rafmacalaba/datause-encoder-data huggingface dataset:
specific measures of de-licensing and FDI reform.<sup>21</sup> We do not take into account the labor market reform, most of which occurred before the first year of our sample 1993 (Ahsan and Pagés, 2009).

To capture the effects of the de-licensing reforms, we use information from Harrison et al. (2011), who extended the data used by Aghion et al. (2008) to 2004, on the basis of Press Notes from the Ministry of Commerce and Industry. The de-licensing variable is a dummy that takes on a value of one if any products in a 3-digit industry have been de-licensed, and zero otherwise. Similarly, the measure of FDI reform was compiled by Harrison et al. (2011) also based on Press Notes from the Ministry of Commerce and Industry. It takes on a value of one if any products in a 3-digit industry have been liberalized, and zero otherwise.<sup>22</sup> In Table 6, we present the results from the modified specification. We find a positive correlation between de-licensing and FDI reform and firm productivity. More importantly for the purposes of this paper, our results on services reform are barely affected by this change.<sup>23</sup>

# **_Instrumenting the Services Liberalization Index_**

In order to ensure that our finding of services reforms improving manufacturing performance is not driven by reverse causality, we instrument the index of services reform. The intuition behind our instrumental variables (IV) approach is that India will react to services liberalization undertaken by other countries, especially economies it views as its competitors, such as China and Indonesia. We measure services liberalization using the WTO commitments in a given sector. More specifically, we focus on the number of commitments made by a country expressed as a percentage of possible commitments. For the years prior to the first full year of the WTO membership of a given country (e.g. 2002 for China), the number of commitments equals zero. To create an instrument relevant to a particular manufacturing sector, the

> 21 According to Harrison et al, by the end of 1991, nearly 85% of industries had been de-licensed, with the share increasing to over 90% of industries by the end of the 1990s. The FDI liberalization occurred somewhat more slowly, and only in 2000 all industries became eligible for automatic FDI approval, except those requiring an industrial license or meeting several other conditions.

> 22 We are very grateful to Ann Harrison, Leslie Martin and Shanthi Nataraj for sharing the data with us.  Industries have been converted from 3-digit NIC 87 industry codes to 4-digit NIC98 industry codes.  Where direct correspondences were not found, averages were used at the 2-digit NIC98 level.

> 23 Including these additional controls in all other specifications presented in the paper would not change its conclusions.

24
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Appendix tablecorrelation between per capita GNP and the reliance on excises as a percentage of indirect taxes. For the world as a whol.e, ovic half of the countries show a reliance on excises as a percentage of indi:ect taxes in a nairow range between 27 and 29 percent. Al shows the data by couniry. The highcst group, showing more than 30 percent of their revenue from excises (omitting India because of its reliance on basically a sales tax labelled excise), are Burundi, Guinea-Bissau, Pakistan, Maldives, and Nicaragua. Those showing no excise revenue are Seychelles, Botswana, Comoros, Oman, Kuwaii, and Syria.

