rafmacalaba/datause-encoder-data huggingface dataset:
# **Annex IIB: Stamp Duty Rates for States and Union Territories FY81-FY2003**

|**Year**|**Hima**|**chal Pradesh**|**K**|**arnataka**|||**Kerala**||**Madhy**<br>**Ch**|**a Prade**<br>**attisga**|**sh and**<br>**rh**|**M**|**aharasht**|**ra**|**M**|**anipur**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Basic**|**SC**<br>**Total**|**Basic**|**SC**|**Total**|**Basic**|**SC**<sup>**6**</sup>|**Total**|**Basic**<br>|**SC**<br>|**Total**<br>|**Basic**|**SC**|**Total**|**Basic**|**SC**|**Total**|
|**1980-81**|8|0<br>8||2|2|**7.5**|5|12.5||2|2||0|||||
|**1981-82**|8|0<br>8||2|2|**7.5**|5|12.5||2|2||0|||||
|**1982-83**|8|0<br>8||2|2|**7.5**|5|12.5||2|2||0|||||
|**1983-84**|8|0<br>8||2|2|**7.5**|5|12.5||2|2||0|||||
|**1984-85**|8|0<br>8||2|2|**7.5**|5|12.5||2|2||0|||||
|**1985-86**|8|0<br>8||2|2|**7.5**|5|12.5||2|2||0|||||
|**1986-87**|8|0<br>8||2|2|**7.5**|5|12.5||2|2||0|||||
|**1987-88**|8|0<br>8|10|2|12|**7.5**|5|12.5||2|2||0|||||
|**1988-89**|8|0<br>8|10|2|12|**7.5**|5|12.5||2|2|10|0|10||||
|**1989-90**|8|0<br>8|10|2|12|**7.5**|5|12.5|7.5|4|11.5|10|0|10|7|0|7|
|**1990-91**|8|0<br>8|10|2|12|**7.5**|5|12.5|7.5|4|11.5|10|0|10|7|0|7|
|**1991-92**|8|0<br>8|10|2|12|8.5|5|13.5|7.5|4|11.5|10|0|10|7|0|7|
|**1992-93**|8|0<br>8|10|2|12|8.5|5|13.5|7.5|4|11.5|10|0|10|7|0|7|
|**1993-94**|8|0<br>8|10|2|12|8.5|5|13.5|7.5|4|11.5|10|0|10|7|0|7|
|**1994-95**|8|0<br>8|6|2|8|8.5|5|13.5|7.5|4|11.5|10|0|10|7|0|7|
|**1995-96**|8|0<br>8|6|2|8|8.5|5|13.5|7.5|4|11.5|8|0|8|7|0|7|
|**1996-97**|8|0<br>8|6|2|8|8.5|5|13.5|7.5|4|11.5|8|0|8|7|0|7|
|**1997-98**|8|0<br>8|8|2|10|8.5|5|13.5|7.5|4|11.5|8|0|8|7|0|7|
|**1998-99**|8|0<br>8|8|2|10|8.5|5|13.5|7.5|4|11.5|8|0|8|7|0|7|
|**1999-2000**|8|0<br>8|8|2|10|8.5|5|13.5|7.5|4|11.5|8|0|8|7|0|7|
|**2000-01**|8|0<br>8|8|2|10|8.5|5|13.5|7.5|4|11.5|8|0|8|7|0|7|
|**2001-02**|8|0<br>8|8|2|10|8.5|5|13.5|7.5|4|11.5|8|0|8|7|0|7|
|**2002-03**|8|0<br>8|8|2|10|8.5|5|13.5|7.5|4|11.5|8|0|8|7|0|7|
|**2003-04**|8|0<br>8|8|2|10|8.5|5|13.5|7.5|4|11.5|8|0|8|7|0|7|

