rafmacalaba/datause-encoder-data huggingface dataset:
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics migrating, formal sector workers tend to avoid adversely affected regions. These findings parallel those of two recent papers studying workers’ location choices in the United States during the Great Recession. Yagan (2014) finds that out-migration does not respond to local labor demand shocks, while Monras (2014) finds that in-migration declines in locations facing labor demand declines. Interestingly, even though 5-year gross migration rates for working-age individuals are large (7.3% between 1995 and 2000), net migration rates are not affected by the trade-induced local shocks. Therefore, it seems that migrants keep moving to harder-hit locations, but they avoid the formal sector in these locations. This pattern results in formal sector net migration (as measured by RAIS) responding to the shocks, whereas overall net migration does not (as measured by Census population). These findings imply that the substantial regional labor adjustment we documented earlier occurred primarily through transitions between formal and informal employment rather than through interregional migration.<sup>38</sup> Recall that informal employment tends to be an absorbing state in the Brazilian context, with very large barriers to shifting from informal to formal employment (Dix-Carneiro 2014).

# **6.4 Earnings**

Along with employment, we consider the effect of the regional trade shock in a worker’s initial location on subsequent formal earnings. Keep in mind that formal earnings effects are likely to be upper bounds on the overall earnings effects, as workers losing formal earnings may partially offset these losses through earnings in the informal sector (which cannot be observed in RAIS). However, we present these formal earnings results for completeness.

For each year _t_ from 1990-2009, we calculate a worker’s average earnings from 1990 to _t_ as a multiple of the worker’s average pre-liberalization (1986-89) annualized earnings: The numerator is the worker’s average post-liberalization earnings from 1990 to _t_ , and the denominator is the worker’s average pre-liberalization annualized formal sector earnings from 1986 to 1989. Note that formal earnings may decline due to lower wages or less time worked in the formal sector. We use this measure because it accounts for worker heterogeneity in initial earnings while still being well defined for workers with zero earnings after 1989, avoiding sample selection issues.

> 38Morten and Oliveira (2014) argue that road construction increased internal migration in Brazil by lowering mobility costs. Such a policy response would be unlikely to increase the mobility response to liberalization, however, because more positive regional tariff shocks tended to occur in traditional migration sending (low wage) locations rather than receiving (high wage) locations.

27
knew SBI to be rupee rich and hence never questioned its ability to meet all debt service obligations in rupees.

Still other factors supporting purchases of diaspora bonds include the satisfaction that investors reap from contributing to economic growth in their home country. Diaspora bonds offer investors a vehicle to express their desire to do "good" in their country of origin through investment. Furthermore, diaspora bonds allow investors the opportunity to diversify their assets away from their adopted country. Finally and somewhat speculatively, diaspora investors may also believe that they have some influence on policies at home, especially on bond repayments.

# **V. Conditions and Candidates for Successful Diaspora Bond Issuance**

The sizeable Jewish and Indian diasporas in the United States, Europe and elsewhere have contributed to the success of Israel and India in raising funds from their respective diaspora. Many members of these diaspora communities have moved beyond the initial struggles of immigrants to become quite affluent. In the United States, for example, Jewish and Indian communities earn among the highest levels of per capita incomes. In 2000, the median income of Indian-American and Jewish households in the United States was $60,093 and $54,000, respectively, versus $38,885 for all U.S. households.<sup>9</sup> Like all immigrants, they are also known to save more than the average U.S. savings rate. As a result, they have sizable amount of assets invested in stocks, bonds, real estate and bank deposits.

Many other nations have large diaspora communities in the high-income OECD countries (Table 4).<sup>10</sup> The presence of tens of millions of Mexican nationals in the United States is quite well known. The Philippines, India, China, Vietnam and Korea from Asia; El Salvador, Dominican Republic, Jamaica, Colombia, Guatemala and Haiti from Latin America and the Caribbean; and Poland from Eastern Europe have significant diaspora presence in the United States. Diaspora presence is also significant in other parts of the world, e.g., Korean and Chinese diaspora in Japan; Indian and Pakistani diaspora in

> 9 National Jewish Population Survey (NJPS) of 2000/01 and the U.S. Census Bureau.

> 10 Data on migration stocks tend to be incomplete and outdated. Recent efforts to collect bilateral migration data in major migration corridors are summarized in Ratha and Shaw (2007).