Data of revenue from various forms of exc. 's are not generally available; the I.M.F. volume provides figures on these categories for only a limited sample. On the basis of information, available, however, some data ^an be given (from the early 1980s). Some countries obtai.a most of their excise revenue from alcoholic beverages: for example (percentages of vxcise tax revenue) Rwanda 99, Burundi 95, Guyana 70, Congo 59, Netherlands A ntilles 57, Belize 53, Suriname 43, Zarnbia 41. Zambia in most years receives about 18 percent of its total tax revenue from beer alone. Latin American countries show relatively high percentages; the Islamic countries the least, since they prohibit or restrict the sales of alcoholic beverages. Several countries depend heavily on tobacco taxes: Indonesia 90, Zaire 80, Somalia 52, Morocco 44 and Sierra Leone 44. Several of these are Islamic countries with little or no reliancc on taxes on alcoholic beverages. In a number of countries motor fuel levies are the major sources; in 14 countries they yield over half of all excise revenue. These include (1980s data) Oman 100, Kuwait 98, Columbia 96, Peru 91, Mexico 88, Israel 79, Costa Rica 76, Mali 74, Trinidad 72, Argentine 69, Niger 67, Singapore 67, Chad 63, Bezswana 59.
in Africa, because manufacturing activity is already highly concentrated in the largest cities. For example, as of 2002, the Dar es Salaam administrative region contained 0.16 percent of mainland Tanzania’s land area, and 8 percent of its population, but 40 percent of its manufacturing employment and 53 percent of manufacturing value added. As of 2008, 55 percent of manufacturing establishments, and 66 percent excluding food, beverages and tobacco, were in Dar es Salaam (National Bureau of Statistics, 2009; National Bureau of Statistics and Ministry of Industry, Trade and Marketing and Confederation of Tanzanian Industries, 2010).<sup>7</sup> Tanzania has relatively low primacy, so if anything, these fractions would likely be even larger in other countries. Although Tanzania has a coastline of over 1,400 kilometers and three other ports, Dar es Salaam handled 95 percent of its port traffic as of 1993 (Hoyle and Charlier, 1995).

If a periphery city already has minimal manufacturing and is more of a market center for exchanging manufactured goods imported from the core with agricultural products from nearby rural areas, then decreased transport costs will not bring increased competition with core manufacturing. Instead, they may increase exports of rural agricultural goods that are otherwise not sold on the market by farmers, and increase imports of manufactured goods, including inputs to agricultural production, in exchange, or at least change the relative consumption of periphery city (and rural) residents.<sup>8</sup> While some non-primate cities in Sub-Saharan Africa clearly have some manufacturing, it is this trading margin that I expect is more relevant to understanding their growth overall.<sup>9</sup> The following model embeds this intuition in a very simple framework in which changes in transport costs drive urban growth. The economy under study is one that exports agricultural goods and imports manufactures. Specifically, the periphery city under study exports its agricultural surplus and imports its manufactured goods, both via a core or port city. The key ingredients are tradable goods prices fixed in the core city by international trade but affected in the periphery city by intercity transport costs.

A representative farmer in the periphery has Cobb-Douglas preferences _U_ ( _a, m_ ) = _a_<sup>_α_</sup> _m_<sup>1</sup><sup>_−α_</sup> _,_ 0 _< α <_ 1, over an agricultural good ( _a_ ) and a manufactured good ( _m_ ).<sup>10</sup> The

> 7These manufacturing statistics are based on establishments with more than 10 employees.

> 8Alternatively, it is possible that these hinterland cities are essentially in autarky: because they already have very high transport costs, they are largely insulated from transport cost changes. This is inconsistent with the empirical results below.

> 9To the extent that manufacturing is substantial in some hinterland cities, this would work against the results I find below in empirical work.

> 10The relevant predictions of the model also hold for quasilinear utility of the form _U_ ( _a, m_ ) = _a_ + _m_<sup>_α_</sup> _,_ 0 _< α <_ 1.

5
statistically different from one, and as Figure 2 indicates, the estimated size of negative real GDP growth shocks is even larger for antidumping alone than all TTBs jointly. One notable difference from the baseline results is that GDP growth in a foreign trading partner has no statistically significant impact on the number of products subject to antidumping. Finally, the general time trend across all of the countries in this sample is that antidumping alone is on average declining over 1995-2010.<sup>19</sup> Column (6) presents a still different approach to construction of the dependent variable whereby we broaden it (relative to the baseline) to include the count of all products subject to TTB _investigations_ , including those that may not have resulted in the imposition of new import restrictions. The results are qualitatively unchanged according to the estimated IRRs and the magnitudes of the effects illustrated in Figure 2. If anything, TTB investigations alone (relative to imposed barriers of the baseline definition) appear slightly more responsive to domestic real GDP shocks and slightly less responsive to real exchange rate appreciations. Furthermore, the overall time trend of products subject to new investigations during this period is strongly increasing.

