rafmacalaba/data-use-mentions-extended huggingface dataset:
political violence. In addition, this variable is less likely to be endogenous to FDI than most subjective measures of conflict because MNE investments are unlikely to cause battle-related deaths directly. Because the battle-related deaths variable is highly skewed, we take the natural log of the inverse hyperbolic sine function.

For our second and fourth hypotheses on the geographic scope of political conflict, we use again the UCDP/PRIO Battle Related Deaths data set. UCDP/PRIO records the warring parties and the incompatibility of each conflict. The geographic scope variable is a dummy, which is 0 if a conflict is localized and 1 if a conflict is nation-wide. First, we coded every secessionist conflict as _localized_ because secessionist fighting is generally confined to the territory that is fought over (Buhaug & Gates, 2002). Subsequently, we manually checked whether conflicts, in which the incompatibility concerned the government instead of a regional territory, were nationwide by analyzing articles on the conflict on the website of BBC news and profiles of the insurgents on the START website (2014). Finally, we visually confirmed our coding using maps based on the UCDP/PRIO Georeferenced Event Database (Sundberg & Melander, 2013) for all conflicts in Africa and South Asia. Unfortunately, UCDP/PRIO GED does not yet collect georeferenced data on conflicts on other continents. We found no inconsistencies in the coding based on the information provided by BBC News or START. We exclude the main effect of localized conflict from our regressions because the type of conflict is virtually country invariant and is hence absorbed by the country fixed effects in our regression.<sup>5</sup> If a country experienced both a localized and non-localized conflict, we coded the observation as having experienced a nationwide conflict. In 22.4% of the observations, there is an ongoing conflict; of those, 56.6% are coded as localized.

Although fDi Markets records FDI data on the project level, it does not include any information on the investing firm other than the name of the
|2.5<br>2.0|2.6<br>2.0|
|3. Contributors to ZUS|1 +.7|14.1|13.6|13.3|12.7|12.6|
|4. Farmers (self-empl.)|4.0|-|-|-|3.4|-|
|_5._Benef.-earn. unempl.|0.0|0.9|1.7|1.3||l<br>-|
|Total population|38.0|38.2|38.3|38.4|38.5|38.6|
|Young < 18|11.4|11.3|11.3|11.2|11.0|10.9|
|Working age<br>|22.8<br>|23.0<br>|23.0<br>|23.2<br>|23.4<br>|23.5<br>|
|Working age<br>Old>65|3.8|3.9|4.0<br>1|4.0|4.1|4.2|

# not available

- annual average (not 31 December)

- ** aged between 18 and 64, there is another definition next page of Rocznik

Note: New definition of all labor force data: Individual farmers were overestimated by GUS from 1989 to 1993 (GUS Bulletin 1994, 7, p. 11 et 40, GUS Rocznik 1994, p.<sup>1</sup> 15, note 3). GUS data on subgroups of the labor force add up to less than the total: the residual is in lb (employed and others).

- Sources: GUS Rocznik 1992, p.42, 203 et 1993, p. 47, 109
# 3. Results

## 3.1 Baseline estimates

Table 2 presents estimation results using OLS and instrumental-variable estimation.

### Table 2

Baseline results: OLS and IV Regression (a) gives OLS estimates (with importer, exporter and time effects) for comparison with instrumental-variable regressions. Regressions (b), (c) and (d) are two-stage treatmenteffect regressions where PSI is instrumented in three different ways. In (b), the first-stage regression (a probit) is on corruption and its square. As expected, the probability of observing a PSI program is a concave function of corruption control, but the turning point is around an index value of 10, so beyond the relevant range it rises in corruption control. Both instruments combined pass Sargan’s overidentification test. In (c) and (d), the first-stage regression is on a variety of instruments including other governance indicators, the amount of OECD aid and several dummy signaling the presence of World Bank Structural Adjustment Programs (identified in Appendix 2), which combined pass Sargan’s test in (c). Results are very similar both qualitatively and quantitatively.

The coefficient on private inspection is stable across specifications and there is no significant bias in OLS estimation. Given the semi-log specification, the coefficient on private inspection is the percent increase (decrease) in trade associated with a change in the private inspection variable from zero to one. The estimates in Table 2 suggest an increase between 8% and 11.5%. While in a plausible range, these estimates are quite high, suggesting, if true, that with an average tariff rate of, say 10% private inspection would roughly just pay for itself _even if it did nothing to tariff avoidance_ .