6. The surcharge of 4% in muncipal area and 5% in corporation of the consideration is levied separately.

67
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# **1 Introduction**

The _U.S. Merchant Marine Act of 1920_ (the Jones Act) requires that maritime vessels moving goods from one U.S. port to another must be U.S.-built, U.S.-flagged, U.S.-owned and U.S.-crewed.<sup>1</sup> This protectionist policy raises the cost of intranational maritime shipping in the U.S., and imposes a disproportionate burden on residents of U.S. islands. These effects of the policy are generally understood as a qualitative matter, but there are few quantitative estimates of the economic burden the policy puts on U.S. outlying areas. In this paper we estimate the economic effects of the Jones Act (JA) on Puerto Rico (PR).<sup>2</sup> We begin by exploring data documenting ship movements in the Caribbean. Supplementary data from the same source reveal each ship’s type, country of build, country of ownership and the flag under which it operates. We use these data to identify ships that call in Puerto Rican ports and satisfy JA regulations. We compare the characteristics of vessels that satisfy JA requirements with the broader fleet calling in PR, and with vessels calling elsewhere in the Caribbean. This exercise reveals a striking pattern; the JA fleet serving PR is dominated by container ships and barges. Other cargo vessels that appear regularly in the rest of the Caribbean are nearly absent from the U.S.-PR market. We hypothesize that the JA raises costs on all-seaborne shipping, but more so on physically heavy and/or bulky products that are difficult to containerize.

> 1Specifically, the Jones Act requires that every vessel serving any U.S. domestic route must be (1) at least 75% owned by U.S. citizens; (2) built in the U.S.; (3) crewed by U.S. citizens or permanent residents; and (4) registered in the U.S. At least 75% of the crew should be U.S. citizens, and all of its officers and engineers (Beason et al. (2015), cited in Olney (2020)). The U.S.-build requirement includes some infrequently used exceptions that happen to be relevant in the Puerto Rican setting.

> 2It is likely that the JA has similar effects on other U.S. islands and outlying areas, including Hawaii (HI) and Alaska (AK). The U.S. does not collect the detailed intra-national trade data for AK and HI that we use here. There is similar data available for other U.S. possessions, but these island economies are quite small, usually more distant from the U.S., and not subject to the same breadth of JA restrictions that apply to PR.

1
Table la we can see that many variables, which one would assume to be _exogenous_ (i.e. not influenced by the program, at least within a three-year period) do in fact differ. For example, the mean levels of education, land size, age and number of laborers differ across the control and treatment villages. Also, the control households are disproportionately concentrated in the Northern region while the treatment households are disproportionately concentrated in the SouthWest region. These differences cause concerns regarding the randomness of program assignment to communities in practice. Below we discuss the methodology employed in the paper to evaluate the impact of the program and to deal with potential problems arising from endogenous program placement and/or self-selection issues relevant to program evaluation.<sup>10</sup>

# _3.2 Methodology_

In line with the above discussion of the data and the implications for estimating program impact, we first compare mean income levels in control and treatment areas based on householdlevel data. To control for observed heterogeneity across households, we then estimate the program impact on income using multivariate ordinary least squares (OLS) regression techniques based on the following equation: log yi =x + 3 X j+ 6 Vpdi + ui where _y,_ is annual household income and Xi includes socio-economic variables such as the age and education of both husband and wife, number of labourers, land size, and location (i.e. regional) dummies. The error terms ui's are assumed to follow independently identical distributions. The variable _Vpd_ is a dummy capturing (village) _program participation -_ that is, takes the value unity for households in "treatment" villages and zero for those in "control" villages. However, _Vpd_ cannot be treated as exogenous if, for example, the choice of program '° For detailed discussion on these issues see, for example, Heckman (1979, 1996, 1997), Heckman and Robb (1985), Rosenzweig and Wolpin (1986) and Pitt, Rosenzweig and Gibbons (1995).

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_For my friends, anything; for my enemies, the law._ _Oscar Benavides, Former President of Peru_

# **I. Introduction**

Over the last decade there have been two major initiatives to measure the ―investment climate‖ that private sector firms face in developing countries, both with the goal of discovering and promoting those government policies and actions that could accelerate (broad based) economic growth.

