rafmacalaba/datause-encoder-data huggingface dataset:
An issue especially relevant to measuring city success is whether there is income convergence across Brazilian cities or regions over time. And if there is convergence, is it related to migration or to convergence in total factor productivity and technologies (see, e.g., Barro and Sala-i-Martin (1995), for the USA versus Japan)? To make these comparisons we compare levels and growth rates in real per capita income across Brazilian cities to see if gaps have narrowed over time and the variation across cities has dropped. Recently, Andrade et al. (2004) tested income convergence across Brazilian municipalities from 1970 to 1996.<sup>5</sup> Their empirical finding suggests a club convergence (a conditional convergence) between the poorer Northern region (the North and the Northeast) and the richer Southern region (the Southeast, the South and the WestCentral).

Conceptually convergence can be measured by (i) β convergence in which poor cities tend to catch up with the rich ones in terms of per capita income, and (ii) σ convergence where the dispersion of per capita income declines over time. β convergence of per capita income across cities can be tested by regressing the annual per capita income growth rates on the log of base year’s per income levels.

Table 5 reports the OLS estimation results for convergence across urban agglomerations. The speed of convergence is calculated using the coefficient estimate and is reported in the last row in the table.<sup>6</sup> The results strongly suggest “ _β_ convergence” across Brazilian agglomerations. The speed of convergence, when regional dummies are added, is stable around 3.4 percent, which is

> 5 They evaluated convergence of  Brazilian municipalities by directly examining the cross-section distribution of income, suggested by Quah (1993, 1997).

> 6 The speed of convergence (β) is calculated using the formula of Barro and Sala-i-Martin (1995), such

ˆ 1 − _e_ −β _T_ that _b_ = − where _b_<sup>ˆ</sup> is the coefficient estimate and T=30. _T_ 15
the NIM provided by BankScope, from financial statements of banks worldwide, we derive a pure spread (or margin) that is comparable across banks in any country across time. This pure spread varies across countries (and over time) according to the degrees of bank competition and macroeconomic (interest-rate) volatility in each country. For example, if a significant proportion of bank margins in a given country are determined by macroeconomic volatility rather than the monopolistic behavior of banks, then policy should be focused on macro-economic policies as a tool for reducing intermediation costs. Alternatively, if a large proportion of bank margins are due to reserve requirements, then a policy of paying interest on bank reserve holdings may reduce intermediation costs.

A few studies have exclusively focused on comparing and identifying patterns related to bank spreads across countries. Most studies use a common measure of the spread such as the NIM, which is comparable across countries. This measure poses a number of problems in terms of its appropriateness for international comparison: (i) Countries with different banking sector specialization can have lower or higher net interest margins, (ii) NIMs do not take into account overhead costs—the costs of doing credit checks, monitoring of the borrower, and recovering the collateral (Beck et al., 1999), (iii) NIMs do not take into account the product mix offered in different countries—for example, in less developed markets most borrowing is done in shorter maturities while in more developed markets, where mortgages are prevalent, borrowing is done with longer maturities.

Many studies use data from the IMF's IFS database to calculate the interest rate spread (lending – deposit rate). The IFS data are not homogeneous across countries because the data for lending and deposit rates are different among countries. The IFS methodology is to report the most relevant interest rates for each country or use the most available interest rates. We cannot solely rely on this absolute spread when evaluating the relative cost of borrowing or the profitability of the banking sector in each country. During the course of this study we have attempted to find the same lending and deposit rates reported by Brazil to the IFS for other countries but it is impossible to find the exact rates. Therefore, we cannot directly compare the spreads in Brazil using the IFS database with other countries because the maturity, type of borrower and the type of interest (fixed or variable) are not the same.

