rafmacalaba/datause-encoder-data huggingface dataset:
possibility of withdrawing the money before retirement. Beshears, Choi, Laibson, and Madrian (2018) supports the evidence that this might be the case.

Caps on fees seem to be high. While the regulation sets caps on fees, they seem to be high for an economy with a sophisticated financial market.

An overall evaluation of the VRSP needs to include some other pension plans existent in Quebec. The low rate of participation in the plan, explained by the presence of other pension plan products, according to the government is related to the fact that in cases that the employer decides to make matching contributions, other pension plans offer them better tax advantages compared with the VRSP. Overall, the success of VRSP needs to be evaluated considering the overall coverage of the private pension system, and not only the VRSP.

# **e. The case of Chile**

Chile has a well-established mandatory funded pension system. In 1981, Chile was the first country in adopting a mandatory funded scheme in an open pension fund system (second pillar), which serves as a reference to other reforms that took place later in other countries. While the system is still in place, the government was concerned about the low coverage of the self-employed.

_Automatic enrollment for the self-employed_ The focus of Chile’s AES is the self-employed. In 2008, a law introduced an automatic enrollment scheme for the self-employed, as a transition for turning their contributions to the pension system mandatory. The system would run with automatic enrollment between 2012 and 2015 and start being mandatory in 2016. The law introduced a gradual process of participation, in terms of the percentage of covered earnings to be used for the purpose of paying contributions, as shown in Table 4. During the years 2012-2014, the self-employed were automatically enrolled in the pension system with the possibility of opting-out. After 2015, the law stated that contributions of the self-employed had to be mandatory.<sup>22</sup> Table 4. Chile: Calendar of Payments of Contributions (% net income)

|Year|% of net income to contribute|Process|
|---|---|---|
|2012|40%|Opt-out|
|2013|70%|Opt-out|
|2014|70%|Opt-out|
|2015|100%|Mandatory|

Source: SII The Internal Revenue Service played a key role in enrolling the self-employed into the pension system. The opt out was implemented in the tax income process. The Tax Revenue Service (SII), determined the amount of contributions that self-employed workers had to pay to the pension system each year. The SII withheld 10 percent of the amount of each invoice or receipt for services provided 22 In 2014, when the time for turning the system mandatory was getting closer, the government decided to postpone until 2018 the moment for making contributions mandatory. Subsequently, a law with these features was approved.

17
at least until almost all pixels exceed _DN_ =6. Note also that the size of the initial _AoL_ will matter more: an observed gain of two pixels will generate a much higher growth rate for an observed starting value of four pixels than it would for an observed starting value of 100 pixels. Experiments with actual data for several countries shows that censoring can result in the reversal of signs in growth rates of _AoL_ and _R_ relative to growth rates from uncensored data. In addition, unlike the blooming problem, the error in observed growth of _AoL_ is not always cancelled out by the error in _R_ .

# **VI. Characteristics of the Dependent Variables**

We use four independent variables. Data for GDP, non-agricultural GDP, and manufacturing value-added are all measured in constant 2005 US dollars. These data come from the World Development Indicators supplied by the World Bank. Data for electricity consumption in kilowatt-hours come from the International Energy Agency via the World Development Indicators. Data for population come from the UN Population Division, again via the World Development Indicators. Data for the value of the stock of physical capital, denominated in constant 2005 US dollars, come from the Penn World Tables Version 8.0.

Several observations stand out when examining the characteristics of the dependent variables. First, as shown in Table 7, the average growth rate for population is much lower than that for GDP, electricity consumption or capital stocks. In fact, annual population growth rates were negatively correlated with annual _GDP_ growth rates for 45 out of 69 countries as shown in Table 8. Population growth rates are also negatively correlated with annual growth rates for electricity consumption and capital stocks in many countries. Second, a comparison with Table 2 shows that the average population growth rate is also lower than the growth rates of _AoL_ and _SoL_ . Third, the standard deviation in growth rates for capital stocks and population are lower than those for _GDP_ and electricity consumption – and much lower than those for _R_ , _AoL_ or _SoL_ . In general, the DMSP-OLS stable night-lights data are much noisier than the dependent variables. That suggests that long-run relationships are likely to be more robust than those constructed around annual data.