Far and away the most famous of these has been the Doing Business (DB) indicators supported by the World Bank. DB assesses 10 elements of a country‘s policies that relate to private firms (e.g. starting a business, hiring workers, trading across borders, dealing with construction permits). DB provides ̳league tables‘ on each of these 10 regulatory areas separately, as well as providing a widely cited overall ―Ease of Doing Business‖ ranking (e.g. in the 2010 ranking, Singapore as ranked number one and Central African Republic at 183).<sup>1</sup> The DB exercise assesses the country‘s ―investment climate‖ with experts that examine the relevant laws and procedures to assess the time and cost of compliance with the variety of governmental regulations, as well as the ease with which a variety of contracts between private parties can be entered into and enforced.<sup>2</sup> A parallel effort, also carried out by the World Bank, has surveyed firms in developing countries. This questionnaire includes modules that ask about the firm and its operations, what the firms perceive as obstacles to their own business and about their interactions with

> 1 A Google search on ―ease of doing business‖ and ―world bank‖ produces half a million hits.

> 2 The Doing Business indicators were developed from collaborations with academics (e.g. Djankov, La Porta, Lopez-De-Silanes and Shleifer, 2002; Djankov, La Porta, Lopez-de-Silanes and Shleifer, 2003; Botero,  Djankov, La Porta, Lopez de Silanes and Shleifer 2004; Djankov, Simeon,  Caralee McLiesh, and Andrei Shleifer 2007.). The DB data, once developed, has then been used to examine the effect of labor regulations (Micco and Pages 2006;), regulation of entry (Bruhn, forthcoming; Klapper, Laeven and Rajan. 2006), or a wide set of regulations (Loayza, Oviedo and Servén, 2006; Bolaky and Freund 2008; Eifert 2009) on cross-country outcomes of interest.

2
transfers) and disposable income (after taxes and transfers).<sup>3</sup> The key distinctive feature of the evolution of income inequality has been the large and sustained increase in the market-based Gini coefficient in advanced economies in each decade from the 1980s through the 2010s.<sup>4</sup> In contrast, income inequality for emerging market and developing economies (EMDEs) as a group has been broadly unchanged since the 1980s.<sup>5</sup> As a result of these contrasting trends, market inequality in advanced economies has surpassed that of EMDEs, on average, in recent decades, from a lower relative level in the 1980s (Table 1). Despite the relatively stable trend for EMDEs, some of the largest emerging market countries—notably China, the Russian Federation, India, South Africa, and Indonesia—have experienced increasing market inequality (Table 1). In addition, inequality varies considerably more across emerging markets and low-income countries—especially the former, where outliers range from a low Gini coefficient in the range 20 to 30 to nearly 70 (Figure 2, panel a). The variation in inequality across countries is especially pronounced when comparing the ratio of income of the top decile relative to the bottom decile of each country’s income distribution (Figure 2, panel b). For emerging markets and low-income countries, the ratio exceeds 20 for several countries.

> 3 For inequality measures presented in the paper, we use the Standardized World Income Inequality Database (SWIID). Its main advantage is coverage in terms of time and country information, as well as pre- and posttax/transfer estimations. This coverage, however, comes at the cost of extensive imputations to fill gaps over time in a country, to extend the data to countries with missing data, and to obtain post-tax/transfer estimates. We recognize this limitation and point out that the World Bank PovcalNet is the unique comprehensive global dataset of nonimputed inequality (and poverty) data. For our purposes in writing this survey paper, the advantages of the SWIID outweigh its limitations.

> 4 This section analyzes trends in poverty and inequality starting in the 1980s. Longer time series on wealth and income inequality have been collected by Piketty (2014) and are restricted mostly to advanced economies. Piketty and Saez (2014) report sustained improvements in wealth and income distribution across Europe and the United States from the 1930s to the 1970s, followed by a worsening of inequality starting in the 1970s to the 1980s. This section captures the rise in inequality in advanced economies starting in the 1980s. Later sections examine several channels that might account for this more recent trend.

> 5 Fabrizio et al. (2017) provide an overview of income inequality trends in low-income countries.

