rafmacalaba/datause-encoder-data huggingface dataset:
parental investments in their children’s health. Cohen and Leker (2014) found that life expectancy at birth was a decisive factor for schooling decisions related to younger cohorts but not for those cohorts who had already completed their schooling.

Hazan (2009) argued that the relevant measure of life expectancy was the expected total working hours over a person’s lifetime, not life expectancy at birth or adult mortality. He argued that the Ben-Porath model would require a positive correlation between longevity and expected lifetime work hours. He did not find evidence of that positive correlation in data on U.S. men born between 1840 and 1970 and all American individuals born between 1890 and 1970. In response, Cervellati and Sunde (2013) argued that an increase in expected lifetime labor supply is neither a necessary nor sufficient condition for higher life expectancy to induce an increase in schooling. They documented that the observed fall in labor supply in the US was due to cohorts spending more time in school, delaying entry into the labor market, and consuming more leisure made affordable by the higher earnings.

Our strategy makes several departures from past studies. First, we base our analysis on 919 household surveys containing information on completed schooling rather than estimates of schooling based on enrollment data. Our strategy is to relate life expectancy at birth for an economy-birth-year cohort to the average education that cohort will ultimately attain. This level of detail will allow us to exploit between-cohort, within-economy variations in life expectancy at birth and schooling. In addition to examining how own life expectancy affects schooling, we also examine intergenerational effects of life expectancy gains by incorporating the effect of changing life expectancy at birth of the parental cohort on the lifetime schooling of their children.<sup>5</sup> Our contribution will be to assess how common are violations of the Ben-Porath hypothesis across

> 5 We see this as a plausible reason for the Lorentzen, McMillan and Wacziarg (2008) finding that adult mortality affects the schooling of younger cohorts.

7
on cardiovascular and respiratory diseases, as well as an increase in mortality (U.S. EPA, 2009, Ch. 2).<sup>7</sup> These negative health effects are particularly important among susceptible populations, such as children, elderly adults, and people with pre-existing conditions like asthma and cardiovascular or lung disease. These populations are at increased risk for the detrimental effects of ambient exposure to particulate matter (U.S. EPA, 2009, Ch. 8). The effects are not necessarily immediate; several studies find a lag between exposure to particulate matter and hospital admissions. There is, however, no consensus on a priori lag times to use when examining morbidity. Some studies find short lags, 0 to 1 days, for cardiovascular diseases and for elderly patients with other diseases, and larger lags, from 3 to 5 days, for asthma hospital admissions.

# **2.2 Air pollution in Lima**

Lima is Peru’s capital and, with a population of more than 9 million, its largest city. It is also heavily polluted. For instance, during the period of this study (2007-2011), the average daily level of PM2 _._ 5 was around 45.6 _μ_ g/m<sup>3</sup> . This level is above the U.S. 24-hour standard of 35 _μ_ g/m<sup>3</sup> and is considered unhealthy for susceptible groups (U.S. EPA, 2012). In fact, in around 70% of weeks PM2 _._ 5 levels in Lima exceeded this threshold (see Figure 1).<sup>8</sup> The main source of air pollutants is exhaust from motor vehicles. According to some official estimates, this source accounts for more than 80% of total emissions in the city (CONAM, 2001). The rest is produced by point sources such as power plants and industrial sites.<sup>9</sup> There are several features relevant for empirical analysis. First, the distribution of PM2 _._ 5 has a wide support with common episodes of moderate to high concentrations (see Figure 2). This feature allows us to study the relationship between pollution and labor supply at different levels of PM2 _._ 5 and explore non-linearities in the dose-response function.

Second, there is intra-urban variation in PM2 _._ 5 levels. Areas in the north, center, and east side of the city have higher exposures to PM2 _._ 5 than areas in the south and closer to the sea shore (like Callao). This is likely driven by the presence of industrial sites in the east side and

> 7In contrast, the evidence linking other sizes of particulate matter to health problems is considered only suggestive or inadequate.

> 8Similarly, levels of other air pollutants such as PM2 _._ 5 and NO2 are above international standards. However, levels of SO2 are very low (DIGESA, 2012). See Figures A.1, A.2 and A.3 in the Appendix for distribution of other air pollutants.

