rafmacalaba/datause-encoder-data huggingface dataset:
Table 6-B presents the summary statistics for a “snapshot” of the sample firms at the end of 2004. From Panel A, the average market cap of the full sample is US$16.98 million (median is US$ 0.84 million), with the NSSI and NSSSBE firms significantly larger than the SSI and SSSBE firms. Panels B to G present key financial items such as earnings per share, net income, retained earnings, external financing through seasoned equity offerings (SEOs), and long-term borrowings and dividend payout ratios for each group of sample firms. For all firms in the sample, the retained earnings are very close to net income (Panels C and D), implying high internal re-investment rates. This is consistent with the finding from Table 5 that retained earnings constitute a vital source of financing. Not surprisingly perhaps, NSSI firms seem to have more access to SEOs than other types of firms (Panel E). Finally, the majority of our sample firms, in particular, small firms (SSI and SSSBE firms), did not pay dividends in 2004 (Panel G), while the fraction of dividend-paying firms is much higher among large firms (NSSI and NSSSBE firms) and these firms pay higher dividends.

Table 6-C provides some evidence on external financing sources at the firm level. The ratios for all the countries (except for India) in the table are taken from LLSV (1997a).<sup>29</sup> Indian firms rely much less on equity financing than LLSV firms: Both ratios of market capitalization to sales and to cash flow for the full sample of Indian firms are much lower than the average of LLSV firms and most of the LLSV subgroups of firms. Among the four groups of Indian firms, large manufacturing firms (NSSI) rely least on equity financing, while small, non-manufacturing firms (SSSBE) rely most on equity financing. On the other hand, Indian firms, in particular, large firms (NSSI and NSSSBE firms), rely more on debt financing (bank loans and bonds) than LLSV firms: Both ratios of debt to sales and to cash flow for the full sample of Indian firms are higher than all LLSV subgroups of firms. When we combine equity and debt financing (sum of rows 1 and 3 or 2 and 4),

> 29 In LLSV (1997a), a ratio (e.g., market cap/sales) for a country is obtained by first finding the _median_ of this ratio across firms within various industries, and then by taking the _average_ of the medians across industries. Each ratio for LLSV countries in Table 6-C is the _median_ of the ratios of countries with the same legal origin. For Indian firms, we follow the approach in AQQ (2005), who take the average ratios of all the sample firms in China.

26
stronger tendency of this inverted ‘U’ shape non-linearity between the number of disaster casualties and income level for floods, landslides, and windstorms than for extreme temperature events or earthquakes. However, they use the number of casualties as disaster intensity, whereas this study employs the higher-order effects and total impacts of disasters. These factors, number of casualties and total impacts may represent different aspects of a disaster, and may not have a perfect correlation with each other. An important common implication between this and their studies is that type of disaster plays a major role for this inverted ‘U’ curve relationship between disaster intensity and development level. And, further extending this line of analysis, inclusion of smaller events, which are excluded in the present study, might potentially reveal more concrete tendencies of the relationship between impact on GDP and GDP per capita. Or, since the global aggregate estimation of disaster impacts is carried out based on the aggregated economic damage data, with no sector disaggregation, the estimated results did not take into account inter-industry relationships, which is considered as the basis for increased vulnerability in middle-income countries. Disaggregating sectors to some extent, for example at least to primary, secondary, and tertiary industries, is necessary to see the intricacy of higher-order effects and their differences among different development levels.

25
Policy Research Working Paper 10711

# **Abstract**

Women often face more hurdles than men in obtaining finance. This is especially so when credit supply is limited and financial markets are less developed. As a result, owners of firms may prefer men over women as top managers of their firms, widening the gender gap in top manager positions. This paper tests this idea using firm-level survey data for small and medium-size formal manufacturing enterprises in 47 developing countries. The results confirm a positive relationship between credit supply and the likelihood of having a woman versus a man as the top manager.

