rafmacalaba/datause-encoder-data huggingface dataset:
5 Table 1. Sample summary averages, 1980

||_Developing_|_economies_|_High-income_|_economies*_|
|---|---|---|---|---|
||_National_|_Urban_|_National_|_Urban_|
|_Item_|(31_countries)_|<br>_(9 cities)_|_(19 countries)_|<br>_(26 cities)_|
|Per capita GNP in 1987 US$b|546|1,589|14,247|15,037|
|Population per sq. km|86|14,982|125|3,989|
|Motor vehicles per 1000 persons|<br>41|60|379|482|
|Autos as percent of total fleet|60|61|86|82|
|Kilometers of road per sq. km|0.2|5.9|1.2|10.9|
|Percent of roads paved|34|-|77|-|
|Meters of road per capita|3.7|0.5|16.5|4.5|
|Meters of paved road per capita|1.0|-|11.2|-|
|Motor vehicles per km of road|12|142|31|152|
|Motor vehicles per paved km|41|-|41|-|

- Not available.

- a. In 1980 high-income countries had a GNP per capita over $4,800 (World Bank, 1992).

- b. Average weighted by population.

_Source:_ Ingram and Liu (1998).

the national and urban levels shown in table 1 suggests that, on average, non-urban national road systems are uncongested whereas urban road systems are congested. Compared with high-income countries, at the national level developing countries have a lower share of cars in their vehicle fleets, a lower share of paved roads, fewer motor vehicles per kilometer of total roads, fewer meters of paved road per capita, and similar numbers of motor vehicles per kilometer of paved road.

The summary averages also show some key differences between cities in developing and high-income countries. Cities in developing countries have much higher population densities, lower road network densities, many fewer motor vehicles per thousand people, and much less road length per person than do cities in high-income countries. The result is the relatively small difference in motor vehicles per kilometer of road between developing and high-income cities.

The overall patterns evident in table 1 are consistent with many of the hypotheses based on simple economic reasoning sketched earlier. For example, the number of vehicles per kilometer of total road at the national level is greater in high-income than in developing countries but similar at the urban level. And autos make up a higher share of motor vehicle fleets in high income than developing countries. These patterns suggest that economic behavior may be an important determinant of motorization and road provision across countries.
including also non‐complying households. We control for stratum dummies and re‐randomization characteristics. Detailed estimation results can be found in the supplemental appendix.

_Source:_ Household data set 2011/2012.

Moreover, Table 8 probes into the question whether _time dedicated to any income generating_ activity increases, which might happen because the higher domestic productivity could set free time for other purposes. We concentrate our analysis on subsistence farmers that constitute 86 percent of household heads and 85 percent of spouses at baseline.<sup>15</sup> Both the head of household and the spouse slightly increase the time they dedicate to income generation (by six and three percent, respectively), but this difference is not statistically significant.

The third most important user group are children between six and 17 years. They use the Pico‐PV lamp mainly for studying (see Table 7). In order to understand changes in the productivity of studying at home, we first need to analyze children’s study patterns and how they divide their study time between daylight time and evening.

As can be seen in Table 9, in around one‐third of the households with children at school age, children do not study after school. There is no significant difference between households in the control and treatment groups. The share of children studying after

> 15 We distinguish as income generating activities between subsistence farmers, governmental employees, independent occupations, and other dependent occupations. The group sizes of the latter three are small at n=2, n=1, and n=2 for spouses and n=4, n=11, and n=9 for head of households. Therefore, these groups are very unbalanced across treatment and control households at baseline (see Table S6.4 in the supplemental appendix). When estimating effects on time dedicated to income generation including these occupation groups, we find a significant postive effect for overall income generation time for spouses. This difference is driven, however, by these non‐balanced sub‐groups and can thus not be interpreted as an effect.
**Table 4. Estimates of job displacement on labor market outcomes**

