rafmacalaba/datause-encoder-data huggingface dataset:
raising interest rates and results in lower investment by crowding out private investment and hence lowering infrastructure development (Aschauer, 2000; Aizenman et al., 2007; Winter, 2017).

Among other variables affecting physical infrastructure, urbanization seems to have a positive and significant effect on infrastructure growth. This implies that rising urbanization increases infrastructure facilities, possibly due to increased demand for the latter (Démurger, 2001 and Li, 2017; Maparu et al. 2021). As per the 2011 census, around 31% of the population lives in urban areas, and is expected to rise to more than 50% by 2050 (UN, 2014). The demographic shift will require huge infrastructure expenditures to increase and upgrade the existing stock of infrastructure facilities such as transport, telecom, water, and energy. On the other hand. Population density has a negative impact indicating the dominance of scale impact over the demand impact (Randolph et al.,1999). Financial development proxies - measured as bank credit to GSDP (CRE) are found to impact infrastructure development positively. The development of the financial sector facilitates long-term project financing with a reasonable cost which is vital for infrastructure development (Agrawal, 2000). Similarly, the social infrastructure index (SINFRA) also positively affects physical infrastructure development supporting the complementarity hypothesis among different types of infrastructure facilities (Cerra et al., 2017).

We also find the coefficient of the dummy variable positive and statistically significant. This indicates that if the center and state governments belong to the same political party or in a coalition government, it would have a positive impact on the state’s infrastructure development. State governments with the same political party or coalition in the central government tend to get higher support in terms of financial resources and relevant policies (e.g., the central government’s support on national highways that pass through the state, building electric power plants and transmission lines, central government’s incentives to promote renewable energy) from the central government for infrastructure projects. We also considered the proportion of the SC/ST population, which is found to be statistically insignificant and hence, dropped from our final estimation.

# **5.2. Determinants of Social Infrastructure**

The results of estimating the relationship between the social infrastructure index and the factors affecting it are presented in Table 6. Like in the case of physical infrastructure, GSDP is found to have a positive and statistically significant impact on social infrastructure, 23
10 income distribution. Also, note once again that dPj is negative for the experiment that is described.

# If household h benefits from the reform, it follows that

while if the inequality is raversed that household's welfare is diminished. If households are ordered according to their income, a necessary condition for expression (7) to hold fo- all admissible social welfare functions is that, for the poorest household consuming x1 and xi, Otherwise, a maxi-min social welfare function that cares only for the welfare of the poorest hous3hold would show a decline in social welfare.

In a similar fashiom. the next poorest household, whose 2 2 consumption pattern is denoted by xi and xj. experiences an increase in welfare if However, aggregate welfare could increase even if the second household were harmed by the reform. With declining marginal utility of income the sum of both welfare changes is positive if <!-- Start of picture text --> x1 +x2 x1 + 2<br>Xi i a X i 2- 0<br>j i<br><!-- End of picture text --> Massll#4/W.lfare Dominance & the Design of Excite Taxation in **the** Cote D'Zvoire*tVhrsk:ab/5/18S88
Table 1. The impact of homicide rates on the number of enrolled students

