rafmacalaba/datause-encoder-data huggingface dataset:
an increase in the share of large firms in the offshore sector. These facts hold both for the entire sample and also when we restrict the analysis for firms in the manufacturing industry.

We then proceed to evaluate outcomes available from firms’ tax records in Figure 7. In panel (a), we document no meaningful differential behavior in the log wage bill for offshore and onshore firms after the reform (again, highlighting the strong convergence of the offshore sector in 2009-2010 but similar trends in 2011-2012). For the full sample we estimate precise zeroes, whereas for the manufacturing sector we have point estimates that are approximately +5% but not always significant. Results for log revenue are similar, showing zero effects in the early years after the reform and larger increases in offshore revenue later in the panel (particularly for the manufacturing sector).

The one outcome for which we find large, negative effects of the reform on the offshore sector are declared profits, presented in panel (c) of Figure 7. We estimate no differential trends in the onshore and offshore sector before the reform, but a large decrease in profits in the two years afterwards: both for the entire sample and manufacturing industry, we see a .4 log-points decrease in profits in 2014 and a 1 - 1.5 log point decrease in 2015. In later years, nonetheless, we see a rapid convergence of profits, such that by 2018 profits are actually estimated to be larger in the offshore sector. Given the consistency of the remaining evidence that the reform did not affect real economic outcomes such as revenue and employment, we consider it unlikely that the large effects we estimate are real decreases in economic profit of offshore firms. This is also consistent with the effects of the tax reform on imports, which appear to be non significant both at the industry(panel (a) in appendix Figure A8) and at firm-level (panel (b) in appendix Figure A8).<sup>20</sup> . More likely, this is a reporting response from offshore firms: once profits go from being taxed at a zero rate to a 10% rate, firms respond by decreasing their reported profits.<sup>21</sup>

> 20The effect on imports appears to be positive even three years after the reform.

> 21We should note that, since we see little to no revenue response, the response in profits is likely driven by an increase in reported costs. It has been extensively documented that costs are much more elastic than revenues when firms respond to changes in tax rates (Bachas and Soto, 2021; Lobel et al., 2024). We also note that the profit variable we use is not the taxable base of corporate income taxes – it is instead a “top line” economic profit measure, after which firms will apply deductions and other adjustments to arrive at taxable income, which we do not observe.

27
# **I.  Introduction**

There is a close relationship between trade costs and the capacity of a country to increase its exports and to integrate in the world economy. The relevance of transport costs, as a component of trade costs, has been increasing as liberalization continues to reduce artificial barriers to trade. In many cases, the effective rate of protection provided by transport costs is higher than the one provided by tariffs (Clark et al., 2004; Hummels, 1999).

One of the most important and evident components of transport costs is distance. In its simplest formulation, the gravity model for trade, introduced by Linnemmann (1966), states that bilateral trade flows depend positively on the product of the GDPs of both economies and negatively on the distance between them, which stands for bilateral transport costs. The impact of distance on countries’ volume of trade is significant: recent estimates of the elasticity of trade volumes with respect to distance indicate that when distance increases by 10 percent, the volume of trade is reduced between 9 and 15 percent (Overman et al., 2003).<sup>3</sup> However, in addition to distance, many other elements influence transport costs. As explained by Limão and Venables (2001), transport costs and trade volumes depend on many complex details of geography, infrastructure, administrative barriers and the state of competition in the transport industry. Provided that distance and infrastructure related costs are major determinants of the success of a country’s export sector, immediate questions arise: what can governments do to “get closer” to markets with high import demand? Can improvements in infrastructure and regulation reduce transport costs? Is it worthwhile to implement policies designed to increase competition in transport markets? Do these policies have a quantifiable impact on transport costs?

Not many papers have tried to estimate the impact on transport costs of policies that improve the quality of regulation and infrastructure or implement new competition regimes. Focusing on infrastructure and using data from maritime shipping companies, Limão and Venables (2001) show that poor infrastructure accounts for more than 40% of predicted transport costs. In a study specific to the port sector, Clark et al. (2004) show that an improvement in port efficiency from the 25<sup>th</sup> to the 75<sup>th</sup> percentile reduces 2
# **TABLE 1 : DOING BUSINESS REPORT: TRADING AMONG BORDERS**