||(1)<br>Logof hourlywage|(2)<br>Hours of work|(3)<br>Formality|(5)<br>Tenure|
|---|---|---|---|---|
|Plant Closing|-0.0749***|1.4492***|0.0262***|-5.0338***|
||(0.006)|(0.159)|(0.004)|(0.110)|
|Fired|-0.0687***|-0.1206|-0.0529***|-5.8829***|
||(0.009)|(0.174)|(0.005)|(0.181)|
|Quit|-0.0229***|1.7405***|0.0401***|-3.3947***|
||(0.005)|(0.149)|(0.004)|(0.097)|
|Closed Own Busines|-0.0532***|0.3016|-0.0714***|-6.2715***|
||(0.005)|(0.197)|(0.007)|(0.119)|
|Observations|1,229,280|1,579,584|1,671,031|1,660,569|
|R-squared|0.3239|0.1302|0.2644|0.3766|

Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.10. All regressions control for year of education, gender, marital status, age, age square, survey period, state and industry fixed effects. Standars errors are clustered at the state level. The ommited category is non-displaced workers.

We now investigate whether the estimates in table 4 persist or tend to disappear over time. Figures 1–9 show lagged-coefficients (and confidence intervals) of different types of job displacement on real wages, weekly hours of work and formality status.

The results show that a plant closing event has a long-lasting negative impact on real wages, as they decline by approximately 11 percent in the first two years after displacement, narrows to 5.5 percent after four years, and to 2 percent after nine years (figure 1). The increase in hours of work is permanent, PC-workers spend 1.5 additional hours per week than non-displaced workers (figure 2).

Finally, plant closings have a short-term negative impact on the probability of working in the formal sector. Within the year of displacement PC-workers have a 2 pp. reduction in the probability of being formal, but a year after the probability increases by 2.7 pp. —above the probability of non-displaced workers— and stabilize to 5 pp. after nine years (figure 3). This finding is interesting for the discussion about remaining potential omitted-variable bias, because if the quality of the 15
Mexico. Inflation was under control, credit did not collapse, and no financial institution required intervention. In this regard, this crisis was very different from previous crises in the country, with effects concentrating in this case on the real side of the economy only (that is, output and employment).

Total employment (figure 2.3, top panel) declined for the last three quarters of 2008 and the first of 2009, after which there were several consecutive quarters of positive job creation. In the second quarter of 2008, just before the crisis started, total employment was about 43.9 million workers, declining to 42.9 million in the first quarter of 2009. Employment growth then resumed, reaching a peak in the fourth quarter of 2009 with 44.5 million workers.

Despite significant job creation for most of 2009, total unemployment by the end of 2010 was 0.9 million workers above pre-crisis levels (figure 2.3, bottom panel). This indicates an economic crisis that, on the one hand, produced a rapid decline and recovery in total employment but, on the other hand, has not been able to produce enough jobs during the recovery so that unemployment returns to pre-crisis levels.

**Figure 2.3: Total Employment and Unemployment, 2008–2010**

<!-- Start of picture text --> year-to-year GDP growth total employment<br>10.0% 45,000,000<br>8.0% 44,000,000<br>6.0%<br>43,000,000<br>4.0%<br>42,000,000<br>2.0%<br>41,000,000<br>0.0%<br>40,000,000<br>-2.0%<br>39,000,000<br>-4.0%<br>38,000,000<br>-6.0%<br>-8.0% 37,000,000<br>-10.0% 36,000,000<br>-12.0% 35,000,000<br>I 2007 II 2007 III 2007 IV 2007 I 2008 II 2008 III 2008 IV 2008 I 2009 II 2009 III 2009 IV 2009 I 2010 II 2010 III 2010 IV 2010<br>number of people<br>annual percentage change<br><!-- End of picture text --> 5
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out of the 75 voluntary pension funds offer more than one portfolio), some asset managers may have extrapolated this revealed preference for conservative portfolios to the much wider universe of second pillar members.

