, and oil prices) and three domestic variables (domestic output growth, inflation, and interest rates). “OP” = oil price shock, “GS” = global supply shock, “GD” = global demand shock, “GMP” = global monetary policy shock, “DS” = domestic supply shock, “DD” = domestic demand shock, “DMP” =domestic monetary policy shock, “Oil and supply” = OP+GS+DS; “Demand” = GD+DD; “MP” = GMP+DMP.

46
financial pressures and aspirations for children’s education can drive women’s decisions to enter the labor market in this context.

_Related literature and contribution._ The contribution of this paper is threefold. First, because of data availability issues, causal evidence on the effects of childcare expansion programs in developing countries is rare (Clark et al. 2019; Vuri 2016). The empirical literature mostly focuses on developed industrial countries with predetermined high female labor supply (Nollenberger and Rodríguez-Planas 2015; Havnes and Mogstad 2011; Kleven 2021). This is problematic because it is unclear whether findings from developed countries (with weaker gender norms on household responsibilities) generalize to this context where household structure is quite different.<sup>2</sup> Our findings indeed suggest that, in such contexts, childcare services can motivate mothers to work not primarily due to time availability, as emphasized in prior literature, but to afford the relatively costly preschool services and provide educational opportunities for their children.

Second, most previously studied childcare expansions have either been smaller in scale or spread over extended periods, leading to gradual increases in childcare access. In contrast, Uzbekistan’s reform was implemented over a relatively short three-year period, during which the number of kindergartens tripled and overall childcare coverage doubled. This rapid and substantial increase in childcare facilities presents a unique case of a strong exogenous supply shock that allows us to examine the effect on female labor supply more clearly (Morrissey 2017; Baker, Gruber, and Milligan 2012).

Third, the existing work primarily emphasizes the labor supply responses of mothers and often overlooks the actual responses of informal care providers to policy expansions. However, it is important to note that childcare can be easily substituted by other adult female members including grandmothers (Vuri 2016). And, thus, the existing studies could underestimate the aggregate response of the female population to childcare expansion policy reform. To illustrate, in Canada, the expansion of childcare facilities not only increased maternal employment, but also grandmothers’ labor supply (Karademir, P. Laliberté, and Staubli 2023). Utilizing household survey data from Uzbekistan allows us to observe not only mothers, but also informal care providers including grandmothers within the household, and observe their responses to this policy expansion reform.

> 2Average household size is 7 where usually 2 working age women co-reside. According to a 2018 survey in Uzbekistan, 43% of unemployed women are not seeking a job because they must take care of their household responsibilities, while only 7% of unemployed men reported the same reason (Strategy.uz, 2022).

