rafmacalaba/datause-encoder-data huggingface dataset:
documented a 16 percent reduction in consumption per adult in households that reported a serious drought shock in the previous two years (Dercon, 2004). Reardon and Taylor (1996) find that poverty increased in Burkina Faso’s agro-ecological zones of Sahelian (2 to 19 percent) and Sudanian (12 to 15 percent) following the 1984-85 drought. Drought in Malawi is shown to reduce household consumption per capita by a third (McCarthy, Brubaker, and de la Fuente, 2016), and a moderate drought that causes a 30 percent yield loss is predicted to reduce consumption by 15 percent and 9 percent in Uganda and Ethiopia, respectively (World Bank 2015, 2016).

Much less evidence exists around the socioeconomic impacts of experiencing successive weather shocks, which in principle makes it even more difficult to smooth consumption (Deaton, 1992; Dercon, 2002). For instance, there is a general consensus that the 1998 Bangladesh floods had a lower impact on the affected population than the 1988 floods, even though the 1998 floods were of a considerably longer duration in most places. One of the reasons for this was that prior to 1988 there had been two major floods, in 1987 and 1984, which left many poorer households in a precarious situation and unable to recover their pre-disaster situation before the next disaster occurred (Beck, 2005).

Furthermore, there is limited evidence on the quality of targeting of large-scale humanitarian relief efforts, and the effectiveness of these efforts in helping households mitigate the adverse impacts of extreme weather events. Understanding the effectiveness of these mechanisms, and how they might be improved, is precisely the type of information we need to better inform humanitarian relief activities and to develop and implement effective climate change adaptation strategies. Yamano et al. (2005) found that food aid offset the surge in malnutrition among children (0.5 to 2 years old) tied to the harvest failure following the 1995-96 drought in Ethiopia. World Bank (2010) reported that households that were affected by the 2007-08 drought in Ethiopia and that also received transfers from the Productive Safety Net Programme (PSNP) consumed 30 percent more calories than non-beneficiaries.

3
Policy Research Working Paper 6998

# **Abstract**

Aid is good for the poor. This paper uses detailed aid data spanning 60 developing countries over the past two decades to show that social aid significantly and directly benefits the poorest in society, while economic aid increases the income of the poor through growth. This new and unequivocal finding distinguishes the current study from past studies that only utilized aggregate aid data and returned ambiguous results. The paper also confirms that none of the elements of globalization (trade, foreign direct investment, remittances), policies (government expenditure, inflation management), institutional quality, nor other plausibly pro-poor factors have systematic effects on the poor or any other income group, beyond their effects on average incomes. The paper finds that trade and foreign direct investment tend to benefit the richest segments of society more than other income groups. Therefore, the presented evidence suggests that aid can play a crucial role in enabling the poor to benefit more from globalization. These discoveries underscore the need to assist developing countries to find the mix of economic and social aid that jointly promotes the participation of the poor in the development process under globalization. In this manner, aid can make greater strides in spurring development.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at yhirano@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
the data, in the full analysis we use the median value of observations of each variable over the period 2010 to 2014.

Of particular interest are the relationships between the composite variables we have constructed using PCA to measure the use of digital technology ( _lint_usage_ for general, household usage, and _ldig_bus_pca_ for business usage), and employment protection legislation (the full composite of the Doing Business indicators _lepl_db_ , and four other PCA variables that each capture different dimensions of employment protection, _dif_hir_inx_ : difficulty of hiring; _rig_hours_ : rigidity of hours; _dif_firing-idx_ : difficulty of firing index; _firing_cost_idx_ : dismissal cost index). Readers should note that our overall employment protection composite variable _lepl_db_ , is the result of PCA applied to 16 labor market regulation indicators of the Doing Business database, and not to the four other PCA composite variables. Each of the four that describes difficulty of hiring, rigidity of hours, etc., results from applying PCA to a relevant sub-set of labor market regulation indicators from Doing Business database. In electing which variables to include in each PCA index, we followed the Doing Business classifications, with one exception: we grouped v34 “Paid annual leave (working days) - 10 years” in our dismissal costs index, since in many countries firms are obliged to cash out unused leave as part of the total severance entitlement due to a dismissed worker.

As is reasonable to expect from the application of a new technique to construct composite measures of countries’ existing labor regulations, our PCA variables of employment protection policies from Doing Business are significantly similar -but not identical- to the de jure labor regulation measures of the earlier-established OECD and ILO methodologies.

