rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Bottom and top decile earnings versus the median: within and between size categories**

|||**Decil**|**e 1**||**Decile**|**10**|
|---|---|---|---|---|---|---|
|**Size class (**_j_**)**|_Nj,D_1<br>_ND_1| ̄_yj,D_1|_Nj,D_1<br>_ND_1 <sup>_∗_ ̄</sup><sup>_yj,D_1</sup>|_Nj,D_10<br>_ND_10| ̄_yj,D_10|_Nj,D_10<br>_ND_10 <sup>_∗_ ̄</sup><sup>_yj,D_10</sup>|
||(1)|(2)|(3)|(4)|(5)|(6)|
||||**Latin A**|**merica**|||
|Self-employed without employees|0.640|11.89|7.59|0.199|4.78|0.95|
|Worker in _≤_10 employee business|0.278|8.71|2.22|0.098|4.15|0.40|
|Worker in 11+ employee business|0.029|8.38|0.21|0.498|5.29|2.61|
|Owner of _≤_10 employee business|0.053|10.59|0.95|0.159|6.08|0.98|
|Owner of 11+ employee business|–|–|–|0.046|12.65|0.57|
|**Total**|**100%**|**9.89**|**10.97**|**100%**|**6.59**|**5.51**|
||||**United **|**States**|||
|Self-employed without employees|0.151|7.11|1.07|0.056|7.27|0.41|
|Worker in _≤_10 employee business|0.210|6.31|1.33|0.062|5.69|0.36|
|Worker in 11+ employee business|0.615|5.94|3.65|0.780|5.24|4.09|
|Owner of _≤_10 employee business|0.024|6.61|0.16|0.073|6.02|0.44|
|Owner of 11+ employee business|–|–|–|0.029|5.93|0.17|
|**Total**|**100%**|**6.49**|**6.21**|**100%**|**6.03**|**5.46**|
|||**L**|**atin America **|**- United **|**States**||
|Self-employed without employees|0.489|4.78|6.51|0.142|-2.49|0.54|
|Worker in _≤_10 employee business|0.068|2.40|0.89|0.036|-1.54|0.05|
|Worker in 11+ employee business|-0.587|2.44|-3.44|-0.281|0.05|-1.48|
|Owner of _≤_10 employee business|0.030|3.98|0.79|0.086|0.06|0.54|
|Owner of 11+ employee business|–|–|–|0.017|6.72|0.40|
|**Total**|**–**|**3.40**|**4.76**|**–**|**0.56**|**0.05**|

**Note** : This table presents results for the different components of the decomposition equation (1), for each business size class. The “Total” row in column (3) reports the overall own-to-median income gap in the respective decile, while in column (2) it reports its within component. The gap between the two is the “between” or “allocation” component. In the other rows, corresponding to individual size categories, column (3) is the product of columns (1) and (2) for a given country (not directly evident in the top panel because the figure shown is an average across countries). The bottom panel presents differences between the middle and top panels. Sources and list of LATAM countries included in the top panel are as in Figure 1 .

The large role played by this between component in LATAM’s larger inequality versus the U.S. reflects the fact that, within D1 workers, the region assigns a much larger fraction of the workforce to the categories with the lowest earnings (i.e., the largest earnings gaps versus the median), compared to the U.S. For D1 individuals, self-employment and ownership of businesses with below 10 employees are the categories that exhibit the largest (negative) gaps versus median 18
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45
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# **Table C.36: Role model analysis: Robustness to different specifications and variable transformations**

