rafmacalaba/datause-encoder-data huggingface dataset:
Moffitt (2002), an explanation for these findings is that the food stamp program is an inframarginal transfer for most recipient households, which makes them nearly equivalent to cash. In table 4 we also present separate estimates on the impacts of transfers on participation in agricultural and nonagricultural activities. Individuals who reported working in the labor market are classified as working in agricultural activities if they reported working in primary sector activities such as caring for animals, farming, forestry or fishing (INEGI, 2007).<sup>33</sup> Individuals who do not work in these activities are considered as performing nonagricultural activities, such as selling clothes, cosmetics, foods, handicrafts, etc. The double difference estimates in table 4 are both negative for agricultural activities which implies that participation in agricultural activities decreases among male adults receiving the cash transfers (T3xR2). Food basket with education is not significant but neither is statistically different from the other treatments. It appears that PAL does not have a statistically significant effect on the participation of adult females in non-agricultural activities.

Thus, PAL may provide partial insurance for food consumption (reduces down-side risk) sufficient to allow recipients to allocate less of their time in agricultural production intended to guarantee food in the event of income and other shocks and more towards nonagricultural activities. This is consistent with the prediction of the basic non-separable agricultural household model with incomplete markets for credit, or insurance. As Morduch (1992) and Bardhan and Udry (1989) demonstrate, an agricultural household that is likely to face binding liquidity constraints will choose a more conservative portfolio of activities that reduces the variance of its incomes, but that also has a lower expected income than the activities

> 33 To better classify individuals working in agricultural and nonagricultural activities we also used the information reported on the type of tasks performed in their work (question 2.17 in the baseline survey and question 2.13 in the follow-up round). We obtained the same qualitative results without the tedious effort of reclassifying individuals based on the information on actual tasks performed.

25
We now turn to our measure of vulnerability. Recall that to calculate vulnerability we need the impact of shocks on welfare, and a probability distribution of a shock occurring. We use the historical distribution of the predicted greenness index for each cluster and the average impact obtained from the pooled regression to estimate the average welfare loss for individuals living in each of the clusters. Our interest is to estimate the number of children who are vulnerable to negative shocks within different probability intervals.

The steps required in order to achieve an estimate of the number of vulnerable children are as follows: First, using the monthly distribution of our shock measure in the period 20002012, we calculate the “expected anthropometric loss” in each month by multiplying the specific value on the predicted greenness index by the coefficients on shocks obtained in the regression above. The validity of this method rests on the assumption that the effect of a shock is linear in values of the predicted greenness index. We also impose the assumption of “no positive gains” (or alternatively no negative loss); meaning that all cases where the drought-measure is 0 or above are seen as normal years and the expected loss is therefore set to equal 0.

Secondly, we follow the common practice in empirical vulnerability studies to calculate the predicted value of our outcome measure for each child instead of using the actual values. This is because vulnerability status is an ex-ante statement about a future scenario that has not yet been revealed. Third, for each child we then proceed to calculating the share of months where the predicted outcome measure (stunting or underweight) _minus_ the expected loss lies below a certain threshold. This share is a rough indication on the probability that a child will be 20
**Policy, Research, and External Affalrs|**

**Wefare and Human Resources** i

WPS 494 Th.s paper- a product of the Welfare and Human Resources Division, Population and Humap Resources - Department was written under the auspices of the African Food Security Unit of the World Bank, as a background paper to the Bank's study, _FoodAid in Sub-Sahiaran Africa._ Copics of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Angela Murphy, room S9-114, extension 33750 (25 pages).

Lavy compared the role of food aid and commercial food imports in offsetting food "shocks" and covering the shortfall in food consumption in 26 countries in Sub-Saharan Africa.

Food aid to low-income countries with transitory or chronic food insecurity has becn criticized on thie grounds that: But Lavy found that: Food aid and commercial food imports stabilize food consumption and neutralize the effects of random shocks to domestic food production. Food aid compensates for up to half of the drop in food production; imports make up an additional 30 percent.

