rafmacalaba/datause-encoder-data huggingface dataset:
and private investment in the roads sector would indeed be expected because of the difficulty of obtaining private financing for non-primary highways.<sup>12</sup> In all cases higher lagged GDP is associated with higher private investment volumes, and, in contrast to the findings of Models I and II, lagged capital stock is negative and significant in all the sectoral regressions apart from the one for water.

Table 3. Fixed Effects regressions by sector

|In_pvt|Telecoms||Roads||Power||Water||
|---|---|---|---|---|---|---|---|---|
||Coef.|t|Coe£|**t**|Coef.|t|Coef.|t|
|Logof**realpublic**<br>**investment**|-0.2942|-3.189**|0.1825|0.409|-0.7662|-2.500**|-1.0194|-1.55|
|Logoflaggedreal<br>GDP|5.8493|4.356**|5.7597|4.312**|4.0419|3.435**|6.4141|4.003**|
|Trend|0.4144|3.608**|0.3525|3.278**|0.5119|4.703**|0.3059|2.852**|
|**Laggedcapitalstock**|0.0000|-2.168**|0.0000|-4.703**|-0.0004|-4.101**|0.0018|0.000|
|Regulatorybody**in**<br>place|-1.0046|-1.077|-0.6995|-0.692|0.1993|0.202|0.3486|0.292|
|**Investmentprice**|1.3759|0.628|4.2064|1.815*|1.6965|0.783|1.7566|0.798|
|**Realrateofinterest**|-0.0058|-0.967|-0.0059|-1.007|-0.0092|-1.620|-0.0205|-3.493**|
|Dummy: Legislation<br>**opening thesector**|5.2836|5.6**|5.4172|5.148**|1.8347|1.780*|1.8979|1.476|
|**Constant**|-67.5412|-4.189**|-66.2719|-4.447**|-41.5051|-2.912**|-76.1823|-3.974**|
|NumberofObs.|159||142||159||109||
|R-sqwithin|0.7068||0.6322||0.5499||0.5274||
|*Significantat10%le<br>**Significantat5%le|vel<br>vel||||||||

The passage of legislation opening the sector to private entry is significantly positively related to private investment volumes in telecommunications and roads but less significant in the power sector and virtually not at all in the water sector. The result on power is somewhat surprising since liberalization of telecommunications and power has been deeper and wider than of the other sectors, with a decline in the importance of government investment going hand-inhand with the increase in private participation. This may reflect the fundamentally competitive

> 12 Consistent with our findings on roads, Dailami and Leipziger (1998)'s analysis of credit risk premiums in foreign currency private loans to greenfield infrastructure projects in developing countries finds that road projects commanded the highest risk premium in 1994-96.

21
indigenous language/dialect are assumed to have more available contacts. As in Bertrand et al. (2000), the social network of an individual is characterized by two key dimensions: the quantity and quality of the network. The quantity of the social network or the contact availability is the fraction of the population in the household’s community (locality) speaking the same indigenous language as the individual. The quality of the network is the fraction of the indigenous households in the country speaking the same indigenous language and participating in the economic activity investigated. Thus the contacts from the same language group with high participation in any given economic activity (e.g. working in handicrafts, working in agriculture etc.), are likely to have a strong influence on the decision to participate in the same activity. An obvious limitation of the use of language as a proxy for social networks is that it ignores networks defined by characteristics other than language and ethnicity.

This paper identifies indigenous peoples using language which raises theoretic and methodological questions. The use of a cultural trait such as language as an identifier of whether someone is indigenous is problematic because it is subject to changes that may or may not lead to a loss of indigenous identity. The language use definition for instance misses those members of the indigenous population that while indigenous in terms of their origins and/or identity, either (i) deny knowledge of an indigenous language and declare Spanish as their native tongue, or (ii) speak no indigenous language. At the same time, the use of for instance self-identification may also lead to underreporting if and when discrimination and social prejudice lead individuals to deny any affiliation with their native origins or at least to downplay their indigenous origins. Due to the way in which census data is collected in Mexico, language variables are the most reliable and objective proxy for differentiating indigenous and non-indigenous households. Self-identification is thus far not widely used, and indigenous people are typically identified through language use or geographic location.