16
39 Bauer and B.S. Yamey 'Economic Progress and Occupational Distribution,' Economic Journal 61, December, 1951. This exception is explained by three arguments. First, the occupational or industry of employment data is inadequate because it assigns individuals to one category, when they may actually be engaged in many, due to imperfect market specialization. Second, the shift between unpaid family production and paid activities can be misleading, and is not measured comparably across countries or over time. These intrinsic problems with interpretations of industry and job-type data across countries have hardly diminished with four decades of data improvements, and should be kept in mind as a serious limitation to the precision of analyses such as are reported in this paper. Bauer and Yamey also dispute Clark's presumption that the income elasticity of consumer demand for manufactured goods exceeds those for primary goods, and that the income elasticity of demand for services also exceeds those for manufactured goods. Subsequent intercountry comparisons by Simon Kuznets and others have given general support to Clark's patterns, but also note the anomalous size and growth of the service sector in many low income countries in the postwar period. Se "Quantitative Aspects of the Economic Growth of Nations: II, Industrial Distribution of National Product and Labor Force," Economic Development and Cultural Change 5 (Supplement) (1957) and Economic Growth of Nations (Cambridge, MA: Harvard University Pres, 1971); A.S. Oberai, Changes in the Structure of Employment with Economic Development, Geneva, Switzerland: IIO, T. P. Schultz (Note 2).

5. Boserup (see Note 3); J. D. Durand, The Labor Force in Economic Development (Princeton, NJ: Princeton University Press, 1975); N. Folbre,
**_Energy demand models for policy_** **_<u>formulation</u>_**

Siddayao, C. M., 1986, Energy demand and economic growth: Measurement and conceptual issues in policy analysis, Boulder, West-View Press.

Sterner, T., 1991, Gasoline demand in the OECD: choice of model and data set in pooled estimations." OPEC Review 15(2): 91-102.

- Suganthi, L. and A. Williams, 2000, Renewable energy in India – a modelling study for 2020-2021, Energy Policy, 28, 1095-1109.

- Suganthi, L. and T. R. Jadadeesan, 1992, A modified model for prediction of India’s future energy requirement, Energy & Environment, 3(4), pp. 371-86.

- Sun, J. W., 2001, Energy demand in the fifteen European Union countries by 2010 – a forecasting model based on the decomposition approach, Energy, 26, pp. 549-560.

Sweeney, J.L., 1983, Energy modeling comparison: An overview, EMF PP6.4, Energy Modeling Forum, Stanford University, California.

Swisher, J. N., G. M. Jannuzzi, and R. Y. Redlinger, 1997, Tools and methods for integrated resource planning: Improving energy efficiency and protecting the environment, UCCEE, Riso (see http://www.uneprisoe.org/IRPManual/IRPmanual.pdf).

Tanatvanit, S., B. Limmeechokchai and S. Chungpaibulpatana, 2003, Sustainable energy development strategies: implications of energy demand management and renewable energy in Thailand, Renewable and Sustainable Energy Reviews, 7(5), pp. 367-95.

- Taylor, L., 1975, The demand for electricity – a survey, The Bell J of Economics, 6(1), pp. 74-110.

Tiwari, P., 2000, An analysis of sectoral energy intensity in India, Energy Policy **28** , (11), pp. 771–778 119
is similar to that of cross-country indicators, which are often based on survey averages at the country-year level.

For some aspects of the local BE, there are no good objective measures, and researchers rely on subjective assessment of various BE aspects. Typically, the survey asks, “to what extent is XX (i.e., a generic name for a specific BE area) an obstacle to the development of the firm?” and the answer is scaled from 0 to 4, where a higher number implies a more severe constraint. To aid interpretation, for each obstacle question, we construct the dummy variable of moderate or severe obstacles for that area (i.e., the values of 3 and 4), and call it XX Obstacles. We then calculate the city-average of XX Obstacles, and view it as the local BE indicator on this XX aspect.

We control for country fixed effects, which capture unobservable differences across countries that are important for firm performance. Therefore, our identification derives from linking differences in firm performances across different city-industry cells to the city-industrylevel BE, while holding country-, industry- and year-level factors constant. Though we have gone perhaps further than others in the BE literature in controlling for omitted factors while employing a global sample, we still face the issue of potential city-industry-level omitted variables. It is conceivable that firms with favorable omitted characteristics may be sorted into cities with particular types of BE, which, as a result, may cause inconsistent estimates.