|Region or<br>Economy|Documents<br>for export<br>(number)|Time for<br>export<br>(days)|Cost to<br>export<br>(US $ per<br>container)|Documents<br>for import<br>(Number)|Time for<br>import<br>(days)|Cost to<br>Import (US<br>$ per<br>container)|
|---|---|---|---|---|---|---|
|East Asia and<br>Pacific|6.9|23.9|884.8|9.3|25.9|1,037.1|
|Europe and<br>Central Asia|7.4|29.2|1,450.2|10.0|37.1|1,589.3|
|Latin America<br>and<br>Caribbean|7.3|22.2|1,067.5|9.5|27.9|1,225.5|
|Middle East<br>and North<br>Africa|7.1|27.1|923.9|10.3|35.4|1,182.8|
|OECD|4.8|10.5|811.0|5.9|12.2|882.6|
|South Asia|8.1|34.4|1,236.0|12.5|41.5|1,494.9|
|Sub-Sahara<br>Africa|8.2|40.0|1,561.1|12.2|51.5|1,946.9|
|USA|6|9|625|5|9|625|
|Argentina|6|16|1,470|7|21|1,750|
|Brazil|7|18|895|6|24|1,145|
|Chile|7|20|510|9|24|510|
|Colombia|6|34|1,745|11|35|1,773|
|CostaRica|7|36|660|13|42|660|
|Mexico|6|17|1,049|8|26|2,152|
|Peru|7|24|800|13|31|820|
|Uruguay|9|22|552|9|25|666|
|_Source: Doing Bus_|_iness Report 2007, _|_TheWorld Bank._|||||

Compared with other regions in the world, Latin America (LAC) shows an acceptable performance of the indicators of official procedures and time necessary for foreign trade (Table 1). Nevertheless, if its performance is compared with that achieved by OECD countries, it can be seen that LAC has much room for improvement. As the time taken for customs and fiscal processing has a direct financial impact on the economic agents participating in the tradable sector of the economy, the “extra” days that the goods are delayed in LAC have a negative effect on its competitiveness. By calculating an average for the indicator, a ranking can be made of the various economies. It can be seen that although Argentina, Chile and Mexico have much improving to do, other countries in the region, such as Brazil (in 107th place in the ranking) and Peru (93rd) need to make reforms far more urgently.

- **World Bank, estimate of logistics costs as a proportion of GDP** [World Bank, 2002]. In an estimate made for various countries in Latin America in 2001, logistics costs were calculated as a percentage of the sale value of the products. The results show that all Latin American

9
year 2004 from Kaufmann, Kraay and Mastruzzi (2005). They present indicators for the following six dimensions of governance:

- 1) _Voice and Accountability:_ a measure of political, civil, and human rights, including the ability of citizens to participate in selection of a Government and media independence.

- 2) _Political Stability and lack of Violence:_ a measure of the likelihood of the government in power being overthrown by violent or unconstitutional means.

- 3) _Government Effectiveness:_ a measure of the quality of public service provision, and the competence of the bureaucracy.

- 4) _Regulatory Quality:_ a measure of the incidence of market unfriendly policies and excessive regulation.

- 5) _Rule of Law:_ a measure of the quality of contract enforcement, the incidence of crime, and the quality of the judiciary.

- 6) _Control of Corruption:_ a measure of the use of public power for private gain, including both petty and grand corruption.

Each of the indicators is scaled to have a mean of zero and standard deviation of one, with higher scores representing better governance. Table 3 presents the results of regressing the cost of a passport relative to per capita income on these measures of governance. As the measures are quite highly correlated with one another, we include each one separately before including them jointly. Each of the different measures has a negative coefficient when entered separately, and all are significant except for voice and accountability and regulatory quality. This shows that higher passport prices relative to

- 9 -
As the primary explanatory variable, we employ the “number of local media articles” mentioning _Doing Business_ . This country-specific variable captures the total number of local press articles discussing economies’ performance on the _Doing Business_ indicators over each _Doing Business_ publication year (for more information see the Methodology chapter).

With the clearly identified dependent and independent variables put in place, we selected a number of controls imperative for the analysis. First and foremost, the model controls for income differences across countries. To this end, it employs the classical control variable of log income per capita in current USD of the World Development Indicators, the World Bank Group. In addition, using Factiva search engine, we collected the data on the number of media outlets per country. As historical data on media outlets is not available, we use the data for the most recent years – 2017/18. The data show that high income OECD countries have on average over 400 media outlets per economy, compared to only 9 in Sub-Saharan Africa.

Another prominent control included in the analysis is freedom of the press variable of the Freedom House. This variable was selected due to its comprehensive global coverage and availability of gap-free historical data. As discussed in the literature section, Freedom House data, and freedom of press variable specifically, are widely used in the existing literature on drivers of reforms. Other sets of variables that we considered and selected as controls only in simple non-panel OLS models include World Justice Project’s rule of law index, Polity IV data on democracy and autocracy, Transparency International’s corruption perception index, the Economist’s democracy classification score and the Worldwide Governance Indicators of the World Bank Group.

