rafmacalaba/datause-encoder-data huggingface dataset:
# Not(ch) Your Average Tax System: Corporate Taxation under Weak Enforcement

Pierre Bachas & Mauricio Soto<sup>_∗_</sup>

## **JEL Classification** : H25, H26, H32, O23

**Keywords** : Corporate Tax, Tax Evasion, Tax Elasticity, Notch

> _∗_ Pierre Bachas: World Bank Research, pbachas@worldbank.org. Mauricio Soto: Banco Central de Costa Rica, SOTORM@bccr.fi.cr - We gratefully acknowledge data assistance by Oscar Fonseca and Jorge Richard Munoz from DGH at the Ministry of Finance and DGIE and DIE at BCCR. We thank Edward Miguel and Emmanuel Saez, for their support and encouragement, and have benefitted from comments and suggestions from Miguel Almunia, Juan Pablo Atal, Alan Auerbach, Youssef Benzarti, Michael Best, Anne Brockmeyer, Natalie Cox, Paul Gertler, Anders Jensen, Claus Kreiner, Etienne Lehman, Joana Naritomi, Alvaro Ramos, Andres Rodriguez Clare, Joel Slemrod, Danny Yagan, Andrew Zeitlin, Gabriel Zucman and seminar participants at Columbia, IDB, IFS, Michigan, Munich, Oxford Center for Business Taxation, Paris II, Princeton, TSE, UC Berkeley, U. de Costa Rica, UPF, World Bank DEC, ZEW, Zurich. Financial support from the Burch Center for Tax Policy, the Center for Equitable Growth and the Julis-Rabinowitz Center for Public Policy and Finance is gratefully acknowledged. The findings and conclusions are those of the authors and do not represent the views of the World Bank, the BCCR or any other institution.
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because the actual products sold differ substantially from one transaction to another.

There are virtually no studies of the relation of quality change to price measures that refer specifically to exported or imported goods aside from the studies mentioned above attempting to measure quality change from unit value data. A partial exception is a set of hedonic price indexes in Kravis and Lipsey (1971) as part of a much broader attempt to measure prices in world trade, but even there, most of the data for the hedonic price indexes actually referred to domestic markets. For years since 1982, there are also recent calculations by the Bureau of Economic Analysis (BEA) of the U.S. Department of Commerce for exports and imports of computers.

Most of the literature on the adjustment of the price quotations for quality change involves the calculation of hedonic price indexes to replace the standard specification-based price measures. There are other types of corrections, such as the cost of improvement calculations used for automobiles by the U.S. Bureau of Labor Statistics, calculations based on prices in second hand markets, and corrections for the bias resulting from the slow introduction of new products into price indexes. However, what is used here are hedonic price indexes that have been calculated in various studies of domestic technological change and prices. The specific series we have used are listed in the notes to Appendix Table 5.

What we did was to collect studies of quality change that had been carried out by others. All referred to domestic prices in the United States, and all the products were in SITC Division 7, Machinery and Transport Equipment. Within Division 7, they were confined to 9 four digit subgroups and 2 three-digit groups and drew heavily on Cordon (1990) and two or three papers on individual products.
Turning to the second of our questions (i.e., which countries would lose/gain from having a non-selective emigration), we simulate the counterfactual quality of institutions obtained when the skilled emigration rate is set to be equal to the unskilled emigration rate. This assumption implies a decrease in the skilled emigration rate. Noting that: e and assuming ms = mu = m, we have: where and<sup>e</sup> h is the resulting share of skilled in the resident labor force. The change in institutional quality is given by: It is straightforward to see that �I < 0 if: or, similarly, if: Figures 3a to 3d show the kernel distribution of zI and vertical lines at values of <u>�2</u><sup>the4indicators(CL,PR,Polity2andEFW)intheunbalanced(thick</sup> �1<sup>for</sup> lines) and balanced (thin lines) samples. The results from this second simulation are similar to those of the ...rst exercise. The fact that the kernel distribution is almost completely to the left of the thresholds suggests an institutional gain for nearly all the countries in our sample from having positively selected migrants (i.e., the counterfactual simulation from equating migration propensities across education groups yields an institutional loss), however this e¤ect is not statistically signi...cant as the distribution lies between the upper and the lower bounds of the con...dence intervals.