4
- 28 -

# Na-wa Cot f Labor (Per.antaoss)

||is"6|Iwo|1075|iWo|1S0I|
|---|---|---|---|---|---|
|Latin<br>Aeric<br>|(1)<br>(2)<br>|(1)<br>(1)<br>|(1)<br>(2)<br> <br>|(1)<br>(2)<br>|(1)<br>(2)<br>|
|_Argntina_<br>|0.43 (0.48)<br>|0.33 (0.51)<br>|0.3<br>(0.50)<br>|0.46 (0.61)|0.4<br>(0."4)|
|Iraz1l<br>|0.54(0.0)<br>|0.54(0.58)<br>|0.85(O.56)<br>|_O.6 (O.")_<br>|0.U5(0.42)|
|C.l ib<br>|0.81 (0.10)<br>|0.43 (0.24)<br>|0.52(0.46)<br>|0.62 (0.50)<br>|0.62 (0.60)<br>|
|Chile|O.3(O.$*)|0.48 (0.40)|0.51 (0.42)|0.26 (0.21)|0.25(0.24)|
|muxlc<br>|0.37 (0.75)<br>|0.8? (0.67)<br>|0.n6(0.71)<br>|0.41 (0.57)<br> <br>|0.45 (0.50)<br>|
|Foru|0.24(0.87)|0.24(0.86)|0.24 (0.37)|0.16<br>(0.)|0.5 (0.13)|
|Atrip<br>||||||
|Keny|n.n.|0.1(0.10)|0.18 (0.71)|0.18(0.46)|0.18(0."4)|
|mer.<br>|B..<br>"|0.14 (0.64)<br>|0.10 (0.66)<br>|0.10 (0.77)<br>|0.10 (0.7")<br>|
|Malawi<br>|.e.<br>|0.14 (0.61)<br>|0.14 (0.66)|0.18_(0.84)_|0.18 (0.46)|
|_Nigirl_<br>|0.06 (0.31)<br> "|0.10 (0.20)<br>|0.10 (0.14)<br>|0.10 (0.16)<br>|0.10 (0.#1)|
|Tanzani<br>|0.10(O.5)<br>|0.10 (0.6)<br>|0.10(0."°)<br> <br>|0.12 (0.10)|0.12 (0.44)|
|Zombie|0.06 (0.11)<br>|0.06(0.24)|0.0<br>(0.84)|0.06 (0.86)|0.06 (0.S8)|
|Zimbabe<br>|0.15(0.78)<br> <br>|0.15 (0.66)<br> <br>|0.15(0.07)<br> <br>|0.10 (0.65)<br>|0.16(0.62)|
|India|0.12<br>(0.96)<br>|0.12<br>(1.06)|0.14<br>(1.24)|0.8(1.16)|0.15(1.08|
|Pakistan<br>|0.15<br>(0.90)<br>|0.15 (0.65)<br>|<br>0.15<br>(0.41)<br>|0.15<br>(0.56)|0.15 (0.42)|
|Sri Lanka|0.26 (0.71)|0.215 (0.6)|0.215 (0.53)|0.16 (0.54)|0.U3(0.88)|
|**E_Bt**Asia<br>||||||
|Hong Kng|0.11(0.06)<br>|0.11 (0.06)|0.15 (0.10)|0.20 (0.10)|0.20 (0.10)|
|Kor--<br>|0.20 (0.24)<br>|0.10 (0.21)<br>|0.20 (0.20)<br>|0.20 (0.21)<br>|0.20 (0.21)<br>|
|Singpore|0.11 (0.14)|0.14(0.06)|0.21 (0.14)|0.3" (0.12)|0.85 (0.17)|
|Europe<br>||||||
|Greese<br>|0.58 (0.58)<br>|0.60 (0.46)|0.55 (0.54)|<br>0.5 (0.6)|0.5(0.90)|
|Portugal|0.16(O.n)|0.11 (0.24|)0.24 (0.40)|<br>_0.n0_(0.87)|<br>0.80(0.85)|
|Austria<br>|||0.76 (0.77)<br>|0.61 (0.78)<br>|0.0<br>(0.76)|
|Prace<br>|||0.70 (0.65)<br>|0.74 (0.66)|O.68 (O.7n)|
|Germany<br>|||0.60 (0.74)<br>|O."(0.78)|0.75 (0.77)|
|*psla|||0.90 (0.65)|0.40 (0.67)|0.40 (0.67)|
|Swede"|||0.45(O.56)|0.02 (0.70)|0.6?(O.6)|
|United Kingde|||0.21 (0.33)|0.6<br>(0.51)|O.3(0.36)|
|USA<br>|||0.81 (0.43)|0.85(0.44)|0.87 (0.44)|
|Caned|||0.<br>(0.81)|0.13(0.3)|0.56<br>(0.85)|
|Japan|||0.14 (0.16)|<br>0.16(0.16)|0.17 (0.17)|

*** NUC are** expressed as a proportion of labor "reinlg. Sooe brackets, the ratio _formd_ by the year equivalent _mentary value_ **of IC of laber end** the hourly par capita incoe.

Souree: fer industrial ceoutries, Bure" of Laor Statistics: Hourly Ctomeention Cost for Produ"ion **Worers** (ratl of aditional comptenstio to hourly *ernlngs); IC figures are nt totally camperabbo bl-m vacation days **and** _vwAtion_ besuose are net considered in the **case** of Industrial countries.