**Figure 1a. The link between IFS and NIM across countries in 2009 is weak**

**Figure 1b. The link between IFS and NIM in Brazil from 1997 to 2009 is non-existent**

<!-- Start of picture text --> 40 IFS 60 IFS<br>35 Brazil<br>55<br>30<br>25 50<br>20<br>45 R 2 = 0.0282<br>15 R2 = 0.1438<br>10 40<br>5<br>35<br>0<br>0 10 20 30<br>-5 30<br>8 10 12 14 16<br>-10<br>NIM NIM<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data 4
# 1 Introduction

When the US granted duty-free and quota-free access to Madagascar, under the African Growth and Opportunity Act 2000, exports from Madagascar exploded, from $170 million in 2000 to $500 million in 2004. Over the same period, Madagascar’s exports to the rest of the world also increased, from $750 million to $875 million. Similarly, based on Comtrade export data, when the EU granted duty-free and quota-free access to Bangladesh under the Everything But Arms Initiative in 2001, knitwear exports from Bangladesh to the EU more than doubled, rising from $823 million to $2,351 million between 2000 to 2004. During the same period of time, knitwear exports from Bangladesh to the US increased from $316 million to $465 million.<sup>1</sup> Exports to countries other than the US and EU rose from $60 million to $190 million.<sup>2</sup> To the surprise of many, such generous trade preferences resulted not in trade diversion from the rest of the world to the preference granting markets, but in trade creation - tempered by the presence of quotas.

The model we develop and estimate in this paper predicts exactly these changes. Trade preferences given by one country have positive spillovers on exports to others in the presence of free entry. For example, preferences given by the EU make the industry more attractive, induce entry, and some of these entering ...rms export to the US so that exports to both countries rise. We use customs data from Bangladesh to estimate a heterogeneous ...rm model based on the ‡agship model of Melitz (2003), but structured to be suitable for trade policy applications. Our work takes a heterogeneous ...rm model literally, and confronts it with micro data and actual trade policies, to estimate all of its structural parameters, including the various levels of ...xed costs. These ...xed costs are at the core of the model and serve as hurdles that productive/fortunate ...rms choose to jump, while those that are less so do not. Our paper then uses the estimated model to evaluate the e¤ects of the di¤erent

> 1This small increase is not unexpected, given that the presence of quotas on most of these items in the US constrained export growth there.

> 2If we base the numbers on import data reported by Comtrade, we get a similar pattern though the numbers are somewhat di¤erent. In the EU, knitwear imports from Bangladesh more than doubled, from $1.3 billion in 2000 to $3 billion in 2004. At the same time the US imports from Bangladesh increased by

> $30 million, and imports from Bangladesh to all countries other than the US and EU rose by $287 million.

3
have indeed been large and the channels diverse. By early 1994 industrial sector output was at least 25% down over end-1989 levels, though with some signs of recovery in 1993. The fall in output was initially fairly common across sectors; only later do we pick up greater variation in changes across branches.

However, it is as well to think of Hungarian firms entering transition with very significant labor hoarding. This implied that major changes to the level of employment were required to bring employment closer to competitive levels. Figures 2-7 provide scatters of output on employment changes for each year from 1989. Because these look at variations rather than levels, it is of course only one measure of the dynamic adjustment; proportional change could still be consistent with an employment bias. They are based on CSO data disaggregated to the 3 digit level and cover 68 branches of industry. The scatters are further disaggregated in terms of branch exposure to ruble or CMEA trade in 1989. Clearly, given what we know about the size of the CMIEA shock to Hungary ', we would expect the shock to output to have been larger for CMEA-exposed firms. This is indeed the case. By 1992 the bulk of CMEA-exposed branches registered output declines in the region of 40-60%; branches with lower CMEA exposure had generally experienced declines of 30-40%. Employment declines were also of similarly higher magnitudes in CMEA-exposed branches. And this seems to hold through 1993 as well 3.

The adjustment of employment in the face of these output losses has several dimensions. First, it is clear that both CMRA-exposed and other branches adjuse employment rapidly and almost equivalently to the output change in 1990. Most changes are close to a 45 degree line for 1990. For 1991 and 1992 the story is somewhat different. For both sets, most observations shift to the left of the 45 degree line, indicating ar. elasticity of employment to output changes significantly below unity. The number of observations and the distance leftwards from the 45 degree line are larger in the case of CMEA-exposed branches.

Official data indicate that employment in industry was down c7.5% in 1993 which, See Commander and Conielli (1993).

