rafmacalaba/datause-encoder-data huggingface dataset:
The PSEs are aggregated across states and across commodities in the same way as the NPC indicators i.e. using the value of production at reference prices as weights. For example, the weighted average (All India) PSE1 for commodity i is: PSEl1 = ( **p** j Di*Qi+SEl) / EjpRi*Qi where, pRj = Reference price of the ith state Qi = Quantity produced by the ith state In defining and aggregating the PSEs we have used reference prices as the numeraire and in the aggregation weights. This differs from some other applications of PSEs, which have related the subsidies to the domestic prices of the products.<sup>14</sup> We believe the method we have used is methodologically preferable in that it is consistent with standard methods of estimating and aggregating the other incentive indicators i.e. nominal protection, effective protection, and effective subsidy. Among other advantages, we avoid the problem that the use of domestic prices for aggregation systematically gives a higher weight to activities with high protection, and vice versa.

# **Incentive Indicators and the Exchange Rate**

The agricultural trade and other policies quantified in these studies are part of a broader system of trade protection and subsidies which directly impacts on all the tradable activities in the economy, especially the manufacturing and mineral sectors. A key economy-wide impact is on the exchange rate, which has in general been supported at a higher level in relation to foreign currencies than the rate that would exist under free trade. In interpreting the incentive indicators for agriculture, it should therefore be recognized that some positive effective protection may be offsetting the indirect disprotection resulting from the exchange rate overvaluation. Furthermore, negative protection indicators represent only part of the discrimination against a crop. But the extent of protection-induced exchange rate overvaluation varies from year to year. These economy-wide effects will be discussed in the book by Gulati and Pursell, but not in these background papers.

14 See for example, United States Department of Agriculture. 1988.

xxvi
transferred, only 27 percent of State B respondents face a similar situation. Interestingly, in State B 39 percent of respondents report a minimum tenure of one year and over two-thirds have remained at post for less than two years. In contrast, less than 40 percent of State A respondents report minimal post tenure of two years. The majority in either state have yet to be promoted. About 30 percent of the respondents report having a private practice.

|**Table 1: Sample HR an**<br>**State**|**d Administ**<br>**A (N=273)**<br>**(inp**<br>|**rative Cha**<br>**and State**<br>**ercent)**<br>|**racteristics**<br>**B (N=266) **<br>|**of Responde**|**nts**|
|---|---|---|---|---|---|
|**Position**|**PHC**<br>**MO**|**CHC**<br>**MO**|**Block**<br>**MO**|**--**|**--**|
|% State A|56.1|30.2|13.7|--|--|
|% State B|53.7|38.1|8.2|--|--|
|**Total Service (yrs.)**|**<2**|**2-6**|**6-12**|**12-18**|**>18**|
|% State A|15.6|36.4|20.8|14.5|12.6|
|% State B|26.7|35.8|17.2|12.5|7.8|
|**No. of Transfers**|**Not Yet**|**1-2**|**3-4**|**5-6**|**>7**|
|% State A|42.5|29.9|21.8|4.6|1.1|
|% State B|27.0|33.5|27.8|8.3|3.5|
|**Maximum post tenure (yrs)**|**<2**|**2 to 5**|**5 to 8**|**8 to 12**|**>12**|
|% State A|17.1|44.1|25.1|3.8|9.9|
|% State B|22.8|26.5|39.1|8.8|2.8|
|**Minimum post tenure (yrs.)**|**<1**|**1 to 2**|**2-3**|**3 to 5**|**>5**|
|% State A|15.0|22.7|19.4|23.9|19.0|
|% State B|41.2|36.1|13.9|5.1|3.7|
|**No. of promotions**|**Not Yet**|**Once**|**Twice**|**Three Time**|**s**<br>--|
|% State A|80.7|15.0|2.2|2.2|--|
|% State B|88.6|11.0|0.4|0.0||
|**Private practice**|**Yes**|**No**|**Can't say**|--|--|
|% State A|27.9|66.0|6.0|--|--|
|% State B|28.4|69.8|1.7|||

