rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3: Poverty headcount and opportunity-sensitive poverty headcount. Levels <!-- Start of picture text --> r = 0.8859 (p=0.00)<br>GR<br>ES LT<br>IT LV<br>PL<br>EE<br>CY<br>FR<br>LU HUBE<br>FI SK<br>NL<br>AT<br>DE<br>CZ<br>0.1000 0.1500 0.2000 0.2500<br> FGT(0)<br>.15<br>.1<br> Op. FGT(0)<br>.05<br><!-- End of picture text --> _Source: Authors’ calculation from EU-SILC (2005)_ as to replicate country _G_ ’s standard poverty incidence, while preserving the crosstype population composition and poverty distribution of country _F_ . The second term corresponds to the _distribution effect_ : with country G’s overall poverty level, it moves from the distribution of poverty across types observed in country _F_ to that of country _G_ .<sup>12</sup> Finally, the third term comprises the _population composition effect_ : it incoporates into the counterfactual distribution the population shares of types in country _G_ .

The decomposition specified in (6) is exact, but it is also path-dependent: the value of each of the three effects would vary if the counterfactual indices were differently specified, corresponding to different ”paths” for the decomposition. One might have changed the population shares first, for example, then the relative poverty incidences across types (given the overall poverty level in country _F_ ), and only shifted to the overall poverty in _G_ in the last step. That such different paths yield different results for the decomposition, and that no path is in any sense more correct than another, is well understood (see, e.g. diNardo, Fortin and Lemieux 1996). So is the solution to the problem, which consists of taking the Shapley-value of each effect across all possible paths of the decomposition (see Shorrocks, 2013). This ”average of decomposition paths” is now commonly known as the ShapleyShorrocks decomposition. With three effects, there are six (3!) possible paths.

Table 3 reports the Shapley-Shorrocks decomposition of the differences in the opportunity-sensitive poverty headcount measure for three pairs of countries: The Netherlands and Lithuania; the Czech Republic and the Netherlands; and Luxembourg and France. These pairs were chosen to exemplify countries ranked very far

> 12Since types are ranked by _Fi_ ( _z_ ), this step includes any necessary re-ranking of types between countries _F_ and _G_ : when the distribution of poverty across types that prevails in country _G_ - _Gi_ ( _z_ ) - is imported into the counterfactual poverty index, the inverse-rank weights associated with each type adjust accordingly.

16
crucial to note that this can have the effect in the limit of making all workers _beneficiaries_ of trade liberalization or of making them all _net losers_ from it. Thus, the common presumption that a phased-in liberalization helps to cushion the blow to affected workers can be exactly wrong in some cases. We offer a simple condition that determines which way it will go.

Note that the dynamics of wages described in our model present a problem for empirical work. Many studies have used reduced-form regressions to study the effect of trade shocks on wages, either across industries (Revenga, 1992), across locations within a country (Kovak, 2013), or both (Hakobyan and McLaren, 2010). An implication of a dynamic model such as ours is that such regressions should be interpreted as measuring the effect of a trade shock on the wage at one point along its evolution to the new steady state, and of course the steady-state effect can be very different from the short-run effect (and as our model makes clear, can even be of the opposite sign).<sup>3</sup> The hazards that these properties present for empirical work are eliminated if one estimates the structural parameters of a dynamic model and uses it as the basis for policy simulations. That is the approach taken in Artu ̧c, Chaudhuri and McLaren (2010) and Artu ̧c and McLaren (2012) on US data; Dix-Carneiro (2014) on Brazilian data; Artu ̧c and McLaren (2010) on Turkish data; and Artu ̧c, Lederman and Porto (2014) on multiple countries’ data.

At the same time, our approach can offer an important piece of assistance to empirical work: In equilibrium, any change that raises the welfare of workers in one location or industry relative to another will result in a rise in flows of workers toward that location or industry, and a reduction of workers in the opposite direction. This can provide a simple but powerful way of looking for welfare effects in a reduced-form study. For example, Kovak (2013) finds that Brazilian workers tend to move out of a state that receives tariff reductions on its major industries, and Hakobyan and McLaren (2010) find an analogous result for US blue-collar workers in response to NAFTA tariff cuts. These can be taken as _prima facie_ evidence that welfare for workers in those locations was reduced relative to other locations.<sup>4</sup>

> 3Another problem for reduced-form studies is that a change in tariff in one industry will affect the wages in all industries. This is generally not accounted for in reduced-form regressions.