> 9These estimates refer to total air emissions not only to PM2 _._ 5.

5
were due to natural resources, including oil products (51%), ferrous metals (13%), wood products (7%), and copper (5%) (Kaulbars 2000).3 Table 3. Estimated tax collections by the Ministry of Taxation in 1999 (based on first 9 months)

||Tota|l||Taxre|venuesac|cruing|to thefe|deralbu|dget||
|---|---|---|---|---|---|---|---|---|---|---|
||||CPT||VAT||Exci|ses|User|fees|
||mn<br>RUB|%<br>Share|mn<br>RUB|%<br>Share|mn<br>RUB|%<br>Share|mn<br>RUB|%<br>Share|mn<br>RUB|%<br>Share|
|Total|760,757|100|70,960|100|138,513|100|69,410|100|8,725|100|
|Fuels|92,945|12.2|8,412|11.9|10,401|7.5|9,052|13.0|5,046|57.8|
|Oil|62,357|8.2|6,987|9.8|6,411|4.6|4,317|6.2|3,272|37.5|
|extr.|||||||||||
|Oilref.|12,044|1.6|825|1.2|846|0.6|3,774|5.4|81|0.9|
|Gas|12,588|1.7|421|0.6|2,211|1.6|954|1.4|1,316|15.1|
|Coal|4,864|0.6|112|0.2|682|0.5|0|0.0|67|0.8|
|Metals|38,274|5.0|79,996|11.3|-4,042|-2.9|14|0.0|1,513|17.3|
|Wood|13,649|1.8|1,997|2.8|1,523|1.1|21|0.0|260|<br>3.0|
|Fish|2,843|0.4|372|0.5|446|0.3|6|0.0|5|<br>0.1|

Source: Russian Ministry of Finance.

1.6 The total contribution of natural resource sectors to tax revenues under current policy is thus very substantial. There are, however, two issues to be raised with the current approach to taxation of these income streams. First, except in the category of natural resource user fees, which represent a small fraction of total Taxation Ministry collections, the contribution of natural resource sectors to tax revenues is indirect, to the extent that it is not the resources themselves that generate the tax revenues, but the value added to these resources. Taxes on profit, social security contributions and excises may burden the capital and labor applied to natural resources.

1.7 Second, failure to capture much of the rent potentially available puts the sustainability of these tax revenues in doubt. This latter issue will become more critical as some of Russia's reserves become scarcer.<sup>4</sup> Of course some of the appropriated rent could be reinvested directly into productive uses without transiting through the budget, but this is far from guaranteed. Russia indeed suffers from massive capital outflows, which is largely associated with natural resources, for example through underrepresented export earnings (Fisher and Sahay 1999; Loungani and Mauro 2000). Flight capital probably is not reinvested in Russia in the short term.

3 Such export duties are an effective way of capturing excess profits since they vary with world market prices and world market prices are not influenced by Russian producers. Nevertheless they are not pure taxes on excess profits and distort the allocation of resources between exports and domestic sales by penalizing exports.

4According to BP Amoco, at the present rate of extraction, Russia has 22 years left of oil in the ground (BP Amoco 1999). Beyond absolute scarcity, the remaining oil becomes progressively more expensive to extract.

3
Cline’s growth-based estimates of poverty reductions stemming from trade liberalization are considerably larger than those obtained by the World Bank Development Prospects Group (DPG), 2004. These authors use a recursively dynamic, CGE model to estimate the poverty reduction in 2015, owing to gradual global trade liberalization between 2005 and 2010. Unlike Cline, they actually track the accumulation of capital in response to increased investment, and the openness/productivity multiplier is also an explicit part of their model. Like Cline, they use a poverty elasticity with respect to aggregate income to convert economic growth into poverty reduction. However, the DPG study uses a uniform poverty elasticity of 2.0. In contrast, Cline uses estimates ranging from 1.0 to 3.5, with those for most of the Asian region, including India and China, at the top of this range (3.5). Overall, Cline’s elasticities, which are based on a simple formula and are a function of each country’s Gini coefficient and the ratio of mean income to poverty income, appear to be larger for the most populous countries in the developing world. Taken together with the more modest rate of capital accumulation due to the absence of Cline’s steady-state assumption, this helps to explain why the DPG study concludes that such trade reforms would reduce $2/day poverty by 320 million – roughly half of Cline’s estimate.