**<u>Table 7:  Sample Averages of Growth Rates of Dependent Variables</u>**

||**GDP**|**Electricity**<br>**Consumption**|**Capital**<br>**Stock**|**Population**|
|---|---|---|---|---|
|Median|3.84|3.65|3.45|1.44|
|Average|3.48|4.10|3.88|1.43|
|Standard Deviation|4.37|6.87|2.77|0.97|
|Skewness|-1.58|1.56|0.81|0.11|
|Kurtosis|9.83|20.48|1.10|-0.33|

_Sources: World Development Indicators, Penn World Tables Version 8, and author’s calculations._

**<u>Table 8:  Count of Negative Correlations of Growth Rates</u>**

||**Electricity**<br>**Consumption **|**Capital**<br>**Stocks**|**Population **|
|---|---|---|---|
|GDP|4|8|45|
|Electricity Consumption|..|9|35|
|Capital Stocks|..|..|31|

_Sources: World Development Indicators, Penn World Tables Version 8, and author’s calculations._
# Introduction

Recent literature has documented the increase in income inequality at the very top of the distribution (e.g. Atkinson and Piketty, 2007, 2010; Alvaredo et al., 2017). Between the mid1980s and the mid-2000s, the income share of the top 1% in the US approximately doubled, while around the same time their share of income from capital declined, and salaries and selfemployment incomes became more important (Figure 1, also see Piketty and Saez, 2007a). These changes in the income composition have been even more pronounced over the long-run, with the share of income from capital among the top 1% dropping from close to 50% in the 1920s to less than 20% in the 2000s (Piketty and Saez, 2003, 2007a). Such an increase in the labor share at the top could arise from a change in the association between labor and capital, or because the share of tax units in the top who receive only capital income declines while there remain separate classes of laborers and capitalists. As part of the hypermeritocratic society (Piketty, 2014), the inequality in the wage distribution has increased substantially (Piketty and Saez, 2007a), providing support for the second channel. At the same time, tax units increasingly have income from both capital and labor (Wol and Zacharias, 2009), and pure rentiers have virtually disappeared (Atkinson, 2009).

While the literature (e.g. Piketty and Saez, 2007a) has focused on the distribution of total income, as well as the distributions of capital and labor income separately, the association between the two income sources has received little attention. To extend the existing literature, this paper examines the association between capital and labor incomes at the top of the US income distribution. Using data based on US tax returns, we can directly measure the association. In a classical society, capitalists are at the top of the capital distribution and bottom in the wage distribution, i.e. the correlation between capital and labor incomes is negative. On the other hand, this correlation is generally positive (but less than one) in modern economies (Piketty, 2014). Our paper also links the study of the functional (capital v. labor) and the personal (rich v. poor) income distributions. For 2
<mark>used IBTs, two used geographically defined tariffs with IBTs, two used a uniform tariff, two used volume-differentiated tariffs (VDTs) with specific thresholds, and two used means-tested discounts.</mark><sup>6</sup> Other studies in the literature that have examined water-only or electricity-only cases confirm this predominance of IBT structures in the developing world.<sup>7</sup> With regard to subsidy “leakage” rates and the progressiveness (or regressiveness) of water and electricity subsidies for the residential sector, it is well documented that subsidies allocated through quantity-based mechanisms (IBTs, VDTs, or other consumption thresholds) perform worse than those allocated through administrative selection—namely, geographic targeting or means testing. Table 1 lists the studies that analyze both water and electricity subsidies for the same location. This table reveals that the same subsidy-targeting mechanisms are applied to water and electricity, and it shows also that the results for quantitative mechanisms have been regressive, while results for administrative-selection mechanisms have been progressive.

> 6 A volume-differentiated tariff, also known as a “ratchet” IBT, is used to calculate a household’s service bill when households use more than the lifeline (lowest-priced) block. In that case, the household is charged—for their entire use of electricity (or water)—the price per kilowatt-hour (resp., per cubic meter) in the highest block within which their electricity/water use falls.

> 7 Appendix A summarizes studies that analyze water-only subsidy incidence. See Cardenas and Whittington (2019) for a summary of electricity-only subsidy incidence analyses.