> 4Although this is an inference about welfare in one industry or location _relative_ to another, at times it can be used to make a case for an absolute welfare change. Hakobyan and McLaren (2010) cite evidence in the literature that the aggregate welfare effects of NAFTA were negligible; given this, the evident large

6
population distribution and typically policymakers want to know the response over the unconditional distribution. For example, policymakers may be interested in knowing how price shocks affect caloric intake for households at the bottom 25<sup>th</sup> percent of the calorie distribution of the total population, before conditioning on factors such as location or socioeconomic status. Such information can be valuable in targeting safety net and povertyalleviation programs, as well as allocating resources more generally.

In this paper we utilize a recent innovation in quantile regression analysis – the Unconditional Quantile Regression (UQR) estimator proposed by Firpo, Fortin and Lemieux (2009b) – to examine the impact of the wheat flour price shocks on several measures of food security. The UQR estimator uses influence functions to estimate the behavioral responses at specific quantiles of the unconditional distribution of the dependent variable. We compare OLS estimates to the UQR estimates at the 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> quantiles to examine whether the effects differ in informative ways across the distribution. We also examine the marginal effects at quantiles corresponding to thresholds that are policy or nutritional relevant, e.g., the commonly used nutritional benchmark of 2,100 kilocalories per person per day. Measuring responses at these thresholds can help to better inform policymakers of how households on the cusp of poverty and/or malnutrition are affected by price increases. Such information may be particularly salient in a conflict-ridden country like Afghanistan, where a large portion of the population lives close to subsistence levels or in poverty; the national poverty rate for 2007-08 was 36% (M. o. E. Islamic Republic of Afghanistan, and the World Bank Economic Policy and Poverty Sector, 2010).

We find disparities in the behavioral responses of households to increases in the price of wheat flour based on where the household lies on the unconditional distribution of the particular food security measure of interest. While the OLS estimates demonstrate the basic quality-quantity tradeoff that households must make (i.e., reducing dietary diversity to buffer the impact of declining purchasing power on caloric intake), they mask important differences across households. Households at the top of the food consumption and calorie distributions experience the largest declines in each of these measures, as might have been expected given that these households can afford to cut back. In contrast, households at the bottom of the calorie distribution cannot afford to make substantial cuts to caloric intake since they are close to the minimum daily energy requirements; accordingly, we see a very small price effect for these households. Finally households at the bottom of the dietary 3
that the distribution of restrictiveness (across economies and sectors) is easily made transparent at each stage of aggregation—or “CES nest” —showing which kinds of policy measures drive the overall distribution of STRI values (Figure 3).

The overall distribution of the resultant scores is shown in Nest 4 (bottom right of Figure 3). The mass point at a score of 0 depicts the 315 entirely open cases, whereas the mass point at 1 reflects the 371 instances in which individual economy-subsector-mode combinations are closed altogether.<sup>26</sup> In between these polar cases, in which an economy-subsector-mode is either entirely open or entirely closed, the distribution of restrictiveness scores is fairly spread out. That said, two important features of the incidence of policy restrictions, and the way the algorithm deals with it, are apparent from Figure 3. First, most measures from the “operations and regulatory recourse” categories carry individual scores of 0.25 or 0.125, and the joint application of several such measures leads to a distribution of restrictiveness scores within the interval of [0.25, 0.50] for those subsectors that are not entirely open (see Nest 3). This is a reasonable result. Second, the overwhelming majority of high scores—those around 0.75—arise from the application of some sort of quantitative limitation, rather than outright ownership limitations (Nests 1 and 2).

**Figure 3: Scores by Principal Stage of Aggregation**

<!-- Start of picture text --> Nest 1: Ownership and Control Nest 2: Quantitative Limits<br>0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00<br>Nest 3: Operations/Reg Recourse Nest 4: Overall Scores<br>0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00<br>1000 1000<br>Frequency 750500 Frequency 750500<br>250 250<br>0 0<br>1000<br>1000<br>750<br>Frequency 750500 Frequency 500<br>250 250<br>0 0<br><!-- End of picture text --> At this point we have aggregated all individual measure scores to one STRI value per economy, subsector and mode. The nested aggregation described thus far is in principle the same within each mode of supply, even though the nature and number of measures within CES nests (such as ‘ownership and control’) will differ across modes.

