rafmacalaba/datause-encoder-data huggingface dataset:
_yi =_ α _+_ β _*Migranti+_ δ _’Xi +_ ε _i_ (2) However, _yi_ is unobserved. For individuals who have finished their schooling, we observe schooling level _S_ if the value of _yi_ falls between two cut-off points, corresponding to grades _S_ and _S+1:_ For individuals with no schooling, we only know that the index falls below the lowest threshold, normalized to zero, and for individuals with the maximum level of schooling, we know only that _yi_ ≥μ _max_ . We classify education grade attainment into seven ordered categories for the purposes of this analysis: no schooling, 1 to 5 years, 6 years (complete primary), 7 to 8 years, 9 years (completed junior high), 10 to 11 years, and 12 and above years (completed high school). Assuming normality of the error terms, ε i, one can then write down the likelihood function, and via this ordered probit model, estimate these cutoff points along with the coefficients of interest (see Greene, 2000). For children who are still in school, we know that they will attain at least their current grade, and hence that for an individual currently in school with _J_ grades of schooling attained, _yi_ ≥μ _J._ One can therefore modify the likelihood function to allow for this censoring, and estimate the censored ordered probit model via maximum-likelihood.<sup>13</sup> To allow for the potential endogeneity of household migration status within the ordered probit and censored ordered probit model we follow the methodology of Rivers and Vuong (1988). In the first stage, a household migrant status is regressed on the instrument and exogenous regressors. The fitted values and residuals from this first stage are then both included in the censored ordered probit model estimated in

> 13 See Appendix A of Glick and Sahn (2000) for specification of the likelihood function. Estimation was carried by programming the likelihood in STATA version 9.

15
existence, making any generalization of findings to the regional or global level difficult to justify. In contrast, this paper relies upon a comprehensive database of PTAs currently in force, where the main addition to existing sources of information on the subject is the inclusion of agreements not notified to the World Trade Organization (WTO), which account for approximately half of all PTAs in existence. The second improvement over the traditional estimates is redefining the dependent variable as trade flows that can be meaningfully considered “preferential.” In order to do this, the paper combines the PTA database with detailed data on trade flows to exclude trade in tariff lines with zero or very low MFN rates, where the margin of preference is not high enough to justify importing goods under the preferential regime. We generate two sets of gravity estimates— one using total bilateral trade as the dependent variable, and one where only preferential trade is considered. The main finding is that, while many control variables are insignificantly different across the two specificaitons, the PTA coefficient is significantly larger in the latter model. The “preferential” specification allows us to estimate the impact of product exclusions and long phasein periods on trade between PTA members, as well as the potential benefits of liberalizing these provisions. We also explore how various types of PTAs affect bilateral trade, finding that the effects vary significantly with the income level of PTA partners and the WTO notification status of the PTA, but are indistinguishable when bilateral PTAs are contrasted with agreements with more than two members.

This paper is organized as follows. Section 2 provides the background on the gravity model of trade, including the main arguments for its use, its theoretical underpinnings, and a brief literature survey. Section 3 summarizes the information in the PTA database, details the methodology used in calculating the share of world trade covered by PTAs, and presents the estimates. Section 4 builds a gravity model of preferential trade, presents the estimates, and uses the model to evaluate the impact of product exclusions and phase-in periods. Section 5 offers concluding remarks.

2
be innovative, fast-growing, and especially ready to adopt new technology. The challenge is to embrace the opportunities of the market while recognizing the potential trade-offs.

# **A Portrait of the Microfinance Industry**

Data on the microfinance industry are available from several sources, each with strengths and weaknesses. We use data from the Microfinance Information Exchange (the MIX), a not-forprofit organization that aims to promote information exchange in the microfinance industry and collects data on microfinance institutions. Some data is publicly available at <http://www.mixmarket.org>, including basic financial measures for a large number of participating organizations. The organization also publishes the _MicroBanking Bulletin_ , which reports more detailed financial information, adjusted in certain ways for comparability, but while group and subgroup averages are available, it is not possible to identify data for specific organizations. The _Bulletin_ is available at <http://www.mixmbb.org/en/index.html>. Another source, the Microcredit Summit Database, contains information on the largest number of microfinance institutions, but provides to the public only limited information about them, including summary information, the number of all borrowers, female borrowers, and “poorest” borrowers. Annual reports for this data are available at <http://www.microcreditsummit.org>.