3 Due to reclassification of branhes and hngcs in size coverage by the Central Statistical Office, the 1993 data are not strictly comparable with the earlier periods.

5
<!-- Start of picture text -->
# of Countries Year of Montreal Protocol (1987) Ratification<br>30<br>25 Zero Nonsignatory Parties<br>20<br>15<br>10<br>5<br>Bo os<br>1990 1995 2000 2005 2010 2015 2020<br>MO non-Article 5 Countries [Article 5 Countries<br><!-- End of picture text -->

<!-- Start of picture text -->
# of Countries Year of London Amendment (1990) Ratification<br>30<br>25 One Nonsignatory Party<br>20<br>15<br>10<br>5<br>0<br>1990 1995 2000 2005 2010 2015 2020<br>MOE non-Article 5 Countries [Article 5 Countries<br><!-- End of picture text -->
|Log of dest. GDP/capita|||0.743***|0.706***|0.801***|
|---|---|---|---|---|---|
|Dest GDP growth|||0.812***|0.890***|0.927***|
|N|13986|13986|11706|11706|10706|

_* p<0.10 ** p<0.05 *** p<0.01. Source: Own calculations based on Lao PDR customs data. The time dependence of the hazard is introduced as log(time) in all models._ Finally, comparisons between the RE logit models (1)-(3) and the logit and Cox in (4) and (5), respectively, reveal small but statistically significant differences which go in the expected direction. One can observe an important effect from unobserved heterogeneity when comparing models (3) and (4): the positive (negative) coefficients are indeed underestimated (overestimated), just as expected. Generally speaking, logit and Cox models should return very similar effects – this is observed in the analysis.

Moreover, to ascertain the importance of the unobserved heterogeneity, Stata calculates a likelihood-ratio test on the hypothesis that the proportion of the total variance contributed by the panel-level variance component is zero. In all the RE models this hypothesis is strongly rejected, confirming the importance of taking frailty into account. This finding agrees with other research which has revealed significant unobserved heterogeneity in similar data (Brenton, Saborowski and Uexkull 2009, Rondinelli and Nicoletti 2009, Hess and Persson 2011b) but conflicts with the minority view that it is not a concern (Esteve-Perez, Sanchis-Llopis and Sanchis-Llopis 2010).

# 5.2 **Investigating Network Effects**

Networks create positive externalities by allowing members to share in the experiences of others for their own benefit. If what is counted in this paper as _peers_ (the number of other firms with similar characteristics) actually forms a true network, then the level of aggregation should matter for the specificity of the benefits drawn from it (information on certifications, on business practices, on quality checks, on shipping and packaging etc.). Consequently, as the network is measured more specifically, its influence should be larger<sup>13</sup> .

**Table 3 - The four dimensions of aggregation along which network specificity is explored**

|Origin|Product|Destination|Time|
|---|---|---|---|
|province|HS-6|country|month|
|region|HS-4|region|year|
|Laos|sector|world||

In this section we investigate in further detail the impact of the variable by looking into the specific levels of aggregation that matter most. We construct these parallel

> 13 It should be reminded that the impact of _peers_ is the net of the negative effect of simple competition among peers and the positive effect of having networks of peers.

22
is therefore the most appropriate dataset to best capture the second great wave of international migration (of the modern era).

Trade data are taken from Feenstra et al (2005), which provides data calculated from the UN _Comtrade_ database<sup>16</sup> for the period 1962-2000. The strength of these data lie in imports being preferred over exports - since they are frequently considered to be more accurate - the extent to which the authors clean the dataset by comparing the import and export data of each bilateral trade flow and the clear documentation the authors provide as to the adjustments made, which facilitates an accurate matching of the migration to the trade data. Since the earliest year to which the trade data refer is 1962, the migration data for 1960 is assumed to be comparable for this decade, under the assumption that these trade flows would have been similar to those two years hence. In cases where countries need to be aggregated to equate them to a trading entity, migrations between these countries are removed from the dataset. Once matched, the dataset comprises 178 countries in total.