By relying on observational data, we acknowledge that endogeneity concerns—especially in the context of a horse race between various alternative explanations—can never be completely eliminated. Since many conventional instruments proposed for a particular variable (such as institutions) are often correlated with other channels in the residual of the performance equation (Bazzi and Clemens 2010; Morck and Yeung 2011), and in our context, we try to run a horse race between a long list of potential factors in determining firm performance, we do not believe the instrumental variable approach is feasible here. We thus instead aim to offer a coherent story to tie various findings together (Rosenbaum 2010).

# **Classifying Key Indicators**

As discussed earlier, we classify our key BE indicators into three distinct categories for ease of interpretation: Basic BE, Refined BE, and Agglomeration Environment (see Figure 1).

10
**Table 8: Cumulative Changes in Sectoral Production 2007-2050 due to Forecasts and the Liberalization of Migration Policy ($US Millions)**

||**Chin**|**a**|**Hong Kong S**|**AR, China**|**Japa **|**n**|**Malay **|**sia**|**Singapo **|**re**|**Thaila**|**nd**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Forecasts**<sup>**a**</sup>|**Liberal**|**Forecasts**|**Liberal**|**Forecasts**|**Liberal**|**Forecasts**|**Liberal**|**Forecasts**|**Liberal**|**Forecasts**|**Liberal**|
|**Rice**|74477|10|5|0|1468|12|784|-8|-1|0|60393|2|
|**Wheat**|55777|7|19|0|1138|-2|9|0|-7|0|52|0|
|**Grains Crops**|651722|-223|1059|7|11103|38|4076|-5|-203|-4|76256|-15|
|**Cattle and Wool**|112796|-40|940|4|4252|12|369|-1|-1|0|1990|-1|
|**Other Animals**|489377|282|5324|49|3372|24|5247|10|-12|0|10243|13|
|**Meat Products**|150836|78|711|61|3261|5|-264|14|127|-3|18422|13|
|**Processed Rice**|75832|10|2|1|1267|14|197|-1|92|-2|32349|16|
|**Other Food**|854926|98|24964|1074|136505|394|82565|-578|9144|-52|53530|14|
|**Forestry**|207409|-25|17|0|6609|-18|12447|-9|-9|0|8281|-1|
|**Extraction**|613449|7|71041|67|27507|2|46065|-5|-70|0|84474|1|
|**Textiles**|1306142|-1977|73981|4225|-11676|-788|8274|-168|-502|-52|39421|-143|
|**Apparel**|701527|-379|29541|3426|2928|-99|2324|-39|-395|-25|25869|-15|
|**Leather Products**|354667|-8|3032|361|-1060|-18|1470|-8|-211|-20|10443|-6|
|**Wood and Paper Products**|943963|-306|68036|1682|96975|90|35254|-180|3069|-107|40764|-45|
|**Motor Vehicles**|435941|-181|35741|300|307680|-483|61687|-185|1312|-15|131973|-34|
|**Electronics**|2068471|3604|133943|1008|14011|1806|944064|-2440|54847|-572|185559|251|
|**Other Machinery and equip**|1085880|-647|157785|1221|161595|-151|148953|-983|-11232|-370|76582|-27|
|**Petroleum and Coal Products**|595971|-136|3172|2|45003|121|52118|-206|41041|121|93406|-49|
|**Chemicals, Rubbers and Plastics**|1814282|-185|164222|1773|123697|199|266086|-699|52096|-473|172358|20|
|**Metals**|539549|-159|-5461|-639|130277|831|36629|-277|13881|-34|10843|1|
|**Metal Products**|289791|11|25143|348|71600|391|25032|-133|6313|-65|13118|1|
|**Other Manufactures**|1032318|1210|5392|62|71440|612|156469|-666|11916|-128|41634|2|
|**Utilities**|530825|-65|47415|961|87727|264|51837|-213|4340|-24|49813|-10|
|**Construction**|75593|-403|250696|1164|632354|3214|77886|-139|53229|219|77574|-111|
|**Transport and Communications**|3087662|-2775|1937158|38613|1491577|1243|206060|-1078|88027|-538|245549|-282|
|**Business Services**|1414860|104|267193|9785|772761|1771|131407|-855|87865|-458|150526|-38|
|**Govt, Health and Educ**|2836662|978|105254|3920|838907|2874|127815|-895|22710|69|179395|17|

Note: a. These are results determined by the model corresponding to forecasts used in the baseline simulation.

37
to analyze chronic poverty in 17 LAC countries for which harmonized cross-sectional micro data are available.