# **Figure 4: Nominal and Real Annual Interest Rate on T-bills, 2000 – 2004**

<!-- Start of picture text --> 14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>-2.0<br>Nominal Interest Rates Ex-Post Real Interest Rates<br>Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04<br><!-- End of picture text --> Source: AKK 30. The average return performance of MPFs has been rather disappointing. As shown in Table 11 and Table 12, the real rate of return net of fees was negative in some years and the annualized average net real rate return in the 1998 – 2005 period amounted to only 3.9 percent p.a., lower than the average real wage growth of 5.3 percent in the same period.<sup>9</sup> This last measure is a measure of the implicit return in a balanced PAYG system and commonly used as a benchmark for assessing the performance of a second pillar. Wage growth in this period was unusually high and was in good part driven by a large increase in public sector wages. For this reason, it may not be a good benchmark for measuring second pillar performance in Hungary during this period. However, the average real rate of return in Hungary has also been low by comparison with other reforming countries, as shown in Table 13.<sup>10</sup>

**Table 11: Net Rates of Return, Wage Growth, and Inflation Rates, 1998 – 2004**

||**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**<sup>**1**</sup>|
|---|---|---|---|---|---|---|---|---|
|Net Nominal Rate of Return|15.7|17.1|7.9|8.0|7.4|3.4|16.3|13.0|
|Wage Growth|16.8|12.1|12.7|13.2|15.0|12.7|6.3|6.2|
|Inflation Rate (Aver)|14.2|10.0|9.8|9.2|5.3|4.6|6.8|3.9|
|Inflation Rate(Dec)|10.3|11.2|10.1|6.8|4.8|5.7|5.5|3.0|

Notes: /1 estimates Source: NBH and HFSA raw data (wage and return), and IMF (inflation).

9 We use December inflation to calculate the real rate of return and average inflation to calculate the real wage growth as the rate of return is realized in December of each year while wages are a flow over the year. 10 Rates of return in Hungary are reported net of asset management fees, while in other countries rates of return are usually reported in gross terms. The average gross rate of return shown in Table 12 and Table 13 was estimated by adding asset management fees of 1 percent of assets.
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Consider, for example, Pronasol's<sup>scholarship-cum-food</sup> package program to reduce primary school drop-out<sup>rates. A double rationale can justify this</sup> program. First, as discussed<sup>in section VI.1, poor parents may not be able to</sup> afford the opportunity cost of<sup>sending their children to school (because of</sup> reduced labor input in the family<sup>farm, say). Second, undernourished children</sup> will underperform even if<sup>they systematically</sup> attend school. Ignoring intrahousehold inequality, Pronasol's<sup>programs can work on these two margins, and</sup> allow parents to make the<sup>investment in ther children's education, a very</sup> desirable outcome. On the<sup>other hand, care must be exercised during</sup> implementation. If the value<sup>of the scholarship-cum-food</sup> package is coo high (relative to household s costs<sup>of sending children to school), parents may</sup> respond to the program by increasing<sup>family size as additional children</sup> become, at the margin, more valuable.<sup>Thus, the program could run counter to</sup> other efforts aimed at reducing<sup>fertility. In practice this effect may not be</sup> that important, but its mention<sup>calls attention to a relevant issue: the need</sup> to gather detailed data on the<sup>characteristics</sup> of the program so that its short and medium term effects can<sup>be fully assessed.</sup> A similar phenomenon occurs with<sup>the program Solidarity Funds for</sup> Production. Given current changes<sup>in the regulations to rural credit</sup> institutions, a program that provides<sup>credit to poor farmers can play an</sup> essential role isolating and protecting<sup>these farmers from the transitional</sup> disturbances associated with such<sup>changes. Over the medium term, however, the</sup> implications of providing credit<sup>at zero nominal interest rates (and hence</sup> negative real rates) must be determined,<sup>and the negative effects of this type</sup> of production subsidy on farmers' choice<sup>oftechnique must be evaluated.</sup> Again, the importance of this issue<sup>at this point is difficult to determine</sup> and, as with other programs, data<sup>on amounts of credit, default rates, output</sup> and input choices, etc., is<sup>required to determine the impact of the program,</sup> and of any changes it may need.