PHC: primary health center CHC: community health center (small rural hospital) block: smallest administration unit 8
does suggest the importance of immigrants to the US growth process. That said, roughly 27% of the male population was immigrant so that overall, immigrants do not appear more or less entrepreneurial than native born. Another indicator is that of fortune 500 firms started by immigrants and here immigrants are slightly underrepresented.<sup>6</sup> The picture from Latin America is dramatically different. In Argentina, the richest Latin country in our sample, 80% of owners/directors were immigrants (see Annex 1 for detail). However, it is also the case that 60% of the male population was comprised of immigrants so that overall they show perhaps 30% more propensity to start businesses than locals. The dominance of immigrants is supported by secondary sources that suggest that much of the dominant meat industry and the railroads, which were sponsored, financed, and constructed largely by nationals in Australia, Canada, and the United States, were undertaken by Europeans. The emblematic meat salting industry was started by two Englishman (Scobie, 1964), and more generally, the beef industry was spearheaded by recent arrivals who formed the _Sociedad Rural Argentina_ in 1866 which led the transformation of the pampas, improving the quality of livestock, pastures, and methods of animal husbandry (Fogarty et al., 1985).

In S ̃ao Paulo, Brazil 50% of large businesses were started by immigrants (70% if we include progeny), 50% more than would be expected given their share in the population. This was also the case in Rio de Janeiro and some parts of Minas Gerais. In the former, between 1878 and 1895, (Prado in Birchal (1999)) most textile mills were founded by foreigners, who owned 62% of wholesale textile trade in Rio, and foreigners soon dominated the manufacturing activities closely linked to their commercial activities. In both S ̃ao Paulo and Rio, the first electricity generating companies were founded by foreigners. Birchal (1999) documents that Juis de Fora, Minas Gerais, a major steel and manufacturing area from 1858-1912, immigrants were responsible for 66% of industries (Birchal, 1999, p.26).

In Chile, 70% of steam powered businesses were started by immigrants, roughly 12 times their share in the male population. Again, this conforms with other historical accounts. Silva Vargas (1977a) notes that “the lack of entrepreneurs and of qualified national workers

> 6We are very grateful to Richard Sutch for the census sample data and to he and Larry Neal for extremely helpful discussions.

7
WPS3815

# **Getting Real about Inequality**

## **Evidence from Brazil, Colombia, Mexico, and Peru**

**Edwin Goñi, Humberto López and Luis Servén** * **The World Bank**

### **Abstract**

Consumption baskets vary across households and inflation rates vary across goods. As a result, standard CPI inflation may provide a very misleading measure of the inflation actually faced by poor households, more so the more unequal the distribution of aggregate consumption across households. Likewise, changes in observed nominal consumption inequality may be very different from those in true inequality, i.e., that measured using household-specific CPIs. We explore empirically these issues using household data covering nine episodes from four Latin American countries (Brazil, Colombia, Mexico, and Peru). We find that in these countries standard CPI inflation typically reflects the inflation rate faced by a rich consumer located in the 80 to 90 percentile of the distribution of consumption expenditure. In most episodes we also find that inflation was anti-rich -- i.e. the inflation faced by the richest consumers was higher than the inflation faced by the poorest consumers. As a result of this bias, the observed increases in nominal inequality generally exceed the actual changes in real inequality. These results are robust to correcting for quality change bias in the CPI, to the use of alternative price indices, and to the use of alternative inequality measures.

JEL classification codes: C43, D12, D31, D63, J10 World Bank Policy Research Working Paper 3815, January 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at_ _<u>http://econ.worldbank.org.</u>_

* This paper was prepared as part of the World Bank.s Latin American Regional Studies program. We are grateful to Eduardo Ley for his insights during initial discussions and to Francisco Ferreira, Bill Maloney and Guillermo Perry for useful comments on earlier versions.
**Table 6: Predicting Baseline Outcomes by Future Orphan Transitions**

||Depende<br>(1)<br>Ln height at<br>baseline|nt Variable<br>(2)<br>Years of schooling<br>at baseline|
|---|---|---|
|Mother died|-0.006|-0.099|
||(0.008)|(0.146)|
|Father died|-0.011|0.237|
||(0.011)|(0.190)|
|Number of observations|718|718|

_Notes_ : OLS estimates with community fixed effects. Standard errors in parentheses. See notes to table 4.