4
Since it is the sophistication of the exports which matter for growth, in this changed global trade scenario, South Asian countries are going to benefit a lot. Their service exports are of very high quality and modern. Over the period it is this high quality of exports which has grown much faster. So, at this troubled time they can exploit their high sophistication to recover from the crisis. However, the policy implication for other South Asian countries which are below the norm is that they should try to catch up with their neighbors and try modernize their service exports.

# **_Service growth and exports are more resilient_**

The globalization of services will continue to be a strong force for two reasons. First, services account for more than 70 percent of global GDP, more than double in size compared to the manufacturing sector. So, there is tremendous scope for services globalization. Second, the cost differential in the production of services across the world is enormous. In the past, the only option to narrowing such cost differentials was through migration, but migration has been heavily regulated and global international migration has remained steady at about 3 percent for decades. Now that service providers can sell services without crossing national borders by making use of the internet (outsourcing), the scope for exploiting cost differentials is much higher. What is more, it is very hard for governments to regulate modern impersonal services, so prospects for rapid expansion in service exports are good.

How resilient is service growth to the decline in investment? To see this we look at the dependence of manufacturing and service sector growth on investments. This will determine whether the slow down in investment will affect South Asia differently.

**Table 3: Cross Sectional OLS Regression: Average Growth in Manufacturing and Services** **<u>and Investment, 1995 - 2005</u>**

||_Average_<br>_Manufacturing_<br>_growth_|_Average Service_<br>_growth_|
|---|---|---|
|Investment (as ratio of GDP)|0.74***|0.16***|
|Initial GDP per capita|0.000|0.000|
|Openness (trade as % of GDP)|0.01|0.003|
|Credit (% of GDP)|-0.08***|-0.01|
|Populationgrowth|0.94**|0.04|
|Control for size|Yes|Yes|
|Observations|157|161|

_Notes_ : Robust standard errors are reported in parenthesis.* represents significance at 10%** represents significance at 5%, *** represents significance at 1%, Country size is measured by area in square kilometers.

20
export, when compared to manufacturing. The difference appears to be smaller than what we find for the Chilean data. Additionally, he also finds that the export intensity of exporting services firms is smaller than for manufacturing. We do not find such a difference in export intensity. The low percentage of export participation among tradable service firms could reflect the fact that Chile’s traditional comparative advantage has been in the primary sector and in low technology manufacturing goods rather than in services. It could also be because Chile specializes in services activities that are less tradable or because Chilean firms in any given services sector are less export-oriented. Also, as shown by Jensen and Kletzer (2005) it is possible that the aggregated categories we use in the “tradable services” group might include some subsectors that are actually non-tradable. Despite these caveats, the tradable services companies still appear to be substantially less export-oriented than manufacturing companies.

# **(ii) Is there a differential exporter premium in manufacturing and services?**

Figure 4 digs deeper into the firm characteristics that are associated with exporting. It shows that size, measured by the number of employees, is related to the probability of exporting, particularly in manufacturing. The figure shoes a monotonic relation between firm size and the probability of being an exporter. More than 85% of relatively large firms in the manufacturing sector export, compared to less than 10% of firms with 10 or less employees. This is consistent with what is now conventional wisdom that size is positively associated with the probability of exporting for manufacturing firms (Bernard and Jensen (1999)).<sup>18</sup> At a much lower scale, also larger firms in the service sector tend to have a higher probability of exporting.

Table 2 below shows the estimates of the size and skill premium by export status and sector. It shows the results of regressions where the outcomes are either log of sales, or log of employment or skill intensity, and the explanatory variables are dummies that reflect the export status and the sector (i.e.

14
Policy Research Working Paper 8114

# **Abstract**

Using firm-level data covering 709 cities in 128 countries, this paper examines the role of a comprehensive list of business environment variables at the subnational level in explaining firm employment and productivity growth. The analysis finds basic protection, access to finance and infrastructure, and the existence of a strong agglomeration environment to be critically important. By contrast, human capital and a list of refined business environment variables related to labor regulations, tax, and land access are found to be relatively unimportant. The analysis also finds that the effects of the business environment vary according to firm size, age, sector affiliation, and the host country’s level of development. The research suggests that it pays to be comprehensive about the business environment and that attention to heterogeneity is important.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lxu1@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
At present, most cotton production in Zambia is carried out under the outgrower scheme.

