rafmacalaba/datause-encoder-data huggingface dataset:
wage rigidity. The estimates also imply that the larger is the share of permanent workers and/or the larger is the EPL index, the stronger is this effect. These results are in line with our expectations, since the existence of permanent contracts complemented with strict labour regulations gives workers more leeway in wage negotiations, which in turn should lead to greater wage rigidity. In particular, it is harder for firms to cut workers’ wages if the threat of lay-off is more difficult to implement. Thus, permanent contracts impose greater wage rigidity than temporary contracts as long as permanent workers are more protected by labour regulations. As a consequence, the effect of permanent contracts on wage rigidity should be more significant in countries with stricter employment protection.

The WDN survey contains information on the structure of agreements applicable for a given firm. Managers were asked if a collective wage agreement exists and if so, whether it is a firm-level agreement or a binding agreement that was negotiated at a level outside the firm (e.g. national, sector level, etc). We use this information to analyse the implications that the union contracts negotiated at different levels have on wage rigidity. For this purpose, we construct three non-nested dummy variables that characterise the type of union contract(s) applying to the firm; the first indicating the existence of only a firm-level agreement, the second signifying only an outside agreement, and the third being equal to one if a firm has both firm-level and outside agreements.

Appendix 7 gives an overview of the cross-country differences in the incidence of union contracts negotiated at different levels. This comparison reveals striking contrasts in the tendency of different types of union contracts across the sampled countries. In particular, there is a group of countries (Austria, Belgium, Spain, France, Italy and Slovenia) where almost all firms have union contracts and also display a very high incidence of higher-level bargaining agreements. On the other hand most of the sampled non-euro area countries (Estonia, Hungary, Lithuania and Poland) have very few firms with higher-level agreements.

We can expect that the effects of union contracts negotiated at different levels will be heterogeneous across countries, since different aspects of wage setting that matter for wage rigidity can be applied at the higher level (sectoral or national) in some countries and at the firm level in others. This is especially relevant regarding wage indexation, which we use as an indicator of real wage rigidity. Similarly, the impact of firm-level contracts is likely to differ across countries depending on the most prevalent wage-setting norm in the economy: a firmlevel contract may buy some additional flexibility in countries where the most common negotiation is outside the firm, while it might impose additional rigidity in a country where 27
**Table 6:  The Role of Taxes and Social Benefits in Inequality Trends, 1994-2002**

||**1994**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|
|---|---|---|---|---|---|---|---|---|---|
|**Concentration Coeff**|**icients*:**|||||||||
|Taxes|0.311|0.307|0.318|0.328|0.322|0.367|0.386|0.400|0.413|
|Benefits|-0.270|-0.316|-0.318|-0.334|-0.374|-0.367|-0.371|-0.365|-0.362|
|**Progressivity**:**||||||||||
|Taxes|-0.070|-0.072|-0.071|-0.069|-0.045|-0.012|-0.010|0.002|0.004|
|Benefits|-0.650|-0.695|-0.707|-0.71|-0.72|-0.746|-0.767|-0.762|-0.771|

Method: * Household ranked by original income. ** Kakwani index.

_Source:_ Authors’ calculations based on HBS.

An alternative way of looking at the effect of fiscal policy on poverty is to consider its redistributive effect. This can be done by comparing the Lorenz curve for original income with the concentration curves for taxes and benefits. Figure 8 compares the redistributive impact of the system in 1998 and 2002. The comparison points to some interesting conclusions. The first surprising result is that in 1998 the concentration curve for taxes dominated the Lorenz curve for original income (i.e., it was above the Lorenz curve for all points in the income distribution). This suggests that the overall tax system was regressive (i.e., that the poor paid more than the better-off as a proportion of their income). However, since the distance between the two curves was very small, the magnitude of the redistribution involved was very limited. In 2002 the situation for the poorest individuals had not changed. The share of taxes that they paid exceeded their share of income, which pointed to a redistribution away from the poor. However, in 2002 the concentration curve for taxes crossed the Lorenz curve, making impossible to derive firm conclusions about the overall level of redistribution.