The next step is to combine the modal scores to one STRI value per economy and subsector. We do this with a weighted arithmetic average, with a set of weights that differs across subsectors, reflecting the relative importance of the three modes for the provision of a given service (Table 6.2 26 Across sectors, this total comprises of 108 cases from the four Financial subsectors, 176 cases from the five Professional Services subsectors, and 87 cases from across all other remained sectors, half of which are in fact railway freight services.

17
- 19 -

demand, when there is a fiscal constraint to increasing supply, an increase in the price will result in an increase in net economic benefits, as long as the increased revenues are used to expand the supply. This is true even where the efficiency price cannot **be** directly measured. v It would be worthwhile to examine the applicability of this argument to other sectors. Should it prove valid, it provides an elegant link between the efficiency objectives and fiscal constraints to public sector prices.

# issues of Price Structure

72. A theoe running through several sections above is the importance of carefully designing the structure of public sector prices to reflect efficiency and other concerns. **We** discuss below how adjustments in price structures have been **used** in some sectors to reconcile conflicting objectives at minimal cost. From **the** efficiency standpoint alone, however, there are strong arguments for a disaggregated pricing analysis.

73. In the utility and railroad sectors, peak and off-peak tariffs, capacity and energy charges, access and **usage tariffs are** essential to properly reflect LRNCs. While this **is** widely recognized, only in the power sector is it routinely applied in the developing countries. The tendency in most sectors seems to be to focus first on getting average tariff or cost recovery levels up to an acceptable standard, and only then to worry about issues of price structure. There are two fallacies in this view. First, it implies a trade-off between financial and economic objectives that is often absent. There are many ways to increase average revenues, of which some will reflect margiial costs much better than others. Where major tariff revisions are needed, it would seem sensible to focus first on those elements of the tariff which are providing the most misleading signals to consumers. By correcting these, consumption patterns as well as revenues would start to move in the right direction. Second, an unfortunate degree of mystery surrounds the idea of marginal cost, making it appear a sophisticated objective that can only be approached through elaborate tariff studies. This is not the case. The information requirements for marginal cost calculations are generally the same as those required for project design and least-cost evaluations. Marginal costs can be calculated on the back of an envelope if need be, and are generally easier to estimate than financial requirements because they do not consider inflation rates, debt repayment terms, historical asset values, and so forth.

74. In many sectors there seems to be an increased focus on aggregate measures of--and covenants on--project or sectoral cost recovery. These are routinely used in irrigation, roads and housing projects. Power and water projects typically include only financial covenants although the EARs generally provide analyses of the adequacy of tariff structures. Where there are clear problems in the structure of prices withir a sector, or where there is a clear price candidate to shoulder the major part of a needed overall increase, more closely targeted price covenants should be encouraged.
**<u>Table 7: Household-level determinants of the difference between recall and diary</u>**

||Entire sample|Completed only|
|---|---|---|
|Household size|10.07***|7.573**|
||(2.936)<br>|(2.052)<br>|
|Head's education|-6.639***|-6.043**|
||(-2.679)|(-2.331)|
|Head's age|-0.139<br>|-0.0956<br>|
||(-0.216)|(-0.140)|
|Main occ. agriculture|41.69*|51.57**|
||(1.862)|(2.152)|
|Production shock|-13.40|-11.94|
||(-0.632)|(-0.533)|
|Owns cattle|-48.97**|-69.55***|
||(-2.189)|(-2.902)|
|No of parcels cultivated|5.790|8.791|
||(0.793)|(1.141)|
|Total area owned|-0.0436|-0.0507|
||(-0.175)|(-0.201)|
|Total area cropped|-66.64***|-66.46***|
||(-17.92)|(-16.77)|
|Log of yield|-165.0***|-165.3***|
||(-14.84)|(-13.79)|
|Share of area managed by females|22.45|27.21|
||(0.998)|(1.136)|
|Share of area pure stand|-50.26|-57.32|
||(-1.390)|(-1.499)|
|Overlap of at least 3 months|-17.80|-13.61|
||(-0.496)|(-0.357)|
|Months in sample = 2|145.2**|169.6**|
||(2.288)|(2.308)|
|Months in sample = 3|204.9***|214.6***|
||(3.213)|(2.943)|
|Months in sample = 4|352.5***|379.0***|
||(5.812)|(5.459)|
|Months in sample = 5|508.5***|548.4***|
||(8.482)|(8.081)|
|Months in sample = 6|601.3***|648.5***|
||(10.41)|(9.939)|
|Constant|124.5|94.08|
||(0.209)|(0.156)|
|Observations|4,238|3,819|
|R-squared|0.422|0.421|
|**_Tests (joint sig. of dummies for):_**|||
|<br>Months in sample (MIS)|20.95***|19.41***|
|Enumeration area|2.91***|2.72***|
|Enumerator|1.09|1.06|