**Table 7: Effect of Attained Schooling and Height on 2004 Consumption Per  Capita**

||Dependent Variable<br>(1)<br>(2)<br>Ln per capita<br>consumption 2004<br>OLS<br>Ln per capita<br>consumption 2004<br>OLS with controls|
|---|---|
|Ln height 2004|2.399***<br>2.154**|
||(0.662)<br>(0.701)|
|Years of schooling 2004|0.078***<br>0.055***|
||(0.009)<br>(0.010)|
|Number of observations|681<br>681|

_Notes_ : (1) controls for sex and a full set of age dummies, while (2) controls also for household and parental characteristics at baseline, and community fixed effects (as in table 4). Standard errors in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1 %.

20
foreign ownership exploring the possibility of heterogenous impacts on firms with different productivity levels (Griffith, Redding, and Simpson 2003, Sabirianova, Terrell, and Svejnar 2005).

This paper is very much part of the growing literature on heterogeneous firms showing how firm heterogeneity interacts with external policy changes generating dynamics that fits much better the empirical evidence (Jovanovic 1982, Hopenhayn 1992, Melitz and Ottaviano 2003, Melitz 2003, Helpman, Melitz, and Yeaple 2004, Bernard, Redding, and Schott 2004, Yeaple 2005). In particular these studies show how the impact of liberalization and globalization is highly asymmetrical depending on the initial productivity of the firms, with more productive firms benefitting disproportionately more from globalization than less productive ones.

# 3 Trade Liberalization and Productivity

## 3.1 The Facts

First of all, before starting to discuss more in details the NAFTA reform and its impact we think it is crucial to draw the attention on the importance of micro-level heterogeneity. With this objective we compare in Fig. 1 the log ratio of the median<sup>6</sup> sectoral labor productivity to the median labor productivity of the entire manufacturing sector (panel a) with the log ratio of the plant-level labor productivity to the median productivity of its sector (panel b).<sup>7</sup> Comparing these two graphs we notice that the productivity spread between different sectors is not much larger than the productivity spread within sector. This confirms the existence of a substantial degree of micro-level heterogeneity within-sector and reinforces the importance of focusing on the potential heterogeneity of the impact of the trade reforms. We are certainly not the first to discover and emphasize the importance of plant-level heterogeneity, in fact in an important review of various plant-level studies Tybout (2000) noticed how one of the distinctive features of LDCs manufacturing sector is its dualism where “large numbers of microentreprsises and a handful of modern, large-scale factories produce similar products side by side”. Further, productivity dispersion is not necessarily a feature of developing countries as remarked by Tybout (2000) and Bartelsman and Doms (2000). In fact, previous empirical studies for both developing and developed countries have confirmed the persistence of a high degree of heterogeneity even after a profound liberalization process. For example, in the case of Chile during the

> 6Using the mean instead than the the median lead to results substantially identical, however we prefer to show the median because we think this is a better descriptive statistic in a situation where the distribution is particularly skewed.

> 7Sector is defined at the most disaggregated level allowed by the Mexican industrial classification system (CMAP), i.e. 6 digits.

5
# **REFERENCES**

Bardhan P & Udry C, 1999. Chapter 4. _Development Economics._ Oxford: Oxford University Press.

- Basist A et al., 2001. Using the Special Sensor Microwave Imager to monitor surface wetness. _Journal of Hydrometeorology_ 2: 297–308.

- Boer G, Flato G & Ramsden D, 2000. A transient climate change simulation with greenhouse gas and aerosol forcing: Projected climate for the 21st century. _Climate Dynamics_ 16: 427–450.

