rafmacalaba/datause-encoder-data huggingface dataset:
# **III.2. The empirical growth model**

To explore the links between growth, inequality and poverty, our empirical strategy generalizes the standard specification used in the literature concerned with the impact of inequality on growth.<sup>17</sup> Building on the illustrative model presented in Section II, we use a specification adding suitable measures of inequality and poverty to an otherwise standard empirical growth regression: where _y_ is the log of per capita income,  _i_ and  _t_ are country- and time-specific effects, _p_ is a measure of poverty, _g_ is the Gini coefficient, _x_ represents a set of control variables, which we shall discuss shortly, and  is an i.i.d error term.

The parameter  in (12) captures the impact of poverty on growth. Thus, if poverty is a growth deterrent, as predicted by the model in Section II (eq. 10), we should find  <0. In turn, the coefficient  in (12) reflects the direct effect of inequality on growth, for given poverty and per capita income levels. However, the overall impact of inequality on growth also depends on how inequality affects poverty - the indirect effect examined in eq. (11). The reason is that, from theory, as well as the very construction of our poverty data (see the Appendix), poverty is a (nonlinear) function of (log) average income ( _y_ ) and inequality ( _g_ ), i.e., _p=p_ ( _g, y_ ). Thus, which means that, even if the direct effect of inequality on growth is nil (i.e.,  =0), its overall effect on growth could still be nonzero through its correlation with poverty.

Identifiability of  and  in (12) hinges on the relationship between poverty, income and inequality. If poverty were close to an exact linear function of (log) per capita income and the Gini coefficient, the estimating equation would feature perfect collinearity. In such scenario, we could drop poverty from (12) and estimate a model including only lagged per capita income and lagged inequality – as done in earlier literature. However, if poverty is not well approximated by a linear combination of _y_ and _g_ , then  is identifiable (as are  and  , and it captures the effect of poverty on economic growth holding inequality and average per capita income constant.

> 17 ‐ See Forbes (2000), Barro (2000) and, more recently, Halter et al. (2014), Dabla Norris et al. (2015), Kraay (2015), Brueckner and Lederman (2018) or Berg et al. (2018), among many others.

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Sánchez-Martín, Escribano and de Arce, 2014. “How regional integration and transnational energy networks have boosted FDI in Turkey” For example, Portugal, Spain and Greece joined the European Community in 1986; however, whereas Portugal and Spain were able to double their average yearly FDI inflows to GDP in 1986-1990 compared to 1980-1985, Greece did not significantly increase its levels during the decade of the 1980s (Figure 2). This could be explained by the fact that, even when membership encompassed similar environmental changes (and opportunities) for the three Mediterranean countries, Greece was by that time relatively isolated in terms of location from the rest of the European markets. Similarly, it is possible to observe that CUSFTA agreement has had a lower impact for Canada than the NAFTA treaty for Mexico. The reason Blomstron and Kokko (1997) provide is that NAFTA is an extended version of CUSFTA that consider deeper integration (stronger “environmental change”); additionally, Mexico has an advantageous location in the region with respect to Canada.

**Figure 2. FDI flows as a % of GDP in selected countries**

<!-- Start of picture text --> 8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Turkey Spain Portugal Greece<br>FDI inflows as % of GDP<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012<br><!-- End of picture text -->

# Source: UNCTAD

Drawing from this methodology, that classifies regional integration agreements according to its probable impact on foreign direct investment, we try to illustrate the Turkish case. The following diagram shows four blue ellipses, which corresponds with the position attributed by Blomstron and Kokko (1997) to their four case studies.

In the same fashion, it might be argued that the Customs Union Agreement signed by the EU and Turkey in 1996 did not help the latter to attract FDI significantly because it did not imply a strong “environmental change”. As it has been mentioned previously, the scope of the agreement was limited 14
variables added as independent variables in the regression (column 4 in Table A-1). The results suggest that differences in community level characteristics do explain some of the location effects on household consumption.

Given that some of the location-specific variables are highly correlated with each other, it is useful to look at their coefficients when they are introduced into the regressions one at a time (see Table A-2). _Travel times to urban centers,_ which proxy access to markets, are important determinants of household welfare in rural Bangladesh. Travel times to the nearest local market (Thana headquarter) and the largest urban market of the country (Dhaka) have the largest positive effects on rural household consumption. The extent of _electrification_ in the _thana_ is associated with higher consumption, more strongly for rural areas than for urban areas (Table A-2).

