rafmacalaba/data-use-mentions-extended huggingface dataset:
been solid growth in per capita income. And the growth by sector has followed the expected path of transformation. Growth in value added in agriculture has been slow, while annual growth in industry and services has been double digit. The share of industry in GDP has more than doubled, and is now a respectable 26% while services, including government, account for 50% of GDP (see Figure 1). Within industry, most of the growth did not come from the very capital intensive mining sector, but actually resulted from import-substitution manufacturing and the development of nontraditional exports (World Bank, 2007).

**Figure 1: Trends in sectoral share of GDP in Uganda, 1992-2009**

<!-- Start of picture text --> 100% 700,000<br>90%<br>600,000<br>80%<br>70% 500,000<br>60%<br>400,000<br>50%<br>300,000<br>40%<br>30% 200,000<br>20%<br>100,000<br>10%<br>0% -<br>Agriculture (% of GDP) Industry (% of GDP)<br>Services (% of GDP) Poverty Headcount ($1.25 per day)<br>GDP per capita (in constant LCU)<br>1992 1996 2002 2005 2009<br><!-- End of picture text -->

**Source** : World Development Indicators, 2010 and Povcal Net, 2011

Not surprisingly, this growth resulted in substantial poverty reduction. The poverty head count, measured at the international poverty line of $1.25/day, fell from 70 percent in 1992/93 to 38 percent in 2009/10. Both rural and urban poverty 13
the site.<sup>18</sup> There is considerable time-series variation as well as cross-sectional variation in the data.

Lobbying data were constructed as described in Section 3 from FEC databases for each election cycle between 1991 and 2000.<sup>19</sup> Goods are ordered by the of PAC spending-toproduction ratio.<sup>20</sup> Agricultural protection is primarily measured as nontariff measure coverage ratios, or NTMs.<sup>21</sup> Data on the incidence of nontariff measures are from the UNCTAD TRAINS database for the years 1993, 1996, and 1999.<sup>22</sup> There are over 150 types of NTMs documented by UNCTAD, and the measure used here is simply the coverage ratio of the union of all NTMs. This measure, though it aggregates price-type, quantity-type, and investigations-type of NTMs, each with different price-effects, has nevertheless been used in prominent studies of the political economy of trade protection (Trefler, 1993; Gawande and Bandyopadhyay 2000; Goldberg and Maggi, 1999). The TRAINS database also allow narrower NTM classifications such as Price NTMs (P) and threat NTMs (THR), 18 The USDA system is not originally kept at the HS level. The coding into HS was done manually from verbal description. They fit HS 4- or 5-digits descriptions almost perfectly.

19 Numerous checks have been applied to the lobbying data constructed for this paper. For example, the data are in close proximity to the numbers reported in Opensecrets (2002). A detailed data appendix that describes steps in the construction of the PAC data is available from the author.

20 Production value is not immediately available, since the PSD database keeps data in quantities, not values, and units of measurement are disparate across goods. But imports can be recovered by dividing the import-to-output ratio by imports (from UNCTAD, see below) in order to recover value of production. Where imports are zero this method does not allow measurement of production,
2010) constructed cross-country narrative series at annual frequency, but such data were not available at quarterly frequency until recently. In this section, I use a newly-constructed IMF quarterly panel dataset of narrative tax series to study expansionary austerity.

The narrative dataset is compiled by the fiscal affairs department at the IMF and it is described in Dabla-Norris and Lima(2018). The dataset contains detailed information on the expected revenue impact, motivation, announcement and implementation dates of nearly 2,500 tax measures of fiscal consolidation across 10 OECD countries between 1978 and 2014. The authors rely on contemporaneous primary sources including budget documents, reports from the Ministry of Finance and tax authorities, technical reports and notes produced during the legislative procedure, and discussions on tax reforms during parliamentary debates. When necessary, these are complemented by information from secondary sources such as IMF staff reports, OECD Economic Surveys, Stability and Growth Pact documents, and news articles 8
# **EXECUTIVE SUMMARY**

# **Research Strategy**

This study tests empirically the key hypothesis that households in the Europe and Central Asia (ECA) region lack access to efficient rental housing. This hits especially the young and mobile, as well as the poor and the elderly. Domestic policy makers, who are increasingly talking about the need for rental housing choice, should consider realigning their housing policies and start enabling and promoting a competitive and efficient formal rental market.