For LDCo, ***s** AAn_Ix 1.
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 17 Table 8. The global length of protected coast under CBA Protection under all the climate and socio-economic scenarios from 2015 to 2030. Antarctica is not considered.

|Scenarios||Length|Protected|
|---|---|---|---|
|Climate|Socio-economic|Distance (km)|Proportion of globe<br>(%)|
||SSP2|1.95E+05|28.20%|
||SSP3|1.69E+05|24.47%|
|RCP8.5|SSP5|2.19E+05|31.75%|
||SSP2|1.79E+05|25.88%|
||SSP3|1.57E+05|22.76%|
|RCP4.5|SSP5|2.00E+05|28.88%|
||SSP2|1.69E+05|24.48%|
||SSP3|1.52E+05|22.06%|
|RCP2.6|SSP5|1.92E+05|27.80%|
# **The Growing Role of the Euro in Emerging Market Finance**

**by Paul R. Masson**<sup>*****</sup> **Rotman School of Management University of Toronto Toronto, ON M5S 3E6 CANADA**

**tel 416-946-0434, fax 416-978-5433 Email:** <u>paul.masson@rotman.utoronto.ca</u>

> *I am grateful to Benjamin Cohen, Mansoor Dailami and Gabriele Galati for comments and discussion.  An earlier version of this paper was prepared as a background study to the World Bank’s 2006 Global Development Finance report. The opinions expressed are those of the author and do not necessarily represent those of the World Bank Group or its Executive Directors.
_Connecting Lagging and Leading Regions: The role of labor mobility_ share of those migrants moving from the lagging Northeast to the leading Southeast region (World Bank 2008). And even today, young workers migrate in large numbers (Figure 1 shows migration of 25-35 year old men in 1991 and 2000).

Why people migrate depends on forces that “pull”, as well as those that “push” them to leave. One big pull is economic density in leading regions of countries. Differences in economic opportunity between lagging and leading regions often provide the main motivation for internal migration. On the other hand, people are “pushed” off their land where agriculture is in severe decline, by the pressures of population growth and where environmental change makes cultivation no longer viable. Historically, droughts have had sudden and prolonged impact on the distribution of the population, particularly in Sub-Saharan Africa<sup>2</sup> and South Asia.<sup>3</sup> Conflict has also pushed people to migrate across Sub-Saharan Africa, and in many other developing regions.

But in many low and middle income countries, another important “push” propels internal migration—the lack of adequate public services in rural areas or in economically lagging regions. To a large extent, this topic has been overlooked in empirical analysis of migration decisions. However in reality, the location of schools, health care centers, hospitals, and public and private amenities is correlated with the location of economic activity. In Africa, disparities in school enrollment and neo-natal care between cities, town and villages are attributable to the near absence of schools and health facilities in outlying areas.<sup>4</sup> Evidence from Central Asia shows that in the isolated parts of Tajikistan, schools are inadequately heated, drinking water is scarce, and there are no arrangements to clear garbage and sewage.<sup>5</sup> So as market forces encourage the concentration of economic mass, public services are under-provided in smaller towns, villages, and lagging regions.<sup>6</sup> Although voluntary, migration in response to limited access to public

> 2 Iliffe (1995) on the historical impact of drought on population distribution in Africa; Bryceson (1999) on the Sahel and Sudan; and Hardoy and Satterthwaite (1989) on Mauritania.

> 3 Wandschneider and Mishra (2003), cited in Deshingkar and Grimm (2004), on the drought-induced migration of 60,000 people out of Bolangir, in the Indian state of Orissa, in 2001. 4 Sahn and Stifel (2003).

> 5 Anderson and Pomfret (2005).

> 6 Venables and Kanbur (2005).

4
participated in the first and second study only, and one that participated in both studies. Two broad trends emerge regardless of which definition of value added was used. First, the control group selected for the first study was more comparable to the treatment group – in terms of average pre-participation labor productivity – than the control group for the second study. Firms in the second control group had labor productivity levels that were over twice that of the sample of CIMO firms; in contrast, the first study’s control group had productivity levels that were “only” 50 percent higher than that of CIMO firms. It would be a formidable challenge for any program, however effective, to overcome such a large initial productivity disadvantage for the group of CIMO firms. And it suggests that greater attention should be placed on selecting more appropriate (and comparable) control groups for evaluation studies.

