rafmacalaba/datause-encoder-data huggingface dataset:
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||**2008 – 2011**|**2012**|
|---|---|---|
|**Real**|All companies are eligible, n|o major changes|
|**Lump sum**|<br>Turnover of below<br>RwF20 million|<br>Turnover of between<br>RwF12 and RwF50<br>million|
||<br>4 percent of turnover|<br>3 percent of turnover|
|**Flat amount**|Not applicable|<br>Turnover of below<br>RwF12|
|||<br>Turnover based fee|

# **Annex 2: Tax declarations, tax revenue and taxable employment**

This paper relies heavily on tax declarations data to make inference on tax revenue and employment; this annex clarifies the relationship between the three.

Tax declaration data, unlike realized revenue, is available at company level, and can be checked against company registrations (see Annex 3 for detail). Although not perfect, there is a tight relationship between tax declarations and tax revenue at an aggregate level; with differences being accounted mostly by withholdings, pre‐payments, arrears, VAT refunds, audit and post‐audit adjustments, and fines.

This paper focuses on the three main tax categories: Corporate and Personal Income Taxes (CIT and PIT), employees’ withholding taxes (or pay‐as‐you‐earn, PAYE), and Value added Tax (VAT). The three constitute 70‐80 percent of overall tax receipts, with the remainder accounted for by excise taxes, land, property and rent taxes, and charges applicable to roads and motor vehicles.

Due to the above mentioned discrepancies between tax declarations and realized revenue figures quoted in this paper do not exactly correspond to national revenue declarations. To avoid confusion, this paper refers to tax figures in percentage of overall declarations.

As tax declarations are only available from 2008 onwards, this paper focuses on the contribution of active companies registered after 2008.

## **Taxable employment**

Using PAYE declarations, it is possible to quantify the number of tax‐paying jobs (both permanent and casual) created by newly registered companies. As incomes below Rwandan francs (RwF) 360,000 per year are tax exempt, it is not possible however to quantify total job creation. This paper refers to incomes above RwF 360,000 as taxable employment.

34
**Table 8** Attrition: Male Sample <u>(Male</u> Data)

||(1)<br>(2)|(3)|(4)|
|---|---|---|---|
||_×_**Boys**_×_**Goal**<br>_×_**Boys**|_×_**Goal**|**Levels**|
|Panel A. Diferential Attrition by|Treatment Status, fully-in|teracted||
|Boys||-0.001<br>(0.031)|-0.006<br>(0.021)|
|Goal|||-0.009<br>(0.027)|
|Panel B. Diferential Attrition by|Treatment Status and Ke|y Measur|es|
|Never talk to dad about SRH|-0.069<br>0.014|0.088|-0.062|
||(0.-78)<br>(0.067)|(0.065)|(0.058)|
|Age|0.018*<br>0.002|-0.013|-0.001|
||(0.010)<br>(0.006)|(0.009)|(0.005)|
|Highest grade|-0.004<br>-0.010<br><br>|0.009<br>|-0.000<br>|
||(0.014)<br>(0.011)|(0.013)|(0.011)|
|Married or cohabiting|-0.113<br>-0.070|0.167|0.026|
||(0.160)<br>(0.057)|(0.143)|(0.046)|
|Own house|0.038<br>-0.079|-0.040|0.050|
||(0.103)<br>(0.056)|(0.079)|(0.046)|
|House has electricity|-0.138<br>0.030|0.075|-0.025|
||(0.100)<br>(0.043)|(0.079)|(0.035)|
|Number of household members|0.031<br>0.011|-0.005|-0.005|
||(0.030)<br>(0.015)|(0.025)|(0.014)|
|Boys||-0.221<br>(0.227)|0.023<br>(0.129)|
|Goal|||0.044<br>(0.200)|
|Observations|1,466|||

_Notes._ Each panel presents coefficients from a separate regression where the outcome, _Yic_ , is an indicator equal to 1 if the male was not resurveyed at endline. In each panel, the rows list variables included in the modell, and the columns indicate interaction terms. Column 1 presents coefficient estimates on interactions between the row variables and a treatment indicator of being assigned to both the _Boys_ and _Goal_ treatment, column 2 presents estimates of coefficients on interactions of the row variables with being assigned to the _Boys_ treatment, column 3 presents estimates of coefficients on interactions of the row variables with being assigned to the _Goal_ treatment, and column 4 presents estimates of coefficients on level controls of the variables at the start of the rows. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates. .01 ***, .05 **, .10 *. _Source._ Male baseline data.