**o** The international response to food crises is slow, meager, and inefficient.

- Food aid is discriminatory, depending on the recipient country's political and economic oricntation.

* Food aid discourages domestic food production and encourages dependence on donors.

* Food aid depresses commercial imports of fooa, reducing the amount of food available.

In other words, every one-ton drop in cereal production is offset by the delivery of 0.8 tons of cereal and dairy products from abroad. There is a lag in this response over a four-year period, but most of the aid is received in one to two years.

Surprisingly, the pattern of aid flows provides no evidence of discrimination by donors. Countries classified as socialist with military governments, and countries that do not protect human or political rights, receive an equal amount of aid during acute food shortages.

* By alleviating shortages food aid allows countries to postpone or cancel politically costly economic reform.

Thc PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Rcsearch, and External AffairsComplex. Anobjective ofthcscries is to get thesc findings outquickly, even ifpresentations are less than fullypolished. The findings, interpretations, and conclusions in these papers do not necessarily rcprcsent official Bank policy.

Produced by tlc IPRE Disscmination Ccnter
controls would be to authorize asset swaps between domestic and foreign institutional investors, especially pension funds. This would allow risk diversification while averting a capital flight. Another option would be to maintain exchange controls for general purposes, but permit contractual savings institutions to embark on a slow and gradual increase in their holdings of foreign securities. A third option would be to combine these two measures, i.e. allow a small amount of foreign investments as well as limited use of asset swaps.

# **III. Anatomy of International Assets Swaps**

The basic mechanics of international asset swaps are easy to present.<sup>2</sup> Pension funds that already own domestic equities engage in a swap with a global pension intermediary, probably an investment bank with operations in London, New York, Hong Kong, Singapore, or other international financial center. The international swap counterparts must have very high credit ratings, or alternatively the swaps must be guaranteed by third parties with strong credit ratings, otherwise they would result in risk substitution rather than risk diversification.

In an equity swap, the total return per dollar on the domestic stock market is exchanged annually for the total return per dollar on a market-value weighted-average of the world stock markets. Equity swaps effectively transfer the risk of the domestic stock market to foreign investors and provide the local pension funds with the risk-return pattern of a well-diversified world portfolio. Since there are no initial payments between parties, there are no initial capital flows in or out of the country. Subsequent payments, which may be either inflows or outflows, involve only the difference between the returns on the two stock market indices. No principal amounts flow across the exchanges from the swap arrangements.

For example, on a notional or principal amount of $1 billion, if, _ex post_ , the world stock market earns 10 percent and the domestic market earns 12 percent, there is only a flow of (.12 - .10) x $1 billion or $20 million out of the country. Furthermore, the local investors make net payments overseas precisely when they and their country can “best”

> 2  This and the following section draw extensively on Bodie and Merton (2002).

4
# **Table 1. Duration regression of lead times**

||**Column(1)**|**Column(2)**|**Column(3)**|**Column(4)**|
|---|---|---|---|---|
|Log(size of deposit, mt cu)|1.000|1.000|1.010|1.010|
||0.770|0.900|0.660|0.610|
|Copper|1.74***|1.72***|1.74***|2.29***|
||0.000|0.000|0.000|0.000|
|Comm. price upswing at discovery|0.940|0.950|0.950|0.990|
||0.160|0.270|0.290|0.860|
|Comm. price upswing x Copper mine|||||
|Size|0.91**|0.92*|0.930<sup>†</sup>|0.910<sup>†</sup>|
||0.040|0.070|0.130|0.100|
|Comm. price change during lead time|||||
|to production|1.00***|1.00***|1.00***||
||0.000|0.000|0.000||
|LIC|1.33***|1.25***|1.020|1.260<sup>†</sup>|
||0.000|0.000|0.850|0.120|
|MIC|1.42***|1.33***|1.11|1.55***|
||0.000|0.000|0.290|0.000|
|Debt>40%||1.16***|1.16***|1.38***|
|||0.000|0.000|0.000|
|Inflation>10%||1.080|1.080<sup>†</sup>|1.010|
|||0.160|0.150|0.920|
|Corruption|||0.92**<br>-0.020||
|Quality of government||||1.120<br>0.630<br>|
|Copper x Quality of government||||0.710<sup>†</sup>|
|||||0.140|
|**Non-linear interaction terms**|||||
|Copper x Comm. price upswing<br>+ Comm. price upswing|0.85**|0.87**|0.89**|0.9 †|
|Copper x Quality of government<br>+Qualityofgovernment||||0.79|
|Kappa|0.92|0.88|0.86|0.49|
|N|948|948|943|921|
|Log Likelihood|-1080.04|-1072.31|-1059.94|-1166.18|
|Akaike Information Criterion|2180.09|2168.61|2145.88|2358.36|