For the analysis in this paper, we use a more detailed version of the data from the Microfinance Information Exchange that is not publicly available, but to which the World Bank Research Department has access through a negotiated agreement. These data include outreach and impact data, financial data, audited financial statements, and general information on specific microfinance institutions. The data set is relatively large, covering 346 institutions with nearly 18 million active microfinance borrowers and a combined total of $25.3 billion in assets (in purchasing power parity terms). Most of the borrowers -- about 10 million--are in the top 20 largest institutions, which shows how the microfinance world has segmented into some very large organizations alongside many smaller, community-based organizations with membership in the thousands.<sup>3</sup> We look at the most recent data during the period from 2002-2004.

> 3  In the larger data set of Gonzalez and Rosenberg (2006), which includes the data from the Microcredit Summit Database, 91 percent of the 1565 institutions they analyze in 2003-4 are small, collectively serving just a quarter of the borrowers.  The other three-quarters are served by just 145 institutions.

7
# **Table 9. Effect of Chinese Exports of Machinery: Regression Signs**

|**Dependent variable:**|Equation (1)<br>Log of value pe|r unit|Equation (2)<br>Change in glob<br>share|al market|Equation (3)<br>Share of exports<br>in components to<br>China|
|---|---|---|---|---|---|
|**Main explanatory variables**:|China's expans<br>volume|ion in export|Change in Chi<br>market share|na's global|China's global<br>market share in<br>finished product|
||**Components**||**Components**|||
|**Results**|**/1a**|**Finished /1b**|**/2a**|**Finished /2b**|**Components /3**|
|**Overall**|(+)|(-)|n.s|n.s|(+)|
|**By main machinery**||||||
|Power generating eqp|(-)|n.s|n.s|n.s|n.s|
|Industrial mchn & eqpm|(-)|(+)|n.s|n.s|n.s|
|Office data & telecom|(+)|n.s|n.s|n.s|(+)|
|Electrical & apprtus|(+)|(-)|n.s|n.s|(+)|
|Road vehicles|(+) [a]|(-)|n.s|n.s|n.s|
|Other transp. Eqpm|(+)|n.s|n.s|n.s|n.s|
|**By country**||||||
|Indonesia|(+)|(+)|n.s|n.s|(+)|
|Malaysia|n.s|(-)|n.s|n.s|n.s|
|Philippines|(+)|(+)|n.s|n.s|(+)|
|Thailand|(+)|(-)|n.s|n.s|n.s|
|Estimation method|OLS||OLS||Tobit|
|Sample classifications|6-digit HS||4-digit SITC||3-digit SITC|

Note: Signs are statistically significant at least at 5% level n.s: not statistically different from zero

[a] significant at 10 % level /1a α 2, /1b α 2 + α 3 /2a δ 2, /2b δ 2 + δ 3 /3 γ 2

> <sup>28</sup>
# **1. Introduction**

A large literature going back to Hernando de Soto (1989) has argued that complex entry regulation prevents informal firms from registering with the government and may also discourage new firm creation.<sup>2</sup> However, studies that have measured the impact of reforms to simplify entry regulation have found only small effects on firm formalization and creation.<sup>3</sup> These studies typically examine the implementation of one-stop shops that integrate various subnational and national registration procedures. For example, with a one-stop shop set up in Mexico, firms that previously had to register with different municipal and federal authorities can complete several steps of the process at a single agency, reducing the number of days required to register a business from 30 to 1 and the required procedures from 8 to 3 (Bruhn, 2011).

Creating a one-stop shop is one of the most popular business registration reforms, but another type of entry reform is to make registration an administrative (not judicial) process (World Bank, 2006). Business registries managed as an adjudicative rather than an administrative function are prone to discretionary powers and increased uncertainty. Even simple procedures such as reserving a name are complex due to being subject to discretionary approval of a judge. The inconsistent interpretation of processes by judges makes automation particularly complex, since it requires standardization of procedures. In the World Bank’s Doing Business report, countries in which courts operate business registries rank significantly lower than countries with administrative business registries (table 1).

|**Table 1: DB Ranking of Countries with Administrative vs. Court Business Registries**|
|---|

|**Administrative Registries**|**Ranking DB 2018**|**Court Registries**|**Ranking DB 2018**|
|---|---|---|---|
|New Zealand|1|France|25|
|Canada|2|Slovenia|46|
|HongKongSAR,China|3|Croatia|87|
|Georgia|4|Germany|113|
|Jamaica|5|Austria|118|
|Singapore|6|Bosnia and Herzegovina|175|

Source: Doing Business 2018 data obtained from www.doingbusiness.org

> 2 See for example Djankov et al. (2002), Klapper, Laeven and Rajan (2006), and Ciccone and Papaioannou (2007).