- CIESIN (Center for International Earth Science Information Network), 2004. Columbia University and Centro Internacional de Agricultura Tropical (CIAT), Gridded Population of the World, Version 3. Palisades, NY: CIESIN/Columbia University

- Cline WR, 1996. The impact of global warming on agriculture: Comment. _American Economic Review_ 86: 1309–1312.

- Darwin R, 1999. The impacts of global warming on agriculture: A Ricardian analysis: Comment. _American Economic Review_ 89: 1049–1052.

- Deressa T, Hassan R & Poonyth, D, 2005. Measuring the economic impact of climate change on South Africa’s sugarcane growing regions. _Agrekon_ 44(4): 524–542.

- Dinar A, Hassan R, Kurukulasuriya P, Benhin J & Mendelsohn R, 2006.  The policy nexus between agriculture and climate change in Africa.  A synthesis of the investigation under the GEF / WB Project, _Regional Climate, Water and Agriculture:  Impacts on and Adaptation of Agro-ecological Systems in Africa._ CEEPA Discussion Paper No 39, Centre for Environmental Economics and Policy in Africa, University of Pretoria.

- Emori, S, Nozawa T, Abe-Ouchi A, Namaguti A & Kimoto M, 1999. Coupled oceanatmosphere model experiments of future climate change with an explicit representation of sulfate aerosol scattering. _Journal of the Meteorological Society of Japan_ 77: 1299–1307.

- FAO (Food and Agriculture Organization), 2003. The digital soil map of the world: Version 3.6 (January), Rome, Italy.

- FAOSTAT, 2005. Estimates of agricultural land use, Internet Database. Rome, Italy. http://faostat.fao.org/

- Gbetibouo G & Hassan R, 2005. Economic impact of climate change on major South African field crops: A Ricardian approach. _Global and Planetary Change_ 47 **:** 143–152 **_._**

- Houghton JT et al., (eds), 2001. _Climate Change 2001: The Scientific Basis._ Contribution of Working Group I to the third assessment report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.

22
**_<u>________________________________________________________</u>_**

# **Annex H.   Sample of Infrastructure/Facilitation Indicators (*means available through World Development Indicators 2005)**

# **_Energy_**

- Access to electricity Network (% of population)*

- Electricity production*

- Average end-user prices (US cents/kwh)

- Quality of electricity services

- Electricity supply time  (hours/day)

- Waiting time for electricity connection

- Electricity outage time (hours/day)

- Technical efficiency index

- Electric power consumption (kwh per capita) *

- Electric power transmission and distribution losses (% of output) *

- Commercial energy production/use *

- Combustible renewables and waste (% of total energy)*

- Energy imports, net (% of commercial energy use)*

- Energy production (kt of oil equivalent)*

- Energy use (kg of oil equivalent per capita)*

- Energy use (kt of oil equivalent)*

- Energy use per PPP GDP (kg of oil equivalent per constant 2000 PPP $)*

- GDP per unit of energy use*

# **_Roads_**

- Roads, total network (km) *

- Road traffic (vehicle per km) *

- Roads, goods transported (million ton-km) *

- Access to “all season” road (% of population)

- Road density, population (road km/1000)

- Travel time to work in main cities (minutes/one-way work trip)

- Paved road (% of total road) *

- Perception of services rendered by road department/public works

- Perception of quality of air transport /railroad services

- Road in good/fair condition

- Access to formal transportation service (% of population)

- Passenger cars (per 1,000 people)*

- Pump price for diesel fuel (US$ per liter) *

- Pump price for super gasoline (US$ per liter) *

- Two-wheelers (per 1,000 people)*

- Vehicles (per 1,000 people)*

- Vehicles (per km of road)*

# **_Railways_**

- Rail lines, total (km) *

- Rail lines density

- Railways, good hauled (ton-km)*

59 ____________________________________________________________________________________________________________
Svensson (2007), using data collected from 126 Ugandan firms, show that a 1% increase in the bribery rate implies a reduction of 3% in firm sales growth. Obviously, corruption may amplify the hampering role of credit constraints (see above) when it involves bank officials responsible for screening the entrepreneurial initiatives (see Beck, Demirgüç-Kunt and Maksimovic, 2005).