55
impact of climate change on the number of poor people exposed is evident. For instance, a 25‐year event with high climate change impacts has the same exposure as a 200‐year historical event (at around 41% of poor people being exposed).

_Table 4. Number and percentage of poor exposed to flood risk in Vietnam, across the 16 flood hazard scenarios examined._

|**Scenario**|**Exposure**||**Return**<br>|**period**<br>||
|---|---|---|---|---|---|
|||25|50|100|200|
|Historical|Estimated poor exposed (million)|5.28|6.19|6.88|7.24|
||Percentage of today’spoor|30%|35%|39%|41%|
|Low climate|Estimated poor exposed (million)|6.27|6.64|7.32|7.54|
|<br>chane|Percentage of today’s poor|35%|37%|41%|42%|
|g|Increase due to climate change|16%|7%|6%|4%|
|Medium|Estimated poor exposed (million)|6.80|7.16|7.69|8.00|
|climate|Percentage of today’s poor|38%|40%|43%|45%|
|change|Increase due to climate change|22%|14%|11%|10%|
|Hih lit|Estimated poor exposed (million)|7.33|7.66|8.14|8.56|
|g cmae<br>chane|Percentage of today’s poor|41%|43%|46%|48%|
|g|Increase due to climate change|28%|19%|16%|15%|

Based on the statistics provided in Table 4, there is no strong signal that poor people are more exposed than non‐poor people, at the national level. However, this may not be the case in specific regions or within specific districts.

To examine which districts have a confluence of poverty and flood risk, we classify both each district’s poverty headcount rate and flood exposure into three categories: low, medium, and high. We create 3 quantiles for each. We examine both absolute and relative numbers, overlaying the number of poor and number of flood exposed, and the percentage of poor and percentage of flood exposed.

The results suggest that areas of the Northern Mountains and the Mekong Delta exhibit districts with high flood and high poverty (darkest shade of brown in Map 12). The results are slightly different when comparing relative and absolute numbers. When using absolute (the number of poor and number of flood exposed) more areas of high flood and poverty are visible in the Mekong and Red River Delta, as well as along the eastern coasts.

16
**BUSINESS ENVIRONMENT, ECONOMIC AGGLOMERATION AND JOB CREATION AROUND THE WORLD**<sup>**1**</sup>

George Clarke Yue Li Lixin Colin Xu JEL codes: J20, J50, L50, O10.

Key words: job creation, business environment, agglomeration, firms. Sector Board:FSE

> 1 We are grateful to comments and discussions of Martin Rama. The research has benefited from the support of the World Development Report on jobs in 2013.

George Clarke:Texas A&M International University; Yue Li and L. Colin Xu: World Bank.
# **4. Empirical Results**

We turn to the empirical implementation of the model on the above data. In order to assess in a transparent manner the robustness of the empirical results to the potentially very large degree of heterogeneity in our country sample, we break the latter into country groups that should be expected to be less heterogeneous, as follows. First, we divide the overall sample into two groups according to their per-capita income level. More specifically, using the World Bank's World Development Report income classification, we form one group consisting of industrial economies and high-income and upper-middle income developing economies **-** a total of **26** countries. Its complement is the group of low and lower-middle income developing economies (22 countries).

Next, anticipating the fact that our portfolio diversification model ma) be **of** limited applicability in environments of severely restricted capital flows, we also group the sample countries according to their respective capital account barriers. This poses some difficulties, however. The only available indicators of capital account restrictions with broad time-series and cross-country coverage are the IMF's Exchange Rate Restrictions, which include qualitative information on various types of measures that hamper international portfolio diversification **--** (a) multiple exchange rate practices, **(b)** current account restrictions, (c) capital account restrictions, and **(d)** mandatory surrender of export proceeds. To combine all these indicators into a summary measure of portfolio restrictions, we sum them and compute the average for each country over the period **1965-97. If** for a country the average is greater than or equal to three (implying that, on average, restrictions exist in at least three of the four categories during the sample period), we classify the country as having high capital controls. This procedure yields asubsample of **33** countries with low capital controls and **15** with high capital<sup>controls.17</sup> Table **3.1** presents some descriptive statistics on the net foreign asset **/** wealth ratios for the full sample and the various country groups just defined. For the overall country sample, both the mean and median of country averages are negative, an indication of the fact that few countries possess net creditor positions. However, the figures reflect some systematic differences across country groups. Among higher income countries, as well as countries with moderate

> **17** The countries in each subsample are listed in the appendix.