Before turning to our results, an analysis of simple visual representations of the relationship between key variables helps to establish the quality of the data, particularly the variables resulting from our application of PCA (additional figures are provided in the annex). Figure 1 presents how our two PCA composite measures of digital technology use are related to countries’ levels of development, as proxied by the level of GDP per capita (in 2011 PPP US$), general household usage in the top panel and business usage in the bottom panel. As is reasonable to expect for a very diverse sample of countries, both measures of digital technology usage are strongly and positively associated with a country’s level of economic development. The countries of the Latin America and Caribbean region do not exhibit a very different relationship from the rest in our sample. Although the strong, positive relationship comes as little surprise, it gives us confidence in the index variables we have constructed with PCA.

10
Gottschalk and Moffitt (1998) found from Michigan Panel Study of Income Dynamics (PSID) that households with lower earnings, lower educational levels and lower marriage propensities are more prone to attrition. Their dataset had a high 50 percent attrition rate, still they found that there is no relationship between attrition rate and magnitude of attrition bias and that even large attrition causes unbiased estimation if attrition is random.

One can formally test if attrition biases estimates, namely, if reduced sample size because of attrition matters in our estimation. The issue we would like to investigate is, what are the determinants of thana attrition and to what extent attrition biases the estimates? We carry out a formal test for attrition bias. We regress per capita consumption of 2000 on the exogenous X-variables (age, sex and education of household head, maximum education of household’s adult males and females, and so on), attrition dummy (meaning 1 for those thanas which were absent in 2005 and 0 otherwise) and attrition dummy interacted with X- variables. Since our purpose is to determine whether coefficients of the explanatory variables differ for those thanas that were dropped from 2005, we will perform a joint significant test (F-test) of the attrition dummy and its interaction variables. A similar fit will be made for the HIES survey of 2005, where attrition dummy includes the thanas excluded in 2000 HIES survey.

# **4. Does income seasonality affect consumption?**

This section discusses the results of the impact of income seasonality on consumption. Table 5 presents summary statistics of the outcome and explanatory variables. As Table 5 shows, most welfare indicators – both at household and village levels – improve from 2000 to 2005. For simplicity, the country is divided into two regions—greater Rangpur and the rest of the country. Although seasonal shares of crop income capture seasonality of 19
Act of 1947, is a piece of federal legislation governing industrial policies, there have been several amendments at the state level since its adoption. All states started at the same point but diverged over time (Besley and Burgess, 2004) such that today employeremployee relations are governed by quite different rules. Minimum wage legislation in Table 5 shows one aspect of disparity between the states.

# **Table 5:  Minimum Wage Schedules in Selected Indian States**

|**Central**<br>**Government/States/Union**<br>**Territories**|**No. of Scheduled**<br>**Employments for**<br>**which Minimum**<br>**Wages have been**<br>**fixed/revised**|**Range**<br>**Wages**<br>**Min ***|**of Minimum**<br>**per day (in Rs.)**<br>**** Max**|
|---|---|---|---|
|Andhra Pradesh|62|25.96|78.77|
|Goa|18|21|140.26|
|Kerala|35|30|184.26|
|Madhya Pradesh|36|51.8|74.34|
|Maharashtra|63|8.46|119.35|
|Orissa|83|40|40.4|
|Uttar Pradesh|65|58|83.42|
|West Bengal|45|48.22|96.17|

_The information is based on the notifications received in Labour Bureau till 12.06.2001 from different States/Union Territories and excludes wages fixed on piece rate basis. India has minimum wages that vary by industry._ _Source:_ _<u>http://labourbureau.nic.in/wagetab.htm</u>_ While all states in India collect property taxes, states are now putting forth new initiatives in an effort to improve the transparency and effectiveness of their tax collection methods; yet these initiatives are independent of each other. The traditional method of levying property taxes was based on the annual rental value of the property. In practice this value was not estimated through the “market” but relied a great deal on 22
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Table 4 reports returns for two groups of households. The two groups are formed based on the magnitude of their hazard ratios for the transaction cost variables, which are estimated from US household level hazard regressions described in Section 5. Hazard ratio on adjusted Amihud’s illiquidity ratio, AdjIlliq, is used as a proxy for how much attention the individual pays to transaction costs. To get robust estimates, households are required to have made at least 50 trades during the sample period to be included in the analysis. 1-, 6-, and 12-month returns are calculated starting from the date of purchase. Characteristics adjusted returns are calculated as returns net of characteristics matched portfolios, as in Daniel et al. (1997). Spread is estimated as in Barber and Odean (2000) and is equal to trading price divided by the CRSP closing price minus one for sales. The ratio is multiplied by minus one for purchases. Round trip spread is the sum of sale and purchase spreads and is reported below. Transactions with a purchase or sale price less than $2, and holding periods less than 2 days, are excluded from the sample. Panel A reports returns for the two groups where the hazard ratio of the transaction cost variable, AdjIlliq, is estimated with a minimum significance level of 10%. Panel B reports returns for the full sample. Statistical significance at the 10%, 5% and 1% levels is denoted by *, **, and ***, respectively.