Note: P-values are given below coefficient estimates.<sup>†</sup> indicates statistical significance at 15%, * at 10%, ** at 5%, *** at 1%. The Pagan-Harding measure of commodity prices is based on the Pagan-Harding algorithm (2002) which identifies turning points in a times series as local minima and maxima. These are used to identify up-cycles (when gold and copper prices are rising). Higher values of the Corruption indicator correspond to better outcomes (i.e. lower corruption) as do higher values of the ICRG Quality of Government indicator. As interaction terms are nonlinear, the combined impact of these is shown separately.

16
Table 6: Baseline Difference-in-difference Estimation: Gorontalo (Restaurants)

|**Control vs. POS reci**|**pient resta**|**urants**||
|---|---|---|---|
|**Dependent Variable:**|**(1)**|**(2)**|**(3)**|
|**Log (restaurant tax receipt)**|**RPOS**|**RPOS-**<br>**Grab**|**TMD**|
|ATET (given RPOS vs. not)|0.134<br>(0.081)|||
|ATET (given RPOS-Grab vs. not)||0.682**<br>(0.335)||
|ATET (given TMD vs. not)|||0.155<br>(0.150)|
|Constant|0.949***|0.895***|1.015***|
||(0.031)|(0.033)|(0.032)|
|Observations|13,920|11,388|11,738|
|Adj. R<sup>2</sup>|0.000|0.008|0.000|

Robust standard errors in parentheses *** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1 Table 7: Baseline Difference-in-difference Estimation: Gorontalo (Hotels)

# **Control vs. POS recipient hotels**

|**Dependent Variable:**<br>**(1)**<br>**Log (hotel tax receipt)**<br>**PAC**<br>**Hotel**|**(2)**<br>**TMD**|
|---|---|
|ATET (given access to PAC Hotel vs. not)<br>0.141<br>(0.129)||
|ATET (given TMD vs. not)|0.213<br>(0.153)|
|Constant<br>0.325***<br>(0.008)|0.373***<br>(0.008)|
|Observations<br>14,119|14,228|
|Adj. R<sup>2</sup><br>0.002|0.006|
|Robust standard errors in parentheses<br>*** _p <_0_._01, ** _p <_0_._05, * _p <_0_._1||

19
Policy Research Working Paper 8365

# **Abstract**

Does increased import competition lead to higher returns to skill within an industry and, therefore, to greater incentives for skill acquisition? Does it also induce skill upgrading by the industry’s existing workforce? To answer these questions, this paper follows individual workers across skills/occupations, firms, and industries using a longitudinal matched employer-employee data set covering all workers and firms in Portugal over 1986–2000. To identify the effects of international competition the analysis uses two exogenous measures of changes in international competition at the industry level. The first is a quasi-natural experiment based on the strong appreciation of the Portuguese currency during 1989–1992 and preexisting differences in trade exposure across industries in a differences-in-differences estimation. The second is source-weighted real exchange rates defined at the industry level. Based on both empirical strategies, and two definitions of skill, the paper shows that international competition increases returns to skill and induces skill/occupation upgrading within industries.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean 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 jsilva@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
-9 -