## 3.2 Robustness

Table 3, Table 4 and Table 5 report several robustness exercises. First, while the baseline results are realized using exports data RHS, we check if they are robust to using mirror data instead. Table 3 presents the results with imports data
42

# Appendix Table 1. Continued

|Cell references<br>1996/97|
|---|
|Policy Analysis - Consumers|
|Subsidized purchase price by consumers<br>(18) = (16)<br>450<br>(per ton of flour)|
|Income transfer to consumers<br>(19) = (17)<br>645<br>(Egypt LE/ton)|
|Rate of subsidy to consumers (percent)<br>(20) = (19)/[(13) + (15)]<br>58.9<br>(Income transfer to consumers divided<br>by free market, non-subsidized price)|
|Notes: All figures in Egypt LE/ton, unless otherwise noted. In 1997 1 Egyptian LE = US $ 0.295.<br>CIF = Cost, insurance and freight<br>CS&T = Collection, storage and transport<br>CF = Conversion factor for wheat grain into flour; for subsidized baladi bread this ratio is 82 percent.|
|Sources: Border CIF price computed as sum of average export price for US No.2 Hard Red Winter Wheat and<br>freight rate from US Gulf to Alexandria, Egypt (International Grains Council, World Grain Statistics 1996/97,<br>London, England: Tables 1 and 42).|
|All other data from unpublished data, Ministry of Trade and Supply.|
44|0.26|0.44|0.84|
|Industry lends or provides equipment to program for student training|0.40|0.49|0.41|0.49|0.81|
|Somebody in charge of industry relations|0.83|0.38|0.80|0.40|0.45|
|Program has staff assigned to collect grads' employment data|0.42|0.49|0.39|0.49|0.45|

73
both the _Boys_ and _Goal_ treatment, column 2 presents estimates of coefficients on interactions of the row variables with being assigned to the _Boys_ treatment, column 3 presents estimates of coefficients on interactions of the row variables with being assigned to the _Goal_ treatment, and column 4 presents estimates of coefficients on level controls of the variables at the start of the rows. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates. .01 ***, .05 **, .10 *. _Source._ Female baseline data.

54
The cycle dates for the US are also a useful contrast to those for the euro area and Japan. The US has had two periods of QT. The first QT program began in 2017, 15 months after the first rate hike in December 2015, which marked the start of the corresponding tightening phase. The second QT program began around the same time as the March 2022 rate hike, which marked the start of the most recent tightening phase. This accelerated start to QT after the pandemic—either at the same time as the first rate hike or soon after—occurred in most countries, although the United States is the only country to have made meaningful progress in reducing the size of its balance sheet before the pandemic.<sup>16</sup> The more frequent turning points in the United States, and corresponding larger number of easing and tightening phases relative to Japan and the euro area, are more typical of other countries in the sample. The dates of the turning points for the US correspond to well-known shifts in monetary policy, as well as to the obvious peaks and troughs in the policy rate data. This is noteworthy given the greater volatility of policy rates earlier in the sample when the Federal Reserve and some other central banks relied on different tools and monetary frameworks (e.g., sometimes focused on the control of monetary aggregates). Despite these changes in how monetary policy is conducted, the dating algorithm, together with the additional criteria discussed above (and in Appendix 1), appears to have been successful in identifying the major turning points in policy interest rates that generally correspond to shifts in monetary policy.

While these differences in rate cycles across the euro area, Japan, and the United States are useful to understand the dating algorithm, there are also several fairly consistent patterns across most economies. Policy interest rates tended to be higher and substantially more volatile in the first half of the sample—reflecting higher neutral rates and inflation combined with monetary policy regimes that generally focused less on
- _increasingpressure on land:_ Orissa's population, growing at an annual rate of 2 percent, continues to depend substantially on agriculture for their livelihoods. The 'cultivable land' frontier is therefore being pushed progressively further onto government and common land.

# _4.3.1 Typology and extent of encroachment_

In Orissa, all types of common land are under threat of encroachment. In private discussions, government officials freely acknowledge the widespread encroachment of government land, but official data is either not gathered or not readily forthcoming. For example, survey and settlement operations do not recognise claims established through encroachment. The reason for this is to preserve original rights in land, but the opportunity is missed to acquire more reliable aggregate data on the extent and forms of encroachment. An encroachment register is maintained at tehsil level, for purposes of proceeding with eviction cases from government land. Owing to the incentives for rent-seeking at this level, these data must be regarded as highly suspect. They are not, in any case, aggregated at any higher level, although in principle some monitoring of encroachment is carried out at district level.