||||**Panel A: Person**|**al income (Taka**|**)**|||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|exposed|235_._54 **|135_._37|249_._89|171_._59|249_._89|84_._09|_−_0_._09|
||(117_._85)|(272_._77)|(274_._32)|(114_._50)|(274_._32)|(_._)|(0_._07)|
|N|488|488|488|488|488|488|488|
|Non-exposed mean|9387_._21|9387_._21|9387_._21|9387_._21|9387_._21|9387_._21|9_._61|
|Non-exposed SD|2863_._31|2863_._31|2863_._31|2863_._31|2863_._31|2863_._31|1_._45|
|||**Panel B: Women**|**’s decision-maki**|**ng power (Index**|**)**|||
||(1)|(2)|(3)|(4)|(5)|(6)||
|exposed|0_._36***|0_._23**|0_._36***|0_._21**|0_._36***|0_._30***||
||(0_._09)|(0_._09)|(0_._10)|(0_._09)|(0_._10)|(0_._08)||
|N|488|488|488|488|488|488||
|Non-exposed mean|_−_0_._26|_−_0_._26|_−_0_._26|_−_0_._26|_−_0_._26|_−_0_._26||
|Non-exposed SD|1_._08|1_._08|1_._08|1_._08|1_._08|1_._08||
|||**Panel C: M**|**en’s expenditur**|**es (% in househ**|**old income)**|||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|exposed|_−_1_._06 **|_−_1_._19 ***|_−_1_._15 ***|_−_1_._32 ***|_−_1_._15 ***|_−_1_._22 ***|_−_0_._74 ***|
||(0_._44)|(0_._42)|(0_._40)|(0_._44)|(0_._40)|(0_._40)|(0_._23)|
|N|488|488|488|488|488|488|488|
|Non-exposed mean|4_._69|4_._69|4_._69|4_._69|4_._69|4_._69|6_._75|
|Non-exposed SD|5_._25|5_._25|5_._25|5_._25|5_._25|5_._25|2_._05|
|Method|PDS|OLS|OLS|OLS|OLS|PDS|PDS|
|Controls|PDS|No|No|Linear|Theory|PDS with interactions|PDS|
|Factory F.E.|PDS|No|Yes|No|Yes|PDS|PDS|
|Enumerator F.E.|PDS|No|Yes|No|Yes|PDS|PDS|
|Month F.E.|PDS|No|Yes|No|Yes|PDS|PDS|
|Imbalanced controls|PDS|No|Yes|No|Yes|PDS|PDS|
|Transformation|||||||IHS|

Notes: Exposed is a dummy variable indicating whether the female sewing-line operator was working on lines to which the newly selected female supervisors were assigned to trial for the promotion program. The non-exposed group is a dummy variable indicating whether the female sewing-line operator was working on lines unrelated to the promotion program, and represents the comparison group for the exposed group. Standard errors clustered at the production line level in parentheses. IHS = Inverse hyperbolic sine transformation of the Taka amount.

98
21 When all effects are taken together, the net impact of higher food prices in 2008 in Brazil was substantially different in rural and urban areas – and this was driven primarily by the offsetting positive market income effect. The negative expenditure effect was in fact stronger in rural areas, where people tend to be poorer and spend a greater proportion of their incomes on food. But higher earnings from food production may have considerably alleviated those effects. Under the more optimistic assumption of 100% pass-through from food prices to agricultural wages, there may have been substantial welfare gains among the rural poor. Rural extreme (moderate) poverty incidence would have fallen by approximately six percent (two percent), and the higher-order FGT indices would also have declined. Under a less optimistic pass-through scenario (α = 0.5), poverty and inequality would have increased as a result of higher food prices, even in rural areas, but much less markedly than when the market income effect is ignored. For the country as a whole, the net effect of higher food prices on poverty is positive (i.e. poverty increases) for all three measures and for both poverty lines, even if α = 1.0. But instead of rising from 11.0% to 13.5% (the expenditure effect), extreme poverty increases to 12.3% in net terms. Moderate poverty rises from 31.3% to 33.4% when all effects are taken into account, instead of to 35.1% under the expenditure effect alone.

# **6. Conclusions**

In this paper we combined spatially disaggregated data on changes in the consumer prices of sixteen food categories with detailed information on household consumption patterns, individual occupations and incomes in Brazil, to investigate the distribution of the welfare consequences of rising food prices in 2007-2008. These price increases were substantial: while headline inflation averaged 5.3% in the two-year period, food price inflation peaked at over 18% in mid-2008.