The regression specifications also include _thana_ level indicators for _microfinance coverage_ and _increase in coverage_ between 2003 and 2005 (see columns 2 and 4 of Table A-1). For rural households, the coefficient for _thana_ level microfinance coverage is insignificant, while that for _thana-_ level increase in microfinance coverage (2003-2005) is positive and significant.<sup>7</sup> This is consistent with the trend suggested by Table 5: on average, areas where microfinance membership grew more also experienced higher poverty reduction between 2000 and 2005. The caveats with the microfinance data discussed in Section II are relevant here, which imply that these correlations do not

> 7 The coefficient for _thana_ level microfinance coverage is negative and significant for urban households. This is likely to be a spurious correlation – given the extremely limited coverage of MFIs in urban areas, it is hard to see how consumption of urban households would be impacted by microfinance expansion. The negative coefficient probably reflects a deliberate attempt by MFIs to expand coverage in areas that were poorer to start with (also see Annex, Table A-1).

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We use a dataset of 117-151 countries spanning the period 1970-2009. The four decades are split into 8 time periods, each covering a 5-year window. Data availability does not allow to observe each country in each period, the resulting database has the structure of an unbalanced panel and the numbers of countries considered depends of the empirical specification. Most of the variables enter as averages over the 5 years or initial values measured at the start of each period are used. Table A.11 in the Appendix provides an overview of the countries and periods included in the analyses.

The dataset combines data from various sources. Per capita GDP (PPP converted, at constant 2005 prices) is taken from the Penn World Table (PWT) Version 7.1 (Heston, Summers, and Aten, 2012). Growth rates of per capita GDP are computed as log-differences of that variable. The value of the logarithm of GDP per capita at the start of each period is used as a right hand side variable.

Government consumption, investment as well as the measure for openness and inflation are obtained from the same source. Data on educational attainment are taken from an updated version of the IIASA/VID Dataset on Educational Attainment (Lutz, Goujon, K.C., and Sanderson, 2007). To control for the level of democracy of a country, an index from the Quality of Government Standard Data collection (Teorell, Charron, Samanni, Holmberg, and Rothstein, 2011) is used, and in order to extend the time coverage we additionally use data on democracy provided by Bollen (1990).

A summary and brief description of the variables used can be found in Table 1.

# **5 Results**

Column 1 of Table 2 shows the first baseline regression including the initial GDP per capita, human capital as well as an index for democracy, the openness ratio, government consumption and investment as shares in GDP, inflation and 7 period dummies, where the first period (1970-74) constitutes the reference category. The negative and significant coefficient of the lagged GDP per capita variable indicates that countries with a relatively low initial income grow faster towards their country specific equilibrium. The institutional variable, an index of democracy, does not show significant effects in this specification. Likewise, education is not identified as a significant driver of growth rates. This is a common problem in cross country growth regressions and often attributed to differences in the quality of education across countries and over time (Hanushek and W ̈ossmann, 2008), the lack of including the demographic structure of educational attainment (Lutz, Cuaresma, and Sanderson, 2008), omitting (in)equality measures of schooling within a country (Sauer and Zagler, 2012) or heterogeneous effects across countries or over time. The specification in Column 1 assumes a linear relationship between education and growth and it does not allow the impact of human capital to differ across countries. In the following, an interaction of the initial GDP per 7
**Figure 1 Active portfolio of public-private partnerships in infrastructure in South Asia, 1990–2018**

<!-- Start of picture text --> (a) In billion dollars (b) As percentage of GDP<br>350 12<br>300<br>10<br>250<br>8<br>200<br>6<br>150<br>4<br>100<br>2<br>50<br>0<br>1990 1994 1998 2002 2006 2010 2014 2018 1990 1994 1998 2002 2006 2010 2014 2018<br>Year Year<br>Source : PPI database and World Development Indicators.<br>Figure 2 Number of active public-private partnership projects in infrastructure in South<br>Asia, 1900–2018<br>1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Year<br>Percent<br>Billions of 2019 US$<br><!-- End of picture text --> _Source_ : PPI database.

India implemented the largest number of infrastructure PPPs in the region, accounting for over three-quarters of the 1,232 infrastructure PPPs in South Asia. Pakistan and Sri Lanka implemented 81 and 79 PPPs, respectively, and are followed by Bangladesh with 45 and Nepal with 38 PPPs. Afghanistan and Bhutan implemented only two, while Maldives had one PPP in infrastructure (table 1). PPP investments in India—amounting to $283 billion—account for more than 85 percent of the aggregate investment in PPPs in the region. India is followed by Pakistan and Bangladesh with total investments of $31 billion and $6.9 billion, respectively. Sri Lanka’s PPP program had 7
Table 4: Average predicted percentage change in sales relative to the same period of 2019.