|**Dependent Va**|**riable: Logarit**|**hmof the nu**|**mber of stud**|**ents**|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||2000-|2010|||||||2007|-2010||||
||(1)<br>Basic School<br>(years 1-9)|(2)<br>Primary<br>School<br>(years 1-6)|(3)<br>Secondary<br>School<br>(years 1-9)|(4)<br>Preparatory<br>School<br>(years 10-12)|(5)<br>Basic School<br>(years 1-9)|(6)<br>Primary<br>School<br>(years 1-6)|(7)<br>Secondary<br>School<br>(years 1-9)|(8)<br>Preparatory<br>School<br>(years 10-12)|(9)<br>Basic School<br>(years 1-9)|(10)<br>Primary<br>School<br>(years 1-6)|(11)<br>Secondary<br>School<br>(years 1-9)|(12)<br>Preparatory<br>School<br>(years 10-12)|(13)<br>Basic School<br>(years 1-9)|(14)<br>Primary<br>School<br>(years 1-6)|(15)<br>Secondary<br>School<br>(years 1-9)|(16)<br> <br>Preparatory<br>School<br>(years 10-12)|
|Homicide Rate|-0.0100***|-0.00953***|-0.0104***|-0.0155***|4.53e-05|0.000191|-0.000475|-0.00291*|-2.64e-06|0.000183|-0.000418|-0.000492|||||
|Drug Related|(0.00302)|(0.00305)|(0.00325)|(0.00586)|(0.000342)|(0.000383)|(0.000411)|(0.00154)|(0.000280)|(0.000328)|(0.000567)|(0.000871)|||||
|Homicide Rate|||||||||||||-0.000131<br>(8.87e-05)|-9.99e-05<br>(7.47e-05)|-0.000183<br>(0.000127)|-0.000178<br>(0.000373)|
|Constant|7.896***|7.588***|6.494***|4.892***|7.896***|7.614***|6.418***|4.327***|7.773***|7.449***|6.405***|5.257***|7.770***|7.447***|6.402***|5.257***|
||(0.0307)|(0.0306)|(0.0339)|(0.0526)|(0.00236)|(0.00238)|(0.00318)|(0.0183)|(0.00157)|(0.00251)|(0.00422)|(0.0122)|(0.00305)|(0.00347)|(0.00482)|(0.0120)|
|Observations|25,194|25,195|25,208|25,041|25,194|25,195|25,208|25,041|9,807|9,807|9,807|9,703|9,811|9,811|9,811|9,707|
|R-squared|0.001|0.001|0.001|0.001|0.985|0.984|0.983|0.936|0.998|0.997|0.991|0.967|0.996|0.995|0.990|0.966|
|Fixed|||||||||||||||||
|Municipality<br>and Year<br>Effects<br>**Notes.** Dependen<br>(general homicide <br>clustered at county|No<br>t variable is the lo<br>rate of 8.3 and dr<br>level in parenthes|No<br>garithm of coun<br>ug related homic<br>es. *** p<0.01,|No<br>ty total students <br>ide rate of 2.6). <br>** p<0.05, * p<0.1|No<br>(INEGI) by school l<br> Drug-related hom<br>|Yes<br>evel. To prevent lo<br>icides are collected|Yes<br>osing data from<br> by the Nationa|Yes<br>observations eq<br>l Council of Publi|Yes<br>ual to zero, w e cal<br>c Security (Consej|Yes<br>culated the log(tota<br>o Nacional de Segu|Yes<br>l students+1). H<br>ridad Pública) o|Yes<br>omicide rates ar<br>f the Federal Go|Yes<br>e normalized acc<br>vernment and in|Yes<br>ording to the natio<br>clude all deaths b|Yes<br>nal homicide ra<br>y "presumed de|Yes<br>te per 100,000 i<br>linquent rivalry".|Yes<br>nhabitants in 2007<br> Standard errors|

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in the OECD ICIO database. The average effect of having an RTA is slightly smaller in magnitude than the baseline results, but having a services agreement has a positive and significant effect for developing countries’ GVC-exports and for those services agreements that allow services exports without local presence.

# **6 Conclusion**

The link between excellent services and the functioning of GVCs has long been emphasized both by economists and policy makers. This paper is one of the first attempts to empirically assess the importance of this link, in particular, the connection between services trade agreements and GVC participation in manufacturing. Using inter-country input-output tables and detailed data on services trade agreements, I show that having a services agreement is associated with higher gross trade and GVC-trade between developing countries, and from developing to developed countries. The effect is proportionately larger for GVC-trade, suggesting a larger importance of services linkages for fragmented production processes. The finding that services trade agreements asymmetrically benefit developing countries has important policy implications: when the production of goods involves intermediate inputs crossing borders multiple times, lowering tariffs and liberalizing trade in intermediate goods are not the only options available for developing countries to take part in GVCs. Liberalizing trade in services can provide new pathways for developing countries to utilize their comparative advantage in labor-intensive stages by joining GVCs, even when they lack comparative advantage in the integrated process.