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development of clusters and inclusion in global supply chains as part of their strategies, but with a lower average ranking. As such, we can infer that, for the majority of EPAs, increase in overall exports and diversification are more often their top priorities, not necessarily through inclusion in global supply chains, clusters or attraction of multinationals.

Table 4: Share of EPAs targeting new products and diversification

|Region|Destination|Products|Major markets|Regional markets|Other markets|
|---|---|---|---|---|---|
|Asia|100.0|92.3|75.0|66.7|18.2|
|ECA|93.3|53.3|86.7|53.3|40.0|
|LAC|100.0|91.3|87.0|95.7|38.1|
|MENA|100.0|91.7|81.8|100.0|22.2|
|OECD|82.4|70.6|92.9|50.0|76.9|
|SSA|93.3|86.7|66.7|86.7|6.7|
|Total|94.7|81.1|82.2|76.7|34.5|

Note: The table provides the simple average of the ranking across crountries, where 1 is the most important objective, and 8 is the least important. ECA = Eastern Europe, LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; OECD = Organisation for Economic Co-operation and Development; SSA = Sub-Saharan African countries.

Most EPAs indicate that the diversification of products and destinations is one of their main goals (table 4). A breakdown of diversification strategies by region reveals that, on average, EPAs in Asia, Latin America and the Caribbean (LAC) and the Middle East and North Africa (MENA) regions give higher priority to the diversification of both markets and products. As expected, the more diversified economies of Eastern Europe (ECA) and OECD countries in general do not prioritize product diversification as much. Moreover, when defining the main targets, most EPAs indicate they are targeting major and regional markets. Because most of these countries are already trading with others considered as major and regional markets, it is more likely that the new destinations target will be effective by promoting new products or firms into markets that might be new for them, but not necessarily for the country.

# **2.3 Resources and Expenditures**

Regarding their size, a median EPA has about 86 employees, while the top quartile has more than 300. Yet, there is a large variability across regions, and OECD countries usually maintain the larger EPAs.

EPAs’ budget (or expenditures) range from US$ 100,000 to US$ 443 million, with a global average of US$ 42.7 million (in 2010 USD).<sup>8</sup> The average budget in low income countries (LICs) is US$ 3.8 million USD. However, the budget in those countries had the highest average annual

> 8The 2010 survey requested information on the total budget of the EPAs over the last 6 years. The 2005 survey gathered data for the 2005 budget only.

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Non-farming households are found not to benefit from higher agricultural profits in the long-run, but they benefit from higher wages at a sufficient level to make their long-run poverty outcomes favorable. The only group of households that does not benefit from large food price shocks in the long run is the urban households who experience no significant change in poverty as a result of large food price shocks even though they appear to benefit from shocks that do not exceed 100 percent.

Our results also suggest that poverty impacts of price increases affecting wheat and rice tend to have the largest adverse impacts on poverty in the short run. However, the impacts of large price increases in these commodities are quite sharply reversed in the long run, when there is the opportunity for wage rate changes and output adjustments to come into effect. The analysis of the effects of changes in the World Bank food price index makes clear that the poverty impact based on indices of this type—i.e. constructed using trade shares—is likely to depend heavily on the specific commodity whose price has increased.

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_-5-_ Table 4. Paistan's Social Indicators - An International Comparison

||Life exp<br>at b|ectancy<br>irth|Inf<br>mor|ant<br>taliy|Ad<br>Illite|ult<br>racy|Prim<br>enrol|ary<br>lment|Seco<br>enrol|ndary<br>lnent|
|---|---|---|---|---|---|---|---|---|---|---|
|-|1970|1991|1970|_1991_|1975|1991|1970|1990|1970|1990|
|Pakistan|46|59|142|97|79|65|40|37 at|13|22|
|India|47|60|137|90<br>|.64|52|73|97|26|44|
|Bangladsh|45|_51_|140|103|78|65|54|73|-|17|
|Nepal|41|53|157|101|Bi|74|26|86|10|30|
|SriLanka|64|71|53|18|17|12|99|107|47|74|
|South Asia|49|59|138|92|-|54|67|88|25|39|
|East AsiaandPacific|59|_68_|76|42|-|24|88|127|24|49|
|Latin America and the<br>Caribbean|61|68|82|44|-|.16|95|107|28|49|
|Middle East and North<br>Africa|53|64|135|60|-|45|68|97|24|36|
|Sub-Saharan Afiica|43|51|144|104|-|50|46|68|6|17|
|World<br>||<br>58<br>||<br>66||<br>8S|5<br>S3|-|35|83|104|15|39|