Finally, the lack of an adequate infrastructural endowment including roads and railways, basic utilities such as electricity and water supply, and ICT networks, is singled out by the literature as a significant shortcoming in preventing young and small firms in DCs from growing (see Aterido, Hallward-Driemeier and Pagés, 2009; Goedhuys and Sleuwaegen, 2010<sup>19</sup> ; Ghani, Kerr, and O’Connell, 2011b).

Having discussed regulatory, institutional and infrastructural conditions, the focus of this study is rather on those microeconomic and personal characteristics that _coeteris paribus_ may have a role in determining the post-entry performance of entrepreneurial new firms; the following section discusses this issue.

# **<u>4.2 Empirical microeconomic studies</u>**

From an empirical perspective, a relatively recent stream of literature has focused on the post-entry performance of firms and has investigated the survival, growth and early exit of newborn firms (among the early studies, see, for instance: Reid, 1991; Boeri and Cramer, 1992; Baldwin and Rafiquzzaman, 1995). Within this field of research, it is possible to analyze the relationship between the _ex-ante_ features of entry on the one hand, and both survival and, conditional on survival, the post-entry performance of newborn firms on the other. The following subsections are devoted to investigating what have been found to be the most important ‘ex-ante’ characteristics affecting the post-entry performance of new businesses.

> 19The authors, using data from the World Bank Investment Climate Survey covering 947 manufacturing SMEs in 11 Sub-Saharan countries, show that firms with their own transport facilities and their own website exhibit higher growth rates, measured in terms of employment creation.

18
program. Some researchers define the exposure at the school level (Bellei 2009; Cerdan-Infantes and Vermeersch 2007), while others rely on self-reported measures of exposure (Llambí 2013). As a consequence, these evaluations measure the intent to treat rather than actual treatment. This makes it difficult to have precise estimates for effects per year, as well as for multiple-year effects. The exception to this is panel studies, such as the one conducted by Arzola González (2010). Another consideration is that different evaluations consider different grades. Given the program designs and the availability of data, some evaluations focus on primary education (Llambí 2013; Cerdan-Infantes and Vermeersch 2007) while others focus on secondary education (Bellei 2009) or a mix of both levels (Arzola González, 2010). Primary and secondary education levels strongly differ in terms of pedagogical approaches, so there is no reason to think that effects across grades would necessarily be similar. These limitations should be kept in mind when reviewing the evidence presented here, and remembered as caveats when overall conclusions are drawn.

# **5. Costing Considerations: The Case of Uruguay**

The evaluation evidence provides limited evidence that extending the school day may be effective for improving learning (among other desired outcomes); however, is it _cost_ effective? This section provides a framework for assessing the cost of extending the school day, through the case of Uruguay. The Uruguayan case was selected for the following reasons: (i) there are more than 15 years of implementation history to learn from, (ii) the country adapted its policy midcourse due to cost considerations, and (iii) good data were available on a topic where data scarcity prevails. Indeed, education studies in development often report little to no cost data (McEwan, 2015). This section is organized as follows: first, we present the contextual background and evolution of Uruguay’s full-time schooling policy. Second, we present the cost information along three dimensions: staffing costs, infrastructure and materials, and food. Third, we consider costeffectiveness, using some assumptions on the returns to full-time schooling.

# **5.1 Addressing Equity in the Context of a Demographic Transition**

Two factors helped usher in the policy reforms that brought about the extended school day model in Uruguay. First, results from the 1996 National Learning Assessment revealed dramatic differences in income quintiles in student performance. More than 85.4 percent of students from the most advantaged backgrounds scored satisfactorily or better on the test, as compared to only 19