The paper’s second finding highlights the importance of non-establishment rights in services liberalization in the GVC context. I find that allowing cross-border supply of services without local presence significantly increases countries’ participation in manufacturing GVCs. This novel finding warrants further investigation since it is likely to gain relevance as advanced communication technology enables more modern services to be supplied and consumed from distance.

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# **1. Introduction**

Informality is pervasive amongst firms in developing countries, yet simple comparisons of formal and informal firms usually reveal that formal firms are more productive and profitable. A series of high-profile sector studies by the McKinsey Global Institute around the world comparing the operation of formal and informal firms concluded that informality has a very negative impact on productivity, even going so far as to conclude that “in Portugal and Turkey, for instance, informality accounts for nearly 50 percent of the overall productivity gap with the United States” (Farrell 2004). However, such estimates ignore the fact that formality is a choice of firms – the lower productivity of informal firms may therefore just reflect less productive firms choosing to remain informal rather than be the consequence of informality.

This paper seeks to provide more credible evidence of the impact of being formal on business profits, using new survey data on firms in urban Bolivia, the country with the highest levels of informality in Latin America (World Bank, 2007). We control for a rich set of owner characteristics, which provide a more comprehensive set of measures of owner ability, background, and motivations for entering business than existing studies of formality. We then identify the impact of registering for a tax identification number on firm profits by using the GPS-measured distance of a firm from the tax office where registration for taxes occurs as an instrument for whether or not a firm is registered for taxes. After controlling for the distance of the firm to the city center and other locational characteristics, we argue that this distance affects the information a firm has about registration, but does not independently affect profits. We show our results are robust to several potential threats to this identification assumption.

We find that registering to pay taxes leads to significantly higher profits for the firms that the instrument affects. Nevertheless, we also provide evidence of heterogeneous effects of formality on profitability. In particular, we find that although registering for taxes

- 2 -
living or working in the nearby neighborhoods but not suffering any asset damage, and an third set of individuals living and working outside the tsunami-affected area. We refer to the first group of individuals as “directly affected,” the second as “indirectly affected,” and the third as “unaffected.” We believe these data are unique in allowing us to examine the recovery process of small scale enterprise in a low-income country.

The first survey is a panel of 618 household enterprises, interviewed quarterly between April 2005 and April 2007, and again in October 2007 and April 2008. The April 2005 baseline survey asked owners which assets were damaged or destroyed by the tsunami, and all waves of the survey ask about the repair or replacement of those assets. The July 2005 survey has questions on grants and loans obtained to replace assets. Enterprises in the panel were also subject to random capital injections in May and November 2005, as described in more detail in Section 3 below. Note that all of the data on asset damage and aid received are self reported. The small scale enterprises and households which comprise the majority of the samples seldom keep written records of assets or business transactions. Thus, while the surveys provide very detailed micro-level data, we have no way to verify any of the responses. We discuss this issue in more detail later in the paper.

In July 2007 we conducted two additional surveys—one with a sample of 456 wage workers and the other with a sample of 424 enterprises with between five and 50 employees. The wage workers all live in the same neighborhoods as the microenterprise owners.<sup>5</sup> Because we did not find enough larger enterprises in these neighborhoods, the enterprise sample was drawn from surrounding areas as well. These surveys asked households and enterprise owners about damage suffered from the tsunami, the extent to which the damaged assets had been replaced or repaired, and the sources of funds used to

> 5 Sri Lanka is divided into 25 districts, which are divided further into 324 Divisions. These are further divided into just over 14,000 Grama Nalidaris, or GNs, which are the smallest administrative units. There are about 400 households in a typical GN. The microenterprise sample was drawn from 25 GNs in the districts of Kalutara, Galle and Matara.

7
a certain degree. Based on this benchmark, the estimated reductions in protection resulting from the UR would have been smaller and the impacts on world prices correspondingly lower.

