rafmacalaba/datause-encoder-data huggingface dataset:
|Quintile_5|98.0|2.1|121.5|25.7|3.7|251.1|
|---|---|---|---|---|---|---|
|Total (Scenario 1)|495.3|10.1|566.4|137.8|14.7|1,224.4|
|Total (Scenario 2)|1,042.6|21.2|1,136.2|265.2|27.4|2,492.5|
|% of govt. exp. (Scenario|||||||
|2)|1.60|0.03|1.75|0.41|0.04|3.83|
|Source: Libyan Household Consumptio|n Survey 2007/8; Lib|yan authorities;|and Bank staff calcu|lations.|||

Reforming gasoline and electricity prices would bring the greatest savings to the government budget. Figure 7 illustrates, for each energy product, the direct impact on government expenditure (measured on the _y-axis_ in LYD) versus a percentage reduction in subsidy ( _x-axis_ ). The values that correspond to 30% and 100% reductions are the same as those reported under the two scenarios in table 24. For all products, government expenditures are a decreasing function of subsidy reduction. The marginal returns to reducing subsidies would diminish as prices get closer to market levels, because fewer and fewer quantities would be bought at subsidized prices given fixed household expenditure levels.

**Figure 7: Magnitude of Decline in Government Spending Following Reform Scenario 2 (Libyan Dinars)**

<!-- Start of picture text --> Gasoline<br>Diesel<br>1.00e+09<br>Electricity<br>LPG<br>Government<br>Kerosene<br>savings<br>5.00e+08<br>0<br>0 20 40 60 80 100<br>Percentage decrease in subsidies<br><!-- End of picture text --> Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

Energy subsidy reform could have a substantial impact on poverty. A 30% reduction in subsidies, assuming unchanged consumption patterns, would increase poverty (measured by the national poverty line) by four percentage points, from 18.5% to 22.5% (Table 25). The increase in poverty following a 27
from 0.23 (maize) to 0.50 (palm oil), implying that, on average, a 10 percent increase in the price of crude oil is associated with a 3 percent increase in food prices. These estimates confirm findings of earlier studies that have used the price of energy as an explanatory variable and concluded that energy plays a key role in food price movements.

The strong relationship between energy and non-energy prices has been established long before the recent boom. Gilbert (1989), for example, using quarterly data between 1965 and 1986, estimated transmission elasticity from energy to non-energy commodities of 0.12 and from energy to food commodities of 0.25. Hanson, Robinson, and Schluter (1993) based on a General Equilibrium Model found a significant effect of oil price changes to agricultural producer prices in the United States. Borensztein and Reinhart (1994), using quarterly data from 1970 to 1992, estimated transmission elasticity to non-energy commodities of 0.11. Baffes (2007), using annual data from 1960 to 2005 estimated elasticities of 0.16 and 0.18 for non-energy and food commodities, respectively. A strong relationship between energy and non-energy prices was found by Chaudhuri (2001) as well.

Pindyck and Rotemberg (1990) analyzed the nature of the comovement among seven commodity prices (cocoa, copper, cotton, crude oil, gold, lumber, and wheat) and concluded that not only these prices co-moved, but they also co-moved in excess of what the macroeconomic fundamentals could explain, in turn leading to the “excess comovement” hypothesis. Interestingly, they assumed that none of these commodities is used as a major input for the production of another, clearly a strong assumption considering that energy is the most important cost-production component to most agricultural commodities (either directly through fuel or indirectly through chemical inputs, especially fertilizers).<sup>**9**</sup>

# Income

The results show no contemporaneous effect of income on food prices (maize is an exception, but the sign of the parameter estimate is negative). Although table 2 reports results for GDP purchasing power parity (PPP) of low and middle income countries, we run the models by using a total of six measures of GDP: Global, low and middle income countries, low income countries, each in PPP terms and current prices. None of these measures resulted in coefficients significantly different from zero.

Income growth by emerging economies has been often cited as a key driver to the post-2004 food price increases. For example, the June 2009 issue of _National Geographic_ noted that demand for grains has increased because people in countries like China and India have prospered and moved up the food ladder. Similarly, Krugman

> **9** Subsequent research, however, challenged the “excess” part of the excess comovement hypothesis. See, for example, Ai, Chatrath, and Song (2006); Cashin, McDermott, and Scott (1999); Deb, Trivedi, and Varangis (1996); and LeyBourne, Lloyd, and Reed (1994).