> 3 See Bruhn and McKenzie (2014) for a summary of the studies on Brazil, Colombia, and Mexico, and see Branstetter et al. (2013) for Portugal.

2
The results suggest that the energy price collapse impacted the credit allocation of banks that were more exposed to energy-related sectors. Banks that were more exposed increased lending to firms in the affected sectors and contracted credit to firms in other sectors. If borrowers were not able to switch credit suppliers, the contraction in bank lending might have had a material impact on their real outcomes.

Using firm-year-level data for the sample of firms in non-energy sectors, we run the following specification: where the real outcome _yf,y_ of firm _f_ in year _y_ corresponds to one of the following variables: total lending, loans for working capital, loans for investment projects, total liabilities, assets, and revenue.<sup>13</sup> _AvgExposureEnergym,Aug14_ is a measure of exposure to the energy sector in August 2014 of a firm headquartered in municipality _m_ . _Posty_ is an indicator that the yearly observation is after 2014. _β_ is the coefficient of interest, as it measures the extent to which the real outcomes of firms in municipalities with more banks with greater ex-ante exposure were affected by the drop in energy prices. Finally, _γf_ and _γy_ are fixed effects at the firm and year levels, respectively, and _εf,y_ is the error term clustered at the municipality level.

The results of this exercise are displayed in Table 5. Starting with information from the credit registry of bank loans, we find that an increase of one standard deviation in the exposure of banks with which a firm has relations reduces total lending by 2.1 percent. Again, the impact is much larger for financing for investment projects. Total loans for working capital contract by 1.9 percent, and loans for investment contract by 12.4 percent. These results suggest that non-energy firms are unable to smooth the shock that their banks receive. We also find evidence that other firm outcomes (liabilities, assets, and sales) were negatively impacted by the municipality’s exposure to the energy sector. An increase of one standard deviation in a municipality’s exposure to the energy sector reduces total firm liabilities by around 2.9 percent and total firm assets by around 2.6 percent. However, we do not find any impact on total sales. Overall, we uncover evidence suggesting that non-energy firms experienced a larger contraction in their liabilities

> 13 Orbis information tends to refer to the month of December. For the credit registry outcomes (total loans, loans for working capital, and loans for investment) for each firm-year pair, we selected the December value.

17
# **II. The Size of Tax Expenditures and Their Fiscal Effects**

# **A. The Size of Tax Expenditures**

According to published country tax expenditure data, the size of tax expenditures varies among countries. In 2003, the size of tax expenditures was over 2 percent of GDP in the Netherlands, 4 percent of GDP in Australia, and about 7 percent of GDP, in Canada and in the US. Table 1 below gives the estimates of the average size of tax expenditures in Australia, Canada, the Netherlands, and the US from 2000 to 2003.

By comparing tax expenditures to integrated tax expenditures and direct expenditures (referred to as “total expenditures”), Table 1 shows that governments have used tax expenditures to implement a significant portion of their total expenditures. The Australian government has used tax expenditures to allocate 15 percent of its total expenditures; the Canadian government has used tax expenditures to allocate about 30 percent of its total expenditures; the Netherlands government has used tax expenditures to allocate 4 percent of total expenditures, and the US government has used tax expenditures to allocate 26 percent of its total expenditures.<sup>4</sup> Like direct expenditures, tax expenditures tend to grow. Sometimes, the growth of tax expenditures is faster than that of direct expenditures. For example, according to recent released data from the Government Accountability Office (GAO) of the US, the federal government’s tax expenditures increased from about US$400 billion in fiscal year 1993<sup>5</sup> to US$850 billion in fiscal year 2004<sup>6</sup> (which represents a 113 percent increase from 1993 to 2004). Federal direct expenditures were US$1,409.5 billion in 1993 and US$2,292.2 billion in 2004 (a 62 percent increase from 1993 to 2004). There are many similar examples from other countries.