**Figure 8: Progressivity of the Tax and Benefit System in 1998 and 2002**

<!-- Start of picture text --> 100 100<br>1998 2002<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 45 degree line 30<br>45 degree line<br>Original inco me<br>20 20 Original inco me<br>Taxes<br>10 B enefits 10 Taxes<br>B enefits<br>0 cumulative % of population 0 c umulative % of population<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>cumulative amount, % of total<br>cumulative amount, % of total<br><!-- End of picture text --> It is in cases like this that it becomes important to use an index of progressivity such as the Kakwani index, which measures (twice) the area between the Lorenz curve for pre-tax income and the concentration curves for tax liabilities and received benefits.<sup>12</sup> Table 6 above gives the values of the Kakwani indexes for taxes and benefits over the period 1994-2002. These values

> 12 Kakwani (1977).

12
# VII. CONCLUSIONS

In this paper we assess the performance of a “no-price” method of deflating for cost-of-living differences over time and space. Such methods are relied upon by some researchers because many large developing countries do not have spatially disaggregated price surveys. Yet such countries are exactly the place where spatial deflation is needed since it is implausible to assume that prices are the same everywhere, with high internal transport costs and an absence of major brands and chain stores setting prices on a national basis. Moreover, for developing countries emerging from a planned economy past, like China and Vietnam, spatial price differences may be growing since urbanization and the development of urban land price differentials is starting from a low base, and so the need for spatial deflation is unlikely to be reduced in the near future.

The method assessed here relies on estimating a food Engel curve and defining the deflator as that needed for nominally similar households to have the same food budget shares in all regions and time periods. This method has been widely used in the literature examining bias in temporal price indexes, where it generally yields results that concur with what theory and other empirical approaches have suggested, in terms of the CPI being an upwardly biased measure of changes in the true cost of living. But there is much less guidance from either theory or from other empirical approaches about spatial deflators. The Balassa-Samuelson effect leads one to predict that the price level will be higher in regions and sectors where nominal incomes are higher, so spatial deflation should show less inequality, but one also can conceive of pathways by which people living in poor areas face higher costs (Muller 2002). Consequently, with more diffuse priors about spatial patterns in the cost-of-living, any empirical evidence—including from “no-price” approaches like the Engel curve method—may be quite influential. This makes the experience of Vietnam in 2010 34
for GSP LDC products, which raises the concern that their estimated positive impact on nonapparel products confounded the gains from GSP LDC and AGOA. Column (8) of Table 2 uses their 1998-2006 sample period and shows a significantly larger response for apparel products and a smaller response for GSP LDC products, relative to our baseline responses. The differences may be due to our longer pre-AGOA period but also to our control for differential pre-existing trends for treated countries and products.

# **_6.3 Robustness:  Export Creation or Export Diversion?_**

Our findings of an average increase in African exports resulting from US trade preferences under AGOA and GSP LDC can have two interpretations. First, that the increase did not represent new export creation but simply the redirection/diversion of African exports from other trading partners to the US in response to the preferences. Second, that there was indeed a real increase in total exports, possibly accompanied by an increase in exports to other destinations, but it was primarily due to domestic developments in exporting countries, favoring specific products in specific years, such as the establishment of SEZs for apparel which are country-product-year level changes not possible to control for even by our stringent set of fixed effects.

We assess how African exports to other destinations changed in response to the AGOA preferences. If exports to other destinations decline as much as those to the US increase, then trade diversion is likely. If exports to other destinations also increase, then LBD or domestic sectorspecific supply side factors are (also) likely to be playing a role. We re-estimate Eq. (1) using EU imports as the dependent variable, given the EU’s importance as a major trading partner for most African countries. Column (2) of Table 3 shows a negative and significant coefficient on the AGOA apparel term, suggesting that the AGOA trade preferences reduced African apparel exports 22
**Figure 4. Kernel density estimates of the proportion of time experienced as pleasant by gender and type of activity**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Total work Labor market work<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Household work Leisure<br>Women (N=91) Men (N=70)<br>2.5 2.5<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>2.5 8<br>2<br>6<br>1.5<br>4<br>1<br>2<br>.5<br>0 0<br><!-- End of picture text -->