This positive relationship is much stronger in industries that are more dependent on external sources of finance for technological reasons. It is also stronger in countries with poor coverage by credit bureaus and low competition between banks, which is consistent with “statistical” and “taste-based” discrimination against women borrowers. The main result is robust to several endogeneity checks, sample alterations, and alternative measures of credit supply and financial development.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mamin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
transition from the production of light, labor‐intensive manufactured goods to more sophisticated manufactured goods.

From that perspective, industrial policy should not only focus on innovation, but also help to strengthen the quantity and quality of labor and the access to advanced ICTs. In this sense, industrial policy also has an “education” dimension and an “infrastructure” dimension, in line with policies aimed at avoiding, or escaping from, a middle‐income trap <u>(see Box 2.1).</u> A broader reform of the state is also needed to reduce corruption and improve governance (including in managing public investment) and improve productivity in the delivery of public services through digitalization. The increase in investment in advanced infrastructure advocated earlier could come not so much from more public investment, away from spending on basic infrastructure, but also improved management of investment projects. In that sense, the issue is not to invest _more_ , but to invest _better_ . Reducing government bureaucracy (with respect for instance to property transfer, construction permits, and trading across borders, and so on) is important to improve the business environment. Reforming the tax system is also important to bolster the contribution of the fiscal sector to growth. These reforms should aim to widen the tax base, improve tax administration and tax collection, reduce the number of VAT rates, and possibly reduce income tax rates. As shown in Figure 3.4, the direct income tax ratio in Morocco, when related to the country’s per capita income, is much higher than in comparator countries. In fact, it is possible that Morocco is on the _wrong side of the Laffer curve_ , with respect to income taxation; if so, reducing the average income tax rate would in fact raise revenues – both by increasing compliance and by reducing incentives to engage in informal activities.<sup>1</sup>

**Figure 3.4 Selected Countries:  Direct Income Tax Ratio and GDP per capita (Averages for 2005‐2016)**

<!-- Start of picture text --> 14<br>Ratio Income tax Italy<br>12<br>10<br>Morocco Portugal<br>8 Chil<br>Peru Spain<br>Romania<br>6<br>Costa Turkey<br>4<br>China Rica<br>2<br>0<br>3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.5 4.6<br>log of GDP per capita<br>gains (% of GDP)<br>Taxes on income, profits and capital<br><!-- End of picture text --> Note: the income tax ratio is calculated as the ratio of taxes on income, profits and capital gains divided by GDP; GDP per capita is at 2011 PPP prices. _Source_ : IMF, World Economic Outlook Database, World Development Indicators (World Bank).

47
35 dramatically as education has expanded. Birdsall, Ross and Sabot (1995) find in cross national data that the estimated returns to schooling are higher the higher the growth in manufactured exports. They argue this reflects differences in the expansion of demand for educated labor. Of the few countries for which long time series on rates of returns do exist, some do show a modest, but not a dramatic, decline in estimated returns while others show a modest increase, but it must be admitted that the countries with the available data may be atypical.

Table I 1: Growth of enrollments and of wage employment in selected Sub-Saharan African countries, from date of study estimating Mincerian return study to 1990 (or most recent).

|Country|Change in<br>enrollments<br>( 000)|Change in wage<br> <br>employment<br>('000)|Ratio. expansion of<br>enrollment to wage<br>employment|Wage employment as<br>percent of total labor<br>force.|
|---|---|---|---|---|
|Botswana|157|122|1.3|50.4|
|Burkina Faso|351|35.4|9.9|3.8|
|Cote d'Ivoire|323|-7.7||9.0|
|Ghana|1312|80.0|16.4|3.8|
|Kenya|1709|436|3.9|14.1|
|Lesotho|142|14.9|9.5|_5.4_|
|Malawi|546|143|3.8|13.7|
|Senegal|180|45.4|4|_5.5_|
|Sierra Leone|257|8.9|28.9|4.9|
|Tanzania|-49|9||5.6|
|Uganda|2254|13.2|17.1|4.7|
|Zambia|446|-4.3||13.1|
|Zimbabwe|135|111.1|1.2|36.6|
|Source: Bennell. 199|4. table 5.||||
12. **The growing middle class, however, is the most unstable group and the people who comprise it have non-negligible chances of moving to the vulnerable or poor groups.** Table 3.2 shows a mobility matrix between different classes during 2003-2009 (upper bound). In particular, 39 percent of the poor in 2003 moved to the vulnerable and 13 percent moved to the middle class in 2009. About 24 percent of the vulnerable in 2003 moved to the middle class in 2009. About 16 percent of the middle class fell in to poverty and about 55 percent moved to the vulnerable group. This makes the middle class very unstable contrary to expectations that it comprises the most stable strata of society.