|Panel A: Observations with Ad|jIlliq Hazard Ratio at>10%<br>|Significance<br>||
|---|---|---|---|
||Group 1|Group 2|Group 1 - Group 2|
||Pays Attention to<br>Transaction Costs|Doesn't Pay Attention<br>to Transaction Costs||
|1 Month return|0.018|0.018|0.001|
|1 Month return Char. adj.|-0.001|-0.001|0.001<br>|
|6 Month return|0.079|0.066|0.013<sup>***</sup><br>|
|6 Month return Char. adj.|-0.001|-0.002|0.010<sup>***</sup><br>|
|12 Month return|0.161|0.132|0.029<sup>***</sup><br>|
|12 Month return Char. adj.|-0.010|-0.035|0.025<sup>***</sup><br>|
|Spread (%)|0.675|3.457|-2.782<sup>***</sup><br>|
|HoldingPeriod(days)|100.0|156.9|-56.9<sup>***</sup>|
|Panel B: All Observations|Group 1|Group 2|Group 1 - Group 2|
||Pays Attention to|Doesn't Pay Attention||
||Transaction Costs|to Transaction Costs||
|1 Month return|0.018|0.017|0.001<sup>**</sup>|
|1 Month return Char. adj.|-0.001|-0.002|0.002<sup>**</sup><br>|
|6 Month return|0.079|0.070|0.009<sup>***</sup>|
|6 Month return Char. adj.|-0.010|-0.019|0.009<sup>***</sup>|
|12 Month return|0.162|0.146|0.016<sup>***</sup>|
|12 Month return Char. adj.|-0.009|-0.027|0.018<sup>***</sup>|
|Spread (%)|1.093|3.321|-2.228<sup>***</sup><br>|
|HoldingPeriod(days)|115.6|147.4|-31.8<sup>***</sup>|

# **Table 5: Change in Illiquidity around Stock Splits**

46
# 1 Introduction

The paper analyzes whether financial contagion affects differently international stocks in contrast to pure domestic stocks when there is an emerging market crisis, all other stock characteristics being controlled for. We define contagion as the transmission of a shock in one country to the stock price in another country. We define international stocks as emerging market stocks that are listed in both their local market and an international capital market through cross-listing, and pure domestic stocks as stocks only traded in their domestic market.

We develop a rational expectations model that provides a measure of the cost of contagion when emerging market firms cross-list their stocks in a common developed market exchange. In that context, contagion occurs through the action of investors buying and selling cross-listed stocks when they are faced with a liquidity, information, or country specific shock. In that purpose, our model makes the distinction between emerging markets and developed markets; and, in each market, between international stocks and pure domestic stocks. We depart from the common assumption that all financial markets are perfectly integrated. Instead, we argue that markets are segmented along emerging markets’ borders. Specifically, emerging market investors mostly invest in their domestic market, whereas developed market investors aka international investors invest in the cross-listed stocks.

As an application, we calibrate our model using a unique dataset of crosslisted stocks and pure domestic stocks in Brazil, Chile, and Mexico. The calibration exercise allows us to investigate whether our model can explain the transmission of the 1998 Brazilian crisis to Mexico and Chile through the action of international investors buying and selling stocks cross-listed in the U.S. And in that context, the model reveals what type of shocks were mostly transmitted.

Finally, we assess whether the benefits of internationalization offset the costs of contagion in our model by performing a welfare-comparison of financial cross-listing versus financial autarky for all investors.