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<!-- Start of picture text --> A. Discrete | B. Discrete II<br>ad ad<br>oO<br>g° 3<br>£ £<br>7]o BoO<br>~ wD<br>c cy<br>22<br>ooO<br>5 5<br>ow fo)<br>oO: oO<br>_ ard<br>2003 2004 2005 2006 2007 2008 2003 2004 2005 2006 2007 2008<br># R1=1500 @ R1=2500<br>® R1=3500<br>Confidence Intervals at 95% level.<br><!-- End of picture text -->
15

# Empirical Results from the Adult Sample

The adult sample consists of 5,644 women aged 20-59. For comparison we also analyzed a sample of 5,241 men of the same ages. Table A.2 shows the sample by current place of residence and by birth year. The individuals in the sample are, on average, nearly equally distributed across Lima, other urban areas, and rural areas, with a slightly larger proportion in rural areas.lI The spatial distribution of the sample, however, differs by cohort. More than 40 percent of the oldest men and women live in rural areas, compared to about a third of the youngest adult cohorts.

# Educational Profiles by Gender and Residence

Table 3 gives the educational profiles of the sample by gender and place of residence. There are clearly large differences in levels of education between men and women. Women have completed an average of only five and a half years of school, compared to seven years for men. While only 8 percent of the men did not attend school, 25 percent of the women never enrolled. This percentage of women with some secondary and higher education was 28 and 12, respectively, compared with corresponding percentages of 35 and 18 for men. These results are very similar to the 1981 census results (table A.1). Since 1981, however, the average number of years of schooling has risen XV We do not estimate the models separately by current residence because this location could differ from the place of residence at the time of schooling. Thus place of residence is included as an explanatory variable in the regressions.
as data on the presence of several organized crime groups from the Armed Conflict Location and Event Data and the Global Terrorism Database.

Our first key result is that municipalities affected by forced displacement experienced sharper declines in economic development relative to other municipalities. We estimate that nightlight density declined by 7.5 percent for every additional percentage point of the municipal population displaced from Venezuela to Colombia, representing an estimated 2.1 percent decline in GDP (Henderson et al., 2012). Considering that approximately 14 percent of Venezuela’s 1990 population is registered as living in Colombia, our estimates imply that total displacement outflows of this magnitude would translate into a GDP contraction of roughly 29 percent. This amounts to about half of the total GDP contraction Venezuela experienced during our period of analysis. Our results are not mechanically driven by population loss since data from ENCOVI show consistent increases in poverty rates and unemployment. Moreover, inequality also rose markedly in municipalities with higher displacement outflows, although these coefficients are estimated with less precision.

How does an autocratic regime remain in power during such a profound economic and social crisis? Our second key finding is that forced displacement contributed to the survival of autocratic leadership by simultaneously weakening political opposition and expanding organized crime, which provided illicit income and coercive capacity to the regime.

We find that displaced individuals disproportionately backed opposition candidates and that their departure also changed the composition of remaining voters.<sup>3</sup> For a municipality experiencing a 14 percent population decline, our estimates imply a reduction of 21.1 percentage points in opposition support and 39.3 percentage points in electoral turnout.<sup>4</sup> These results are corroborated by the fact that only 60,000 out of approximately eight million Venezuelans abroad were permitted to vote in the 2024 presidential election. Our findings include the

> 3This is consistent with the evidence in Holland et al. (2024), in which 12.1 percent of displaced Venezuelans in Colombia supported the left and only 0.1 percent supported Maduro’s government.

> 4These figures are computed as the product of the estimated coefficients and the share of the 1990 population displaced: 14 _×_ 1.51 and 14 _×_ 2.81, respectively.

6
**_Figure 4: Distribution of foreign worker nationalities by state (percent, an average during March 2017- February 2018)_**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Bangladesh Indonesia India Myanmar Nepal Philippines Pakistan Thailand Others<br>Johor Kedah Kelantan Melaka Negeri Sembilan Pahang Pulau Pinang Perak Perlis Sabah Sarawak Selangor Terengganu W.P. Kuala Lumpur W.P. Labuan W.P. Putrajaya<br><!-- End of picture text --> _Source_ : MOHA 2017-18.