Farmers and firms understood the importance of honoring contracts and the benefits of maintaining a good reputation. The outgrower programs were perfected and there are now two systems utilized by different firms: the Farmer Group System and the Farmer Distributor System. In the latter, firms designate one individual or farmer as the distributor and provide inputs. The distributor prepares individual contracts with the farmers. He is also in charge of assessing reasons for loan defaults, being able, in principle, of condoning default in special cases. He is in charge of renegotiating contracts in incoming seasons. In the Farmer Group System, small scale producers deal with the ginneries directly, purchasing inputs on loan and repaying at the time of harvest. Both systems seem to work well.

# **1.2 International Markets and World Prices**

The production of cotton and the international trade of cotton products are, and have traditionally been, subject to significant interventions. The distortions include taxes (either directly or indirectly through state marketing monopsonies (parastatals), border intervention (tariffs, quotas), production and export support, and input subsidies. Aksoy and Beghin (2004) summarize the markets for different commodities including cotton. They suggest that the combined support in cotton production in the major world producers (US, China, Greece, Spain, Turkey, Brazil, Mexico and Egypt) between 1997/98 and 2001/02 ranged from $3.8 to $5.3 billion.

We turn next to briefly review some of the interventions adopted by some of the main producers, namely the United States and the European Union. Our summary is based on Baffes (2004), who provide more details on cotton markets, policies, and prospects. In the United States, intervention in cotton production is regulated by Farm Bills, like the 1996 and 2002 bills.

6
- Li, Z., M. Wu, and B. R. Chen (2017): “Is road infrastructure investment in China excessive? Evidence from productivity of firms,” _Regional Science and Urban Economics_ , 65, 116–126.

- Mahlknecht, G. (2014): “Greg’s Cable Map.,” working paper, National Bureau of Economic Research.

- Malamud, O., S. Cueto, J. Cristia, and D. W. Beuermann (2019): “Do children benefit from internet access? Experimental evidence from Peru,” _Journal of Development Economics_ , 138, 41–56.

- Martincus, C. V., J. Carballo, and A. Cusolito (2017): “Roads, exports and employment: Evidence from a developing country,” _Journal of Development Economics_ , 125, 21–39.

- Masaki, T., R. Granguillhome Ochoa, and C. Rodriguez Castelan (2020): “Broadband Internet and Household Welfare in Senegal,” Policy Research Working Paper Series 9386, The World Bank.

- Matas, A., J.-L. Raymond, and J.-L. Roig (2015): “Wages and Accessibility: The Impact of Transport Infrastructure,” _Regional Studies_ , 49, 1236–1254.

- Mbekeani, K. K. (2010): “Infrastructure, Trade Expansion and Regional Integration: Global Experience and Lessons for Africa,” _Journal of African Economies_ , 19, 88–113.

- Melo, P. C., D. J. Graham, and R. B. Noland (2010): “Impact of Transport Infrastructure on Firm Formation: Evidence from Portuguese Municipalities,” _Transportation Research Record_ , 2163, 133–143.

- Moneke, N. (2020): “Can big push infrastructure unlock development? Evidence from Ethiopia,”Technical report, Mimeo.

- Mouelhi, R. B. A. (2009): “Impact Of The Adoption Of Information And Communication Technologies On Firm Efficiency In The Tunisian Manufacturing Sector,” Working Papers 506, Economic Research Forum.

- Muller-Crepon, C., P. Hunziker, and L.-E. Cederman (2020): “Roads to rule, roads to rebel: Relational state capacity and conflict in Africa,” _Journal of Conflict Resolution_ , 65.

- Murdock, G. P. (1959): “Africa its peoples and their culture history.”

- Nelson, A., and U. Deichmann (2004): “The African Population Database. Version 4,”Technical report.