Because Brazil is a large producer and net exporter of food, rising international food prices should generate aggregate income gains for the country. Any meaningful estimate of the welfare consequences must therefore seek to account for impacts on the income side, as well as for the standard expenditure effects usually captured by the compensating variation of the price increases for consumers. Furthermore, agricultural production in Brazil – and in many other emerging and advanced economies – relies predominantly on a wage-earning labor force, rather than on the traditional family farms that both buy and sell food. Estimates of the impact of rising food prices on labor incomes could not, therefore, rely only on net household purchases (or sales) of food, even if
> <sup>16</sup>

Johnson and Robinson 2001, 2002). In all cases, each of these instruments has been introduced into the literature as a measure of underlying political institutions rather than as a direct estimate of property rights. As a consequence, to the extent that they are valid instruments for property rights in growth equations, one would expect them also, logically, to be reasonable instruments for institutions of various kinds in the growth equations. In the current context, however, only colonial origin variables are (generally) valid.

# **Other controls**

The specification here is parsimonious, allowing the estimates of institutional variables to reflect both their direct and indirect effects. However, two additional controls are always included, land area and total population. These capture exogenous variation across countries in the size of the market, which in turn might have a significant impact on the development of the financial sector. Extensions of the base regressions encompass other variables, including the legal origin of countries.

# **Results**

Tables 1 and 2 present the base results that support the main contentions of the paper. Tables 1a and 1b, using, respectively, ordinary least squares and two-stage least squares estimates of equations (1) and (2), show that objective measures of political checks and balances and, to a lesser extent, competitive elections, have a significant influence on financial sector development. These results stand in contrast to those reported by Beck, et al. (2001), but are consistent with the evidence in, for example, North and Weingast (1989).<sup>5</sup> After controlling for endogeneity the results strengthen: both magnitudes and statistical

> 5 Beck, et al. (2001) show that initial values of subjective measures of democracy from the Polity III database are not significant determinants of financial sector development.  They also use _current_ , not initial, values of the checks and balances measure used here and find it has a significant impact on financial sector development.
# **Croatia - Sectoral ETCR indicators**

<!-- Start of picture text --> Airlines<br>6.0<br>5.0<br>4.0<br>Croatia<br>3.0 EU-15 average<br>OECD average<br>2.0<br>1.0<br>0.0<br>1975 1985 1995 2005<br><!-- End of picture text --> <!-- Start of picture text --> Airlines - Entry Barriers<br>6.0<br>5.0<br>4.0<br>Croatia<br>3.0 EU-15 average<br>OECD average<br>2.0<br>1.0<br>0.0<br>1975 1985 1995 2005<br><!-- End of picture text --> <!-- Start of picture text --> Airlines - Public Ownership<br>6.0<br>5.0<br>4.0<br>Croatia<br>3.0 EU-15 average<br>OECD average<br>2.0<br>1.0<br>0.0<br>1975 1985 1995 2005<br><!-- End of picture text --> 57
At a glance, Malawi’s cluster distribution for exclusive rights is quite balanced. Country level statistics, however, mask differences between matrilineal and patrilineal communities. When parsing the Malawi data into matrilineal and patrilineal marriages (Figure 8b), one observes stark gender differences in the compositions of clusters. Women landowners in matrilineal marriages are more likely to be in exclusive clusters (31 percent, compared to 16 percent for men) and vice versa for patrilineal marriages (12 percent, compared to 35 percent for men). For the “not hold” cluster, the majority in both customs are female. There are more joint landowners in patrilineal marriages as well.

# **4.2 Rigidity of Rights Bundles within a Cluster**

Within a cluster, we find substantial variation in bundles of rights (Table 7). In Malawi, for example, the cluster in the first column reflects about 60 percent of observations (landowner-parcel level) that do not have the right, in the following order, to bequest, collateral, rent, and sell (“NNNN”). The second most commonly recurring bundle type is ENNN (bequeath is exclusively held and no other rights held), reflecting about 7 percent of observations.

Cluster bundles vary in terms of their rigidity, or how much rights vary within a cluster. Ethiopia has the least variation in rights bundles. The majority in the “exclusive” cluster (approx. 84 percent) hold exclusive rights for all four rights, while 88 percent in the “joint” cluster hold all joint rights. When one randomly chooses a parcel-landowner combination from Ethiopia who has a right to bequeath, for example, there is a high probability that this landowner will also have the other two transfer rights. Within Ethiopia, for the cluster where rights are mostly not held, 65 percent of landowners hold no rights at all (NNNN) — a substantial number but nevertheless reflecting less rigidity than Tanzania.