||Men-led<br>businesses|Women-led<br>businesses|Gender<br>difference|Statistically<br>signifcant|
|---|---|---|---|---|
|Aggregate unconditional|-43.2|-45.2|-2.0|*|
|Aggregate conditional|-43.4|-45.2|-1.8|*|
|Micro (0-4)|-47.9|-49.9|-2.0|*|
|Small (5-19)|-45.1|-47.4|-2.3|*|
|Med and large (20+)|-38.7|-40.0|-1.3||
|Manufacturing|-42.0|-43.5|-1.5|*|
|Retail and wholesale|-39.2|-39.8|-0.6||
|Hospitality|-60.4|-67.8|-7.4|*|
|Other services|-46.8|-49.6|-2.7|*|
|Others|-41.2|-42.1|-1.0||
|Low and lower middle|-43.2|-46.8|-3.5|*|
|Upper middle and high|-43.6|-42.8|0.8||
|Q1 in mobility drop|-40.5|-41.9|-1.4||
|Q2 in mobility drop|-45.7|-44.3|1.4||
|Q3 in mobility drop|-40.7|-44.6|-4.0|*|
|Q4 in mobility drop|-46.7|-50.0|-3.2|*|

* indicates statistical significance of the gender difference at the 5% level. Averages over the full sample. Full set of results available in the appendix. The aggregate unconditional average is the estimate for _β_ in Equation 1. The other predictions exploit the estimates for Equation 2. The aggregate conditional average is the predicted value of the outcome if every business in the sample were led by a man/woman. The predicted averages in the following rows condition on other characteristics of the business in addition to the gender of the owner or manager.

# **5.4 Financial risks**

The COVID-19 shock was associated with a sharp decline in firm liquidity as many firms reported severe difficulties in meeting their financial obligations (Apedo-Amah et al., 2020). The BPS and WBES surveys measure liquidity constraints and financial risks among firms using two questions that seek to capture the ability to meet their current financial obligations (e.g. rents, wages, interest payments, etc.) and the likelihood to be in or soon fall into arrears.<sup>15</sup> At the time of the interview, women-led businesses reported on average less cash available to cover their costs (Table 5). Men-led businesses report on average the predicted equivalent of 70 days of cash available to cover costs, whereas women-led businesses report only 61 days, a statistically significant gap. When controlling for size and sector using the conditional model, the difference remains statistically significant, indicating that most of the difference in available liquidity between men-led and women-led businesses does

> 15The two questions read _(i_ )As of today, for how many days could this establishment continue paying all costs and payments with the cash available? _(ii)_ Is it expected that this establishment will fall in arrears in any of its outstanding liabilities in the next 6 months?

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# Table 1a: Average budget shares of Mexican households

|**Budget shares**|Average|Quintile1|Quintile2|Quintile3|Quintile4|Quintile5|Urban|Rural|
|---|---|---|---|---|---|---|---|---|
|Wheat|2.9%|3.6%|3.5%|3.0%|2.6%|1.6%|2.7%|3.2%|
|Maize|4.0%|7.1%|5.4%|3.8%|2.5%|1.1%|3.4%|5.5%|
|Alcohol Tobacco|0.8%|1.1%|0.8%|0.8%|0.7%|0.6%|0.8%|0.9%|
|Meat|8.9%|7.0%|10.3%|10.7%|9.9%|6.4%|9.0%|8.5%|
|Legumes|2.1%|4.7%|2.8%|1.7%|1.1%|0.5%|1.9%|2.8%|
|Dairy|5.8%|6.0%|6.9%|6.6%|5.8%|3.8%|5.8%|5.9%|
|Oil Fats|1.4%|2.6%|1.8%|1.3%|0.9%|0.4%|1.2%|2.0%|
|Vegetables|3.6%|5.3%|4.4%|3.7%|3.1%|1.7%|3.5%|4.0%|
|Fruit|1.4%|1.1%|1.5%|1.6%|1.6%|1.3%|1.5%|1.3%|
|Sugar|0.9%|2.1%|1.1%|0.7%|0.4%|0.2%|0.8%|1.2%|
|Other food|7.7%|9.5%|8.6%|8.1%|7.2%|5.0%|7.3%|8.4%|
|Total Food|39.5%|50.1%|47.1%|42.0%|35.8%|22.6%|37.9%|43.7%|
|Non Food|60.5%|49.9%|52.9%|58.0%|64.2%|77.4%|62.1%|56.3%|

Source: Author’s calculation using INEGI households surveys 1984-2000.

Table 1b: Share of expenditure in the 10 food groups as percentage of total food expenditures.

|**Budget shares**|Quintile1|Quintile2|Quintile3|Quintile4|Quintile5|
|---|---|---|---|---|---|
|Wheat|7.2%|7.4%|7.1%|7.3%|7.1%|
|Maize|14.2%|11.5%|9.0%|7.0%|4.9%|
|Alcohol Tobacco|2.2%|1.7%|1.9%|2.0%|2.7%|
|Meat|14.0%|21.9%|25.5%|27.7%|28.3%|
|Legumes|9.4%|5.9%|4.0%|3.1%|2.2%|
|Dairy|12.0%|14.6%|15.7%|16.2%|16.8%|
|Oil Fats|5.2%|3.8%|3.1%|2.5%|1.8%|
|Vegetables|10.6%|9.3%|8.8%|8.7%|7.5%|
|Fruit|2.2%|3.2%|3.8%|4.5%|5.8%|
|Sugar|4.2%|2.3%|1.7%|1.1%|0.9%|
|Other food|19.0%|18.3%|19.3%|20.1%|22.1%|

Source: Author’s calculation using INEGI households surveys 1984-2000.