Who encroaches on what types of land? Table 13 summarises the current status of encroachment on different types of public and common land. An important type of interaction between the state and the land user is the de facto 'privatisation' of common lands through encroachment. Successful encroachment _of peromboke_ lands and other common lands (such as uncultivated wastelands) depends upon the degree of influence the individual encroacher enjoys, both with regard to other villagers and to government revenue department officials. Individuals of widely differing socio-economic status encroach upon _peromboke_ land. Landless and near-landless households are perhaps the most numerous, but their position is vulnerable to better-off and relatively more powerful groups who are able to employ intimidatory tactics to evict them. Those with a greater degree of influence may be in a position to see that weaker groups are evicted, yet also manage to evade the law themselves through bribery to allow their encroachment on public and common
Individuals can access their pension savings at age 65 for men and 60 for women, with three withdrawal options: Programmed Withdrawals ( _Retiro Programado_ ), purchase an annuity from an insurance company ( _Renta Vitalicia_ ), or a mix of phased withdrawals for a period of time and a deferred lifetime annuity. The law also allows for early retirement, provided that the worker has pension funds sufficient to generate a pension amount equal to or greater than 110 percent of the minimum pension guaranteed by the state.<sup>13</sup> Prior to the 2008 pension reform, the state provided noncontributory retirement income transfers through two mechanisms. First, a welfare or assistance pension, known as the _PASIS_ pension, equal a little less than a third of the minimum wage was available for program applicants above 65 years of age, irrespective of their contribution history, was allocated based on an index of economic vulnerability, called “ficha CAS”.<sup>14</sup> The second transfer was a minimum pension guarantee (MPG) equal to about twice the PASIS pension. Individuals with more than 20 years of contributions received the MPG if their accumulated contributions could not finance a higher pension. Both of these benefits took the form of a top-up, that is, the benefit was equal to the difference between the guaranteed level and the pension financed by the worker’s account.

# **3.1 Gender pension gaps and the 2008 reform**

In 2008, the pension system underwent significant reforms aimed at alleviating old age poverty and reducing gender gaps in pension accumulations. An analysis of pension contribution histories at the micro level (e.g. Arenas de Mesa et. al. (2007)) showed that most individuals were expected to have low pension accumulations upon retirement.<sup>15</sup> Only 37 percent of women were projected to have a pension above the MPG level, in comparison with 67 percent for men. The average projected replacement rate for women under the pre-reform pension
Policy Research Working Paper 6024

# **Abstract**

This paper investigates the impact of increases in wheat flour prices on household food security using unique nationally-representative data collected in Afghanistan from 2007 to 2008. It uses a new estimator, the Unconditional Quantile Regression estimator, based on influence functions, to examine the marginal effects of price increases at different locations on the distributions of several food security measures. The estimates reveal that the negative marginal effect of a price increase on food consumption is two and a half times larger for households that can afford to cut the value of food consumption (75<sup>th</sup> quantile) than for households at the bottom (25<sup>th</sup> quantile) of the food-consumption distribution. Similarly, households with diets high in calories reduce intake substantially, but those at the bottom of the calorie distribution (25<sup>th</sup> quantile) make very small changes in intake as a result of the price increases. In contrast, households at the bottom of the dietary diversity distribution make the largest adjustments in the quality of their diets, since such households often live at subsistence levels and cannot make large cuts in caloric intake without suffering serious health consequences. These results provide empirical evidence that when faced with staple-food price increases, food-insecure households sacrifice quality (diversity) in order to protect calories. The large differences in behavioral responses of households that lie at the top and bottom of these distributions suggest that policy analyses relying solely on ordinary least squares estimates may be misleading.

This paper is a product of the Economic Policy and Poverty Sector, South Asia 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 adsouza@ers.usda.gov and djolliffe@worldbank.org.

_The
budget data from the Ministry of Finance, the 2008 Agricultural Yearbook from the Ministry of Agriculture and Irrigation, and the latest Household Budget Survey (HBS 2005/2006). These data sources have been complemented with information from the International Monetary Fund and the World Bank. The model is very detailed at the production, commodity, factor, and household levels and includes 65 production activities, 65 commodities, 15 factors of production, and 12 household types.<sup>10</sup> Factors of production include labor according to the skill level (unskilled, semiskilled, skilled) and employment by the public and private sectors.

In addition to the SAM as the main data source to calibrate to a set of parameters in both production and demand functions, a DCGE model also requires several elasticities. The main elasticities include the substitution elasticity between primary inputs in the value-added production function, which determine the ease with which, for example, users of fuel can substitute fuel for other inputs; the elasticity between domestically produced and consumed goods and exported or imported goods; and the income elasticity in the demand functions. The income elasticity with regard to fuel, for example, decides how consumers react to higher prices. We estimated the income elasticity for Yemen from a semi-log inverse function suggested by King and Byerlee (1978) and based on the data of HBS 2005/2006 for rural and urban households separately. These elasticities range from, for example, 0.31 for cereals to 2.2 for transport and 1.95 for fuel, where most elasticities are lower for urban households than for rural.<sup>11</sup> For

> 10 For a detailed list of production activities, commodities, factors of production, household types, and other accounts of the 2007 SAM, see Table A.3 in the Appendix.