Prior to testing the aforementioned hypothesis, we first run a number of simple correlations and perform data robustness checks. First, we correlate year on year Factiva and Google data, which yields an average correlation coefficient of 0.6. This is not surprising as Factiva’s data only captures local media, while Google does not differentiate between foreign and local press coverage. Therefore, in the subsequent analysis the two data sets are used as complementary independent variables rather than substitutes. However, we give more prominence to Factiva’s data due to our interest in targeted local media that specifically focuses on domestic politics and local economic development. We then check all the variables for outliers and isolate exorbitant cases. For example, in some of the OLS regressions we limit the number of media articles per year to either 100 or 200 as few of the countries with 200+ articles considerably skew the relationships, thus exaggerating coefficients.

16
Prices received by the exporting countries for their coffee on the world market vary depending on the coffee type exported. The ”indicator prices” are calculated by the ICO on the basis of the daily spot prices of the relevant types coffee traded in the New York and Bremen/Hamburg markets. The ICO prices distinguish between four main groups of coffee: Colombian milds, Brazilian milds, other milds robustas. The main exporters of each type of coffee are listed in Table 2. In the country-by-country analysis the price of the appropriate coffee type is used as the world market price.

The quality of coffee and, correspondingly, the actual price paid for it varies within each of the four groups depending on the origin. The quality may change over time. In fact, in some countries the quality has been declining in the recent years. The actual export prices for each country cannot be inferred from the ICO data, which represents an average price for all coffee traded within each type. An indication of how export prices vary across countries and over time can be obtained from other sources, for example from trade statistics. US import data can be used to calculate the per unit import price of coffee. In 2002 the US imported one third of all green coffee traded on the world market. The per unit prices of US imports of raw coffee can therefore serve as an estimate of the actual export price received by a country. The monthly US import prices can obtained from the Tariff and Trade DataWeb of the US International Trade Commission<sup>2</sup> . As can be seen from Figures 2-5, the US import prices followed closely the ICO indicators in most cases, Ethiopian coffee being a notable exception.

It would not be appropriate to use the US import prices to explain the variation in the domestic producer prices, however. First, per unit prices that are calculated using the trade data are not observable until the transactions take place and customs collects all the information on values and quantities traded and publishes it. ICO indicators, on the other hand, can be easily observed as they are published daily and can be used as a basis for negotiating local prices. Second, interpreting the data correctly may be problematic because of the methodological difficulties. USITC reports imports of arabica and robusta together until 1989. After that the classification distinguished between “arabica” and “other” green coffee, the latter not necessarily being robusta. For example, a great deal of coffee from Colombia and Mexico, both of which only produce arabica, are classified as “other” in the USITC statistics. According to US Customs<sup>3</sup> , misclassification is very common. If the invoice for the coffee sale does not specifically mention that the coffee is of the arabica sort, the importer automatically lists coffee as “other”. Since both types have zero tariff, it makes no difference for the

> 2 http://dataweb.usitc.gov 3 A phone interview with an Imports Specialist from the US Customs Service Center

12
According to equation (12), per capita income growth is the sum of the labor productivity growth rate, growth in the labor force participation rate, and growth in the fraction of the population of working age. Substituting equation (12) in equation (10) yields the growth rate of per capita income represented by equation (13) below: (13) Equation (13) provides the basis for inferring the relative contribution of women to economic growth from the growth accounting framework. The initial parameter values in the baseline scenario are selected to be consistent with the production structure of most African economies. For instance the elasticity of output with respect to the male-owned land input is defined such that the share of agricultural value added expressed in GDP. Similarly, this choice reflects the fact that the distribution of land continues to be biased against women. The base case scenario assumes that and that the contribution of capital is gender neutral.

# **IV Data and empirical results**

Except in a few cases (annual hours worked ( ) and capital stock ( , most data are obtained from the World Bank’s World Development Indicators database. Data on annual hours worked are very difficult to obtain even for OECD countries, and are not available for countries in SSA. For all practical purposes, we assume that the hours worked variable is time-invariant and genderneutral , even though women tend to put in longer hours than men in most countries and societies [Boserup (1970)]. Understandably this hypothesis imposed by data limitations is likely to result in an underestimation of the relative contribution of women to growth.<sup>21</sup> National product and income accounts generally do not report a series for the capital stock ( . This variable is constructed using a perpetual inventory method as follows:

# ,

where is the constant depreciation rate of capital and is real investment. The perpetual inventory method requires data on real investment, a value of and a value for the initial stock of capital . In the absence of estimates of capital stock for most countries in the region, the initial stock of capital is taken as zero.<sup>22</sup> This base value assigned to the initial stock of capital will not markedly affect the

> 21 However, the large fraction of long hours of work invested by women is not necessarily captured in national account estimates, as they largely fall in the realm of informal sector and home production. 22The initial stock of capital could also have been chosen such that the capital output ratio in the initial period equals the average capital output ratio over the reference period. However, this would have required data on the initial capital stock.