# **3. Data and methodology**

## **3.1.Data**

**The principal data source used is a transaction-level data set of remittances obtained from BNM.** Since 2016, BNM has compiled outbound remittance transaction data reported by remittance service providers (RSPs) as part of its banking supervision responsibilities. These include transactions carried out by post offices, money changers, and money wholesalers, but exclude those by commercial banks. Furthermore, the data set does not include informal remittance transactions such as cash carried out by a traveler or sent through an informal money transfer channel (e.g., _hawala_<sup>§</sup> ).

**The data are individual daily transactions for the period of March 2017 through February 2018** . The RSP data include objectively known variables for each transaction such as unique identifiers of remitters, remittance amount, transaction currency, destination country of the transfer, and the state location of MSPs. They also contain other variables, such as occupation and employment sector of the remitter, which are recorded during the account registration process at the MSPs. We do not use these variables in this paper, as the information could be outdated or incorrect.

## **3.2.Methodology**

§ Hawala is an informal method of transferring money without the movement of cash, but through transactions across a network of money brokers: To illustrate, a money sender goes to a money broker A in the current location and gives a transaction amount to be sent to the recipient in another location; a hawala broker B in that location pays out the amount to the recipient, under the promise that broker A settles the debt at a later date.

7
almost doubles (column 4 and 8, Table 2). Hence, undernutrition levels at the _woreda level_ explain a relatively large share of the variation in z-scores, even controlling for other _household level_ proxies, and would seem like a valuable targeting metric in addressing stunting and underweight.

**Table 2. OLS regression of z-scores targeting alternatives**

|||**Height**|**for Age**|||**Weight**|**for Age**||
|---|---|---|---|---|---|---|---|---|
|Column|1|2|3|4|5|6|7|8|
|Dependent variable: z-score|HAZ|HAZ|HAZ|HAZ|WAZ|WAZ|WAZ|WAZ|
|**Potential targeting variables**|||||||||
|HH has access to rotected water|0.05|-0.05|-0.04|-0.04|0.07|-0.01|-0.02|-0.00|
|p|(0.10)|(0.11)|(0.12)|(0.11)|(0.07)|(0.07)|(0.08)|(0.06)|
|HH h t tilt|-0.16*|-0.02|-0.04|-0.09|-0.13*|-0.02|-0.03|-0.01|
|as no access o oe|(0.09)|(0.10)|(0.11)|(0.10)|(0.06)|(0.07)|(0.07)|(0.07)|
|HH utilize health service|0.15*|0.06|-0.02|-0.01|0.13*|0.05|0.00|0.03|
||(0.09)|(0.09)|(0.09)|(0.10)|(0.07)|(0.07)|(0.08)|(0.07)|
|Wealth||0.00***|0.00**|0.00||0.00***|0.00|0.00|
|||(0.00)|(0.00)|(0.00)||(0.00)|(0.00)|(0.00)|
|Mothers’ education|||||||||
|(no education is the excluded category)|||||||||
||||0.18*|0.16*|||0.23***|0.16*|
|Primary|||(0.10)|(0.09)|||(0.08)|(0.08)|
||||0.62**|0.57**|||0.68***|0.62***|
|Secondary|||(0.24)|(0.25)|||(0.15)|(0.14)|
|Ti|||1.14***|1.06***|||0.93***|0.95***|
|ertary|||(0.31)|(0.32)|||(0.35)|(0.33)|
|Imputed woreda||||-3.15***||||-3.42***|
|stunting/underweight level||||(0.45)||||(0.42)|
|C|-1.52***|-1.40***|-1.48***|-0.19|-1.27***|-1.17***|-1.29***|-0.27*|
|onstant|(0.08)|(0.08)|(0.09)|(0.20)|(0.07)|(0.07)|(0.07)|(0.15)|
|Number of observations|4,220|4,220|3,838|3,838|4,372|4,372|3,976|3,976|
|**R2**|0.004|0.012|0.018|0.034|0.006|0.015|0.025|0.052|

The asterisks indicate the significance level: *** p<0.01, ** p<0.05, * p<0.1. Standard errors in parentheses. Regressions take sample design and household weights into account by using Stata’s svy command. Data: EMDHS 2014.