Poland (central government only), for example, has not established tax benchmarks that could be used to compose a full list of tax expenditures, but the size of a number of commonly recognized tax incentives is large. According to a Ministry of Finance document<sup>7</sup> , over 300 tax incentives in the nature of tax expenditures have been established sine 1992. Among them, there were 200 tax incentives relating to personal income tax (PIT), and 100 tax incentives relating to corporate income tax (CIT) and VAT. According to the Polish Ministry of Finance, the estimated size of a group of 18 tax incentives in the PIT (less than 10 percent of the total of 200 PIT tax incentives) was 1 percent of GDP (annual average) from 1993 to 2000. If all the 300 tax incentives were estimated, the total cost would be significant.

> 4 The aggregate total of tax expenditures has not taken into account changes in the behavior of taxpayers, nor the amount of tax reduction that may result when a taxpayer takes advantage of a tax expenditure; it may lower his/her tax bracket to a lower level in a progressive tax rate schedule.

> 5 “Tax Policy, Tax Expenditures Deserve More Scrutiny,” GAO, USA, June 1994.

> 6“Understanding the Tax Reform Debate,” GAO, USA, August, 2005.

> 7 Poland Ministry of Finance, “The White Paper for Tax,” 1989.

7
12. Roback, J. 1982. "Wages, Rents and Quality of Life," Journal of Political Economy, vol. 90, p.1257-78.

13. Roy, A. D. 1951. "Some Thoughts on the Distribution of Earnings," Oxford Economic Papers, vol.3, p. 135-46.

14. Solon, G., Page, M. E. and Duncan, G. J. 2000. “Correlation between neighboring children in their subsequent educational attainment,”Review of Economics and Statistics, vol. 82(3), pp. 383–92.

15. Shilpi, Forhad. 2011. “Mobility costs and regional inequality: Evidence from Bangladesh,” Journal of Globalization and Development, 2(1).

16. World Development Report 2009. Reshaping Economic Geography, World Bank, Washington DC.

17. World Bank, 2013. Bangladesh: Assessing a Decade of Progress in Poverty Reduction, World Bank, Washington DC.

31
of the variables used in matching and main outcomes variables (i.e. patent counts) prior to sanctions. The mean and variance of control variables ( _X_ ) are similar between the sanctioned and unsanctioned firms after re-weighting. The last two columns perform a t-test between the two groups, and reveals that the sanctioned firms and unsanctioned firms have similar patents outcomes prior to sanctions.

Table A.2: Summary Statistics after matching and balancing

||**San**<br>**F**|**ctioned**<br>**irm**|**Unsa**<br>**F**|**nctioned**<br>**irm**|**Dife**|**rence**|
|---|---|---|---|---|---|---|
||Mean|Std. Dev|Mean|Std. Dev|Dif|Std|
|Total Patents|30.63|62.78|27.99|63.12|2.63|(3.60)|
|High-Tech Patents|14.38|28.25|12.01|29.95|2.37|(1.67)|
|Domestic Patents|30.48|62.56|27.86|63.05|2.62|(3.59)|
|EPO/USPTO/PCT Patents|4.90|15.56|3.65|18.96|1.26|(0.99)|
|Triadic Patents|0.51|2.97|0.22|1.51|0.29**|(0.13)|
|Controls|||||||
|Patent Age|5.07|3.08|5.07|3.16|0.00|(0.18)|
|Patent Stock (log)|2.53|1.68|2.53|1.65|0.00|(0.10)|
|Prior Average Paent App.|0.47|0.54|0.48|0.57|0.00|(0.00)|
|Dummy Patent|0.78|0.42|0.78|0.42|0.00|(0.02)|
|Lagged Total Patent App (log)|2.02|1.58|2.02|1.60|0.00|(0.09)|

Note: This Table compares the mean and standard deviation of sanctioned and unsanctioned firms before firms being sanctioned. Patent is a dummy variable that equals 1 if a firm at year _t_ has also patented at _t −_ 1. Patent _×_ Total Patent refers to the number of patents firm at year _t_ patented at _t −_ 1. Last column is the standard error of t-test. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

Table A.3 compares summary statistics of firms in the identified sanctioned IPC field and unsanctioned IPC field. We find firms in the sanctioned IPC fields files significantly higher patents than firms in other IPC fields. Hence, we use the similar matching method to construct a comparable control group and then apply entropy balancing to reweights the control group observations. More specifically, for each firms _i_ of the sanctioned IPC field _j_ , we find a firms in the unsanctioned IPC field that share the same patent trajectories before field _j_ being sanctioned. Table A.4 provides summary statistics of the variables used in matching and main outcomes variables (i.e. patent counts) prior to sanctions.