**Table** **<u>3.2:  Mobility matrix, 2003-2009, upper bound</u>**

||Origin<br>||Percentage<br>|movingto 2009<br>||
|---|---|---|---|---|---|
||(In 2003)|Poor|Vulnerable|Middle Class|Total|
|Poor|72|48|39|13|100|
|Vulnerable|22|29|47|24|100|
|Middle class|6|16|55|30|100|
|Total|100|44|41|16|100|

Source: Authors’ calculations based on ECAPOV data. Notes: transition matrix is based on synthetic panel for 2003-2009.<sup>5</sup> Threshold for poor is 162 somoni and for middle class 294 somoni per month. Explanatory variables include year of birth cohort, number of children, education of the head of household, rural/urban and regional dummies, and different interactions between these variables and means are the regional level. Sample: heads of household 25-55 years of age. Welfare aggregate is as used to calculate national poverty rates.

13. **Upward mobility is much stronger in urban areas, while volatility of class transitions is more pronounced in rural areas.** Tables 3.3ab contain mobility matrixes for urban and rural areas. Consistent with higher poverty in rural areas, there are lower chances to be in the middle class for rural residents. Moreover, rural areas are characterized by much higher volatility than urban areas. For example, in urban areas only 7 percent of the middle class fell into poverty in 2009, while in rural areas this ratio is more than 3 times higher. This is consistent with - higher volatility of earnings in rural areas that are strongly dependent on seasonal and climate-dependent agricultural activities.

**Table 3.3a:  Mobility matrix for urban areas, 2003-2009, lower bound**

||Origin<br>||Percentage<br>|movingto 2011<br>||
|---|---|---|---|---|---|
||(In 2002)|Poor|Vulnerable|MiddleClass|Total|
|Poor|66|43|41|16|100|
|Vulnerable|25|25|43|32|100|
|Middle class|9|7|48|46|100|
|Total|100|37|42|21|100|

**Table 3.3b:  Mobility matrix for rural areas, 2003-2009, upper bound**

||Origin||Percentage|movingto 2011||
|---|---|---|---|---|---|
||(In 2002)|Poor|Vulnerable|Middle Class|Total|
|Poor|74|51|38|11|100|
|Vulnerable|21|32|49|19|100|
|Middle class|5|25|62|13|100|

> 5 Brief non-technical explanation of the synthetic panel methodology is provided in box 2 in annex.

11
**Figure 1: Structural change and agriculture in the Turkish economy, 1980-2012**

<!-- Start of picture text --> $US 2005 Share (%)<br>thousands<br>12 GDP per capita 0.6<br>Agricultural income per worker<br>Share of workforce in agriculture<br>10 Agriculture's share of trade 0.5<br>Agriculture's share of GDP<br>8 0.4<br>6 0.3<br>4 0.2<br>2 0.1<br>0 0.0<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text -->

**Source: FAO (2014); World Bank (2014).**

**Figure 2: Shares of rural, urban and national populations below national poverty line, 2002-2012**

<!-- Start of picture text --> Share of<br>population<br>0.4<br>Rural<br>Urban<br>0.3<br>0.2<br>0.1<br>0.0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text -->

**Source: World Bank Development Indicators (2014)**

20
**Figure 7: Estimated change in incomes due to future climate change versus current income, municipal level in Chile**

<!-- Start of picture text --> 0<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br>-14<br>0 500 1000 1500 2000<br>Current income per capita (USD/month)<br>climate change (% change)<br>Change in incomes due to future projected<br><!-- End of picture text -->

# **8. Conclusions**

In this paper we first used a municipality level cross-section database to estimate the general relationship between climate and income in Chile. We found that incomes are negatively related to temperature, while life expectancy is not significantly related to average temperatures. Both income and life expectancy were higher for either very low levels of rain or high levels of rain, whereas intermediate levels of rain were less favorable.