Note that access to water and toilet and education are also potential variables (as EA averages) in the imputation models, and the z-score was, of course, used to obtain the SAE estimates in the first place. However, for the purposes of the analysis above, these dependencies are not a concern.

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**Figure 2: Evolution of United States farm size and nonfarm manufacturing wage**

<!-- Start of picture text --> 250 14<br>12<br>200<br>10<br>150<br>8<br>6<br>100<br>4<br>50<br>2<br>0 0<br>1910 1930 Farm size1950 1 Wage970 1990<br>Average farm size (ha)<br>Manufacture wage (1992 $s/hr)<br><!-- End of picture text --> _Source_ : Based on Gardner 2002.

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Greene, W (2012), Limited Dependent Variables - Truncation, Censoring, and Sample Selection, Chapter 19, Econometric Analysis, Pearson.

Hausman, J and D. Wise (1977), “Social Experimentation, Truncated Distribution, and Efficient Estimation,” Econometrica, May 1977.

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Mazumder, Bhashkar, (2015). “Estimating the Intergenerational Elasticity and Rank Association in the U.S.: Overcoming the Current Limitations of Tax Data,”, Working Paper Series WP-2015-4, Federal Reserve Bank of Chicago.

Mitnik, Pablo, Victoria Bryant, David B. Grusky, and Michael Weber, New Estimates of Intergnerational Income Mobility Using Administrative Data, Mimeo, Statistics of Income, Internal Revenue Service, 2014.

Neidhofer, G (2016), Intergenerational Mobility and the Rise and Fall of Inequality: Lessons from Latin America, Working Paper.

Nimubona, A, and D. Vecatachellum (2007), “Intergenerational education mobility of black and white South Africans,”, Journal of Population Economics.

Nybom, Martin, and J. Stuhler, (2017). “Biases in standard measures of intergenerational income dependence,” Journal of Human Resources, Summer, 2017.

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Shevlyakov, G, and Hannu Oja (2016), Robust Correlation: Theory and Applications, Wiley Series in Probability.

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Solon, Gary (1999). Intergenerational Mobility in the Labor Market, in O. Ashenfel23
five countries. This holds true for both stunting as well as for height-for-age outcomes. Second, in four of the countries (Tanzania, a food surplus country, is the exception), children in households in the second and third lowest wealth quintile are also significantly affected by droughts. Third, we show that for the top two quintiles, the impacts vary by country. Mozambique, Tanzania and Malawi show considerably muted impacts, while Zambia and Zimbabwe have impacts that are comparable across the DHS wealth quintiles.<sup>28</sup> Taken together, this suggests that while the impacts of droughts are most pronounced for the lowest wealth quintile (which corresponds to what practitioners refer to as the ultra-poor population), broader segments of the rural populations of all five countries are affected by droughts.

# **4 Pragmatic Drought-Contingent Targeting Strategy**

While machine learning applications to early warning frameworks, and poverty targeting more generally, hold considerable promise — particularly when timeliness and accuracy are critical — the majority of these approaches are not interpretable. Our contribution here is to emphasize the value of simple approaches in contexts that are both controversial and uncertain. Baumol and Quandt (1964) provide a useful characterization of four necessary attributes for _optimally imperfect decisions_ : i) objectively measurable inputs and outputs; ii) objectively communicable decision criteria and an absence of human judgment; iii) a unique and deterministic mapping of inputs to decisions; and iv) decision algorithms that involve inexpensive and verifiable calculations.

It is worth examining a poverty targeting framework from this perspective. McBride and Nichols (2016) seminal application of machine learning to poverty targeting is the first to use an approach that is explicitly designed to improve out-of-sample prediction. However, along with Sohnesen and Stender (2017), they employ a black-box random forest methodology.<sup>29</sup> Mark Schreiner’s insightful and widely-used approach to designing proxy-means tests (e.g. Schreiner (2007), Schreiner (2008)) emphasizes interpretability and ease-of-use, in addition to out-of-sample performance. However, as Diamond et al. (2016) argue, his approach is not based on well-understood statistical methods and, while it does well on

> 28These results may be complemented by surveys that contain more information on wealth, income and consumption to better understand the reasons for these differences. For example, we are unable to separate the mechanisms through which drought shocks impact stunting. Possible channels involve crop income, farm labor and market prices. Further, there are social and economic channels through which shocks could be mitigated. In Zambia, different from the other countries, the mining industry provides a critical source of income and employment for several local economies.