Cline’s synthetic estimates – as well as those from DPG – highlight the potential for trade liberalization to have a substantial long run impact on poverty. However, in order to get to this estimate, he must follow a long and arduous path, crossing several research “minefields” in the process: “steady-state” CGE analysis, growth theory, and cross-country regression analysis, in addition to the responsiveness of poverty to economic growth. It will be some time before these individual pieces are strong enough to support anything more than back-of-the-envelope estimates of potential long run poverty impacts of trade reform. In the meantime, we expect that most of this literature will continue to emphasize the short- to medium-run income distributional impacts of trade reform on poverty resulting from comparative static estimates of the ensuing commodity and factor price changes. To the extent that most policymakers focus on this shorter time frame, and because short run impacts are especially important for households facing extreme poverty, we believe this emphasis is justified. With this in mind, we turn next to a potential research agenda that emphasizes the short- to medium run impacts of trade liberalization on poverty.

24
and Klenow 2009).<sup>11</sup> This implies that holding for all other explanatory factors (location, sector and economic activity, for example), firms of the same age, across different economies should employ a similar number of people and make about the same sales revenue if economies are all equally efficient in allocating resources to the most productive firms. If some economies are not allocating the resources that firms need to grow, in economic terminology, they exhibit allocative inefficiencies.

<!-- Start of picture text --> Figure 3: Older firms in Russia employ fewer workers and earn less sales revenue than similar firms in other economies<br><!-- End of picture text -->

**Figure 3: Older firms in Russia employ fewer workers and earn less sales revenue than similar firms in other economies**

<!-- Start of picture text --> Age predicts size of labor force (log) Age predicts sales revenue (log)<br>Russia vs. Rest of the World Russia vs. Rest of the World<br>Rest of the World<br>Rest of the World<br>Russia<br>Russia<br>0 2 4 6 8 10 15 20 25 30<br>Linear prediction Linear prediction<br>95% CI Russia 95% CI Russia<br>Rest of the World Rest of the World<br>Source: Authors' calculations based on comprehensive dataset of Enterprise Surveys (May 2012) Source: Authors' calculations based on comprehensive dataset of Enterprise Surveys (May 2012)<br>Age predicts size of labor force (log) Age predicts sales revenue (log)<br>Russia vs. Rest of ECA Russia vs. Rest of ECA<br>Rest of ECA<br>Rest of ECA<br>Russia Russia<br>0 2 4 6 8 10 15 20 25<br>Linear prediction Linear prediction<br>95% CI Russia 95% CI Russia<br>Rest of ECA Rest of ECA<br>Source: Authors' calculatons based on Enterprise Survey comprehensive dataset (May 2012) Source: Authors' calculations based on Enterprise Surveys comprehensive dataset (May 2012)<br>8 30<br>6 25<br>4 20<br>2 15<br>Actual size of labor (log) Actual sales revenue (log)<br>0 10<br>8 25<br>6<br>20<br>4<br>15<br>2<br>Acutal sales revenue (log)<br>Actual size of labor force (log)<br>0 10<br><!-- End of picture text --> One way to determine the relative allocative efficiency of economies is to compare firm-size and age data across economies. As firms get older and grow, they employ more workers and increase their sales revenue. For that reason, there should be a positive relation between firm size and age and this relation should demonstrate a statistical regularity across economies. Figure 3 depicts this relationship between firm size and age for Russia and other comparator economies. The size of the

> 11 Hsieh, Chaing-Tai, and Peter J. Klenow "Misallocation and manufacturing TFP in China and India." The Quarterly Journal of Economics:124.4 (2009): 1404-447. Print.