**Table 3.  Mean Labor Productivity**

|**Groups**|**Value**|**added Per**|**Worker**|**Production**|**value minus mat**<br>**worker**|**erials cost per**|
|---|---|---|---|---|---|---|
||**1991**|**1993**|**1995**|**1991**|**1993**|**1995**|
|_Firms only in_|_the first st_|_udy_|||||
|Non-CIMO|47,391|34,964||63,898|49,140||
|CIMO|31,741|35,146||35,078|37,024||
|_Firms only in_|_the second_|_study_|||||
|Non-CIMO||178,734|207,989||186,299|225,120|
|CIMO||79,704|84,114||71,735|78,964|
|_Firms in both_|_studies_||||||
|Non-CIMO|46,738|65,459|128,253|57,752|55,658|113,618|
|CIMO|30,874|51,815|51,372|38,174|38,884|51,203|

_Source:_ Bank staff calculations. _Note_ : All figures in 1994 Mexican pesos.

The second point that emerges, abstracting from level differences, is that labor productivity in both treatment and control groups rises over time. CIMO participants improve their post-program performance relative to their **own** pre-participation levels, particularly in the 1991-1993 period, but because their productivity grew faster than that of the control group, in which growth rates were actually negative, the productivity gap between CIMO and non-CIMO firms shrank over the 1991-1993 period. This would indicate a positive net impact of program participation on performance. However, the data for the 1993-95 period is mixed – labor productivity in both groups grew, and the productivity gap did not fall – suggesting an 11
# Where do the unbanked get basic financial services?

The unbanked in both countries tend to rely on cash transactions and on services provided by commercial outlets, although in México the unbanked rely on informal forms of saving and borrowing not present in the U.S. At the same time, the unbanked in México show a high rate of home ownership, suggesting an alternative form of saving not present in the U.S.

In both countries the majority of unbanked are paid by check or by deposit to a third party account. Less than a fifth of the U.S. group and a tenth of the Mexican group are paid in cash. This suggests an important cost to the unbanked in transaction costs. At the least it involves a trip to a bank, at most, a commission paid to cash a check or to access a third party account.

|**Payments received**|**USA**|**Mexico**|
|---|---|---|
|By Check|50.5%|85.6%|
|_(Check cashing operations charges 2-3%)_|||
|In Cash|18.8%|7.8%|
|Direct bank deposit|16.6%|6.3%|
|(to third party account)|||

The unbanked in both countries face additional costs in making payments. In the U.S. unbanked persons generally rely on payment services associated with the “check cashing outlets”, a service industry which has grown up in response to the unbanked’ needs. Among the “CCOs” those associated with “payday lending<sup>4</sup> ” have been under investigation recently for predatory lending. In Mexico the unbanked carry out their transactions in cash at a cost of travel and time, although focus groups also report that the unbanked frequently circulate signed checks as a form of cash or cash checks in stores where they also (by obligation) make purchases.

|**USA- How the Unbanked make payments**|**Mexico- How the Unbanked make payments**|
|---|---|
|Cash 36%|Cash 95-99%|
|Check 5%|Check 2-3%|
|Money order/||
|Bill payment services 35%||
|(_Mostly through check cashing operations)_||

In terms of borrowing, surveys looked at what credit sources the unbanked have used in the previous three years. Interestingly, family, friends and third parties make up the principal sources of loans, in both countries suggesting the importance of informal support network for marginalized groups in the United States. (Recall that a high percentage of the unbanked are recent immigrants and minority groups.) In Mexico, friends cover about the same percentage for credit needs as in the US (between 8-9%) but the retail stores lead in importance as lenders to about half of the unbanked population. Government programs –absent in the US - also have a small but significant presence providing credit to about 6% of the unbanked in Mexico.

> 4 Payday lending actually refers to loans made between paychecks, guaranteed by the turn over of the paycheck to the lender on payday.

8