For Tanzania and Malawi, the “joint” cluster has more variation of bundles; 52 percent of landowners have all joint transfer rights in Tanzania and 36 percent in Malawi. As discussed earlier, the joint cluster does have fewer observations in these two countries compared to other clusters. In Malawi, especially, those with joint rights tend to also have some exclusive or no rights in their bundles.

Given the variation in rights bundles observed in the data, having a machine learning algorithm prevents researcher bias when assigning individuals to a cluster and is more efficient in doing so compared to assigning individuals manually.

22
Australasia and Latin America and subsequently in other parts of the world. Increasingly comprehensive systems of tariffs and quotas, often supplemented by exchange controls, were-used to limit imports. In some countries credit was extended by the public sector on favorable terms to import-competing industries, and a variety of other measures were adopted to promote their gr&wth. The use of such policies may well have been related to the choice of debt management strategy. Countries like Argentina, for example, which continued to service their debts did so by retaining resources in the export sector and by relying on imports fot the provision of other goods. Other countries like Brazil, which reduced or suspended debt-service payments, had neither as urgent a need to generate foreign-exchange receipts nor as favorable access to certain creditor-country markets, and had reason to shift resources into the import-competing sector.2=/ The simple correlation of -0.30 between the measure of default in Table 3 and the import share of GNP (pooled time-series cross-section data, defined as the deviation of the import share from the country average for the sample period) is consistent with this view.

The consensus view of such policies as they operated in the last quarter century is that they have proven inferior to the alternative of export promotion.2V Experts indict import substitution strategies for having insulated producers from the chill winds of competition, for diverting resources into sectors far removed from comparative advantage, for raising the cost of inputs into domestic production, and for discouraging industry from achieving minimum efficient scale. But it need not follow that import substitution was an inferior strategy in the 1930s. Some country studies suggest that import substitution worked rather well in particular instances.29/ It is conceivable, for example, that the effectiveness of import substitution relative to export promotion may depend on the rate of expansion of international trade. When trade is expanding rapidly, as in the 24
country (Hidden Imports Barriers and Transparency of Government Policies, whose ratios to the mean are about 1). In all other counts, Mexico is well below the mean. For instance, in Port Facilities Mexico’s level of 0.478 is 25% below the average of 0.636 for the countries in the sample. These facts suggest that there is wide room for improvement in key domestic variables that are linked to international trade.

# **4.2 Trade Flows and Other Variables**

We use bilateral trade flow data available at the Commodity and Trade Database (COMTRADE) of the United Nations Statistics Division, for 2000, 2001, 2002 and 2003. In contrast to WMO(2004) that uses yearly data for 2000 and 2001, to avoid our results be driven by particular years in what follows we use as dependent variable the average trade level for the four years.<sup>7</sup> We focus our attention on trade in manufactured goods, defined as commodities in categories 5 to 8 in SITC 1 digit industry except those in category 68 (non-ferrous metals). As indicated above, we extend what was done in WMO (2004) by using data for 4 main industrial sectors: Food Beverages and Tobacco, Textiles, Machinery except Transport Equipment, and Transport Equipment. World trade of these sectors is about 64% of total trade. For Mexico, these sectors represent 79% of non-fuel exports and 65% of imports, respectively.

Tariff data were derived from the Trade Analysis and Information System (TRAINS) of the United Nations Conference on Trade and Development (UNCTAD). We use the weighted average of applied tariff rates for the manufactured goods in 2002 where bilateral trade values corresponding to each tariff line are used as the weights. The data on gross domestic product (GDP) and _per capita_ GDP were derived for years 2000-2003 from the World Development Indicators published by the World Bank. All nominal figures were converted to constant US$ of 2002 using as deflator the US Wholesale Price Index.

# **5. The Gravity Model**

The standard gravity formulation includes various measures of market size (GDP), similarity of demands (GDP per capita), measures of remoteness (distance and adjacency), and measures of kinship (regional trade arrangements, and language/ethnic similarities). To this basic formulation, we add tariffs as well as the trade facilitation indicators and some additional factors,

> 7 For a rationale of doing this, see Soloaga and Winters (2000).

8