along with a summary of the variables and definitions, means and standard deviations,<sup>are listed in</sup> Appendix Tables 1 - 4. The total fertility rate is for 1985 (TFR) and represents the number<sup>of children</sup> that would be born to a woman if she lives to the end of her childbearing years and<sup>bears children at each</sup> age in accordance with prevailing age-specific fertility rates. The infant mortality rate<sup>(IMR), 1985, is</sup> defined as the number of infants who die before reaching one year of age per 1000 live births.<sup>Both the</sup> TFR and the IMR are taken from the World Bank's World Development Report, (WDR)<sup>1987. Desired</sup> family size (DFS) is taken from the World Fertility Survey data from 1983 to 1988.<sup>It is based on the</sup> conventional method of measuring "preferred" family size and is available only for 37 countries<sup>from</sup> (Lightbourne, 1987). Contraceptive prevalence (CP) represents contraceptive use among<sup>currently</sup> married women for the latest available year ranging from 1982 to 1989, as assessed in 1988<sup>from the</sup> United Nations in Levels and Trends of Contraceptive Use and the Popu!ation Division's 1989<sup>database</sup> on contraceptive use found in the United Nations "Women's Indicators and Statistics<sup>Spreadsheet</sup> Database for Microcomputers" (WISTAT).

3.2 Gross secondary enrollment rates for males and females for 1970 and 1975 (MED, FED) are taken from UNESCO in the World Bank's Economic and Social Database (BESD). Gross<sup>Domestic</sup> Production (GDP) per capita, 1985 is corrected for purchasing power parity and is expressed<sup>in 1985</sup> international prices from Summers and Heston (1991). The family planning services score (SERV),<sup>1982</sup> is based on part of a broader index developed originally by Mauldin and Lapham (1982) and<sup>updated by</sup> Mauldin and Ross (1991). The score used In this paper assesses the availability and strength<sup>of service</sup> and service-related family planning programs in developing countries (Ross et al. 1992).<sup>The index</sup> reflects 13 separate measures of service and service-related activities, including commnunity-based 7 **This** index **is** one of the four components which make up the family planning program effort<sup>score in 1982 of Mauldin</sup> **and** Ross (1991). **We** have chosen this component to isolate a non-endogenou **s** upply variable.
**Do Economic Crises Lead to Health and Nutrition Behavior Responses? Analysis Using Longitudinal Data from Russia**<sup>**1**</sup>

# **Zlatko Nikoloski (LSE)**

# **and**

**Mohamed Ihsan Ajwad (World Bank)**

JEL Classification: I14, I15, I24, I25, J17, J63, P46 Keywords: Income shocks, coping strategy, crisis, health.

> 1 The views expressed in this paper are those of the authors and should not be attributed to any organization. Comments on earlier versions of the paper are also gratefully acknowledged from Basab Dasgupta, Owen Smith, Alexander Plekhanov, and Antonio Giuffrida.
the results in Table 13. We do not look at model 4 as we have seen that the results are quite robust across model specifications and this model requires a larger number of observations to meet the degrees of freedom requirements. Because we are dealing with time-series data, we test for the presence of serial correlation, and use the CochraneOrcutt procedure to correct for these effects when present.

Consistent with the analysis in section 3, we find decentralization of maintenance has the strongest impact on resource costs (see table 10). The coefficients for decentralization of maintenance are significant and negative (first order), then positive (second order) and negative (third order), implying that resource costs decrease with increasing levels of decentralized maintenance. The effect of decentralization on unit costs of construction is mostly linear, since the first order effect is the only significant variable of decentralization. Furthermore, we see that economies of scale are lost as these costs increase with increasing degrees of decentralization. The effect of decentralization on unit costs of administration in Korea is not very significant, although the overall effect is to reduce these costs, as was found earlier. However, total decentralization has a strong non-linear impact on resource costs. The overall effect is to reduce the total costs of providing roads (since the negative second order coefficient dominates).