3
Table 4 presents the estimation results of elasticity regression (8). Columns (2)-(5) show that the elasticity is around 1.55. The coefficient of 1 _/N_ is statistically significant at the 1 percent level in column (2) and at the 10 percent level in column (5), supporting our identification assumption with the number of effective observations capturing cross-country variability in the variance of nighttime light growth. The coefficients of area and cloud cover are also statistically significant at the 1 percent level when included alone, indicating that they also affect the noise in nighttime light growth. Across the different specifications, the estimated elasticity stays nearly the same, varying from 1.51 to 1.60 when the controls are included separately and rising to 1.64 when they are included together. The elasticity is hence higher than suggested by the simple regression presented in table 3. It is also higher than the central unity estimate in Henderson, Storeygard, and Weil (2012) and the 1.3 estimated in Hu and Yao (2021), both of which use annual DMSP nighttime light data from 1992.<sup>18</sup> However, our estimate is somewhat below the implied elasticity between VIIRS nighttime lights and GDP per capita in China, which Chor and Li (2021) estimate to be 2.1.

Table 4: Elasticities between Nighttime Lights and GDP

||Var|iance of nig|ht light g|rowth: va|r(_z_)|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|cov(_y, z_)|1.46***|1.55***|1.51***|1.60***|1.64***|
||(0.55)|(0.54)|(0.54)|(0.54)|(0.54)|
|1_/N_||522.0***|||341.4*|
|||(139.8)|||(198.6)|
|Area|||0.12***||0.026|
||||(0.045)||(0.061)|
|Cloud cover||||4.32***|3.47***|
|||||(1.14)|(1.19)|
|Obs|327|327|327|319|319|
|Adjusted _R_<sup>2</sup>|0.018|0.056|0.036|0.058|0.074|

_Note_ . This table presents the results of regression (8) and its variants with other covariates. The first row represents estimates of the elasticity between nighttime lights and GDP. The sample includes 84 emerging markets and development economies between 2013Q2 and 2020Q4. Each time period that is used to estimate the variances and covariances covers two years, i.e., 2013-14, 2015-16, 2017-18, and 2019-20. Throughout, standard errors are in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

> 18However, it is within the range of elasticities presented in table 5 of Henderson, Storeygard, and Weil (2012).

15
30 After subtracting (3) from (2), then adding and subtracting zipijHKwijHK, vi pijHKwijUS and i Pijuswij HK, the difference between US and Hong Kong unit prices of apparel group j can be expressed as: _(4) Pius_ - p HK - i pijHK(wijUs _witH)_ + Zi (Pi USPijHK)W,jHK + Z, (Pius PijHK)(wiJUS, w,JHK)

- where Ei wijus vi wijHK _ 1, so E i (wiius. wijHK) -0. In other words, the difference in the US and Hong Kong unit prices of apparel group j can be broken down into a composition effect (differences in weights), a price effect

(differences in prices of component MFA categories) and an interaction of the two effects, Note that we have information only on PjUS, PjHK, PijHK **and** wiJHK. Since the US production data is not broken down into MFA categories, we do not know Pi)S and wijU. Let pij denote the difference between US and Hong Kong weighting of the ith category of apparel group j. Suppose we make the simplifying assumption that: (5) Pij US - aJ + pijK Then aj captures the extent of rent sharing as it denotes the price difference between the US and Hong Kong of apparel group j.1<sup>5</sup> If there is no rent sharing, aj equals zero. Putting (5) in (4) gives us:

- (6) PUS pJHK _ a + Z, OijPiJHK

where Ei Pit - 0.

Now we want to test whether the price difference between the US and Hong Kong for each apparel group J is due to differences in the composition of the group or due to rent sharing. Therefore we are interested in the significance of the Pij's and aj. Specifically, if our null hypothesis states that there is no compositional difference between US and Hong Kong apparel groups, and no
60% of the unbanked have an educational level of completed primary education or below, while the majority of the banked population has a completed secondary or university degree. Experience in the US has shown that low education is frequently associated with the lack of financial literacy. This points to the important role of financial education in increasing demand for formal financial services.