The trade data are divided into two distinct periods, 1962-1983, for which data are complete (i.e. 178*178 countries) and 1984-2000. For this latter period bilateral trade flows values at less than $100,000 are omitted i.e. missing. Moreover, complete data are only available for 72 countries, which are reduced to 68 once the necessary aggregations have been made.<sup>17</sup> For this more recent period, the trade data are available for 68*178 countries therefore. The sample selected for estimation comprises those trade flows for those 68 countries for which data are available in each period (i.e. 68*68), termed sample 1. Additional samples are also estimated to check for robustness. Sample 2, relates to the largest possible sample (68*178), sample 3 comprises origins with fewer than 30 missing observations and sample 4 comprises those nations with fewer than 20 missing observations. The percentage of world migration and trade captured in each sample is detailed below (see table 1).<sup>18</sup> The list of countries in each sample can be found in the appendix.

<u>Table 1. The percentage of world trade and migration captured in each sample in every decade</u>

|**Decade**|**Samp. 1**|**Samp. 1**|**Samp. 2**|**Samp. 2**|**Samp. 3**|**Samp. 3**|**Samp. 4**|**Samp. 4**|
|---|---|---|---|---|---|---|---|---|
||**%WT**|**%WM**|**%WT**|**%WM**|**%WT**|**%WM**|**%WT**|**%WM**|
|**1960**|88|80|94|88|76|69|69|50|
|**1970**|90|76|95|83|79|68|72|51|

> 16 See: http://comtrade.un.org

> 17 For example Belgium, Belgium-Luxembourg and Luxembourg to a single entity over time, named Belgium-Luxembourg.

> 18 Note that the denominator used when calculating each migration figure in table 1, refer to the total 178 countries in the sample, in other words those countries/regions for which internal migration has been removed.

14
**Figure 6: Reductions in Average Global, Annex I and Non-Annex I Emission Intensities Required to Cause a Decline in Global CO2 Emissions before 2030**

<!-- Start of picture text --> 0.55 Annex I - SC1 & SC2<br>Non-Annex I - SC1<br>0.50 Non-Annex I - SC2<br>Global - SC1<br>0.45 Global - SC2<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>2005 2010 2015 2020 2025 2030<br>Metric Tons CO2 Per Thousand of GDP at 2000 PPP<br><!-- End of picture text --> Would it be feasible to achieve the required emission reductions through intensity based targets meaning that without affecting the expected economic growths? To answer this question is beyond the scope of this study. Climate change policy models, particularly based on dynamic general equilibrium settings, might be able to answer this query. Further studies are hence needed to investigate whether or not intensity based targets could lead to the atmospheric stabilization of CO2 concentrations at the level implied by the Article 2 of the UNFCCC.

-30-
where ziα ≡ α0+ α1 x1i+ α2 x2i+ α4 x4i; η*i ≡1+[u*2i/exp(ziα)]; x4 is a variable included in the selection equation but excluded from the behavioral equation; and the correlation between ln(η*i) and ln(ηi) is set at 0.5.<sup>9</sup> The dependent variable in (6) is observed when y2i>1.

The results for standard estimators with this DGP are presented in Table 5 while those based on limited-dependent estimators are presented in Table 6. Results for average bias and standard errors across the four cases are summarized in Appendix Table 4. Zero trade flows account approximately for 30% of the observations in this sample.

In Table 5, many of the results are similar to those obtained using Eaton-Tamura data sets. For example, comparison of the truncated and censored versions of each estimator reveals consistently better performance of the truncated versions. Also similarly, the NLS estimators generally have smaller biases and standard errors in cases 1 and 2 while the than in cases 3 and 4. With this DGP, the censored OLS in logs estimator has quite serious bias in case 3, even though the form of the dependent variable is consistent with homoscedasticity.

The Poisson estimator outperforms the standard log-linear estimators in most cases. Specifically, in cases 1 2 and 3 the bias on the estimate for β1 is about 14%. The bias is smaller in case 4 but the standard error is substantially larger. The bias on the—frequently more important—estimate of β2 is substantially lower than for β1. The truncated PPML performs better than the censored PPML in almost all cases, reinforcing our finding using the ET-Tobit DGP. As in the case of the Tobit DGP we find that as the number of zeros increases so does the bias and standard error of the parameter estimates generated by the PPML estimator.<sup>10</sup> The GPML and NBML estimators suffer from very large bias in almost all cases. The standard errors associated with these estimators are also large. This finding reinforces our earlier findings—and those of SST—that these estimators do not appear to perform well for estimation of the gravity model.