**Table 10: Impact of Decentralization on Resource Costs--Case of Korea**

|Variable|Construction|Maintenance|Administration|Total Effect|
|---|---|---|---|---|
|year<br>|::<br>'**25**<br>year1|146...**.**..<br>(L_9)|1.093.4<br>(1.57)**|(2.92.|
|per capita GNP||||.....**..**|
|decentralizationl|(1.61 )*(09 Es|3|||
|fiscal|8286||||
|decentralization<sup>3</sup>|(0.89)|g(.1|||
|Constant|_|.23<br>!3.|9<br>-2180.05||
|rho<br>_____________|-<br>.6<br>(570<br>-|-04<br>;<br>2|0.32<br>(1 .56)*|05;<br>(.2|
|R-square|0.75|0.97|0.76|0.81|
|Observations|24|_<br>24|24|24|

Fiscal decentralization measured separately as the percentage locaI government contrib.tion to construction, maintenance, administration, and road provision in general in the first, second and third order respectively.

Shaded portions significant at 90% level of confidence.

* significant at _85%_ level of confidence.

** significant at the 80% level of confidence.

28
<!-- Start of picture text --> Ethiopia Tanzania<br>0 0.15 0 0.50<br>-0.2 0.10 -0.2<br>0.30<br>-0.4 0.05 -0.4<br>0.10<br>-0.6 0.00 -0.6<br>-0.10<br>-0.8 -0.05 -0.8<br>-0.30<br>-1 -0.10 -1<br>-1.2 -0.15 -1.2 -0.50<br>Undervaluation (left scale) Growth per capita (right scale) Undervaluation (left scale) Growth per capita (right scale)<br>1.0 Uganda 0.2<br>0.1<br>0.0<br>0<br>-1.0<br>-0.1<br>-2.0<br>-0.2<br>-3.0 -0.3<br>Undervaluation (left scale) Growth per capita (right scale)<br>1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003<br>1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002<br><!-- End of picture text --> Note: Undervaluation zone is above zero. Source: Nguyen (2014) and Rodrik (2008).

24
Various explanations have been advanced for deviations from household portfolio efficiency, including inadequate financial education,<sup>27</sup> weak investor protection<sup>28</sup> and lack of trust (Guiso, Sapienza and Zingales, 2005).<sup>29</sup> These issues have been little considered in the developing country context, and the limited data on age and education patterns are only moderately informative here. In practice, the use of non-cash financial assets is highly correlated with education levels in China, Ghana and Vietnam (but education in turn is correlated with income).<sup>30</sup> In addition to advocating a more adventurous portfolio, financial planners often recommend that the riskiness of the household portfolio should decline as households near retirement – a form of turnpike theorem for investment planning. Looking, therefore at variations in the share of risky assets with age, no strong pattern emerges consistently across countries for which data is available. There is a hump-shaped pattern—as would be recommended by financial planners – for the US, the UK and Germany (Figure 2); but a trend increase for the Netherlands and (at least as far as participation is concerned) for India. The reduced share of risky assets held by older people in the US is less than would be recommended by financial planners (Ameriks and Zeldes, 2004).<sup>31</sup> There is no pronounced age-risk correlation for any of the developing countries for which we have data. Given that different age groups also represent different cohorts in these data, however, there is an identification problem in separating the effects of calendar time and age.

Supply conditions can also strongly affect household portfolio composition in developing countries including the choice between risky and safe assets, especially, but not only,

> 27 Lusardi and Mitchell (2006) found astonishing levels of financial ignorance when they added a module on financial education to a 2004 US survey on health and retirement issues.

> 28 Giannetti and Koskinen (2004) present data for 26, mostly advanced, economies on the percentage of households holding equities; they find a positive correlation between these percentages and measures of investor protection.

> 29 Rosen and Wu (2004) show that households with poor health status hold less risky assets.

> 30 See Figure 3 for Ghana and Vietnam.  For urban households in China, the share holding risky assets increases monotonically with education categories from 0.1% of those with below primary level to over 10% for those with post-graduate qualifications (CASS survey special tabulations).

> 31 However, as Ameriks and Zeldes show, the apparent downward trend with old age is not confirmed by a more thorough multivariate microeconometric analysis of the US data—an observation which should act as a caveat for conclusions based on the simpler univariate approach adopted here.

20