Finally, column (7) of Table 3 presents the results from the empirical model of temporary trade barriers for an important subsample of emerging economies G20 members; i.e., Argentina, Brazil, China, India, Indonesia, Mexico, South Africa, and Turkey.<sup>20</sup> The results for this set of countries are broadly similar to those for the larger sample of emerging economies.

# **5 Investigating the Impact of the WTO on New Import Protection**

Thus far our estimates for the emerging economies’ use of TTBs have been undertaken on samples of data beginning in 1995. Our argument is that this is the period during which emerging economies faced a relatively common set of rules under the WTO regarding how to implement import protection through TTB policies. In this section we investigate empirically whether this new environment has affected how aggregate-level shocks feed into new import protection by identifying potential changes across time associated with the GATT versus WTO institutional regimes. We are able to do so because

> 19 Bown (2012a) shows country-by-country evidence for which the overall increase in TTB import coverage over this period is due to inclusion of TTB policies such as safeguards.

> 20 Collectively, by 2010 these eight countries accounted for 18 percent of world merchandise imports and 20 percent of world GDP.

<mark>17</mark>
exceptions to these very low savings rates; in nearly all cases they are oil economies (Nigeria, Chad, Cameroon, and Côte d’Ivoire) or resource-rich non-oil producers (Namibia and Zambia). See figure 1.2.

**Figure 1.2 African focus countries’ gross domestic savings rates**

<!-- Start of picture text --> 40<br>Gross<br>30 domestic<br>savings as<br>% of GDP<br>20<br>10<br>0<br>-10<br>%<br>Benin Burkina Faso Cameroon Cape Verde Chad DRC CTMte d'Ivoire Ethiopia Ghana Kenya Lesotho Madagascar Malawi Mozambique Namibia Niger Nigeria Rwanda Senegal South Africa Sudan Tanzania Uganda Zambia LIC AICD LMIC AICD UMIC AICD Oil exporters Non-oil Xers (ex. SA) Non-resource rich AICD excl. South Africa<br><!-- End of picture text -->

# Sources: World Bank, GEM and WDI databases.

Note: The World Bank WDI database calculates gross domestic savings as the difference between GDP and total consumption. LIC AICD = Low-income AICD countries (Benin, Burkina Faso, Chad, Democratic Republic of the Congo, Côte d’Ivoire, Ethiopia, Ghana, Kenya, Madagascar, Malawi, Mozambique, Niger, Nigeria, Rwanda, Senegal, Sudan, Tanzania, Uganda, Zambia).

LMIC AICD = Lower-middle-income AICD countries (Cameroon, Cape Verde, Lesotho, and Namibia). UMIC AICD = Upper middle-income countries (South Africa). Oil exporters = Cameroon, Chad, Côte d’Ivoire, Nigeria. Non-resource-rich = All AICD countries except Cameroon, Chad, Côte d’Ivôire, Namibia, Nigeria, and Zambia.

Using savings rates as an upper-limit proxy for funds available for investment in infrastructure, the countries can be grouped into four categories: those with high potential to generate domestic funds for infrastructure projects (Nigeria, Chad, Namibia); those with solid potential (Côte d’Ivoire, Mozambique, Zambia, Cameroon, South Africa, Sudan, Madagascar, Tanzania); those with limited potential (Niger, Burkina Faso, Senegal, Kenya, Uganda, Ghana, Benin); and those with severely limited or no potential (the remaining six countries). Chad and Nigeria, which top the list, are net oil exporters with savings rates in excess of 35 percent. Limited capacity to absorb high oil-export revenues in the domestic economy and a desire to reduce debt explains why major oil exporters may be saving more of their oil export revenues. The second category is made up of other resource-rich commodities exporters, as well as South Africa.