The LASSO-PMM approach introduced in this paper is more flexible and entails fewer assumptions compared to these two methods. As such, it does not require estimating any error correlation terms or assuming a binormal distribution of residuals in the underlying regressions as in Dang and Lanjouw (2013). In addition, the LASSO-PMM method not only provides transition probabilities, but also predicts income growth for every observation between two rounds of crosssectional data as if we had actual panels. Finally, unlike Dang et al. (2014), the LASSO-PMM method obtains point estimates instead of bounds of welfare dynamics.

We use 36 panels from four countries to validate our estimates of mobility using harmonized variables frequently used in many regional and global studies, which facilitates implementation by researchers and policymakers in developing countries. The LASSO-PMM approach for predicting poverty and mobility approximates observed welfare changes. In general, validation results show that LASSO-PMM predictions are statistically indistinguishable from actual poverty rates, transitions, and income changes calculated using panel data. A series of validation exercises support the method’s predictive performance. We divide these exercises into: i) _sensitivity tests_ , in which the actual poverty rates and transitions do not change; and ii) _robustness exercises_ , when we change the estimation procedure, sample, or other attributes that change the actual welfare estimates. Both of these validation exercises show that the LASSOPMM approach for predicting poverty and mobility accurately approximates actual welfare changes. The two main conclusions from this validation are: i) changing parameters to make the method more computationally intensive provides only marginal changes in estimates; and ii) model specification matters. All these findings are sufficiently encouraging in general to suggest 4
Policy Research Working Paper 10778

# **Abstract**

This study examines the impact of farm input subsidies, food and cash transfers, and irrigation investments on the dietary diversity, food consumption scores, and coping strategy index in Malawi. Despite the potential for synergies to address a range of vulnerabilities affecting food consumption, very few studies focus on combined program effects. The analysis employs three-waves of integrated household panel surveys for Malawi from 2013, 2016, and 2019, and uses instrumental variable Poisson and Tobit regression to address endogeneity. The findings show weak joint program participation effects, which may be due to program design or data limitations in this evaluation. Households that receive food and cash transfers showed improvements in diet diversity and the food consumption score. Input subsidies were less effective in helping households cope with food insecurity and reduced diet diversity and the food consumption score. This suggests that overreliance on agricultural input subsidies may lead to reduced variety in food consumption. Policies that are aimed at more linkages between programs should also diversity and rebalance public spending to reduce food and nutrition insecurity.

This paper is a product of the Agriculture and Food Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mirriammatita@gmail.com.

_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
# Table 2: Summary Statistics

|_Average before episode of_<br>**Latent Conflict**|||
|---|---|---|
|Ethnic Income Inequality|0.472|0.578|
|Natural Resource Rents (% of GDP)|8.291|13.099|
|Ethnic Fractionalization|0.480|0.549|
|Religious Fractionalization|0.445|0.387|
|Spatial inequality|0.456|0.534|
|Male 15-29 Pop. Share|0.136|0.137|
|History of Conflict|0.077|0.116|
|**Institutions**|||
|CPIA|3.749|3.393|
|Freedom House|3.730|4.739|
|Political Terror Scale|2.265|2.685|
|Log GDP per capita|8.392|7.873|
|Log Population|15.542|15.954|
|**Shocks**|||
|GDP per capita Growth|0.024|0.016|
|TOT change|0.012|0.010|
|Natural Disaster|0.487|0.551|
|Neighbors in Conflict|0.490|0.720|
|No. obs.|422|69|
|Notes: This table reports means of the correlates of conflict consid|ered in the paper, separate|ly by conflict and non-conflict episodes.|

37
# Table 14: Program Impact on Child Participation in Fetching Wood

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||<br>|6-15 years old<br>|<br>|<br>|6-12 years old<br>|<br>|1<br>|3-15 years ol<br>|d<br>|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
|School||||||||||
|Meals|0.064|0.108|0.011|0.081|0.107|0.054|0.009|0.11|-0.13|
||[0.026]**|[0.033]***|[0.039]|[0.029]***|[0.037]***|[0.043]|[0.060]|[0.079]|[0.091]|
|Dry Rations|0.056|n.a.|0.035|0.079|n.a.|0.072|-0.026|n.a.|-0.083|
||[0.033]*|n.a.|[0.039]|[0.037]**|n.a.|[0.043]*|[0.073]|n.a.|[0.087]|
|Follow-up|-0.077|-0.1|-0.05|-0.096|-0.114|-0.081|-0.021|-0.05|0.034|
||[0.018]***|[0.024]***|[0.027]*|[0.020]***|[0.026]***|[0.030]***|[0.041]|[0.055]|[0.062]|
|Girl|0.204|||0.181|||0.292|||
||[0.013]***|||[0.014]***|||[0.029]***|||
|Constant|0.048|0.113|0.193|0.06|0.114|0.188|0.322|0.332|0.619|
||[0.019]**|[0.025]***|[0.025]***|[0.020]***|[0.026]***|[0.027]***|[0.040]***|[0.056]***|[0.049]***|
|Observations|6047|2462|3585|4757|1931|2826|1290|531|759|
|R-squared|0.17|0.11|0.16|0.16|0.11|0.17|0.19|0.20|0.14|