# **Source:** Figure 4.2 from Seymour and Floro (2016).

**Notes:** The dotted lines show the mean values among men and women, respectively. Only the difference for household work is statistically significant by conventional thresholds. Total work includes: work as employed; own business work; farming; construction; fishing; other work; collecting water; collecting firewood; vegetable gardening; animal husbandry; caring for children; caring for the sick/elderly; cooking; shopping/going to the market; cleaning the home; weaving, sewing, and textile care; and other domestic work. Labor market work includes: work as employed; own business work; farming; construction; fishing; and other work. Household work includes: collecting water; collecting firewood; vegetable gardening; animal husbandry; caring for children; caring for the sick/elderly; cooking; shopping/going to the market; cleaning the home; weaving, sewing, and textile care; and other domestic work. Leisure includes: traveling; watching television; listening to radio; reading; sitting with family; social activities; and other leisure.

33
- Haushofer, J. and J. Shapiro (2018). The long-term impact of unconditional cash transfers: experimental evidence from kenya. _Busara Center for Behavioral Economics Working Paper, Nairobi, Kenya_ .

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36
The 71 percent of countries in _Europe and Central Asia_ with interest rate caps had a lower proportion of people receiving loans in the past year, 60 percent had lower competition as measured by H- statistics<sup>27</sup> , and 71 percent had a credit-depth information index lower than the regional average. In addition, 57 percent of the countries with these caps had a higher share of state-owned banks than the regional average, while those not imposing interest rate caps had a ratio of state-owned banks below the regional average.

In _Latin American and the Caribbean_ **,** most of the countries with interest rate caps have a creditto-GDP ratio, account penetration, and strength of legal rights index lower than the regional average (79 percent). Another interesting fact is that in only 6 percent of the countries with caps the share of stateowned banks is higher than the regional average. The concentration of banking assets and competition in the banking system do not seem to be common financial system characteristics among the countries in the region with caps.

In the _Middle East and North Africa,_ most of the countries with interest rate caps have a credit-toGDP ratio lower than the regional average (67 percent) and a lower proportion of people receiving a loan in the past year (80 percent). In addition, 67 percent of them have a three-bank asset concentration index higher than the regional average. Similar to other regions, private bureau coverage, depth of credit information, and strength of legal rights are below the regional average in countries that cap interest rates.

In _South Asia_ **,** two of the three countries that cap interest rates on loans have a lower share of people taking out loans from formal financial institutions than the regional average. These countries also have less a competitive banking sector, according to the H-statistics index, and two of them trail the Lerner index<sup>28</sup> **.** Banking concentration, however, is lower than the regional average in all three countries, and the average credit-to-GDP ratio for the period 2008–12 is higher than the regional average in all of them.

Although _Western European_ countries with interest rate caps score high on financial inclusion as measured by account penetration, the share of people receiving loans is lower than the regional average in all of them. They also score low on competition in the banking sector, according to Lerner index (73 percent), low on the depth of credit information (82 percent), and low on legal rights (73 percent). As in

> 27 H-statistics measures the elasticity of banks revenues relative to input prices. Under perfect competition, an increase in input prices raises both marginal costs and total revenues by the same amount, and hence the H-statistic equals 1. Under a monopoly, an increase in input prices results in a rise in marginal costs, a fall in output, and a decline in revenues, leading to an H-statistic less than or equal to 0 (GFDD, 2013)

> 28 The Lerner Index is a measure of market power in the banking market. Higher values indicate less bank competition .