These estimated relationships were then used to simulate the effects of both past (19582008) and future (2008-58) climate change. Past changes in climates were analyzed using historical data from seven meteorological stations spread across the territory, and estimating average trends for each station. It was found that trends varied systematically across the country, with northern regions having warmed over the last half century, southern regions having cooled, and central regions having experienced little or no change in temperatures. The central region did experience significant reductions in precipitation (about 10%), whereas the regions to the north and to the south experienced no changes.

The consequences of these past climatic changes were then simulated using the estimated cross-section models. The findings indicate that past climate change (reductions in temperatures and reductions in precipitation) has been favorable for the central part of Chile, where the majority of the population is concentrated, whereas the sparsely populated northern regions have suffered from warming.

24
-37-

Table 10: Indices of Trade Compatibility in Europe, 1962

|||||Imports<br>|||||
|---|---|---|---|---|---|---|---|---|
|||BLX|FRA|GER|ITA|NLD|AVG|Xcc|
||BLX|0.58|0.46|0.50|0.46|0.55|0.49|0.16|
||FRA|0.64|_0.56_|0.57|_0.54_|0.66|0.60|0.14|
|Exports|GER|0.56|0.50|0.40|0.49|0.58|0.54|0.19|
||ITA|0.55|0.48|0.49|0.41|0.56|0.52|0.18|
||NLD|0.52|0.47|0.53|0.46|0.58|0.50|0.15|

Source: Estimated from data in Comtrade, U.N. Statistical Office
- 15 -

The real problem was that the accounts showed cheques at their face values when it was evident that there was a large real deficit in the system. Not only was it impossible to tell the clients' true position, but if some dealers were forced into bankruptcy their deficits would imperil other dealers because of the heavy linkages. In fact, by December, the A Panel had already referred nine dealers to bankruptcy proceedings. Their deficits with the clearing company were KD 4.3 billion 2_/ . After a generous valuation of their assets, the dealers were estimated to have a net deficit of KD 3.4 billion. Both the existing deficits in the system and the rapid deterioration in asset prices made it difficult to assure that third parties were not adversely affected. Creditors who settled their disputes first received larger payouts and higher quality assets.

A second problem with the legal approach was handling the flow of assets from net payers to net receivers. The clearing company's figures showed that 83 percent of the registered trades covered transactions in Gulf companies and KSCCs, whose shares which were not supported by the government and could not be placed as security against a loan. The cheque-by-cheque approach meant that assets had to physically change hands, and since receivables and payables could not be matched under the system, assets had to be liquidated in an already thin Kuwaiti market and a rapidly falling parallel market. The clearing company could not perform multilateral clearing operations on cheques whose real values were not known and whose values changed as the market declined. The links between dealers meant that as the value of a cheque changed, it would work through the system to affect other dealers.

Third, the approach did not solve the problems of the large dealers in deficit. Bankruptcy proceedings at best would take years. This meant that most payments would effectively come to a stop since no payments could be made by the large dealers, and all the dealers in the network would be unable to meet their commitments.

# A COMPREHENSIVE SOLUTION?

It was not surprising that these risks were not addressed either by the Chamber or the government. They could not be addressed because the decision had been made to route the cases through the courts. The legislators disapproved of the dealers, whom they saw as speculators whose risky activities had upset the financial system and who deserved to be punished. The laws reflected this objective. While alternatives were presented they were not politically acceptable.

_The Clearln4 Solutlon._ A satisfactory response could have been based on simplifying asset movements between dealers through the clearing company. The basic assumption was that the cheques were only worth the value of the dealers' underlying assets and that was what final receivers would get regardless of the law.