# Summing Un.

The government's recent efforts at poverty<sup>alleviation are very welcome</sup> indeed. In spite of the stringencies<sup>imposed by the macroeconomic</sup> stabilization program, more resources<sup>are being channeled to the poor. This</sup> is a significant achievement.<sup>The programs through which resources are being</sup> channeled, moreover, appear to<sup>be, on the whole, well designed. The</sup> replacement of generalized price<sup>subsidies by targeted programs, and the</sup>
finanaW needs. Lastly, **as** notes under a RUF are isued regularly and partly to non-bank investors, a borrower's name would beome established in a market which is effoctively a bridge between the banking and capital markets, making it posdble ultimately to tap the capital market by way of FloatiUng Rate Note and Euro-bonds.

Though so far NIFs have been arranged overwhelmingly for developed countries, a number of developing countries, notably South Korea, Singapore, India and Indonesia, have begui to arrange NIF., mostly for small amounts. First RUF for an Indian borrower was undertaken by the London branch of the State Bank of India at the end of 1984 which was then refinanced in 1986 partially by way of **a** Euro-Commercial poper program-again a first for an Indian borrower. This experience enhanced India's credit worthiness; though the firt paper was traded at the margin of a few basis points above LIBOR, it **was** then placed with investors at rates below L1BOR 1I The first issuance of Eurocommercial paper (ECP) from India has been by the Industrial Credit and Investment Corporation of India (ICICI) in 1987. The Bank of Baroda is currently contemplating (1988) a NUF in the U.S.A. for tha purpoce of lending in the U.K. markot While the advantaes of the securitized market obviously apply to developing countries, who generate enough bank and investor interest, it also has, in a different form through some methods, a limited application to other countries. These methods may be called _credit enhancement_ scheme..<sup>12</sup> In essence, the security-a bond or a note-issued by a borrower would have to be strmetured in a way to mske the risk acceptable tc international investors. One way to enhance creditworthiness would be through collateralising the debt instrument Such credit support may take the form of direct collateralization by t.e assignment of certin foreign currency revenues to back principal and interest payments on a bond. Commodity indexed bonds or bonds linked with export performance or performance of specific projects are also instances of such schemes. Credit 11. Erc Roll. wInternationd Capita md Creit **Mrksts** and Dovelopin Countri..," _Stat. Bea of India Montiuy Reuiw,_ Volume 36, No. 6, (June 1987). 2. iid.
significant part of the costs of production, it will dampen the net increase in profits. Similarly, increase in water prices can also affect productivity and income of farmers. In places like the EU, where farmers are already granted higher than market prices through price supports, it seems unlikely that the farmers will be granted an even higher price as a result of the competition from biofuels. Such farmers will not experience an additional increase in rent. In the absence of such supports however, the volatility of the energy situation will mean a risk of major losses from shocks that may lower energy prices. Dumping of oil by the Organization of Petroleum Exporting Countries is an example of such a shock. Although subsidies and standards requiring use of biofuels can reduce such uncertainties, market power by agribusiness firms involved in seed and processing can reduce the benefit of such subsidies to farmers. Also when land is scarce, landowners will capture most of the benefits going to farmers. Economic studies should emphasize the role of market structure to determine the likely distribution of benefits of biofuels along the supply chain.

- **Agribusiness** : Modern biofuels have major implications for agribusiness. The conversion of energy crops into modern biofuels requires sophisticated processing technology. If energy prices are expected to remain high or if subsidies are likely to remain in place, there will be major investments made in crop production and processing. The result is likely to be strategic alliances between farmer

cooperative agribusiness and energy sector merger and acquisition of agribusiness by energy firms. Vertical integration in new fuels from agriculture to processing and distribution is also likely in the case of new fuels like cellulosic ethanol. The food processing industry will be negatively affected due to increase in cost of inputs like grains, sugar, oil, and meat.

