rafmacalaba/datause-encoder-data huggingface dataset:
findings are quite similar regardless of the distribution: women are 40% of taxpayers in the top 1% – the share is stable between the top 1% - 0.1%, and then starts to decline at higher incomes until the range 20-30% within the top 0.01%.

These shares of female presence among the top 1% of earners are high by international comparison. Atkinson _et al._ (2018) document that the share of women in top 1% are consistently in the range of 15-25% in several high-income countries. In Sweden, Boschini _et al._ (2017) document that the share of women in the top 1% in 2013 was 23% when capital gains were included but only 16% when it was not included. Neef & Robilliard (2021) document that the share of women in the top 1% of wage earners is in the range of 20-30% in Brazil, US, France and Costa Rica. Our estimates of 40% female participation within the top 1% are therefore higher than in all those settings.

These differences between female participation among top earners in Honduras and other countries could be driven by several reasons. First, most of the countries for which granular data on top earners exists are much richer than Honduras – the threshold to be at the top 1% in Sweden in 2013 was approximately USD 140,000 PPP, for example, five times more than in Honduras where that value is stable around USD 30,000 since 2014. An yearly income of USD 140,000 PPP would be equivalent to being at the top 0.05% of the distribution (using tax records income) in Honduras. In that sense, it is not strictly comparable to consider gender gaps among the top 1% in countries with vastly different income levels.

Boschini _et al._ (2017) also document that women showing up at the top of the distribution in Sweden are particularly likely to have received large capital gain incomes, which are often one-offs - they are also less likely to persist at the very top of the distribution than men. We first show that women at the top 1%, conditional on income, have a larger share of wage and capital income, and a lower share of mixed income, when compared to men. In Table 4, we compute gender gaps in the share of wage, mixed and capital income for those individuals at the top 1%, conditional on log-income. We document that, on average, women have a larger share of wage (3.5 percentage points) and capital income (1.1 p.p.), and a lower share of mixed income (4.6 p.p.) - the latter is usually driven by individual entrepreneurs, suggesting that the gender gap might be more relevant in those activities.

We can also provide a deeper dive into the gender gaps in income composition within the very top earners. In Figure 10, we investigate whether the composition of income at top quantiles differ between women and men. For those below the top 0.1%, the composition between labor and capital income is remarkably similar: both groups receive approximately 85-90% of total income from labor and less than 10% from capital. That figure changes, nonetheless, for those within the top 0.1% - the increase in the share of capital income vs. labor income is more pronounced for women than for men. At the top 0.01%, the share of capital for women is above 50% vs 40% for 19
<!-- Start of picture text --> Figure 6: Compliance and Labor Productivity 2009-10<br>100<br>se 80<br>2<br>= 60<br>Ww<br>3<br>& 40<br>v7<br>—<br>S 20<br>0)<br>0-50 50-100 100-150 150-200 200-<br>Labor Productivity (Thousand Rupees)<br><!-- End of picture text --> <!-- Start of picture text --> Figure /: Distribution of Labor Productivity 2009-10<br>=<br>=<br>=<br>=<br>Lak}<br>o<br>a]<br>) 5 Log Labor10Productivity- 15 20<br><!-- End of picture text -->
and rural wages and income composition in the last decade. Section 3 analyzes wage determination and differences in returns across the wage distribution. Section 4 addresses correlates of nonfarm employment and the likelihood of being employed in the high/lowproductivity sectors. Finally, Section 5 concludes and gives policy recommendations.

# **2. Rural Labor Markets**

The Mexican rural labor market is important for poverty reduction. Employment is key to lifting poor rural families out of poverty. The rural labor market can be analyzed in many ways. One way is to consider the agriculture and nonagricultural sector or off-farm sector. Rural off-farm employment has traditionally been seen as a low productivity sector, producing low quality goods. The sector, in this view, is expected to shrink as the economy develops and incomes increase. However, recent research shows that this view can be very socially costly. For example, the rural nonfarm sector has a positive role in absorbing a growing rural labor force and slowing rural-urban migration. Moreover, the nonagricultural sector contributes to national income growth and to promote a more equitable distribution of income (see Lanjouw and Lanjouw 2001).

Lanjouw and Lanjouw (2001) also finds that the nonagricultural sector is large and growing in developing countries. In Latin America alone, 47 percent of the labor force in rural settlements and rural towns are employed in off-farm activities. Moreover, 79 percent of women in the Latin American rural labor force are employed in off-farm activities.