# D. TRADE DATA

The welfare effects will partially depend upon the trading position in the commodities affected by liberalization. In general, net-food importers would be expected to benefit from reforms which lower the prices of their food imports, and suffer from price increases.'<sup>2</sup> In aggregate, total food imports accounted for about 8% of LDC total merchandise imports over the 1990-93 period. Cereals accounted for approximately 3%. However, in some countries (e.g. Bangladesh, Egypt, Mozambique), total food comprised of 20-25% of total merchandise imports during the same period.

The data on net trade positions (imports-exports) in value terms are derived from FAO's commodity balance database (CBD).13 The net trade values used in the RUNS model are also based on this database; hence, consistent commodity coverage are ensured. For each primary and processed commodities, the commodity balance database contains a comprehensive picture of the pattern of a country's commodity supply and utilization. The total quantity produced in a country added to the total quantity imported and adjusted to any change in stocks gives the supply available. On utilization, a distinction is made between the quantities exported, fed to livestock, used for seed, used for manufacturing and other uses, or lost during storage and transportation, and food supplies available for human consumption. The FAO data on imports covers all movements into the country of the commodity in question. In includes commercial trade, food aid granted on specific terms, and estimates of unrecorded trade.'<sup>4</sup> Exports covers all movements out of the country of the commodity in question. FAO also reports whenever possible trade in processed commodities expressed in the originating primary commodity equivalent. For example, wheat are imported as bulk grain and/or as processed (e.g. wheat flour, wheat bran, wheat germ, wheat gluten, etc). The value of imports and exports of these processed products in terms of the

> 12. However, as indicated by Tyers and Falvey (1989), this need not always be true in the case where significant and multiple distortions exist in the food sub-sectors.

> 13 The FAO data are based on official country trade statistics on standard trade classification (SITC). Trade data are included in both the Commodity Balance Databases (CBD) and the Supply and Utilization Database or Food Balance Sheets (SUA). The difference in the databases mainly concerns the processed products that are expressed in primary commodity equivalent in CBD whereas in SUA, trade accounts are shown by product weight, often in different detailed categories. However, the differences are relatively small and the grouping "total food" in CBD are very similar to those used in the supply and utilization accounts.

> 14 The import data are reported in terms of net weight, i.e. excluding the weight of the container.

12
- FALVEY, R., D. GREENAWAY, AND Z. YU (2004): “Intra-industry Trade Between Asymmetric Countries with Heterogeneous Firms,” _GEP Working Paper 2004/05_ .

- FALVEY, R., AND H. KIERZKOWSKI (1987): “Product Quality, Intra-Industry Trade and (Im)perfect Competition,” in: Kierzkowski, H. (Ed.), _Protection and Competition in International Trade_ , Basil Blackwell, Oxford, pp.143-161.

- FEENSTRA, R. AND J. ROMALIS (2006): “A Model of Trade with Quality Choice,” University of California, mimeo.

- FOSTER, L., HALTIWANGER, J. AND C. SYVERSON (2006) “Reallocation, Firm Turnover and Efficiency: Selection on Productivity or Profitability,” _NBER Working Paper_ , No. 11555.

- GREENAWAY, D. (1995): “Vertical and Horizontal Intra-Industry Trade: A Cross Industry Analysis for the United Kingdom,” _The Economic Journal_ , 105, pp. 1505-1518.

- GROSSMAN, G.M., AND E. HELPMAN (1991): _Innovation and growth in the global economy_ . Cambridge, MA: MIT Press.

- HALLAK, J. C. (2006): “Product Quality and the Direction of Trade,” _Journal of International Economics_ , 68(1), pp. 238-265.

- HELPMAN, E., M. MELITZ, AND Y. RUBINSTEIN (2004): “Trading Partners and Trading Volumes,” Harvard University, Department of Economics.

- HELPMAN, E., M. MELITZ, AND S. YEAPLE (2004): “Export Versus FDI with Heterogeneous Firms,” _American Economic Review_ , 94 (1), pp. 300-316.

- HELBLE, M. (2006): “Border Effect Estimates for France and Germany Combining International Trade and Intra-national Transport Flows,” _Review of World Economics,_ 143(3), pp. 433-463.