43
# **7. Concluding Remarks**

In this we data from Mexico’s census data to the paper, exploit plant-level quantify output losses that arise from the marked differences in growth rates between formal and informal plants. Unlike Hsieh and Klenow (2014), we are able to focus on a panel of registered and unregistered plants to estimate their life cycle employment profiles. We build on the model developed by Guner et al. (2018) where plants grow by investing in their productivity. Our innovation is introducing a tax on output that some plants can escape from by operating informally due to incomplete enforcement. Our policy experiments suggest that reducing the tax rate while improving the enforcement technology to keep the tax revenue constant would lead to output gains of 16.4% relative to the benchmark. It also implies an increase in the average growth rate by 25.3%.

26
Table 6: How the type of discovery affects arms imports

||Oil vs. gas|
|---|---|
|(1)<br>(2)<br>(3)<br>SIPRI<br>SIPRI<br>COMTRADE|(4)<br>(5)<br>(6)<br>(7)<br>(8)<br>COMTRADE<br>Import share<br>Import share<br>GDP share<br>GDP share|
|Oil<br>0.243<br>0.616|0.430<br>0.484|
|(0.307)<br>(0.425)|(0.352)<br>(0.370)|
|Gas<br>0.144|0.284<br>0.174<br>0.211|
|(0.167)|(0.251)<br>(0.209)<br>(0.238)|
|N<br>2359<br>2415<br>2353|2402<br>2353<br>2402<br>2391<br>2432|
|R-sq<br>0.69<br>0.72<br>0.69|0.69<br>0.42<br>0.42<br>0.38<br>0.37|
|On|shore vs. ofshore|
|(1)<br>(2)<br>(3)<br>SIPRI<br>SIPRI<br>COMTRAD|(4)<br>(5)<br>(6)<br>(7)<br>(8)<br>E<br>COMTRADE<br>Import share<br>Import share<br>GDP share<br>GDP share|
|Onshore<br>0.111<br>-0.180<br>(0.269)<br>(0.360)|-0.283<br>-0.209<br>(0.327)<br>(0.351)|
|Ofshore<br>0.245|0.853<sup>_∗∗∗_</sup><br>0.661<sup>_∗∗_</sup><br>0.713<sup>_∗∗_</sup>|
|(0.222)|(0.297)<br>(0.242)<br>(0.260)|
|N<br>2371<br>2430<br>2359|2425<br>2359<br>2425<br>2397<br>2455|
|R-sq<br>0.70<br>0.71<br>0.68|0.70<br>0.41<br>0.42<br>0.37<br>0.38|
|Dista|nce to nearest border|
|(1)|(2)<br>(3)<br>(4)|
|SIPRI|COMTRADE<br>Import share<br>GDP share|
|Discovery<br>1.082|0.682<br>0.661<br>0.422|
|(1.063)|(2.035)<br>(1.743)<br>(1.866)|
|_×_ Min Dist<br>-0.186|-0.060<br>-0.089<br>-0.029|
|(0.212)|(0.378)<br>(0.323)<br>(0.348)|
|N<br>921|874<br>874<br>883|
|R-sq<br>0.69|0.63<br>0.31<br>0.32|

Notes: The dependent variable is in inverse hyperbolic sine. Discovery dummies are for the past 5 years. Min Dist is the log of the country minimum of the country’s discoveries’ minimum distance to the nearest border. Period limited to 1990-2010 due to data availability. The regressions include country and year fixed effects. Robust standard errors in parenthesis, and * stands for statistical significance at the 10% level, ** at the 5% level and *** at the 1% percent level.

23
higher education is slightly higher, and they are more likely to have a playground, adequate sanitation for preschool, and a fridge. We also observe that quality indicators are fairly homogeneous across municipalities of different size.

## **2.5 Additional data**

To perform validity tests to our empirical strategy, we use additional data from _Instituto de Pesquisa Economia Aplicada_ (IPEA) on a number of time invariant attributes of municipalities. These include the area of the municipality (in squared Km), its altitude, latitude, and longitude, as well as the distances to the state and federal union capitals. In addition, we use IBGE data from the 1991 census on a number of municipal attributes, notably the share of females aged at least 10 years old that were economically active, the share of black population, and income per capita.