To test the key hypothesis, given the considerable resource constraints for this study, a sixcountry sample was used to reflect different housing sectors and housing reform environments— Armenia, Poland, Lithuania, Romania, the Russian Federation, and Serbia (without Montenegro). The study used disaggregated household datasets, other available statistical and survey evidence, and interviews to arrive at an empirical picture of the rental sector in those countries and test the null hypothesis. The study also considered investor types and legal, regulatory and taxation issues that prevent swifter rental sector development.

# **Main Empirical Findings**

Rental supply in housing sectors in the ECA region is smaller than expected in mature market economies with similar patterns of demographics, incomes, mobility needs, and building inventory.

This is especially so in Armenia, Romania, and Serbia where the formal rental sector is a barely traceable residual. In Russia, the rental sector is significant, but only because it consists primarily of social (municipal) rental housing slated for eventual privatization. In Lithuania, social (municipal) rental housing has been decimated, but there is a lively, mostly informal, private rental market in individual apartments. In Poland, the significant rental sector includes formally restituted prewar rental apartment buildings, and cooperative rental apartment stock, as well as the “regular” social (municipal) rental apartment stock.

There is also evidence of a large shadow rental sector that survived the privatization wave. With the notable exception of Poland, in the broadly defined apartment sector, ownership titles to common
# **<mark>I. Introduction</mark>**

In November 2021, representatives from nations from all over the world attended the COP 26 meetings in Scotland where negotiators sought a pathway forward to achieve a global carbon budget to limit global warming to 1.5 degrees Celsius. Analysts who have studied the necessary carbon emissions reductions for achieving this goal posit that only 11% of the carbon budget is still available (Dasgupta, Lall, and Wheeler 2022).

As observed at the COP 26 meetings, global cooperation to cap greenhouse gas (GHG) emissions faced pushback from many developing countries. A type of bargaining game is now playing out. Many academic economists have argued that there is a global Hicksian Pareto improvement possible if the developing world is compensated for not engaging in fossil fuel intensive economic development (see <mark>Kleinnijenhuis, Adrian and Bolton 2022)</mark> .

Yet, efforts to reduce GHG emissions require incurring upfront costs in return for uncertain future benefits of less climate change risk. The emerging middle class in the developing nations could bear much of these costs as they are now buying and operating energy intensive durables ranging from vehicles to air conditioners to computers. Owners of these durables represent an interest group with a stake in opposing carbon pricing. In the absence of targeted transfers, such a Pigouvian policy would introduce a negative income effect. The French Yellow Vest protests of 2018 may offer a preview of the future in the developing world if the emerging middle class views the low carbon policy agenda to be elitist and to lower their standard of living by raising the prices of energy and limiting their personal freedom.

In this paper, we use several data sets to explore the consumption patterns, carbon emissions and environmental attitudes of people with different incomes and educational attainment levels. First, we use data from the World Bank’s Living Standards Measurement Surveys to estimate durables Engel curves. We provide new evidence on the extensive margin of demand for a variety of carbon intensive durables – cars
.9|52.1|
|Price shocks|0.2|3.0|––|4.4|4.9|21.1|42.0|––|––|1.7|3.2|
|Employment shocks|6.4|4.3|––|9.3|3.1|7.7|3.4|6.4|1.5|1.9|0.7|
|Health shocks (death, illness)|6.9|14.0|30.2|23.2|33.8|10.1|18.0|9.1|8.9|11.8|14.9|
|Personal and property crime|1.8|6.6|0.9|5.8|1.9|8.5|8.4|3.2|3.1|6.6|8.7|
|Familyand legal disputes|––|––|1.9|0.0|0.9|1.7|4.3|0.7|0.3|––|––|

**Table 3. Households in developing countries face many shocks. Percentage of respondents reporting type of shock. Source: (World Bank, 2013) based on data from household surveys, various years 2005-2011. U=Urban, R=Rural.**