8
Achieving that growth will not be easy.<sup>39</sup> It will require investments in agricultural research and development (R & D), tailored to African (often rain-fed) conditions, and efforts to bring research results to African farmers. China would seem to be in a good position to help African countries build up their agricultural research and extension systems. (SSA’s total public spending on agricultural R & D increased by barely 20% in real terms over 1981-2000; over the same period it increased three fold in China; See World Bank, 2007, Table 7.1.) Higher agricultural growth will also require investments in rural infrastructure, which is worse now in many African countries than it was around 1980 in China, when the rural reforms began.<sup>40</sup> Provided that Africa makes the right investments in supporting agricultural growth there should be no difficulty finding the market for its produce, including in China, which is now more open to agricultural imports (after its entry into the WTO).

African observers of China’s success might be tempted to conclude that rising inequality is the inevitable price of lower absolute poverty. Looking forward, that would be worrying in Africa, where inequality is already rather high, with many countries having levels of inequality reaching (and in a few cases exceeding) the levels found in Latin American, where inequality measures tend to be the highest of any region of the world.<sup>41</sup> However, it should not be presumed that poor countries necessarily face an aggregate growth-equity trade off. Upward pressures on inequality can certainly be generated by the growth process, such as stemming from skill shortages and higher returns to schooling in the labor market. On the other hand, to the extent that the growth comes from relaxing the constraints facing poor people in access to key markets, it may help put downward pressure on inequality. The net outcome is an empirical issue, and will vary from country to country, as is indeed found to be the case in cross-country comparisons of growth rates and changes in inequality (Ravallion, 2007).

So Africa should be wary of drawing the lesson from China that rising inequality is the inevitable price of higher growth and less poverty. Indeed, as noted above, China’s experience actually provides counterexamples (in some time periods and some provinces) to the view that rising inequality is the unavoidable by-product of sustained economic growth in a poor country.

> 39 See Dorward et al. (2000) on the constraints facing agricultural development in Africa.

> 40 World Bank (2007, Part 2) contains a detailed discussion of the policy instruments available for promoting pro-poor agricultural development.

> 41 See the inequality measures by country in World Bank (2005).

21
29 Figure 4: Relative contributions of different border policy instruments to trade reduction from agricultural policies (ITRI), 1960 to 2004 (percent)

# (a) Developing countries

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>‐10<br>‐20<br>1955‐59 1960‐64 1965‐69 1970‐74 1975‐79 1980‐84 1985‐89 1990‐94 1995‐99 2000‐04<br>Import tax Export subsidy Export tax<br>Import subsidy All border measures<br><!-- End of picture text -->

# (b) High-income countries

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>‐10<br>1955‐59 1960‐64 1965‐69 1970‐74 1975‐79 1980‐84 1985‐89 1990‐94 1995‐99 2000‐04 2005‐07<br>Import tax Export subsidy Export tax<br>Import subsidy All border measures<br><!-- End of picture text --> Source: Authors’ calculations using data in Anderson and Valenzuela (2008).
use cameras with a tamper-proof date and time function, to monitor teacher attendance in India when teachers were provided financial incentives for attendance.

While the formal rules of the program make it an unconditional program, this appears not to be the case in the perception of a substantial part of the potential beneficiaries. Before the actual implementation of the program there was a publicity campaign, which mentioned the need for households to enroll their children in school and take them to health care centers. Some surveys indicate that 1/3 of the beneficiaries state that they believe that the transfers are conditional, so that they will probably respond to the program as if it poses explicit requirements with regard to school enrollment and visits to health care centers.

An interesting feature of the design of the program’s impact evaluation is that it consists of a randomized experiment and of a regression discontinuity design. In the experiment 1309 families around the first quintile of the poverty index were randomly assigned to treatment and control groups. For the regression discontinuity design data were collected from 1221 families around the second quintile of the poverty index, which is the program’s threshold for eligibility. Hence our estimates pertain to groups at two different locations of the poverty distribution, thereby giving insight into the potential heterogeneity of the program’s impact. If the cash transfer lifts a credit constraint, it is likely that the impact is larger among poorer families. Moreover, since school enrollment prior to the program’s implementation is lower among poorer families there is also more scope for an increase in enrollment among these families.

Our empirical findings show that there are indeed heterogeneous treatment effects according to this pattern. School enrollment of children in families around the first quintile increases by about 10 percentage points in response to the cash transfer, while school enrollment of children in families around the second quintile is unaffected by the program. Our findings suggest that the program’s effectiveness can be enhanced by lowering the poverty threshold for program eligibility, so that unresponsive families are no longer covered (i.e. do not receive a windfall).