# **3 Empirical method**

Here we present the econometric strategy for examining the impact of FPM transfers on the supply of municipal and private preschool in Brazilian municipalities. As shown by Brollo et al. (2013), the allocation system of FPM transfers over the time period considered in this paper makes it possible to apply a (fuzzy) Regression Discontinuity (RD) design. A municipality will receive high versus low federal transfers (the treatment) depending on its population size (the running variable) in a stochastic manner, but the likelihood of being treated conditional on the running variable is known to have sizable discontinuities at multiple thresholds.<sup>8</sup> In the neighborhood of a given population threshold separating two population intervals of the FPM revenue-sharing mechanism, “theoretical” transfers sharply increase from a lower to a higher level. Theoretical transfers are therefore a step function of population (the running variable). Due to imperfect compliance or measurement error, transfers effectively received by municipalities may differ from theoretical transfers. Theoretical transfers can

> 8Litschig and Morrison (2013) use the same Brazilian data set on federal transfers for an earlier period in a RD design.

11
98) the median time is 3 months. The preliminary finding based on the Kaplan Meier estimates is important because it shows that graduates from the 1998 Survey found jobs faster than those from the 1994 Survey. If only a simple average of values for surveyed individuals had been taken, the mistaken conclusion would be reached that graduates of the 1998 Survey search for a job longer than individuals of the 1994 survey do.

**Table 17. Kaplan Meier Estimates Job Search after graduation from CONALEP (Months)**

||Time Es|timated|
|---|---|---|
|_Probability (T>t)_|_Survey 94_|_Survey 98_|
|0.25|12.0|7.0|
|_0.5_|4.0|3.0|
|0.75|1.6|1.0|
|Mean|9.2|6.8|
|Median|4.0|3.0|
|Cases censored|169 (20.4%)|124 (3.8%)|
|Total number of cases|<br>827|3273|

Table A4.1 shows the hazard ratio or risk of finding a job and a respondent's region."<sup>4</sup> Graduates from the 98 Survey have a 45 percent greater probability of finding a job than graduates from the 94 Survey do. Graduates from the North or Center of Mexico have a higher probability of finding a job (between 60 and 45 percent) than graduates from the South (22 percent) do. The 1993-1996 cohort had a 4 percent higher probability of finding a job than the other cohorts did. Also, the higher the level of schooling of the household head, the higher the chance of the CONALEP graduate of finding a job.

> 14 Since employment is not conventionally a risk, we shall refer to the technical "risk" of finding employment as the "probability" or "chance."

40
**Table 6.5: Dispersion of Income 1987-1991**

||Accra|Other<br>Towns|Rural|
|---|---|---|---|
|GLSSI(1982)||||
|Standard||||
|deviation||||
|Household|287.430|274.250|354.073|
|Income||||
|Income per|82.945|77.588|107.690|
|capita||||
|Incomeper<br>.AEU|113.517|100.035|127.928|
|Income per|187.696|188.929|199.129|
|Earner||||
|GLSS3(1921||||
|Standard<br>deviation||||
|Household|622.456|633.112|475.932|
|Income||||
|Income per|235.581|199.547|172.763|
|capita||||
|Income per<br>AEU|266.569|230.138|183.478|
|Income per|575.250|571.772|404.939|
|Earner||||

_Source: GLSSl and GLSS3_ Reference must be made at this point to the findings in the Extended Poverty Study of the World Bank. The poverty line in this case was defined as two-thirds of mean household expenditure per capita in 1988 for the country as a whole, and expressed in constant May 1992 Accra prices. The strong conclusion of this work was that between 1988 and 1992 "poverty (as represented by both the headcount and poverty-gap ratios) declined steeply between 1988 and 1992 in all areas except Accra." (World Bank 1995, p.42). This trend is reconcilable with the evidence of widening income differential between Accra and the rural areas only if the degree of inequality in the distribution of income increased in Accra relative to the rural areas in the 1987-92 period. This is exactly what the statistics in Table 6.5 show, if we measure the degree of inequality of income by its standard deviation. Although income in the capital city grew relatively to 43