Araujo, de Janvry and Sadoulet (2003) measures the role of social networks on behavior applied to rural off-farm employment in Mexico. The authors find that income from off-farm sources is especially important for rural households, and explore the determinants of participation in off-farm nonagricultural employment using data for rural Mexico. In particular, they seek to understand the role of social networks on the individual decision to participate in off-farm labor markets. Araujo et al. find that neighbors' participation in off-farm nonagricultural employment has a significant impact on the individual choice of occupation, even after controlling for availability of opportunities. The role of neighbors' employment choices is more important for groups that are less likely to participate in nonagricultural rural employment such as women, indigenous, the elder, and land-owners. This finding suggests an important role for networks and referrals in the job-search process of rural households. Finally, relative to endowments such as education that are relatively scarce in rural Mexico, Araujo et al finds that social networks compensate more to those who are less endowed and therefore less likely to participate in off-farm nonagricultural employment.

## **Demographics**

In 2000, more than 25 million of Mexico’s total of 97.5 million people lived in rural areas, defined as localities with less than 2,500 inhabitants. The population is slowly moving to urban areas. In 2000, 25 percent of the Mexicans lived in rural areas, 2
2

[Renaud (1988)]. In the last two decades, Chile for example, suffered two crises in its housing financial system; one in 1975/76, the second between 1983 and 1986. In both cases, the government was forced to intervene, purchasing financial assets related **tu,** housing and real state financing. However, as shown by the case of Hungary studied here, it is the dimensions of subsidized mortgage credit in socialist economies in transition what makes the issue a focal point in the process of reform.

The Hungarian case also provides a vivid illustration of the potential difficulties encountered in dealing with these problems, difficulties stemming from the economic and social ramifications of housing finance. Facing a legacy of subsidized mortgage credits, the Polish Parliament quickly approved the extreme and drastic solution of re-writing all existing housing loan contracts to eliminate subsidies. But it took nearly three years of analysis and discussions before the Hungarian authorities could design a proposal that would be acceptable to Parliament. Other reforming socialist economies may have to face a similar ordeal. Section **It** of this paper presents the origins, dimensions and implications of the problem of the subsidized housing loan portfolio in Hungary. Section III analyzes options to deal with this problem. Section IV presents conclusions.'

# H. Housing Finance in Hungary in the 1980s

# A. Overview

As in many other countries, in Hungary the issue of the availability of housing has been a major concern for a long time. In the past, housing in the cities and larger towns was typically produced and managed in the socialist sector by large construction enterprises and local councils; rural housing remained mostly in the private sector. More recently, since 1983, the focus of State support for housing shifted from construction and management of State-owned units to direct financial assistance to families for private construction. For clarity purposes, it is useful to divide the decade - wo periods: before, and after 1989.

The re-1989 Housing Financial System The housing financial system established by the Housing Act of 1983 and in place until December 31, 1988 led to the accumulation of a stock of housing loans at increasingly subsidized interest rates. Up to that point, housing loans were financed by captive deposits from the household sector which, as per the system in place, had no alternative financial intermediary to deposit or borrow from. Both lending and borrowing rates were administratively set. Loans were granted with maturities of up to 35 years, and interest rates were fixed for the life of the loan. As of December 31, 1988 such stock amounted to Ft 274 billion (approximately $ 5.3 billion) and was equivalently to approximately 20% of 1988

> 5 **The** statistical analysis was carried out with **figures** available as of April 1990, when the **first** version of **this paper was prepared. Data** up **to** December 1989 were provided by the National Planning Office. **All** other **figures,** unless otherwise **specified, have resulted from the authors'** projections, estimations and simulations.
in the mid term as a result of additional deforestation, but increases thereafter with the increase in protected lands. The consumption of all other land-based goods and services, and biofuels increase modestly in the long term.