When we turn to limited-dependent variable estimators, we find that the ET-Tobit estimators are frequently seriously biased. In some cases, such as the ET-Tobit estimator

> 9 Note that ln(η*i) and ln(ηi) correspond exactly to the two error terms U1i and U2i in Heckman (1979), respectively. The correlation between ln(η*i) and ln(ηi) is assumed to be  0.5 in order to rule out the case of independence between the two error terms in which case, the standard least squares estimator may be used on the selected subsample without introducing bias.

> 10 The simulation results with higher percentages of zero observations are available upon request by the authors.

20
Turkey, as it was not a part of the former Eastern Bloc. Descriptive statistics for the main variables used in the analysis are in the Appendix. For the country-level analyses, we also use data on country tax rates compiled by the World Bank and Price Waterhouse Cooper for the years closest to the BEEPS 2012/2013 implementation (PricewaterhouseCoopers &World Bank Group, 2013).

This study uses these data to address two key hypotheses:

**_H1:_** _The greater the frequency of interactions between tax authorities and a business, the greater the likelihood of perceived corruption in tax administration._

**_H2:_** _Corruption in tax administration (experienced and perceived) disproportionately accounts for negative perceptions of corruption in general._

Hypothesis H1 is a derivation from the Chander and Wilde (1992) model, which suggests that in the presence of corruption, audit rates will be higher. If so, then it is plausible that firms experiencing more interactions with tax officials will also experience a tax-related bribe and/or other corrupt practices in tax administration. Accordingly, H1 is based on the assumption that the greater the number of such interactions, the more opportunities there are for corruption of one sort or another in the administration of taxes. Hypothesis H2 extrapolates the reasoning in H1 by suggesting that tax administration and corruption in tax administration may disproportionately account for overall perceptions of corruption.

# **<mark>4. ANALYSIS</mark>**

# <mark>4.1 BIVARIATE COUNTRY-LEVEL COMPARISONS</mark>

This section represents a series of bivariate country-level comparisons. Although the study is based on only two related hypotheses (see above), these hypotheses involve analysis of multiple tax-related experiences as well as multiple proxies of the level of corruption. Therefore, the purpose of this section is to 1) establish preliminary support (if any) for the proposed general hypotheses, and 2) introduce and clarify the multiple operationalizations of corruption and tax-related firm experiences used in this paper. Thus, we start with a “snowball” approach at the country level where one relationship is used to formulate and test the next, before formulating multivariate firm-level models. The following bi-variate analysis and scatterplot illustrations serve to introduce the relationship rather than test causalities of 4 | P a g e
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 2. Until 2007, infrastructure’s contribution to growth high, even as power holds back the economy**

<!-- Start of picture text --> a. Infrastructure’s contribution to annual per capita economic growth in selected countries, 2003–07, in percentage points<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>Telecom Electricity Roads<br>b. Potential contributions of infrastructure to annual per capita economic growth in selected countries, in percentage points<br>5<br>Main Telephone Lines<br>4 Electricity Generating Capacity<br>Length of Road Network<br>3<br>2<br>1<br>0<br>-1<br>-2<br>(2001–05 vs. 1991–95)<br>% changes in growth per capita due to changes in infrastructure<br>leader (Mauritius)<br>capita from improving<br>Potential changes in growth per infrastructure to level of African<br><!-- End of picture text --> _Source:_ Calderón 2009.

Looking ahead, the contribution of infrastructure to growth promises to increase even more as improvements in the power sector are achieved. Simulations suggest that if Botswana’s infrastructure could be improved to the level of the African leader, Mauritius, annual per capita growth rates would be 1.2 percentage points higher than they are at present. This impact would come mainly from an increase in power generating capacity (figure 2b).

4