6
**<u>Table 2: Details on land market participation at household level</u>**

||**Ethiopia**|**Malawi **|**Niger **|**Nigeria**|**Tanzania**|**Uganda**|
|---|---|---|---|---|---|---|
|Autarkic (%)|73.99|89.65|91.12|89.04|92.69|80.55|
|Rent in (%)|20.81|10.06|7.29|10.80|6.17|18.65|
|if yes, mean area rented in|0.63|0.49|1.87|0.71|0.82|0.52|
|if yes, area of cultivated area (%)||74.69|48.14|87.54|72.64|57.11|
|Rent out (%)|5.20|0.30|1.59|0.17|1.14|0.80|
|Total area rented in (with weights)|1,516,979|130,155|262,079|1,069,316|400,091|492,903|
|Total area rented out (with weights)|206,339|5,092|80,486|15,404|197,619|27,824|
|Gini of owned area|0.50|0.40|0.45|0.53|0.59|0.49|
|Gini of operated area|0.50|0.39|0.45|0.53|0.59|0.48|
|Purchased any during last 5 years (%)|||3.58|||9.63|
|ifyes size of areapurchased|||2.13|||0.57|

_Source:_ Own computation from LSMS-ISA household surveys.

15
Applying 2015 tariffs has little negative impact on the per capita wellbeing of households. Given small increases in tariffs that are focused mostly on rich consumers, expenditures per capita are expected to decline on average by JD0.6 or about 0.03 percent (table 13). This would bring no changes in poverty and poverty gap measures.

**<u>Table 13. The impact of 2015 tariffs on economic wellbeing</u>**

|Quintiles|Impact, JD|Impact, %|
|---|---|---|
|Quintile 1|-0.02|0.00|
|Quintile 2|-0.05|0.00|
|Quintile 3|-0.15|-0.01|
|Quintile 4|-0.31|-0.01|
|Quintile 5|-2.48|-0.06|
|Total|-0.60|-0.03|

Source: Authors’ calculations based on extrapolated HEIS 2010 data. <u>Notes: Population quintiles based on consumption per capita before the reform.</u> Full removal of subsidies, in contrast, will have a considerable impact on economic wellbeing. Fully removing subsidies with a flat tariff rate is expected to reduce consumption per capita on average by JD 72.5 or by 3.6 percent. The negative impact is expected to be the strongest for the poorest households, with the bottom quintile experiencing on average a 5.7 percent reduction in per capita consumption. The negative burden on the poorest households can be reduced if a progressive increase of tariffs is applied. In this case, the negative impact would be less pronounced for the poor even though the average household consumption would drop by the same amount. Nevertheless, both sub-scenarios are quite severe and would be difficult to implement. A semi-progressive increase in tariffs leading to a smaller reduction in subsidies, as depicted in scenario 3, is perhaps more realistic; the relative impact on households across the distribution would be almost equal, with a 1.2 percent reduction in per capita on average (table 14).

**<u>Table 14. Different scenario for electricity tariff reforms</u>**

|iil|Total<br>i|fu|**Scena**<br>ll eliminatio|**rio 2**<br>n of subsid|ies|**Scen**<br>semi-progress<br>tar|**ario 3**<br>ive increase in<br>iffs|
|---|---|---|---|---|---|---|---|
|untes|consumton er|||||||
||<br>capita|fl<br>|at<br>|Progr<br>|essive<br>|||
|||Impact,<br>JD|Impact,<br>%|Impact,<br>JD|Impact,<br>%|Impact,<br> JD|Impact,<br>%|
|Quintile 1|843|-49|-5.8|-33|-3.9|-10|-1.1|
|Quintile 2|1,240|-59|-4.7|-42|-3.4|-13|-1.0|
|Quintile 3|1,624|-66|-4.1|-54|-3.4|-18|-1.1|
|Quintile 4|2,198|-78|-3.5|-71|-3.2|-24|-1.1|
|Quintile 5|4,336|-109|-2.5|-157|-3.6|-58|-1.3|
|Total|2,048|-72|-3.5|-72|-3.5|-24|-1.2|

Source: Official tariff instructions replacing electricity tariff instructions 17/6/2012. <u>Notes: Population quintiles based on consumption per capita before the reform.</u>

**Full elimination of subsidies has the strongest negative impact on poverty and inequality.** In particular, poverty is expected to increase by 2.4 percentage points, poverty gap by 0.7 percentage points and inequality by 1.9 percent (table 15). Planned tariffs for 2015 year will not have negative impact, while semi-

14
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

suggests that the combined effect of the disadvantages in these factors result in a per capita income that is approximately 30% of that of rich countries.