<!-- Start of picture text --> Change in Land Use Relative to Baseline Change in GHG Emissions Relative to Baseline<br>200 1.5 40 Natural Land<br>150 Food Crops 30 Conversion<br>100 Livestock Feed 1 20 Livestock<br>50 Biofuels Crops 0.5 10 Biofuels<br>Pasture Land Offsets<br>0 Protected Forests 0 0 Use of<br>-50 -10 Fertilizers<br>Managed Forests -0.5 Forest<br>-100 Unmanaged Forests -20 Sequestration<br>-1<br>-150 -30 Net GHG<br>Accumulation<br>-200 -1.5 -40<br>2005 2030 2055 2080 2105 2005 2030 2055 2080 2105<br>Change in Consumption of Land Based Good Change in Consumption of Biofuels Relative to<br>and Services Relative to Baseline Baseline<br>0 40 10%<br>30 8%<br>-10 6%<br>-20 Processed Livestock 2010 4%2% Consumption of 2gBiofuels<br>Ecosystem Services<br>-30 0 0% Consumption of 1g<br>Processed Timber -10 -2% Biofuels<br>-40 Energy Services -4%<br>-50 Processed Grains -20-30 -6%-8% Share of Biofuels inLiquid FuelConsumption<br>-60 -40 -10%<br>2005 2030 2055 2080 2105 2005 2030 2055 2080 2105<br>Million Ha GtCO2e/yr GtCO2e<br>USD Per Person Million toe<br><!-- End of picture text --> Figure E.2: Sensitivity Analysis: 50% Increase in Elasticity of Substitution between Agricultural Land and Fertilizers Figure E.2 shows the eects of a 50% increase in elasticity of substitution between agricultural land and fertilizers. Given the increase in fertilizers' costs in baseline scenario, better substitution between agricultural land and fertilizers implies additional demand for agricultural land, which increases by about 150 million Ha in 2100. All forest areas decline. GHG ows increase with the additional conversion of natural lands. However, the decline in fertilizers' use results in smaller GHG ows. The former eect dominates, and net GHG accumulation increases by 30 GtCO 2 e in 2100. The consumption of all land based goods and services declines. There is also a small decrease in the consumption of biofuels. Figure E.3 shows the eects of a 50% improvement in second generation biofuels production technology. This improvement makes second generation biofuels a more competitive alternative to petroleum products, and they enter 30
In Figure 4 we show box plots for log energy intensity for 1990, 2000, 2010 and 2014. A box plot displays the median, the first quartile, the third quartile and the largest and smallest adjacent values.<sup>3</sup> The following observations are noteworthy: First quartile, median, third quartile and largest value all decrease over time. The outside values get closer to the largest adjacent observation over time. <mark>The smallest adjacent value also decreases over time, though the largest change occurred between 1990 and 2000. After 2000, the smallest adjacent value stayed fairly constant. The smallest value can</mark> be considered as the “technology frontier” or “minimal energy consumption required per unit of output”. According to this picture, the frontier did not move very much between 2000 and 2014, rather the rest of the distribution moved closer to the frontier. Given that there is a generally increasing trend for per capita income across the sample, this result appears to be consistent with our earlier conclusion that as income rises above a certain threshold, reducing energy intensity becomes more difficult.

To examine whether there is also a difference in the speed of convergence on different sides of the income threshold, we divide the sample into two subgroups based on whether a country‐year observation lies below or above the cutoff point at log per capita GDP of 8.5. We calculate the � conditional convergence rate according to ���� ~~�~~ � ~~�~~ log ���1�� (Barro and Sala‐i‐Martin, 1992; Islam, 1995), where _T_ is the length in the time dimension under consideration (25 years in our case) and � is the coefficient obtained from equation (6). The results are reported in Table 5. The rate of unconditional convergence for groups below and above the income threshold is roughly 0.10 percent and 0.14 percent, respectively. The difference between the two groups is small and the lower‐income sample even has a lower convergence rate. The result could reflect different economic growth rates of the two groups.

# **V. Index Decomposition**

In this section, we investigate how much of the improvement in energy intensity is due to structural change and how much is due to efficiency improvements, and whether the income threshold effects identified earlier play a role in the relative importance of structural vs. efficiency changes. To this end we use the Fisher Ideal Index Decomposition for structural change in energy intensity as described in Boyd and Roop (2004). We consider three sectors: industry, agriculture and services. As described in Boyd and Roop (2004) we first calculate a Laspeyres and a Paasche index of energy intensity for structure and efficiency using the following formulas from Boyd and Roop (2004): S: agriculture, industry and services share of GDP I: energy intensity 0: first year

> 3 Values that are more than 1.5 times the interquartile range (distance between first and third quartile) away from the median are plotted separately.

9
|**Level of Data Aggregation**<br>**Estimation Method [Dep. Variable]**<br>**Table 8. Foreign**|**Direct Investment**<br>**Poisson [No. Links]**<br>**Region-Region**|**- Growth in Active**<br>**Poisson [No. Links]**<br>**Country-Region**|Marginal Effect<br>**Links: Robustn**<br>**Logit [Dumm**<br>**Cou**|Baseline<br>Probability<br>**ess**<br>**y=1 if Non-z**<br>**ntry-Country**|Probability<br>Growth<br>**ero Link]**|
|---|---|---|---|---|---|
|North-to-North Trend|0.003<br>(0.005)|0.003 ***<br>(0.001)|0.004 ***<br>(0.001)|92%|0.4%|
|North-to-South Trend|0.044 ***<br>(0.002)|0.044 ***<br>(0.004)|0.021 ***<br>(0.001)|27%|5.2%|
|South-to-North Trend|0.028 ***<br>(0.003)|0.029 ***<br>(0.002)|0.018 ***<br>(0.001)|48%|3.3%|
|South-to-South Trend|0.051 ***|0.053 ***|0.015 ***|9%|8.3%|
||(0.002)|(0.003)|(0.000)|||
|**Wald Tests on Trend Coefficients:**||||||
|1. South-to-South = North-to-North|0.048 ***|0.050 ***||||
|2. South Destination = North Destination|0.032 ***|0.032 ***||||
|3. South Source = North Source|0.016 ***|0.018 ***||||
|**Fixed Effects**|R-R|C-R|R-R|||
|**R-R Fixed Effects x Post-2008 Dummy**|Yes|Yes|Yes|||
|**Number of Observations**|72|11,673|291,604|||