We find that international cross-listing is costly because of contagion: the price response in one emerging country to a shock in another emerging country is significantly stronger for cross-listed stocks than for pure domestic stocks, all other characteristics being controlled for. However, we find a magnitude of contagion that is smaller than reported in other models of contagion that assume perfectly integrated financial markets, which suggests that the magnitude of contagion may have been overestimated in previous studies. Furthermore, shocks in one emerging market are transmitted to all the emerging markets 2
An interesting issue is that the HIV/AIDS epidemic has two effects on the returns to education. The fi rst is to lower drastically the overall life-expectancy with the consequences that incentives to invest in education should be reduced. But, the second effect of the epidemic is that more educated people react more to the information about the epidemic, and this tends to increase the returns to education. The calculations described above only considers that second effect, since it is the only one that is potentially causal. However, it would be interesting, in further research, to try to estimate and disentangle both effects.

# References

The word “processed” describes informally reproduced works that may not be commonly available through library systems.

Ainsworth, Martha, Kathleen Beegle, and Godlike Koda. 2001. "The impact of adult mortality on primary school enrolment in Northwestern Tanzania." UNAIDS, Geneva. Processed.

Ashenfelter, Orley, and Michael Greenstone. 2004. "Using Mandated Speed Limits to Measure the Value of a Statistical Life." The Journal of Political Economy 112 (1,1): S226—68.

Becker, Gary S. 1993. Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education, 3rd Edition. Chicago. Ill: The University of Chicago Press.

Becker, Gary S., and Casey B. Mulligan. 1997. "The Endogenous Determination of Time Preference." Quarterly Journal of Economics 112(3): 729—58.

Blanc, Ann K. 2000. "The Relationship Between Sexual Behavior and Level of Education in Developing Countries." Report prepared for UNAIDS, Geneva. Processed.

Chamberlain, Gary. 1982. "Multivariate Regression Models for Panel Data." Journal of Econometrics. 18: 5—46.

Clark, Robert C., and Desire Vencatachellum. 2003. "Economic Development and HIV/AIDS Prevalence." Scienti fi c Series 2003s—25, Centre interuniversitaire de recherche en analyse des organisations (Cirano), Montreal.

33
Policy Research Working Paper 6200

# **Abstract**

In many African countries, the timing of important life events—such as school-leaving, first marriage, and entry into the labor market—is thought to be strongly tied to migration. This paper investigates the relationship between major life events, household characteristics, and migration among adolescents and young adults in contemporary Malawi. The specific research questions are twofold. First, what are the socio-economic and demographic determinants of migration? Second, how do school attendance, first marriage, and employmentseeking relate to migration patterns? The study uses panel data collected from a survey designed specifically to explore socioeconomic and demographic aspects of youth transitions to adulthood and which tracked respondents as they moved to new dwellings. Among the sample, they find that moves are not uncommon, and the predominant reasons for moves are non-economic. Although historically ethnic traditions in this area have held that girls and women usually did not move upon marrying, the data show that women were more likely to move between survey rounds than boys and men, and that marriage was the main reason for doing so. Closer ties to the head of the household are associated with less movement for both women and men.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kbeegle@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table E2: Additional Descriptive Statistics - Ecuador

||Mean|Std. Dev.|N|
|---|---|---|---|
|||**Panel A: **|**Worker Panel**|
|**_Controls (pre-global fnancial crisis)_**||||
|Male|66.6%|47.2%||
|Age|33.25|9.61||
|Higher education or more|10.8%|31.0%||
|Previous formal sector attachment|79.5%|29.2%||
|**_Other_**||||
|Young (16-34)|61.5%|48.7%||
|Middle age (35-49)|31.3%|46.4%||
|Old (50-65)|7.1%|25.7%||
|||**Panel B: **|**Firm Panel**|
|**_Controls (pre-global fnancial crisis)_**||||
|Importer dummy|72.5%|44.7%||
|Firm Avg. Growth in total employment|20.7%|55.3%||
|Firm Avg. Growth in total wage|26.3%|59.0%||
||**Panel **|**C: Worker**|**Panel - Full sample**|
|Male|68%|47%|15,882,150|
|Age|33.1|10.3|15,882,150|
|Higher education|14%|35%|15,882,150|
|Previous formal sector attachment|39%|43%|15,964,933|
|Average months worked (cumulative)|5.9|4.6|15,021,274|
|Average real wages (cumulative)|1.7|12.4|8,368,115|

_Note:_ The worker-level statistics in panel A are based on the sample of workers in firms with a non-missing GFC firm shock measure in 2008 used in the regressions. The firm-level statistics in panel B are based on all firms with non-missing GFC shock measure in 2008 used in the main regressions. The worker-level statistics in panel C are based on all workers in the IESS database. Real wages are expressed in local currency in 2010 prices.

59
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