**18**
North America would enjoy the benefit of the doubt under the decisions rule adopted here to decide the existence of a concentration problem. Latin America and Europe—both east and west—are once again the two regions in which the concentration levels are the highest. Eastern Europe and Asia are not that far behind.

While the concentration indicators are useful, once more, they do not tell the full story. First, it may be useful to point out that in 2003, Deutsche Telekom and SBC shared 40% of the market controlled by these 9 companies in the world. Second, actors relatively small at the global level can be very important are the regional level. This is the case of Telstra who is big in Asia but relatively small globally. Finally, however strong the presence of these firms may be in developing areas, for most of them the core business continues to be in developed countries, most importantly in Western Europe and North America.

**<u>Table 4: Regional HHI for Telecoms – Fixed</u>**

||<br>**GLOBAL**|<br>**Africa**|<br>**Asia &**<br>**Oceania**|<br>**Latin**<br>**America**|<br>**North**<br>**America**|**Eastern**<br>**Europe**|**Western**<br>**Europe**|
|---|---|---|---|---|---|---|---|
|**Market indicator**||**Nu**|**mber of w**|**orking lines**|**per country**|||
|**HH min**|**2009**|888|2265|2859|524|2075|3443|
|**HH max**|**3551**|2342|4169|3929|1796|4499|4570|
|**Aggregate market share**|**39%**|23%|42%|46%|30%|45%|48%|
|**Number of firms**|**9**|2|2|4|3|2|6|
|**Number of countries**|**35**|6|4|9|2|3|11|
|**Cases**|**42**|6|4|12|4|4|12|
|**Main actors**|**Bell South, BT,**<br>**Deutsche Telekom,**<br>**France Telecom,**<br>**SBC, Telecom Italia,**<br>**Telefonica de España,**<br>**Telstra, Verizon **|France<br>Telecom,<br>SBC|France<br>Telecom,<br>Telstra|SBC,<br>Telecom<br>Italia,<br>Telefonica<br>de España,<br>Verizon|Bell South,<br>SBC,<br>Verizon|Deutsche<br>Telekom,<br>France<br>Telecom|BT, Deutsche<br>Telekom, France<br>Telecom, SBC,<br>Telecom Italia,<br>Telefonica de<br>España|

# b. **Mobile Telecoms**

Globally, based on our sample of 65 countries in which the top 9 operators are present, the measure proposed here suggests that it is difficult to make an assessment of the degree of market power in the sector. The HH max is fairly high but the HH min is below the 1000 threshold level which makes the indicator inconclusive. The fact that 9 firms cater to 37% of the market seems to suggest a relatively large number of actors. But this does not tell the full story. Indeed, in 2003 Vodaphone controls 33% of the number of working lines credited to these 9 companies. Telefonica de España, SBC and Verizon added up to a further 40% of that total. This suggests that at the global level, 4 actors are particularly important.

14
**TABLE 1. DISTRIBUTION OF WORKERS’** **<u>CHARACTERISTICS</u>**

|Gender||Training||
|---|---|---|---|
|Women<br>Men|27.2%<br>72.8%|None<br>In-house training|41.1%<br>39.8%|
|Education||External formal training|19.1%|
|Lower secondary or less|71.2%|Belongingto a union||
|Upper secondary|18.6%|No|57.8%|
|Universityor more|10.2%|Yes|42.2%|
|Experience in the firm||Type of Contract||
|Three years or less|47.6%|Temporary|10.7%|
|From 3 to 10 years|37.5%|Permanent|89.3%|
|More than 10years|14.9%|||

Source: Calculations based on ENESTYC 2001.

According to the survey, we estimate that the majority of employees receive some form of training, though most was internal. The share of employees receiving formal inhouse-sponsored training was 40 percent, while the number not receiving any training was roughly the same at 41 percent, with only one-fifth of employees receiving external formal training. The differences in education and training (both in-house and external) are very small between women and men in Mexico. In-house training was 40 percent for men and 36 percent for women, while external training was 20 and 16 percent, respectively.