Importantly, the evidence reviewed in this section suggests households consider their vulnerability to natural risks like floods and droughts when making other risk-related decisions in other domains – such as creating a business or migrating to a city. Because these risks interact, the existence of natural risk can reduce the willingness to take these other risks, which are necessary for development and growth. Empirical evidence on innovation and entrepreneurship suggests for instance that increased risk-taking behaviors are associated with higher economic growth
*|10121|2048|17086|8721|

The main sources of energy demand data are energy balance tables available from China’s energy statistical yearbook. For energy consumption in the industrial sub-sectors and technologies we used several sources such as _China Statistical Yearbook 2006-2011_<sup>[24]-[29]</sup> , _Industrial Statistical Yearbook_ 2010-2013<sup>[30]-[33]</sup> , _China Steel Statistics 2011_<sup>[34]</sup> , _China Chemical Industry Yearbook_<sup>[35][36]</sup> , _China Nonferrous Metals Industry Yearbook_<sup>[37][38]</sup> , _China Energy Statistical Yearbook 2006-2013_<sup>[39]-[42]</sup> . We also use technical parameter documents on production lines of major industrial sectors, as well as calculation and collation from related literature. The data of the transport sector are from: _Research into China's Medium and Long-Term Development Strategy on Traffic and Transportation_<sup>[43]</sup> , _Automotive Energy Outlook 2012_<sup>[44]</sup> , _China Transport Yearbook_<sup>[45]-[47]</sup> , _China Bulletin on Motor Vehicles Pollution Prevention_ , _China Energy Statistical Yearbook 2010-2013_ , as well as data research and calculation based on major literature. For the building sector, the data sources include _The Annual Development and Research Report on Building Energy Efficiency in_ 44
(RCRE). The survey collected detailed household-level information on income and other household characteristics. The starting point is a sample from 1987 of 4,847 households drawn from 82 continuously observed villages in nine provinces.<sup>16</sup> Originally planned as an annual longitudinal survey, by the end of our sample (2002) there was significant attrition of households, on the order of 30 percent.<sup>17</sup> We are able to follow a “pure” balanced panel of 3,424 households every year between 1987 and 2002, excluding 1992 and 1994 when there was no survey.

> 16 The complete RCRE survey covers over 22,000 households in 300 villages in 31 provinces and administrative regions.  RCRE’s complete national survey is 31 percent of the annual size of the NBS Rural Household Survey.  By agreement, we have obtained access to data from 9 provinces (Anhui, Gansu, Guangdong, Henan, Hunan, Jiangsu, Jilin, Shanxi and Sichuan), or roughly one third of the RCRE survey.

> 17 The RCRE survey continued past 2002; however, there were significant changes in survey design from 2003 that introduced serious comparability problems for income.  The post-2002 data cannot be pooled with previous surveys.

_Inequality and Growth in Rural China_ , Page 11
of variation indicating that there are high carbon areas. The people and the political leaders of these regions will be less likely to support the low carbon agenda and they are likely to argue that Pigouvian policies are elitist unless there are spatial transfers to such regions. The United States has wrestled with how to compensate coal miners for their anticipated dislocation (Eyer and Kahn 2020).

> 3 This project maps CO2 emissions across Europe. The aim is to estimate an emissions inventory for each of the ~116 000 administrative jurisdictions across Europe and the UK. The model spatially disaggregates each country's official (Eurostat) CO2 emissions inventory to places using OpenStreetMap. Vehicle emissions are attributed across fuel stations, train emissions at stations, aviation bunker fuel emissions at airports, and so on. Industrial source emissions are located at the registered address where these emissions physically occur or are legally controlled. Data are for the year 2018.