46
which have contributed to decreasing cargo dwell time as well as the role of public stakeholders, mainly Customs, the port authority and the port operator. Section four explains the justification of reducing cargo dwell time from an operational approach. Then, the main lessons for SSA ports are presented. In the sixth section two of the main remaining challenges to reduce cargo dwell time are discussed. The final section concludes and presents some factors, which could contribute to improve port competitiveness in general.

# **2. Cargo Dwell Time at the Port of Durban**

“Normal” cargo dwell time differs between ports and even more importantly between port users and stakeholders<sup>6</sup> . In the case of Durban, 28 days is the time limit for Customs to consider “abandoned cargo” and this then represents the end of “normal” cargo dwell time. Shippers, who represent the automotive industry, consider 3 days already as excessive dwell time. However, some small shippers would tend to consider “normal” cargo dwell time around 4-5 days.

In the absence of an irrefutable and uncontested benchmark of what is considered to be abnormal cargo dwell time, TNPA/TPT decided to target a dwell time of 3 days for the Port of Durban. In reality, it seems as if this target has been achieved. Data obtained for the Durban Container Terminal (DCT)/ Pier 2 (and confirmed by shippers and shipping lines), indicate a 3-4 days dwell time for that terminal since 2006.

The figure below shows that the average dwell time at the port is below 4 days for both imports and exports with a slight peak of 5 – 7 days around May 2010 which correlates with the 2010 Transnet labor strike period.

> 6 See Refas and Cantens (2011) for a discussion on various definitions.

6
3 workers with formal documentation) was almost halved, from just under 60% to just over 30% of all workers. And not least, despite the morass, real GDP per capita and mean household per capita income (for the spatially undeflated national distribution) were both some 22.5% higher in 1996 than 1976.5 Importantly, however, this increase was closely associated with rural-urban migration; accumulated growth in mean urban household per capita incomes was a mere 4.3% (spatially deflated). See Table 1 below. And yet, despite the macroeconomic turmoil and continuing structural changes, however, a casual glance at the headline inequality indicators and poverty incidence measures reported at the bottom of Table 1 might suggest that little had changed in the Brazilian income distribution between 1976 and 1996.

|__________________________|________<br>**l1976**|**1981**|**1985**|**1996**|
|---|---|---|---|---|
|GNP (inconstant 1996Reais- thousands)*|434,059|538,475|599,130|778,820|
|GNP per capita (in constant 1996 Reais)*|4,040|4,442|4,540|4,945|
|Annual Inflation Rate <sup>1</sup>|42%|84%|190%|9%|
|Open Unemployment <sup>2</sup>|1.82%|4.26%|3.38%|6.95%|
|Average Years of Schooling 3, 4|3.23|4.01|4.36|5.32|
|Rate of Urbanization 4|67.8%|77.3%|77.3%|77.0%|
|Self-employed as share of Labor Force <sup>4</sup>|27.03%|26.20%|26.19%|27.21%|
|Share of Formal Employment <sup>4</sup>* 6|57.76%|37.97%|36.41%|31.51%|
|Mean Household Per Capita Income 4,_5_|265.10|239.08|243.15|276.46|
|Inequality (Gini) 4|0.595|0.561|0.576|0.591|
|Inequality (Theil - T) 4|0.760|0.610|0.657|0.694|
|Poverty Incidence (R$ 30/month) 4|0.0681|0.0727|0.0758|0.0922|
|Poverty Incidence (R$ 60/month) 4|0.2209|0.2149|0.2274|0.2176|

Notes: *) Annual figures.

1) Percent, from January to December. Based on the IGP-DI for 1976, and on the INPC-R for all other years.

2) Based on the IBGE Metropolitan Unemployment Index.

3) For all individuals 10 years of age or older, in urban areas.

4) Calculated from the PNAD samples by the authors. See Appendix 1.

5) Urban only, monthly and spatially deflated. Expressed in constant 1996 Reais.

6) Defined as the number of employees 'com carteira' as a fraction of the sum of all wage employees and selfemployment workers.

5 See Table 1, and Appendix I for details.