The experimental design using data from families around the first quintile has been analyzed before in a recent paper by Schady and Araujo (2008). Although their empirical approach differs somewhat from the approach adopted in this paper, the findings are qualitatively similar; they too find that school enrollment goes up by about 10 percentage points for families around the first quintile that receive the cash transfer. The main novelty of the current paper is that we present the findings from the randomized experiment along with the fresh evidence from the regression discontinuity design, and that we compare and interpret the findings from both designs.

2
To what extent, if at all, are tea households better off than non-tea households? We base our answer to this question on several types of comparisons. Table 9 presents results from a naïve welfare comparison based on both per capita expenditure and per capita income. As noted earlier this type of comparison does not account for any heterogeneity between the two groups. The results suggest that, in tea cultivating regions of Rwanda, average welfare is higher for tea households than for non-tea households.

Table 9. Naïve Comparison of Welfare between Tea and Non-Tea Households

|Outcome|Differencein Means|Tea|Non-Tea|All|
|---|---|---|---|---|
|Per capita Expenditure|1936.43|25397.66|23461.23|24399.25|
|Per capita Income|2571.38|23075.70|20504.32|21721.56|

Source: Authors’ calculations Table 10. Matching Comparison of Welfare between Tea and Non-Tea Households

|Kernel|PerCapitaExpenditure|PerCapitaIncome|
|---|---|---|
|Gauss|6504.71|5488.51|
|Epanechnikov|6765.65|6659.29|
|Quartic|6722.41|6628.20|
|Uniform|6831.99|6770.90|
|Cosinus|6757.93|6651.70|

Source: Authors’ calculations Next we use the regression methods described above in order to account for both observable and non-observable heterogeneity. In the case of the Heckman method, the selectivity correction factor turned out not to be statistically significant. This gave us comfort in our use of the propensity score matching method. The corresponding results are presented in table 10. These reveal that when likes are compared with likes, the welfare advantage that tea households have over the non-tea households is much higher than what the naïve comparison would suggest. Indeed, regardless of the kernel function used among the ones reported in table 10, average per capita expenditure for tea households is more than the average for non-tea households by about 7,000 RWF As a last test for the robustness of our conclusion, we use a two-stage procedure explained by Wooldridge (2002). First estimate the participation equation as a nonlinear 19
recipients into active aid relationships. Selection regressions are reported in Appendix Table A4. The sample in Equation A4.1 is the full set of 15,402 pairings of the 34 donors and 151 eligible DAC recipients for each of the three survey years.

Selection variables include HIPC status, colonial tie, and distance from the donor capital or headquarters city (in log of kilometers). Countries already intensively engaged with donors to qualify for debt relief may have a greater interest in results, as well as lower participation costs, so we expect a dummy for countries reaching the HIPC completion point to be positively related to survey participation.

Colonial ties are shown to influence aid allocations by donors across recipients in Alesina and Dollar (2000), among other studies. We create a colonial tie dummy variable equal to 1 for all donor-recipient pairs (such as UK-Ghana) where the recipient was once part of the donor‟s colonial empire.<sup>15</sup> A colonial tie is present in 4.9% of the PDMS observations but in only 1.9% of the censored cases. Physical distance has also been shown to influence donor‟s aid decisions (e.g. Frot and Perrotta, 2011). Distance is less meaningful and its effect may be weaker for multilaterals, however, as some of them have global mandates (UN, World Bank) or interests (EC). We therefore create two separate distance variables, one for bilaterals (with multilaterals coded 0) and another for multilaterals (with bilateral coded 0).

Appendix Table A4 reports probit regression results for the selection variable, coded 1 for uncensored and 0 for censored observations. The coefficient estimates presented in the table represent marginal effects, evaluated at the means of all of the other regressors. Equation A4.1 includes data from all three survey years. The selection variables are all highly significant. The probability of a donor-recipient pair being uncensored increases by 20 percentage points if they

> 15 Colonies are assigned only to their last colonial master; e.g. Rwanda and Burundi are assigned only to Belgium, which occupied those parts of German East Africa in 1916.

20
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