> 4 https://data.worldbank.org/indicator/EN.ATM.CO2E.PC

12
distribution with correlation coefficient ρ and standard deviations σ 1 and σ 2 respectively.<sup>9</sup> εε εε Quantity (2) can be estimated by where Φ 2 () . stands for the bivariate normal cumulative distribution function (cdf)) (and φ 2 () . stands for the bivariate normal probability density function (pdf)). In equality (4), the parameters β _j_ and σε _j_ are obtained from equation (1), and ρ from the following formula where the simple correlation coefficient ρ _y_ 1<sup>_y_</sup> 2 is approximated from the birth-cohort-aggregated household consumption between the two surveys. Note that in equality (4), the estimated parameters obtained from data in both survey rounds are applied to data from the second survey round (x2) (or the base year) for prediction, but we can use data from the first survey round as the base year as well. It is then straightforward to estimate quantity (3) by dividing quantity (2) function (cdf).<sup>10</sup>

# **III.2. Setting Vulnerability Lines**

Using the given poverty lines zj, quantities (2) and (3) classify the population into two groups, one is poor and the other nonpoor. But we can obtain richer analysis by further

> 9 In other words, this assumption implies that households in period 2 that have similar characteristics to those of households in period 1 would have achieved the same consumption levels in period 1 or vice versa. 10 Further asymptotic results and formulae for the standard errors are provided in Dang and Lanjouw (2013). These studies also provide validation results for the synthetic panels against the actual panel data for several countries including Bosnia-Herzegovina, Lao PDR, Peru, the United States, and Vietnam. Other studies that offer further validation and extension include Cruces _et al._ (2015), Dang and Lanjouw (2017), Dang _et al._
the Business Environment Risk Intelligence (BERI), Freedom House, Gallup International, the World Economic Forum, the Heritage Foundation, and the International Country Risk Guide (ICRG) compiled by the Political Risk Services group. These institutions collect and construct similar variables of institutional quality, such as corruption, that have been widely used in the literature. KKM apply principal components techniques to this set of variables from various sources to construct broad measures of institutional development along six dimensions. By using the KKM measures as broad indicators of institutional development we avoid having to choose among these different but closely related variables (the correlation between these variables is generally high). Each of the KKM components is highly correlated with most of the underlying measures. For example, the index of corruption is highly correlated with the ICRG index of corruption.

16
There is also plenty of empirical evidence available in the literature on the association between a mother’s education and different dimensions of child health such as infant mortality, the number of immunizations (e.g., Desai and Alva, 1998), child nutrition measured by height-for-age (HAZ) or stunting (e.g. Alderman and Heady, 2017), and fertility (e.g. Pop-Eleches, 2010; Kim, 2010; McCrary and Royer, 2011; Monstad et al., 2008). Yet, causal evidence on the effect of a mother’s education on ECD net of its effect of stunting is rather scarce. A better understanding of the channels through which maternal education affects ECD outcomes is essential for the prioritization of policies related to child health. Evidence of a direct effect of maternal education on ECD, separate from its potential effects on child stunting, would suggest that maternal education has the added advantage of compensating for the potentially negative effects of stunting on ECD and thus provide even stronger justification of the need to increase the quantity and quality of female education as an especially important factor in the development and economic growth of Sub-Saharan African countries undergoing a demographic transition.

With this background in mind, the analysis in this paper takes advantage of the variation across households in indicators of early childhood development and applies recently developed methods for more robust inferences based on observational data (Chernozhukov et al., 2018). The results from the conventional ad-hoc specification of a model, i.e., a regression model with the usual suspect confounding factors, chosen based either on custom or economic intuition, are contrasted to a more sophisticated approach, called double machine learning (DML). DML employs data-driven methods to select controls from a much wider set of variables available in the survey along with statistical learning methods that yield information that would not be available from fitting the model only once using the original sample (e.g. Hastie et al., 2009; James et al., 2013;
transfers (as above) as instruments. Multipliers are similar to the parsimonious specication and are signicant at the 5% level.<sup>24</sup> 3.4. Placebo Tests. Even if I cannot control for potential confounding variables, I can test for omitted variable bias by running a placebo regression using counterfactual growth rates when the confounding variable might be inuential, but no transfers were actually paid. A non-zero multiplier during these periods might indicate a spurious relationship. In the rst set of placebo regressions, shown in Panel A of Figure 2, I test whether states receiving large one-o transfers always grew faster during recessions by regressing growth rates during the three quarters of the 19901991 recession on the 2001 and 2008 transfers.<sup>25</sup> The estimated multipliers are always insignicant and are usually close to zero.