> 11 Table A.4 in the Appendix presents the complete list of imputed income elasticities.

9
of the time with more than 5 percent differential attrition.

Panels (c) and (d) of Figure 3 examine how much the estimated treatment coefficient changes using PDS Lasso compared to Ancova as the attrition and differential attrition rates vary. We see a significantly higher mean percentage change in the treatment coefficient when differential attrition is greater than 5 percent, but even so, the mean percent change is only 0.08 standard deviations.

We would also expect PDS Lasso to make more of a difference to standard errors over Ancova when there is no lagged dependent variable available, and PDS Lasso is then potentially able to predict the outcome with other controls. We compared the standard error ratio (PDS Lasso/Ancova) for cases where no lagged dependent variable was available, to those where it was available and also included in PDS Lasso. There is not much difference at the median (0.996 when there is a lagged variable compared to 0.99 with no lagged variable available). However, at the 25th percentile the ratio is smaller (meaning more improvement) at 0.938 with no lag, compared to 0.974 with a lagged dependent variable available.

Finally, using the replication data, we also looked in more detail at the outliers, where PDS Lasso had resulted in the biggest change in standard errors compared to Ancova. Qualitatively we see these arise from cases where there was no lagged dependent variable available, and where just a few<sup>9</sup> controls are chosen by PDS Lasso that help predict the outcome well. For example, the largest reductions in standard errors compared to PDS Lasso occurs in Garbiras-Diaz and Montenegro (2022). They conduct an experiment to look at the effects of encouraging citizen oversight in Colombia. No lagged outcome is available, and the standard error after using PDS Lasso is 50-60% of that using just strata fixed effects. However, almost all of this improvement is coming from the inclusion of a single control: the number of candidates in the
more likely to move for college—such as attending a highly selective college, or having some unobserved traits—also make her more likely to stay in the college city for work.

Through this sorting, population quantity and quality concentrate into big cities. Prior to moving for college, the spatial distribution of ability—as proxied by Saber 11—among high school graduates in our data is already more favorable in big than in smaller cities. Migration only intensifies this advantage, as big cities attract migrants (population quantity) who are highly talented (population quality). Meanwhile, the relatively less able either remain in small and medium cities, or move there from bigger ones.

This sorting, along with agglomeration effects, is reflected in the distribution of wages across individuals and over space. We find that, in cities of every size, migrants earn more than nonmovers.<sup>3</sup> In big cities, top earnings accrue to individuals who move there for college and stay. In medium and small cities, they accrue to individuals who arrived there for work, having gone to college in bigger cities. Importantly, individuals who move to bigger cities after college tend to earn less than those who move to smaller cities.

It is precisely the individuals who move for work, after college, those who separately identify the college and work city size agglomeration effects. Those who move to smaller cities for work are relatively high-ability individuals who attend college in big cities, where they train at selective, private institutions. Although they are not as able as workers in big cities, they are positively selected into their smaller work cities, where they are the most able, highest-earning workers. In contrast, individuals moving to bigger cities for work are not necessarily the most able and have attended less-selective colleges in small and medium cities. Although they are more able than their peers who do not move out of their college city, they are not positively selected into their big work cities. Taken together, the two
Table 9. The impact on Government annual spending and revenues after increase in gasoline prices and <u>providing cash transfers under proposed Government scheme, trillion IRR</u>

|Pre-Ref|Post-Ref|Change|
|---|---|---|
|Subsidies<br>1,170|731|-439|
|Average transfer per capita, daily (IRR)<br>0|13,894|13,894|
|Transfers, Government scheme<br>0|338|338|
|Net total budget<br>1,170|1,069|-101|
|Source: Authors’ calculation using forecasted HEIS 2018/2019.|||
|Note: Transferper capita of 13,894 is the mean transfer based on thegovernment scheme.|||

The proposed mitigating measure (if perfectly targeted) would fully compensate the population from the bottom 50 percent for the direct and indirect impacts from increased gasoline prices. For example, consumption per capita of the population from the poorest decile after the price reform _and_ government transfer will be 8 percent higher than before the reform (Table 10). As a result of cash transfers, poverty might even fall below the pre-reform level. Inequality would be also reduced substantially because the proposed cash transfers play a more important role relative to the budget of poor people (Table 11).

Table 10. Ratio of consumption per capita after reform and after government transfers to pre-reform consumption per capita

||`Daily expenditure`<br>`per capita, pre-`<br>`reform (1)`|`Daily`<br>`expenditure per`<br>`capita after`<br>`reform, (2/1)`|`Daily expenditure per`<br>`capita after reform and`<br>`proposed Government`<br>`scheme(3/1)`|
|---|---