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that large market share firms in a concentrated industry realize different returns compared to low market share We also use firms’ markup of price over marginal cost as a firm-level measure of the firm’s pricing power. De Loecker and Eeckhout [2017] show that average markups have increased from 18% above marginal costs (as measured by Cost of Goods Sold) in 1980 to 67% above marginal cost by 2014.

To estimate markups, we rely on the framework by De Loecker and Warzynski [2012] and De Loecker and Eeckhout [2017] that is based on cost minimization of a variable input of production, without additional assumptions on firm demand or competition. Heuristically, this measure takes the firm’s capital stock as given, and estimates the markup that the firm can charge customers over its variable costs. A high markup is consistent with market power, as competition would erode the firm’s ability to charge above variable costs. Importantly, since markups do not take into account the cost of tangible and intangible capital, high markups are consistent with both high and low rates of return to invested capital. Intuitively, it is natural to think of high markups resulting from a firm’s exercise of market power by reducing sales and thereby realizing high returns on invested capital. However, it is possible for a firm to have a low markup, high sales per unit of invested capital and to be a star firm. Thus, the extent to which star status is related to high markups is an empirical question.

Recent work by Traina [2018] argues that _COGS_ grossly underestimate firms’ variable costs. Other expenses, such as _SGA_ are increasingly a lion’s share of variable costs for US firms. Traina shows that once we include _SGA_ in the calculation of marginal costs, there is no increase in public firm markups. Consistent with Traina [2018], we base our measure of variable inputs on Operating expenses (Compustat item _OPEX_ ) rather than Cost of Goods Sold (Compustat _COGS_ ) as in De Loecker and Eeckhout [2017]. _OPEX_ includes SGA expenses whereas _COGS_ only includes costs of production such as material, labor, and overhead and does not include SGA expenses. COGS has been a declining share of variable costs for US firms as shown in Figure A2 of the Appendix. We differ from Traina [2018] in our adjustment of operating expenses to include the correction for intangible capital.

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Available empirical studies show that net tax revenues from capital income are _negative.16_ For instance, Gordon and Slemrod (1988), based on the US tax system in 1983, estimate that tax revenues would increase if capital income taxes were completely eliminated. For Denmark, the net revenue from taxing capital income in 1986 was estimated to be _minus_ 1.6% of net national income (Sorensen, 1994). Muten (1996) reports that the Swedish government in 1994 did not reduce the tax rate on capital income from 30 to 25% as originally planned though a lower tax rate would have actually _increased_ tax revenues. Such an empirical exercise cannot be performed for Hungary, in the framework of this paper, because data requirements are extensive. When there is precise information on this, all available empirical studies arrive at a similar conclusion: the ability to raise additional tax revenue from taxing interest income is very limited.

There are several reasons behind this empirical conclusion. First, given that capital income is taxed under a progressive income tax, net revenues will be negative when agents with high nominal marginal tax rates borrow from agents facing low rates. Second, there is no incentive to forego the deduction of interest payments but a strong incentive to evade taxes on interest income. As a result, net tax revenues will tend to turn negative (even under a flat-rate tax). Third, many countries operate schemes which combine relatively generous rules for interest deductibility with favourable treatments of certain types of capital income (returns on pension saving, owner-occupied housing, etc). This is particularly the case in Scandinavian countries and in the US. As a result, tax arbitrage implies considerable losses for the tax authority.

In Hungary, however, interest paid by businesses has been treated as a deductible expense all along. Accordingly, the introduction of a tax on interest income will undoubtedly lead to an increase of net tax revenues.

# _Inflation_

Inflation has been stubbornly stuck in the 20-30% range in Hungary in the past few years, with . the rise in the consumer price index averaging 25.7% between 1990 and 1996. This qualifies in the economic literature as "moderate inflation". Projections for 1997 point at a possible decline to about 12-13% by end year.

Inflation influences the burden of taxpayers via two main channels. First, any progressive tax system that has nominal incomes as tax base will produce "bracket creeping" in periods of moderate to high inflation. This is exactly what happened in Hungary since 1990, with bracketcreeping being a major force underlying the increase in the real tax burden on the employed.

16 Note that empirical results find that net revenues from taxing _total_ capital income, and not from taxing interest only, are negative.