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- La Porta, R. and A. Shleifer (2014a). Informality and development. _The Journal of Economic Perspectives 28_ (3), 109–126.

- La Porta, R. and A. Shleifer (2014b). Informality and development. _Journal of Economic Perspectives 28_ (3), 109–26.

- La Silla Vac ́ıa (2012, December). Desplazados dentro de su misma ciudad: las v ́ıctimas m ́as invisibles en Colombia. http://lasillavacia.com/historia/desplazados-dentro-de-su-misma-ciudadlas-victimas-mas-invisibles-en-colombia-40579.

- Lewis, E. (2011). Immigration, skill mix, and capital skill complementarity. _The Quarterly Journal of Economics 126_ (2), 1029–1069.

- Lewis, E. and G. Peri (2015). Immigration and the economy of cities and regions. In _Handbook of Regional and Urban Economics_ , Volume 5, pp. 625–685.

- Lozano-Gracia, N., G. Piras, A. M. Ib ́a ̃nez, and G. J. Hewings (2010). The journey to safety: conflict-driven migration flows in Colombia. _International Regional Science Review 33_ (2), 157– 180.

- Meghir, C., R. Narita, and J.-M. Robin (2015). Wages and informality in developing countries. _American Economic Review 105_ (4), 1509–1546.

- Moya, A. et al. (2012). Violence, emotional distress and induced changes in risk aversion among the displaced population in Colombia. In _Pacific Development Economics Conference, University of California, Davis_ .

- Ottaviano, G. I., G. Peri, and G. C. Wright (2018). Immigration, trade and productivity in services: Evidence from UK firms. _Journal of International Economics 112_ , 88–108.

- Perry, G. (2007). _Informality: Exit and exclusion_ . World Bank Publications.

- Rozo, S. V. (2018). Is murder bad for business? Evidence from Colombia. _Review of Economics and Statistics. Forthcoming_ .

29
economies. As an example, the Chilean customs data, which by international standards are reliable and quite disaggregated, has a single code for services exports.<sup>7</sup> Nevertheless, using some of the identified characteristics of services companies, and drawing upon the existing empirical literature, we can make some conjectures about services firms’ exporting behavior. The existing firm-level literature, focusing on manufacturing firms and pointing to the existence of large fixed costs of entering export markets, has been used to explain the generally (but not invariably) positive relationship between productivity (and hence size) and exporting status (Bernard and Jensen 1999). The argument being that only the more productive (and hence larger) firms can afford to pay the fixed costs required to start exporting (Melitz 2003). Since scale economies appear to be a less important feature of services, it is conceivable that the selection into exporting of the (more productive) larger firms mechanism is weaker in services than in manufacturing (Love & Mansury (2007b)).<sup>8</sup> However, the “immaterial” nature of many services implies a more critical problem of asymmetric information between producers and consumers.<sup>9</sup> Furthermore, services often tend to be less standardized and more designed to fulfill the needs of specific consumers. These characteristics are likely to create the need to invest in developing a solid reputation in specific export markets, to establish a close relationship with consumers and to customize services. These requirements may imply higher fixed costs to enter export markets for services companies and thus larger barriers for smaller and less efficient firms to penetrate export markets. According to Eickelpasch & Vogel (2009), for example, customized services which require intensive communication and interaction with clients should require more geographical proximity/connections than manufacturing. Overall, the correlation between size and export status is therefore a-priori ambiguous. US data reveals that the percentage of service firms that export is low and lower than in manufacturing (Jensen (2008)).

8
- 19 -

# Footuotes

It is worth noting that the CBs who advance more rural credit than the PACS and the LDBs. For example, RBI reports that in 1981 the CBs advanced 776.3 billion rupees to the rural sector, while the coopearive sector (i.e., PACS and LDBs) advanced only 236.7 billion rupees, a third of what the CBs advanced.

2 Credit can enter into the output supply and hence input demand and investment or wage functions if credit is a binding constraint in rural household's input-output decision-making. Assume that a farmer maximizes output function, Q - Ka (i)

# subject to a liquidity constraint,

rx _-_ 5 (ii), where Q is crop output, K is fixed capital such as livestock and irrigation pumps, r is the price of variable inputs (X) such as labor and fertilizer, 6 is the total credit available to purchase variable inputs; and equation (i) is the familiar Cobb-Douglas production function. By simple manipulation, one can derive the input demand-equation as Xc, _r__<sup>_1_</sup> _6_ (iil) and the output supply equation as

## , **QC(** _- r-PP_ (iv)

where Xc and Qc are, respectively, credit-constrained level of input use and crop output. If competitive labor market exists and equilibrium condition is satisfied, one can also show agricultural wage as a function of credit ration available to the farmers.