5
Apart from beneficial changes within firms, persistent changes in comparative advantage could also be due to external economies such as Marshallian externalities across firms within industries leading to a movement across different equilibria for an economy (Chipman, 1970; Ethier, 1982; Grossman and Rossi-Hansberg, 2010; Lyn and Rodriguez-Clare, 2013). For example, LBD benefits may spill over within an industry or country through the movement of experienced workers across firms (Stoyanov and Zubanov, 2012). External economies can also manifest themselves through demonstration effects as firms learn about export capabilities from previous exporters (Hausman and Rodrik, 2003), or the establishment of country reputation in new foreign markets by previous exporters (Rauch and Casella, 2003; Krautheim, 2012). These industry- and country-level profitable opportunities may also not have been realized prior to the trade preferences due to market failures due to financial, information, or coordination constraints.

Generous trade preferences may also have a dynamic effect by relaxing internal constraints within countries relating to underdeveloped markets, coordination failures across industries, and poor policies. For example, strong profit opportunities in export markets fostered by those preferences may attract foreign direct investment that would compensate for underdeveloped domestic capital markets. Even temporary strong export growth could help resolve coordination problems created by pecuniary external economies in professional or transportation services sectors (Murphy et al., 1989; Acemoglu and Zilibotti, 1994). Finally, demonstration effects from export successes in the region could encourage countries to engage in necessary domestic reforms.<sup>5</sup> Our paper’s main contributions are to the empirical literature on the impact of nonreciprocal trade preferences on developing countries’ trade flows.<sup>6</sup> Herz and Wagner (2011), Gil-Pareja et al. (2014), and Ornelas and Ritel (2020) estimate the impact of nonreciprocal trade

> 5 Hanson et al. (2018) empirically demonstrate the importance of thinking about comparative advantage in such dynamic terms. 6 See USITC (2014), Cirera and Cooke (2015), and Ornelas (2016) for reviews of the literature.

8
updated to 1994 using industrial production indices provided by UNIDO.

# **4.2 Did counter-lobbying increase in Mercosur?**

Table 1 reports results of the estimation of (22) in the first column and of (23) in the second column. In both regressions, the coefficients have the expected sign and are statistically significant at the 99 percent level."<sup>9</sup> The regressors "account for" around 60 percent of the cross-sectorial variation in Mercosur's CET.

While it may be stretching the power of the model to interpret the values of /3, **/2** in terms of welfare weights, it is nonetheless worthwhile to note the following. Unconstrained estimates suggest that Argentina and Brazil give relatively similar weights to social welfare in their objective function. However, the constrained estimates suggest that Argentina's trade policy authorities are significantly more concerned by social welfare than Brazil's trade policy authorities _(VA/^aB =_ 1.27). These estimates are consistent with the fact that, on average, Argentina's pre-CET average tariff is lower than Brazil's (see sample data in the appendix).

For our purposes, however, the most important result is that in both regressions, the sector's share of sales to other sectors enters with a negative sign, suggesting that, indeed, as predicted by a political-economy approach to the determination of the CET intermediate goods producing sectors get less protection.

The results seem sufficiently promising to use our estimates to calculate the marginal effect of counter-lobbying on the CET from the coefficient estimates in table 1. The results of this exercise are shown in table 2. Let a z indicate the mean value of _zM_ in the sample, and /<sup>3</sup> the estimated marginal effect of regressor _zM,_ whose value is given in table 1. Then the change in the value of the mean value of the CET, AT is given by: AT = z. In our sample z = 0.68.

> "<sup>9</sup> Note that the negative coefficient on zM also implies that government's objective is closer to the political-economy function used in this paper than one where government's maximise tariff revenue (see footnote 14).