Note: This table shows the trends in foreign direct investment (FDI) active links for 2001-18. The estimation methods are Poisson and logit. For the Poisson regressions, the dependent variable is the number of active links. Active links refer to country pairs that have a positive value. For these regressions, we report the estimated coefficients. For the logit regressions, the dependent variable is an indicator variable that takes value 1 whenever a country pair has a positive value and 0 if the value is zero. Country pairs with a missing value are not considered. For these regressions, we report the average marginal effect, the estimated probability for the first period of the sample (trend=0), and the average annual percentage change in the predicted probabilities. There are three levels of data aggregation: region-toregion (R-R), country-to-region (C-R), and country-to-country (C-C). Regressions include R-R or C-R fixed effects. Regressions also include the interaction between R-R dummies and a dummy that equals 1 after 2008. For the C-R and C-C regressions, standard errors are clustered by C-R and C-C pairs, respectively. *, **, and *** represent statistical significance at 10%, 5%, and 1% level, respectively.
Now consider a farmer who becomes landless as a result of the reform. Clearly she cannot be worse off after the reform than if she had stayed a farmer. Having established above that no farmer loses as a result of the reform for the case in which _g_ 1( _A_ 0 ) ≥ _g_ 0 ( _A_ 0 ) , it cannot be the case that those farmers who choose to become landless lose as a result of the reform. However, when _g_ 1( _A_ 0 ) < _g_ 0 ( _A_ 0 ) there can be losers among the newly landless.

Lastly, consider the pre-reform landless who stay landless. All that matters to the welfare impact for this group is what happens to the wage rate. For this group, a rise in landlessness signals a welfare loss, through the impact on the wage rate. Thus we have verified Proposition 3.

In the above analysis, welfare losses from a land market arise from labor market effects or diminished command over non-land inputs to production. Losses can also occur with lags in labor-market adjustment and distortions in land markets. Suppose that the decision to sell one’s land is made before the new labor market equilibrium is revealed and that there is a sufficiently large transaction cost to prohibit buying-back land; this can arise if the value of the land to the new owner exceeds the prior purchase price, as would be the case when the land purchases allow the consolidation of previously fragmented plots. With a fall in the wage rate due to the reform, some farmers who sell their land will find that they are worse off in equilibrium.

# **4. Data and summary statistics**

We use the unit-record data from four nationally-representative surveys by the General Statistical Office, for 1993, 1998, 2002 and 2004 with sample sizes for rural areas are 3,800, 4,300, 22,600 and 6,900 for 1993, 1998, 2002 and 2004 respectively. All four surveys are representative for the seven regions that Vietnam is commonly divided into. The 1993 sample is self weighted, while the other surveys used stratified random cluster sampling so that sampling weights need to be applied. The first survey was done just before the 1993 Land Law.

17
Figure A.4: Validation with SICONFI data across years - payment

# (a) Minas Gerais (MG)

<!-- Start of picture text --> 2014 2015<br>150 150<br>100 100<br>50 50<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>2016 2017<br>200 200<br>150 150<br>100 100<br>50 50<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> 5 0 5 10 15 20 25<br>2018 2019<br>150<br>100 100<br>50 50<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>2020 2021<br>200 200<br>150 150<br>100 100<br>50 50<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>% difference from SICONFI data - MG % difference from SICONFI data - MG<br>(b) Paraná (PR)<br>2013 2014<br>80<br>40<br>60<br>30<br>40<br>20<br>20 10<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>2015 2016<br>40<br>40 30<br>20<br>20<br>10<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>2017 2018<br>40<br>40<br>30<br>20 20<br>10<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>2019 2020<br>40<br>30 40<br>20<br>20<br>10<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>% difference from SICONFI data - PR % difference from SICONFI data - PR<br>Frequency<br>Frequency<br>Frequency<br>Frequency<br>Frequency<br>Frequency<br>Frequency<br>Frequency<br><!-- End of picture text -->

**Notes:** This figure presents the percentage deviation in total amount of budget payments, at the municipality-year level, between our dataset and SICONFI, the public finance dataset of the Brazilian Treasury. Values are positive whenever the total amount in our dataset, aggregated from individual payments, is larger than that of SICONFI. See Table 1 for more details on our data coverage. We truncate observations at -25% to the left and at 25% to the right.

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