t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1 22
# **1. Inequality in Latin America Unambiguously Declined in the 2000s**

There is substantial evidence suggesting that inequality in Latin America unambiguously 3 declined in the majority of countries in the 2000s. From a weighted average of 0.548 in the late 1990s, the Gini coefficient for household per capita income fell to 0.488 in the late 2000s. While during this period inequality in other regions of the world rose, the Gini coefficient declined in 16 of the 17 Latin American countries for which there are 4 comparable data (Figure 1). The decline is statistically significant in all 16 countries.

**Figure 1: Declining income inequality in Latin America, by country: 2000-2011** _Annual % change in the Gini coefficient_

<!-- Start of picture text --> 3.00<br>2.12<br>2.00<br>1.00 0.82 0.77<br>0.61<br>0.40<br>0.00<br>-0.20 -0.10<br>-0.47 -0.39<br>-1.00 -1.30 -1.24 -1.17 -1.07 -1.03 -0.91 -0.79 -0.74 -0.72 -0.95<br>-2.00<br>-2.05 -1.99<br>-2.64<br>-3.00<br>Nicaragua Bolivia Ecuador Argentina El Salvador Mexico Venezuela Brazil Peru Dom. Rep. Panama Chile Costa Rica Paraguay Uruguay Guatemala Honduras LAC-17 China South Africa India USA<br><!-- End of picture text --> _Source_ : Authors’ calculations, based on data from SEDLAC (CEDLAS and The World Bank), May 2013 for Latin American countries; World Bank (World Development Indicators) for China, India, and South Africa; and OECD (OECD Stats) for the United States. _Note_ : Data for Argentina and Uruguay are for urban areas only. In Uruguay, urban areas covered by the survey represent 80 percent of the total population; in Argentina, they represent 66 percent. The average change in the Gini for each country is calculated as the percentage change between the end year and the initial year, divided by the number of years. The average for the total is the simple average of the changes by country (sixteen countries in which inequality fell). The following time periods were used to estimate the percentage changes: Argentina (2000-12), Bolivia (2001-11), Brazil (2001-11), Chile (2000-11), Costa Rica (2001-10), Dominican Republic (2000-11), Ecuador (2003-11), El Salvador (2000-10), Guatemala (2000-11), Honduras (2001-11), Mexico (2000-10), Nicaragua (2001-09), Panama (2001-12), Paraguay (2001-11), Peru (2000-11), Uruguay (2000-11), and Venezuela (2000-11). The periods used for non-Latin American countries are as follows: China (1996-2005), India (1994-2005), South Africa (1995-2009), and United States (1995-2007).

> 3 See, for example, Azevedo et al. (2012); Azevedo et al. (2013); Cornia (2013); Cruces et al. (2011); Gasparini et al. (2011); Gasparini and Lustig (2011); Lopez-Calva and Lustig (2010); and Lustig et al. (2013), who also suggest that the decline in inequality during the 2000s is robust to the selection of income definition, inequality measure, and data source.

> 4 Colombia is excluded from the regional averages and from the changes shown in Figure 1 due to problems of data comparability across years.