Alternatively, the potential confounding variable might be inuential in the years around the time the transfer is actually paid. To test for this, I counterfactually move transfers backward or forward by up to six quarters (using the Benchmark with controls specication from Table 1). In addition to detecting spurious results, it can also pick up anticipation eects (for short leads, _t <_ 0 ) or delayed eects (for short lags, _t >_ 0 ). For permanent transfers, the results are shown in Figure 2, Panel B. One can see that the largest t-statistic is at t = 0 (when the actual transfers occurred), and all other leads and lags are insignicant at the 5% level (t-statistic less than the red cuto of 2), which is what one would expect to see.<sup>26</sup> 24This endogenous BEA category includes all transfers that are not Social Security Benets, Medicare or Medicaid payments or state unemployment compensation. The BEA classies tax rebates as transfers if they are at least partially refundable, which includes
though the differences are small; these differences may be due to access to a larger variety of foods through home production.

> 9 The household response rate was 99.8%, and the PSU replacement rate was 3%.  Thirty-two households were dropped due to missing female questionnaires; all of these households are located in four communities, suggesting systematic errors in field operations.  Fifty-two households were dropped due to missing consumption data and seven households were dropped due to missing asset data.  One household is missing data on household size and is dropped because per capita measures of consumption and food security cannot be calculated.

8
Table 3 shows the descriptive statistics of the rescaled variables in which we see that the variability of the data is somewhat "normalized." Table A.2 in the appendix gives the correlation coefficients among these rescaled variables for the MENA sample.<sup>29</sup>

**<u>Table 3.</u>** <u>Descriptive statistics of the rescaled data for the MENA and OECD samples</u>

|SC dimension|Variable<br>||MENA<br>|<br>||OECD<br>|<br>|
|---|---|---|---|---|---|---|---|
|||Obs.|Mean|Sd.|Obs.|Mean|Sd.|
||rf_pp<br><br>|266|0.49|0.22|532|0.77|0.14|
|Protection (P1)|rf_rp<br><br>|266|0.46|0.16|532|0.79|0.10|
||rf_rp<br><br>|266|0.47|0.16|532|0.81|0.14|
||rf_ff<br><br>|263|0.10|0.06|532|0.27|0.11|
|Provision (P2)|rf_mf<br><br>|265|0.49|0.21|532|0.55|0.09|
||rf_ff<br><br>|249|0.11|0.10|531|0.50|0.17|
||rf_ifpi<br><br>|244|0.01|0.01|438|0.02|0.
for introducing an even very modest wealth tax. But more needs to be done to exploit those opportunities. The World Bank (and other international organizations), for example, often provide loans and technical assistance to support tax reforms in developing countries. It would be relatively easy to require governments to release (suitably anonymized) distributional statistics to their citizens.

Armed with better data, a number of important research questions can be addressed, including some very basic ones: What is the ‘true’ level and trend of inequality? Can the trends in the micro data and the national accounts be reconciled after accounting for capital incomes and the top of the distribution? What do corporate tax data tell us about the true extent of economic concentration and should it be a concern for competition authorities? How do capital income dynamics compare with the more frequently analyzed dynamics of labor incomes? How does the current system of taxes and transfers affect the distribution of income? Is that fiscal system optimal? How do households in different parts of the income and skill distribution interact with the global economy – as consumers, sellers of their labor, owners of capital – and how is this different in poorer and richer countries?

This essay has provided a global perspective on inequality in an attempt to complement Piketty’s analysis in C21. In contrast to the sharp increases in top incomes in Anglo-Saxon countries observed by Piketty and co-authors, inequality viewed from a global perspective appears to have stabilized or even fallen slightly, driven by convergence in average incomes across countries. After rising strongly in the 1980s and 1990s, inequality within the average developing country has fallen in the 2000s. These results are based on household surveys, which are known to underestimate top incomes, especially capital incomes. Furthermore, these surveys often use consumption expenditures, which might not capture increases in living standards at the top. Hence, although much more evidence has become available in poor countries, these results are subject to considerable uncertainty. Therefore, better data in developing countries remains a top priority