21
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The mix of foreign funding influences consolidated budget infrastructure expenditures. The nature of the influence is similar, but not identical to that found in the case of central buidget infrastructure expenditures. Holding the level of funding constant, countries with a high proportion of official flows spend neither more nor less than those with **a** low proportion of official flows. This finding holds regardless of whether IMF flows are included or excluded from the official flow tally, as can be seen from Table C7. However, as the proportion of commercial bank flows increases, per capita consolidated budget infrastructure expenditures increase at an increasing rate. At sample means, the estimated elasticity is 0.48, much greater than was found for central budget infrastructure expenditures. As was found for central budget infrastructure expenditures, as the share of direct foreign investment increases (holding the total level of foreign flows constant), consolidated budget infrastructure expenditures initially fall, but subsequently rise. The estimated elasticity at the sample mean is -0.08. The trough of the relationship occurs when the level of direct foreign investment reaches about 1.5 percent of GDP, somewhat less than one standard deviation above the sample mean. Direct foreign investment only induces increased consolidated per capita infrastructure expenditures in countries where the level of foreign investment is exceptionally high. These findings are robust across base runs and sensitivity analyses.

# **_14._** _Government Objectives -- Poverty Commitment_

Consolidated per capita infrastructure expenditures do not respond to the level of poverty alleviation commitment as assessed by PCOM. This finding is robust across all base nins and sensitivity analyses. The estimated sign of PCOM is inconsistent across runs, and the coefficient is never significant even at the 15 percent level, as can be seen from Appendix C. Indicators of poverty levels, as opposed to our indicator of poverty commitment, do no better. The signs are inconsistent across runs and the coefficients are actver significant as can be seen from Tables C8.

The findings of regressions including poverty incidence indicators, as opposed to our poverty commitment indicator, are consistent with those found for the central budget regressions. However, poverty alleviation commitment, PCOM, was found to significantly influence central government per capita infrastructure expenditures, while PCOM does not influience consolidated budget expenditures. It may be that at lower levels of government, investments in infrastructure are not viewed as an effective means to alleviate poverty. Hlowever, it may also be that there is a relationship, but because our review of documents only focused on the central government, the variable PCOM is a poor indicator of the extent of commitment to poverty alleviation at lower levels of government. This issue warrants further exploration.
cotton export quota is auctioned, except for the low quality unspinnable _deshi_ cotton, which is left in the open bidding market. Any traders can export _deshi_ cotton if they want.

# **_Quota Allocation Performance_**

Public traders are allocated most of the cotton export quota every year. Usually, the CCI, a central government agency, is allocated the largest portion of the cotton export quota, followed by the Maharashtra State Cotton Growers Co-operative Marketing Federation (MAFED), the Gujarat State Co-operative Cotton Marketing Federation (GUJCOT), and other state marketing federations **(Table 4).** Although cotton situations change every year, the export quota for spinnable cotton is not allocated to private traders even when cotton harvest is exceptionally good. As a result, private cotton exporters specialize in unspinnable _deshi_ cotton, which is not so profitable for exporters compared to other varieties of cotton. There are two major reasons why the government prefers public traders to export cotton. First, the government believes that it is good for farmers, because the export rents captured by public traders can be rebated to farmers by supplying them with better cotton seeds and by R&D activities. Second, the government is afraid that some private traders may manipulate the export transactions and cause capital outflows.

**Table 4:** **_Market Share of Cotton Traders by Ownership_**

|Type of Trader|Share in Domestic<br>CottonMarket|Typical Share in<br>Export Market|
|---|---|---|
|CCI (central government agency)|8 %|25 %|
|State marketing federations|17 -22 %|50 %|
|Private traders|70 - 75 %|25 %*|
|Total|100 %|100 %|

* Mostly _deshi_ cotton.

Sources: EICA (1995) and Lal & Panner (1995) etc.

# **_Incentives to Export Cotton_**

There are a number of factors that make exporting spinnable cotton very attractive for traders. The most obvious incentive is the difference in price between the Indian market and the world market. In the past few years, the difference between nominal domestic cotton prices and export prices has been around 15 per cent (Pursell and Gupta, 1996). Compared to this, the margin left for traders after deducting sales tax and transportation costs is only about 0.5 per cent in the domestic cotton market. This difference between the export market and domestic market is much narrower for unspinnable _deshi_ cotton, which private traders 7