Less than half of these employees, 42 percent, were unionized. Also, 89 percent were employed via permanent forms of contracts.

The characteristics of a firm used in this analysis are size, division of activity, region, whether the foreign capital of the firm exceeds one-half, if the firm exports over 50 percent of its output, investment in research and development (R&D), quality control certification, and technology adoption.

6
# **5.2 An Instrument for Related Lending**

Though ethnic fractionalization poses fewer endogeneity problems than rule of law, the substitution of these variables does not address the potential endogeneity of the related lending policies themselves, which could also be driven by similar unobservable political factors. The difficulty, of course, is finding an appropriate instrument for those policies.<sup>19</sup> We propose as a candidate the index of official supervisory powers constructed by BCL (2001). That index, which is described in detail in Appendix 2, is based on sixteen questions about the powers granted to supervisors in monitoring and disciplining banks. We view this as an indication of a society’s general propensity to use official mechanisms to monitor market activities. To the extent that a society views its banking sector as being tightly regulated and supervised, fears about the negative effects of related lending might be diminished. We would therefore expect a positive relationship between our related lending index and the BCL index of official supervisory powers, and indeed the correlation between the two is 0.42, which is significant at the 1 percent level.

When we use the official supervisory powers index as an instrumental variable in a twostage least squares regression, the index of related lending is not statistically significant at conventional levels (Table 5, column 3). When we then restrict the sample to countries that began our period of study with relatively low levels of credit (private credit/GDP < 0.7 in 1990), the index of related lending is negative and significant at the 5 percent level (column 4). This filter excludes nine countries from the active observation set, eight of which are industrialized.<sup>20</sup> Similar results hold when we restrict the sample using other variables that measure institutional or financial development. The results from the instrumental variable regressions therefore indicate that related lending is more strongly associated with slower private credit growth in less developed countries, which offers support for the notion that the relationships that we uncovered in our baseline regressions are causal.

> 19 Ethnic fractionalization is not a good instrument because we have no strong prediction about the relationship between it and our index of the permissiveness of related lending. Indeed, the significant results in models 1 and 2 of Table 4 derive from the fact that related lending is permitted in some highly fractionalized societies and in some homogeneous societies. The correlation between ethnic fractionalization and the related lending index is 0.15 and not significant at the p=.10 level; i.e., ethnic fragmentation would not be a strong instrument for related lending even if it is exogenous.

> 20 Those industrialized countries are Switzerland, France, Japan, Malta, Netherlands, New Zealand, Singapore, and the United Kingdom.

17
# Efficient Debt Reduction

# 1. Introduction

It is now widely recognized that the overhang of sovereign debt is imposing major costs not only on debtors but also on creditors, by seriously disrupting the debtor economies.1 The World Bank, in the 1988-89 edition of the World Debt Tables has recently recognized that the debt overhang of the heavily indebted countries (the HICs) is "a burden . . . that reduces incentives to undertake sustainable long-term adjustment" (p.xvi) The costs of debt overhang have led to a widespread acceptance of the need for a process of debt reduction, by the World Bank and others. The major commercial banks have, in rhetoric at least, started to advocate debt reduction aa one step out of the current crisis.<sup>2</sup> In order to discuss the potential usefulness of debt reduction mechanisms, it is useful to have a working definition.

lThe economic crisis in Latin America, and the role of tle foreign debt, is analyzed in Sachs (1988), which can be regarded as a companion paper to the current analysis. 2See for example, the recent statement of the Institute of Internationa, Finance, "The Way Forward for Middle-Income Countries", January 1989, which states that "the scopa for voluntary debt reduction should be significantly enlarged" (p.2), and that creditor governments should seek ways to encourage banks to "accelerate the application of voluntary, market-oriented financing techniques that reduce existing bank debt"(p.4). Similar, Morgan Guaranty Trust, in the December 1988 issue of World Financial Markets states that the "multilateral organizations should support bank-debt reduction, for example, by helping debtors to issue new obligations of enhanced quality and retire existing ones at a discount" (p. 2).

1
of spending is significantly higher. Being single or widow/er reduces expenditure by 13.0 and 7.6 percent, respectively, compared to married PA households. This result was as expected since these types of households tend to have smaller numbers of able-bodied male adults who are most likely to be able to get work.