Almeida and Ferreira (2002) examine an interesting issue in the context of general economic performance. The study finds evidence that centralised countries have a more volatile economic performance than less centralised countries. Such a hypothesis has not been tested for sector level performance (pre-reform as well as post-reform). However, if this result does apply to sector level performance, then, as volatility in the electricity sector has considerable political, social and business implications, it need to be considered in the reform design.

As we saw, while there is some evidence in support of the importance of institutions on economic development, the main sources of this influence are less certain. In the context of electricity, it is useful to determine what factors have an impact on the institutions and whether they exert an independent or indirect influence on the performance of reform.

The aforementioned studies of the electricity sector in Bacon and Besant-Jones (2001) and Drillisch and Reichmann (1998) examine aspects of the role of institutions, policies, and endowments as determinants of reform. There is however considerable scope for improved modelling of institutional and country development variables in sector specific studies. This is important because reforms need to be appropriate for the general economic circumstance of the country.

# **3.4  Country case studies**

Cross-country econometric and productivity analysis can be useful for addressing welldefined questions associated with reform. These questions are addressed using a limited number of variables that are similar in measurement units and are comparable in what they represent. However, the issues associated with model specification and accuracy of variables in representing the relevant aspect of reform pose limitations on these types of studies.

The multi-faceted nature of reforms and the varied characteristics of the sector across countries, mean that case studies can address issues that do not easily lend themselves to rigorous quantitative analysis, or could not be addressed due to lack of data. Further, results suggesting that, on average, there is a statistically significant correlation between a dependent variable and certain independent variables, have limited relevance to decision-makers concerned with individual sectors. Further, in electricity reform, dynamics, such as the implementation process, and qualitative aspects, such as regulation and conflict resolution are crucial and, at the same time, inherently difficult to capture through statistical methods.

Case studies can play a complementary role in understanding complex questions and multi-dimensional activities such as electricity reform. A statement in Rodrik (2003) made in the context of general economic development is also valid in the case of electricity sector reform: _“Ideally, case studies can generate novel hypotheses that in_ 26
examine whether there are heterogeneous effects that vary with parent background. Migrant opportunity has a stronger negative effect on the high school enrollment of children from families in which parents are cadres, small scale enterprise owners or managers, have some high school education, or had significant off-farm work experience. Given that children from such families are otherwise more likely to attend high school, finding a stronger effect among these groups provides evidence that we do not simply identify the decisions of families who remain unlikely to enroll children in high school after access to education improves in more rural communities.

Finally, we examine spillover effects of increases in migration on local employment. A larger migrant network outside the village implies a smaller local labor force. As migration from the village increases, the opportunity cost of high school may rise because the net return to migrant employment is increasing and because a shrinking local labor force leads to an increase in the relative returns to local employment. As the migrant labor force increases in size, the probability that high school age children find either migrant or local wage employment both increase, but there is no evidence that youth leave school to work in household agricultural activities or family-based non-agricultural enterprises.

# **2 Background**

# **_A. Rural-Urban Migration in China_**

During the 1990s, China’s labor market changed dramatically with rapid growth in the population of rural migrants moving to work in urban areas. Estimates using the one percent sample from the 1990 and 2000 rounds of the Population Census and the 1995 one percent population survey suggest that the inter-county migrant population grew from just over 20 million in 1990 to 45 million in 1995 and 79 million by 2000 (Liang and Ma 2004). Surveys conducted by the National Bureau of Statistics (NBS) and the Ministry of Agriculture, which include more detailed information on short-term migration, suggest that there were well over 100 million migrants by the early 2000s (Cai, Park and Zhao 2008).

In other settings, referral through networks of earlier migrants is found to be an important feature of the job search and migration decisions of potential migrants (Montgomery 1991; Carrington, Detragiache and Vishnawath 1996; Munshi 2003). In China, Rozelle et al. (1999) emphasize that villages with more migrants in 1988 experienced more rapid migration growth by 4