.2&/ IFA acted as trustee and advisor to some of the dealers. The information is from IFA files.
included in the estimations as vehicle sales may be affected by the economy’s performance. The variables exhibit nonstationarity and seasonality and appear not to be cointegrated under the Engel and Granger test. Thus, the variables are in first differences. BIC indicates that it is sufficient to include two autoregressive terms and a quadratic time trend. The added lags of the dependent variable also account for serial correlation.

Panel (b) of table 6 shows the estimates of equation (4) including GDP growth rate, adding a vehicle price index, and conducting two-stage least squares (2SLS) (see columns (1), (2), and (3), respectively). Although vehicle prices were not available, a price index was created using the ratio of the total monetary value of sales in the country to the total number of new regular car sales.<sup>29</sup> A possible concern when constructing the price index is that it might be endogenous as sales in Bogotá represent almost 50 percent of the total sales in Colombia. To address this issue, 2SLS is estimated by instrumenting the price index with the real exchange rate.<sup>30</sup> Across the three specifications, the short- and long-run effects of PYP2 were positive and statistically significant at the 10 percent level. The long-run estimates range from 5.5 percent to 9.3 percent, though two out of three of these estimates are less precise. This mild evidence of a growth rate increase in regular car sales relative to the moderate restriction is compatible with the results for vehicle registrations. As a robustness check, equation (4) is run for regular car sales in other Colombian cities. Since some cities had already implemented driving restrictions, sales in those cities had probably also increased as a consequence; hence, the simultaneous effect of other restrictions may yield misleading estimates. Therefore, the model is run for the aggregated regular car

> 29. Information on these variables and GDP growth was obtained from DANE.

> 30. The first-stage equation indicates that the instrument is relevant at the 5 percent significance level.

22
Fiscal policy is poverty inducing in Uganda in 2016/17. The poverty headcount index increases by approximately 2.2 percentage points in 2016/17 from pre-fiscal to post-fiscal income (excluding in-kind transfers). Direct transfers in Uganda provide meaningful benefits to those who receive them. However, the magnitude of transfer spending overall is low such that direct transfers received together subsidy benefits captured are, for most households, not enough to compensate for household indirect tax payments, causing poverty to increase slightly.

The Ugandan fiscal system, including all taxes and all transfers (including in-kind), reduces inequality by around 3 Gini points in both 2016/17 and 2012/13. The largest contributor to this equalizing effect in 2016/17 is the PAYE personal income tax followed by the education and health in-kind transfers. Ugandan social protection programs (including direct transfers) are progressive and concentrated among the poorest households but are small and have limited coverage. Indirect subsidies related to water services as well as tertiary and technical education transfers are regressive and would, on their own, increase inequality in Uganda.

_Table 1. Inequality and Poverty Before and After Fiscal Policy_

||**2**|**012/13**|**2**|**016/17**|
|---|---|---|---|---|
|||<sup>**Poverty**</sup>||**Poverty**|
|**Income Concept**|**Gini**|<br>**Headcount**|**Gini**|<br>**Headcount**|
|Market income plus Pensions|0.41|19.8%|0.43|21.39%|
|Net Market Income|0.40|19.8%|0.42|21.44%|
|Disposable Income|0.40|19.7%|0.42|21.42%|
|Consumable Income|0.40|19.9%|0.41|23.70%|
|Final Income|0.38|---|0.40|---|

_Note: Poverty headcount calculations use the national poverty line._ The rest of the report is organized as follows. Section 2 provides an overview of the fiscal structure of Uganda, describing both the main taxes and transfers in the country, and the CEQ methodology and the allocation assumptions are presented in Section 3. The poverty and inequality results are presented in Section 4, and the progressivity and marginal contribution of each fiscal instrument is discussed in Section 5. Section 6 compares the 2012/13 and 2016/17 fiscal incidence analyses. Finally, Section 7 concludes and lists some policy considerations.

# 2. Fiscal Structure

This section presents a categorization of tax and other revenue collections and public expenditures in Uganda for the fiscal year 2016/17 –the UNHS survey enumeration year – and identifies the fiscal policy items included in the fiscal incidence analysis.

7