**Table 4.** Correlates of log expenditure per capita adjusted by economies of scale using OLS, by wave and gender of the household head

|VARIABLES|(1)<br>Female|(2)<br>Male|(3)<br>Pooled|(4)<br>Pooled|
|---|---|---|---|---|
|Female PA|||0.010<br>|-0.011<br>|
||||(0.008)|(0.023)|
|Wave 2017-18 (base: 2013-14)|-0.212***<br>|0.002<br>|-0.003<br>|0.001<br>|
||(0.008)|(0.004)|(0.004)|(0.004)|
|Female x Wave 2017-18 (base: 2013-14)|||-0.203***<br>(0.009)|-0.213***<br>(0.009)|
|_Characteristics of the PA_|||||
|Age|0.004***|0.002***|0.002***|0.003***|
||(0.001)|(0.000)|(0.000)|(0.000)|
|Marital Status (base: married)|||||
|Single|-0.148***|-0.121***|-0.130***|-0.130***|
||(0.020)|(0.014)|(0.012)|(0.012)|
|Divorced|-0.017|-0.051|-0.027*|-0.021|
||(0.016)|(0.033)|(0.014)|(0.014)|
|Widowed/Widower|-0.082***|-0.120***|-0.082***|-0.076***|
||(0.010)|(0.034)|(0.009)|(0.010)|
|Education (base: less than 6 years)|||||
|6-11 years|0.136***|0.134***|0.131***|0.136***|
||(0.010)|(0.006)|(0.005)|(0.006)|
|More than 12 years|0.213***|0.234***|0.224***|0.235***|
||(0.013)|(0.007)|(0.006)|(0.007)|
|Female PA|||||
|x 6-11 years||||0.125***<br>(0.009)|
|x More than 12 years||||0.201***<br>(0.012)|
|_Characteristics of the household_|||||
|Family size|-0.015***|-0.043***|-0.035***|-0.044***|
||(0.002)|(0.001)|(0.001)|(0.001)|
|Able bodied male adults, %|0.152***|0.164***|0.236***|0.172***|
||(0.027)|(0.016)|(0.011)|(0.016)|
|Number of children below 5|-0.076***|-0.035***|-0.044***|-0.031***|
||(0.005)|(0.003)|(0.002)|(0.003)|
|Number of elderly in the hh|<br>-0.181***|<br>-0.085***|<br>-0.106***|<br>-0.094***|
||(0.017)|(0.011)|(0.009)|(0.011)|
|Family type (base: couples with children)|||||

22
empirical studies have focused on quality-related aspects of trade between dissimilar economies, the empirical part therefore provides another valuable extension to the literature. Even though we confront our model with data from developed countries, namely trade between 15 EU countries, the mechanisms that we describe might also be at work for trade among developing countries. One of our key exogenous variables that decide on the export performance of the economy is trade costs. Given the fact that many developing countries still face considerable trade costs, the quality component of our model might help to better evaluate the export performance of developing countries.

A study closely related to our empirical investigation is the contribution of Hummels and Skiba (2004). Analyzing import flows of six countries from the rest of the world, the authors find strong evidence of per unit trade costs. They conclude that their results corroborate the Alchian-Allen effect of “shipping the good apples out”. Whereas their study focuses on the trade costs, our primary concern is the role of quality in international trade.

The paper is structured as follows: Section 2 presents a model of a closed economy in which firms produce different qualities at different costs and in which consumers buy all qualities, but in different quantities. In section 3, the effects of trade between identical economies in the presence of trade costs are examined. Section 4 studies the trade patterns between different economies. Before concluding, we test our model empirically using detailed trade data from 15 EU countries.

# **2. Heterogeneous Quality Firms Trade Model**

# **2.1 The Basic Model**

We suppose a two-country economy, including the home country (Home) and the foreign country (Foreign, denoted<sup>*</sup> ). The countries are identical in their production technology and freeness of trade. In each country a manufacturing sector ( _M_ ) and an agricultural sector ( _A_ ) are producing goods. Our model economy has three types of factor endowment: First, lowskilled labor, _L_ , which is mobile between and employed by both sectors. Second, high-skilled labor, _H_ , which is a production factor specific to the _M_ sector. And finally, capital, _K_ , which is also only used in the _M_ sector and firm-specific (not transferable between firms). All three factors are immobile between countries.

6