> 29These exercises are done in the context of monetary poverty targeting, which involves a larger number of explanatory variables and a higher signal-to-noise ratio than our context.

18
A basically similar picture emerges in relation to the cost of exporting, again sourced from _Doing Business_ (Figure 2). On average, ASEAN does relatively well: $806 per container is slightly lower than the OECD average of $811. However, the range within the region is very wide, from Singapore—which at $382 per container is amongst the top 5 in the world—to Laos at $1420 per container, nearly four times higher than Singapore.

Interestingly, although the cost figures for importing and exporting are generally low even compared with the OECD, the same is not as true for time and document counts (Figures 3-4).<sup>8</sup> Both at export and import, the number of documents required and time taken in ASEAN countries are well in excess of the OECD average: 32 days versus 12 days for importing, and 11 documents versus 6. However, Singapore is once again one of the world leaders in relation to trade times, reinforcing the image of intra-regional heterogeneity that has already been given.

To round out our snapshot, we use results from Kee et al. (2006) to provide an overall assessment of the trade policy environment in ASEAN (excepting Cambodia, Myanmar, and Singapore, which are not included in the Kee et al. (2006) study). Those authors calculate two measures that are of interest. A country’s Overall Trade Restrictiveness Index (OTRI) is the uniform tariff which, if applied, would give the same level of imports into that country as under current policy settings. Its Market Access OTRI (MA-OTRI) is the uniform tariff which, if applied by the rest of the world, would give the same level of exports out of that country as under current policy settings.

Table 4 provides OTRI and MA-OTRI measures for ASEAN member countries, in versions that include tariffs only, and both tariffs and non-tariff barriers. We also differentiate between total

> 8 These data are also sourced from the World Bank’s _Doing Business_ database, and are constructed analogously to the cost data.

- 7 -
Vessels that fished inside a potential closure the day before closure announcement have a much smaller total treatment effect (0.5% increase in total juvenile catch) than vessels that did not (87.2% increase in total juvenile catch).<sup>31</sup> The treatment effect for vessels that fished inside a potential closure the day before closure announcement is not different from zero (delta method standard error is 3.3%), whereas the treatment effect for vessels that did not fish inside a potential closure the day before closure announcement is highly statistically significant (standard error is 5.8%). The difference in treatment effects between the two information groups is also statistically significant (difference is 86.7% with a standard error of 6.7%).<sup>32</sup> The information mechanism also operates at the firm-level. Vessels who had a different member of their firm fish inside a potential closure the day before closure announcement would occur have a much smaller treatment effect (19.4% increase in juvenile catch) than vessels who did not (78.4% increase in juvenile catch). Both treatment effects are different from zero (standard errors are 3.8% and 5.4%), as is the difference in treatment effects (difference is 59% with a standard error of 6.6%).

The results in this section support Proposition 3 that vessels that receive a stronger information shock from closures have larger treatment effects. The information provided by closures is a mechanism underlying the policy’s large spillover effects. The vessel-level result in Figure 8 also explains why vessels underreport percentage juvenile in the raw electronic logbook data (Section II) even though closures provide valuable information (Section V). Closures only provide information to vessels who were not already fishing in the area, so vessels might underreport percentage juvenile to avoid triggering a closure in the area they are already fishing.<sup>33</sup>

> 31I calculate the percentage change in total juvenile catch for both groups and both categorizations in the same way as in Section IV, converting the treatment coefficients into changes in levels and accounting for the reallocation in tons caught in the four fishing seasons the total allowable catch limit was binding.

> 32Note that this result does not reflect mean reversion because I estimate treatment effects across potential closures. I estimate the treatment effect for vessels that fished inside a potential closure the day before closure announcement would occur by comparing juvenile catch by these vessels near potential closures that get closed to juvenile catch by this same group of vessels near potential closures that do not get closed.

> 33Recall that underreporting by vessels does not bias my estimates because I calculate my regressions’

41