2
22 Most pension funds aim at a replacement rate of approximately 50 percent to 60 percent of the coordinated (insured) income. Very few specify an upper bound on this insured income. Together with the income from the first pillar and the fact that there are no social security deductions on pension benefits, the net replacement rate _before_ taxes amounts to 70-80 percent for workers with an uninterrupted working history even for high income groups as shown in Table 7 and Figure 10. Due to the fact that federal and cantonal taxes in Switzerland are progressive, and also as a consequence of the availability of additional children pension benefits, the effective net replacement rate can be well above 100 percent as illustrated in Table 7.

|**Table 7 Pension Benefits as a Function of Pre-retirement Income**|
|---|

|Before retirement<br>Gross income||50|||100|||200||
|---|---|---|---|---|---|---|---|---|---|
|Marital status|Sing|marr|m+2|sing|marr|M+2|sing|marr|m+2|
|Net income|41|42|44|73|77|80|135|143|147|
|After retirement||||||||||
|I = First pillar|20|30|36|25|38|46|25|38|46|
|II = Second pillar|12|12|17|37|37|52|87|87|122|
|Net(I + II - tax)|30|40|52|55|68|89|92|106|139|
|Replacement rates||||||||||
|Gross|0.65|0.85|1.07|0.63|0.75|0.98|0.56|0.63|0.84|
|Net|0.75|0.95|1.18|0.75|0.88|1.11|0.71|0.78|0.98|

(in 1000 Swiss Francs) and marital status (sing = single, marr = married with adult children, m+2 = married with two children under 18/25). The computations are based on the following (very realistic) assumptions: The spouse does not have any second pillar income, but qualifies for the same first pillar pension as the main bread winner (mainly through child care credits and part-time income) in the married adult with children case. For the married with two minor children case, it is assumed that the spouse (for obvious reasons the wife) is too young to claim her own benefits. The pension fund replaces 50 percent of coordinated income (= income - 25,300) with no upper income limit. Children benefits are 40 percent (first pillar) and 20 percent (second pillar) of the main claimant’s benefits each. The tax base is the city of Zürich.

Source: Authors' calculations

**Figure 10  Replacement Ratios as a Function of Income: 1st and 2nd Pillar**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>10% 25% 50% 75% 90% average<br>Pension 1st Pillar Pension 2end Pillar Other income<br><!-- End of picture text --> Note: Only individuals aged between 65 years and 70 years in 2002 are considered. The average monthly incomes per percentile are 2,947; 5,155; 7,521; 9,962; 13,263 with an overall average of 5,765 CHF. Source: Einkommens- und Verbrauchserhebung (EVE) 2002
31 was more than 50 percent greater than the fraction of poorer non-democracies. Compared to rich countries or rich democracies, poorer countries and poorer democracies also exhibit significantly younger ruling parties at the beginning of leader terms and higher average rates of programmatic parties overall.

**Table 6:  The organization of citizens for collective action across richer and poorer countries (2008)** **<u>(Number of countries in parentheses)</u>**

||All c|ountries|Com<br>elec|petitive<br>tions|No comp<br>electi|etitive<br>ons|
|---|---|---|---|---|---|---|
||Poor|Rich|Poor|Rich|Poor|Rich|
|Age of largest governing<br>party when leader took|18.2<br>(73)|37.7<br>(69)|19.4<br>(42)|39.2<br>(62)|16.5<br>(31)|24.4<br>(7)|
|power|||||||
|Average of programmatic|.39|.73|.39|.76|.38|.44|
|dummy variables over all<br>parties|(74)|(69)|(43)|(62)|(31)|(7)|
|Both largest government and|.15|.62|.16|.68|.12|.14|
|largest opposition party are<br>programmatic|(68)|(69)|(43)|(62)|(25)|(7)|

**Note** :  The comparison is of countries in 2008.  Poorer and richer countries are those below or above the median per capita income in 2008; democracies are countries with competitive elections (legislative and executive indices of competitive elections from the Database of Political Institutions equal to seven)

Neither difference is significant comparing poor and rich non-democracies and ruling party age is actually somewhat lower in richer non-democracies. However, oil-rich countries disrupt this last comparison. As Gehlbach and Keefer (2009) argue, leaders of resource rich countries are less likely to institutionalize their political parties, but incomes are still likely to be high. Consistent with this, if Middle Eastern countries are excluded from the comparisons in Table 6, all of the other differences remain approximately the same, but the party years variable remains approximately 16 in the poor non-democracies, but rises to 24 in the (now only seven) rich non-democracies.