- HUMMELS, D., AND P. J. KLENOW (2005): “The Variety and Quality of a Nation's Exports,” _American Economic Review_ , 95(3), pp. 704-23.

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16

# Chart 3-1. ID! Flown in Four Latin American Countries (in US$ billions)

<!-- Start of picture text --> I~~~~~~~~~~~~~~~~~~~<br>* / ~~~~Mexko ##C -<br>0~~~~~~~<br>i 1970 IS7S 1974 t93 I978 11 I s192t Is" 19B<br><!-- End of picture text --> Among the five Asian countries over the twenty-year period, there was a general trend of increasing flows, but there were significant positive and negative deviations from this basic trend in each country. Furthermore, each country graph reveals a distinctive profile over time. Except for a large dip in 1973-74, Indonesia experietLced increasing flows from the late 1960s to the mid1970s, and relative stability since. India experienced overall low levels throughout the period. FDI flows into Korea were relatively stable at low levels but then increased over the 1984-87 period. Malaysia and Thailand both experienced relatively large magnitudes of inflows in 1974 and again in the early 1980s (1982 was a peak year for Malaysia and 1984 for Thailand).
control children being dropped from the height-for-age regressions, and 2 of the 177 treated and none of the 156 control children being dropped from the weight-for-age regressions.

Given the relatively small number of children for whom we have anthropometric data, a concern may be that the estimated impacts of _Pantawid_ on nonbeneficiary children’s height-forage and weight-for-age are driven by outliers in the data. This annex explores this issue in further depth by considering varying thresholds at which to trim the data, using -3 and 3 SD, -4 and 4 SD, -6 and 6 SD, and -7 and 7SD as alternative thresholds instead of the WHO (2006) recommended -5 and 5 SD. These results, presented in Annex 2 Tables 1 through 3, show that the results presented above are highly robust for all three specifications discussed in Table 6 above—simple program impact, interaction with above median saturation, and the interaction with the fourth quartile of saturation. We also consider Winsorizing 5 and 10 percent of the anthropometry data. As shown in Annex 2 Tables 4 and 5, the results are robust to this alternative method of treating the outliers in the anthropometry data.

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We find interesting dynamic effects of the marketing reforms. Compared to the introductory phase of 1997-1998, the failure of the outgrower scheme caused farmers to move back to subsistence and led to significant reductions in farm productivity. The improvement of the outgrower scheme in 2001-2002, reverted these trends: farmers allocated more land to cotton, and productivity (i.e., yields per hectare) significantly increased.

# Appendix 1: Cotton Selection Models

Table A.1 reports a set of results of the selection equation. These estimates are obtained from a Tobit model. Qualitatively similar results are estimated with OLS or the CLAD models. We find that household assets are positively linked to land cotton shares. Total land and whether the household raise livestock can work as collateral perhaps allowing the household to obtain cheaper credit and to purchase inputs or to afford any initial investment. In addition, household assets may allow farms to adopt riskier (but also more profitable) agricultural activities.

The size of the family also affects cotton allocation positively. One explanation is that bigger households can take care of own-consumption needs (food security) and have additional resources needed to embark in cash agriculture. A related finding in Table A.1 is that household with higher proportion of males tend to allocated higher shares of land to cotton. This is consistent with the notion that the availability of labor supply matters in the choice of crops.

Finally, there is some evidence that male-headed households tend to grown more cotton than female-headed families.

# References

Altonji, J. (1986). “Intertemporal Substitution in Labor Supply: Evidence From Micro Data,” Journal of Political Economy vol. 94, no. 3, part 2, pp. S176-S215.

Balat, J. and G. Porto (2005). “Globalization and Complementary Policies. Poverty Impacts in Rural Zambia,” in A. Harrison ed. (2005).

Benjamin, D. (1994). “Can Unobserved Land Quality Explain the Inverse Productivity Relationship?,” Journal of Development Economics 46, 51-84.

Besley, T. and A. Case (1994). “Diffusion as a Learning Process: Evidence From HYV Cotton,” mimeo, Princeton University.

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