- **International trade** : Biofuels are expected to reduce a nation’s dependence on imports for oil. While this is likely to be true, it however may not reduce the dependence on imports for energy unless the biofuel feedstock is produced locally. This has led some to question the effectiveness of biofuels in improving energy security. Simulations for the EU and the United States suggest that these regions are likely to become net importers for agricultural commodities especially

55
that income persistence is lower in the developing world. However, this comparison is difficult to interpret given the differences between the two databases. Specifically, a possible interpretation is that our OLS estimates of � , as well as the MSE-minimizing combination of the OLS and bias-corrected OLS estimates, have greater small-sample downward bias in the PovcalNet sample where the available time series is much shorter than in the WID sample.

When we correlate our estimates of income persistence with per capita GDP across countries, we find a negative correlation within the WID and PovcalNet samples – higher persistence is associated with lower incomes. The reason for this is not obvious, and it is unlikely to be mechanical. Note that if countries were identical in all parameters but the income persistence parameter, then lower persistence would be associated with lower average income (since lower persistence implies both a lower mean and lower cross-sectional variance of log income in the steady state, and therefore lower mean income given our lognormality assumption). This seems to suggest that countries with lower income persistence differ from countries with higher persistence in other ways that benefit their income growth.

Our approach to approximating individual-level distribution dynamics with aggregate data makes it possible for the first time to compare anonymous and non-anonymous growth rates in a wide cross section of countries. We show that the anonymous growth rate of the bottom 40 percent (corresponding to the World Bank’s definition of ”shared prosperity”) understates the growth rate of those initially in the bottom 40 percent by 3 to 4 percentage points per year. This implies that the anonymous growth rate is a poor approximation to the success of those initially in the bottom of the income distribution, and is not a good tool for tracking the effects of policy interventions that were targeted to the initially-poor. The exact opposite holds true when tracking growth at the top end of the income distribution. Growth of top incomes will be larger when measured anonymously. Our estimates suggest that for the top 10% the difference is as large as 10 percentage points in annual terms. Thus, tracking anonymous top income shares substantially overstates the expected income growth rates realized by the average individual in the top end of the distribution.

Approximating income distribution dynamics, under the assumption that individual incomes follow a lognormal autoregressive process, can also be applied to an inter-generational context. The advantage of working with analytical approximations in this case is that it provides a means of rationalizing some of the empirical regularities that have been reported in recent studies on inter-generational mobility. One prominent example of such a regularity is that higher levels of inter-generational mobility are more likely to be observed in countries with lower levels of income inequality (see e.g. Corak, 2013; 25
# Annexes

## Annex 1: Methodological notes

### **Poverty indicators**

Official absolute poverty is estimated based on comparison of disposable income of the household with its budget of subsistence minimum. Disposable income is calculated based on consumption of the households, privileges, and in‐kind income. Budget of subsistence minimum of a household is estimated based on the composition of the household and budgets defined for specific age groups: children age 0‐3, children aged 3‐ 6, children aged 6‐18, working‐age population, retirees.

Moderate poverty is estimated based on the comparison of per capita disposable income with the minimum consumer budget for a family consisting of 4 persons (2 working‐age adults and 2 children).

### **Social groups definition**

We define working‐age individuals who reported income in the form of wages or from self‐employment as employed. We define the fully employed to be those who reported employment related income exceeding BYN 100 for three months in a quarter. Other individuals who reported employment related income are considered partly employed. Non‐employed are those working age individuals who did not report any employment or self‐employment related income. Hence, this group includes the unemployed, inactive population, students, housekeepers, individuals on parental leave.

### **Consumption in real terms**

Consumption of separate goods and services is reported in HBS files in monetary terms, and we convert it into real terms using the consumer price indices of related goods. Subsequently, all goods and services were aggregated into several groups:

- Consumable goods

- Food

- Other consumable goods

- Food related goods and services (alcohol and tobacco, eating out, cultivation of land plot and animal breeding)

- Non‐food goods (clothes, footwear, textile, maintenance of private vehicles)

- Durable goods (household goods and appliances, furniture, purchase of private vehicles)

- Services

- Education and health

- Utilities and public transport

- Other services (communication, culture, recreation and sports, personal care)

27