56. Focus groups in Bogotá have confirmed that lower income groups have limited knowledge about financial services and generally have preconceived notions that banks ‘rob or steal’. Consequently, financial education is an important ingredient of any financial access strategy for Colombia. To this end, various channels can be explored: innovative approaches in the US, for example, work with school children in order to reach their unbanked parents. Financial education can also be effective through NGOs or a comprehensive public awareness campaign.

57. The Government can also play a role in enhancing the use of formal financial services. For example, rather than paying government employees by check, the government could pay wages as well as pensions electronically into recipient accounts. This would significantly decrease transaction costs for employees, pensioners and banks, while encouraging recipients to use non-cash financial services. Paying employees electronically is successfully practiced in some US states.

# **4.4 AREAS FOR FURTHER WORK**

58. In the scope of the present paper it was only possible to provide an initial overview of the state of access to financial services in Colombia, and highlight issues on both the supply and demand side. Further work is encouraged in order to explore a number of the presented topics in more detail.

59. On the supply side, a follow-up survey of Colombia’s major financial intermediaries in Bogotá and other cities would be beneficial. Such a survey should address issues such as banks’ transaction cost structure, account opening requirements, minimum balance requirements, fee structures for checking and savings accounts, payment services, consumer loans as well as microcredit, and interest rates on all of these products. These findings should be benchmarked against international bank practice taking countries’ different income levels into account. On this basis it could be established whether banks in Colombia provide financial services at high costs relative to client income and international experience. The survey could also help identify the potential for start-up of self-limiting assistance to financial institutions for encouraging the provision of basic services.

60. The present paper limits itself to examining access issues among formal financial institutions supervised by the _Superintendencia Bancaria_ . In 66
WBES with other measures of the political, institutional and business environment at the country level. We describe below how we measure our key variables.

**Infrastructure.** Two related measures capture the extent of modern communication infrastructure: telephone density (from WDI indicators), and the local (i.e., city-industry-size) share of firms using websites for their businesses (based on WBES).  Since telephone density and local web intensity are closely correlated, and they both measure the use of modern telecommunication, we rely on factor analysis to obtain their principal factor, and call it Telecom.<sup>10</sup>

**Geography** has been suggested as an important reason for Africa’s lack of development (Collier and Gunning 1999; Sachs and Warner 1997).  In particular, Africa has more landlocked countries, which heightens the need for coordination with neighboring countries. Given the significantly higher trade costs associated with borders, being landlocked necessarily impedes international trade.  Another somewhat subtle aspect of geography for Africa is its small population in general.  The low fertility of land and other aspects of geography imply that African countries tend to have sparsely populated land.  To capture the geographic elements above, we thus rely on (1) a dummy variable for whether a country is landlocked and (2) domestic market size, as proxied by country population.  We also explore in robustness tests the importance of natural resource endowments, such as the share of land in tropical areas.

**Political and institutional factors** .  This category includes three political and institutional factors that might influence firm performance.  The first is ethnic fractionalization, a key feature that affects many formal and informal institutions in Africa (Easterly and Levine

> 10 One omission in our measure of infrastructure is the lack of road network data.  While the WDI has data on paved roads, the data are very unreliable.  For instance, India is said to have longer road network than China, a possibility that is instantly dismissed for anyone who has travelled to both countries.  The WDI road data simply do not capture the quality of roads well.  In contrast, the measures we adopt here make more sense in our judgment.

8
Figure 8: TFPG vs Growth in Loan/GDP <!-- Start of picture text --> 0.06<br>0-.0<br>< -0.02<br>I -0.04-<br>-0.06 -<br>-0.08 '<br>j -0. 1<br>-0.12<br>-0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25<br>Loan/GDP growth<br><!-- End of picture text --> Figure 9: Trends in FDI/GDP and Imported Capital Goods/GDP <!-- Start of picture text --> 0.1 - _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 0.4<br>0.09 0.35<br>0.08<br>0.3<br>0.07-02<br>0.06 02<br>0.05 0.2<br>0.04 01<br>0.03<br>0.1<br>0.02 I (left scale)<br>0.01<br>90 t flg 909 09 )9 0<br>|. _FDI/GDP -I--Imports of K goods/GDP<br><!-- End of picture text -->

22