# Robust standard errors in brackets

* Significant at 10%; ** significant at 5%, *** significant at 1% Dependent variable is whether child has fetched wood the week before the survey.

Regressions also control for child age, orphanhood, parents education, ethnicity, religion, household assets and village fixed effects (not reported).

37
Table 6: COVID-19 and Electricity Consumption in Ghana: Average Effects

|||||logof Co|nsumption||||
|---|---|---|---|---|---|---|---|---|
||A|ll|Resid|ential|Non-Res|idential|HeavyI|ndustries|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|I (Apr-Dec 2020)|0.161***|0.212***|0.176***|0.228***|0.028***|0.071***|-0.159***|-0.126***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.002)|(0.015)|(0.014)|
|I (Apr-Dec 2019)|-0.017***|0.036***|-0.016***|0.037***|-0.020***|0.026***|-0.041***|-0.005|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.002)|(0.014)|(0.013)|
|I (Apr-Dec 2018)|-0.047***|-0.009***|-0.049***|-0.010***|-0.035***|0.001|-0.035**|-0.008|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.002)|(0.015)|(0.015)|
|DID|0.145***|0.189***|0.160***|0.205***|0.021***|0.058***|-0.155***|-0.127***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.003)|(0.003)|(0.018)|(0.018)|
|Climate Ctrls|No|Yes|No|Yes|No|Yes|No|Yes|
|Customer FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District X Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.709|0.710|0.685|0.686|0.774|0.774|0.858|0.858|
|Mean dep. Var|383.1|383.115|123.781|123.781|419.210|419.210|152470.284|152470.284|
|Obs|41958788|41958777|37299880|37299869|4602780|4602780|52317|52317|

Notes: Dependent variable is the log of monthly electricity consumption. I (Apr-Dec 2020) is an indicator variable equal 1 for the period April 1- December 31 2020. Likewise, I (Apr-Dec 2019) and I (Apr-Dec 2018) are indicator for the same period April 1- December 31 in 2019 and 2018 respectively. The reference group is the period Jan-Mar in 2018, 2019, and 2020. DID estimates are derived from the expression _DID_<sup>_ω_</sup> = _β_<sup>ˆ</sup> 2020<sup>_ω−_</sup><sup><u>1</u></sup> 2 �2019 _k_ =2018<sup>_β_ˆ</sup> _k_<sup>_ω_.Climate Ctrls include the monthly</sup> average temperature and total precipitation. Standard errors clustered at district level in parenthesis. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 32
13

# 3. **How** Does DeRosit Insurance Affect Secondary **Market** Prices ?

## 3.1 A Model

This secti6ot-presents a model of the influence of deposit insurance on the pricing of developing country debt. In particular, the model examines how a representative bank that can trade in the secondary **market and that** is subject to deposit insurance, capital **requirements and accepted accounting** principals. Subsequently, the model is simulated by using exposure data for the largest U.S. banks.

In this two period model, the bank's loan **portfolio consists of two kinds f** of loans: domestic and foreign. The bank's exposure to country **i, Lt, is** long term and given by history in the first period. Country **i defaults with** probability **n** d, in which case the bank will not receive **any** payment from the country. In case of no default, foreign **loans pay** the **principal and the** f **h** foreign interest rate r. . The domestic loan portfolio, denoted L , **is** h short-term yielding a certain domestic return r . The bank **can adjust its** domestic loan portfolio by making new loans in the first period, and adjust its foreign loan portfolio by engaging in trading in the **secondary markets.** Deposits, denoted D, are assumed to be partly insured by a deposit **8** insurance agency and the deposit holders are risk neutral . **The average,** e9 effective interest rate, r , that banks have to pay to attract deposits is: where, dS and du are the shares of insured and uninsured **deposits,**<sup>**and rs**</sup> and r are the safe interest rate and the rate on uninsured<sup>deposits</sup> respectively.

Assuming that the bank will be closed in the future **period if the**