22
Table 5

# The Gains From Agricultural Tariff Liberalization Across Commodities

|||Vi|etnam|||||
|---|---|---|---|---|---|---|---|
|Product|Aggregate<br>Gains|St.Dev.<br>Gains|Consumption<br>Efects|Income<br>Efects|Pro-Poor<br>Bias|Gains<br>Poor|Gains<br>Rich|
|Aggregate|1.99|0.82|5.52|-3.53|-1.70|1.04|2.74|
|Animal fats|0.17|0.07|0.17|0.00|0.20|0.28|0.08|
|Apples|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Banana|0.88|0.23|0.92|-0.04|0.59|1.16|0.57|
|Beans|0.01|0.01|0.01|-0.00|0.00|0.01|0.00|
|Beef|0.02|0.07|0.09|-0.06|-0.15|-0.06|0.10|
|Beer|0.12|0.05|0.12|0.00|-0.10|0.05|0.16|
|Cashew|-0.02|0.03|0.00|-0.02|0.01|-0.01|-0.02|
|Cheese|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Cigarettes|0.61|0.17|0.61|0.00|0.31|0.70|0.39|
|Citrus|0.00|0.06|0.04|-0.04|-0.04|-0.01|0.02|
|Cloves|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Cocoa|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Coconut|-0.01|0.01|0.00|-0.01|0.00|-0.01|-0.01|
|Cofee|-0.15|0.12|0.03|-0.18|0.01|-0.13|-0.14|
|Corn|-0.13|0.15|0.01|-0.14|-0.35|-0.36|-0.01|
|Cotton|-0.00|0.00|0.00|-0.00|-0.00|-0.00|-0.00|
|Eggs|0.04|0.06|0.09|-0.05|0.04|0.06|0.02|
|Fish|0.37|0.12|0.50|-0.13|0.16|0.42|0.26|
|Grapes|-0.00|0.01|0.00|-0.00|-0.00|-0.00|0.00|
|Greens|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Milk|0.02|0.01|0.02|-0.00|-0.01|0.01|0.02|
|Other|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Other Dairy|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Other Processed Food|0.92|0.26|0.92|0.00|-0.66|0.55|1.22|
|Other Spices|0.09|0.08|0.13|-0.04|0.17|0.18|0.01|
|Other alcohol|0.17|0.07|0.17|0.00|0.18|0.26|0.08|
|Other cereals|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Other crustaceans|-0.02|0.06|0.03|-0.05|-0.04|-0.03|0.01|

Notes: Authors’ calculations based on HIT data. The table presents the gains associated with the elimination of Vietnam’s own agricultural import tariffs.

34
<!-- Start of picture text --> 2011 Rural: Women & Poverty<br>poverty-female circumcision-girls' education<br>\<br>\<br>Nf<br>| 1.Zl¥<br>\ /<br>x Fs<br>Ty ae<br>——— below_poverty line --------- female_circumcision<br>—-—-- girls_education<br>population: approx. 34677100<br><!-- End of picture text -->

<!-- Start of picture text --> 2011 Urban: Women & Poverty<br>poverty-female circumcision-girls' education<br>\<br>\<br>H<br>\"f<br>j<br>4 /<br>eeal<br>——— below_poverty_line —--------- female_circumcision<br>—'—"* girls_education<br>population: approx. 4989400<br><!-- End of picture text -->
in Dabnid Ham. In addition, Lotte Japan controls 24% of Lotteria, which controls 13% of the votes in Daehong Communications. The latter ownership chain adds to the level of ultimate control since the weakest link in the chain now becomes Deahong which has 24% voting rights in Dabnid Ham. The control of Dabnid Ham by the Kyuk Ho Shin family is ensured with only 4% of the cash-flow rights in the company, through the use of both pyramiding (Samnam Foundation controls Daehong which in turn controls Dabnid Ham) and cross-holding (the additional votes that Lotteria has in Daehong).

We also study the ultimate ownership structure of Pusan Finance. The Kyuk Ho Shin family has a 39% direct control of Pusan Finance. In addition, Lotte Japan has a 27% control of Lotte Industrial, which in turn has a significant stake (12%, not reported in the figure) in Pusan Finance. Surmming up the two chains of ultimate ownership, the controlling family has 51% of the control rights in Pusan Finance, while holding 46% of the cash-flow rights.

**Figure 1: The Lotte Group**

(Immediate Control/Ultimate Cash-Flow Rights) <!-- Start of picture text --> K Yyuk Ho Shin Famnily|<br>34/34 100/tOO<br>La Jao.3 | Samnam Foun.dation ----.<br>41113 2v 32129 23t23 26/26 19(14 19115 71WM 17/l1 39139 329 211<br>27/9 14/5 16/5 27/9 20/6112/4 2 27n' 3116 |2V5 1 /5 23/S 26/5<br>42/4 22/2<br>Honn Ch-ni-lse K..na DiOibw,on<br><!-- End of picture text -->

12
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