_ = Not available.

a/ There is some controversy surrounding ihis number. Other esdmates suggest an improvement in the primary enrolhnent ratio over 1970.

Source: 1) WDR 1993, 1979 2) World Tables, 1997

# SavinEs and Investments

7. - A rapidly expanding economy is usually assciated with a high rate of saving and investment. In the case of Pakistan, both saving and investment rates have increased noticeably. For example, the domestic saving rate grew from an average of 7.3% in 1973-76 to 11.5% in 1988-91, while the toal investment rae expanded from 14.9% to 18.6% over the same period (see Table 6). Nevertheless, a stiking result is that both the saving and investment rates are below the average found in other developing regions except Sub-Saharan Africa. The saving rate is also below that found in Sub-Sabaran Africa.
growth rates. However, in an examination of 13 country-case studies, Wacziarg and Welch find noticeable heterogeneity in the growth response to trade liberalization. Although their small sample does not allow for definite conclusions, it appears that the growth response after liberalization is positively related to conditions of political stability.

This paper starts with the observation that although opening to trade is beneficial to economic growth on average, the aftermath of trade liberalization varies considerably across countries and depends on a variety of conditions related to the structure of the economy and its institutions. A simple exercise may serve to convey this point. Figure 1 plots changes in growth rates of per capita GDP between the 1990s and 1980s versus changes in the volume of trade (as a ratio to GDP) between those two decades for a worldwide sample of 82 countries. Figure 1 has four panels; in each of them we separate the country observations according to whether they belong to the top one-third (diamonds) or bottom two-thirds (circles) of a rank distribution given by, in turn, each of the following criteria: a) secondary enrollment rates (a proxy for human capital investment); b) main telephone lines per capita (a proxy for public infrastructure); c) a subjective index of the quality of governance; and d) a _de facto_ and _de jure_ index of labor market flexibility. (Appendix 2 gives details on variable definitions and sources.) Each criterion used for ranking country observations is measured over the 1980s, the beginning period.

Dividing the country observations into top and bottom groups allows us to compare the corresponding slopes for the relationship between changes in trade volume ratios and changes in economic growth rates. In all panels, the OLS line described by the bottom observations is basically flat, implying no relationship between trade opening and growth improvement in the bottom groups. However, this changes for the top groups: for the top observations, the slope of the OLS line is positive and steeper than that for the bottom group.<sup>1</sup> This clearly suggests that the empirical impact of trade opening on growth may depend on the existence and degree of distortions in non trade areas. Of course, this is quite a simple exercise and it does not control for other growth determinants (such as initial per capita GDP), does not account for joint endogeneity, and does not use all

> 1 For the top observations according to educational investment, public infrastructure, and governance, the slope of the OLS line is significantly positive at conventional levels.  The top observations according to labor market flexibility also describe a positive slope that is larger than that of the bottom group, but it is not statistically significant.  The more satisfactory methods later in the paper, however, indicate that the impact of labor market reforms on the trade opening/growth relationship is in fact strongly significant.

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season. The national rate of 12.7 percent is the same as the rate reported to the OECD by the SPO for 1985(Table-1.4).. . For earlier years there are no national LFSs but only special surveys for urban and rural areas as mentioned above.

For urban areas the LFSs for the years 1982-85 should be fully comparable. The coverage is limited to cities with more than 10,000 inhabitants (tabulations for cities with more than 20,000 inhabitants exist, ibid. pp. 75-88); in 1980 the total population in such cities actually exceeded slightly that in urban areas defined as provincial and district centers. 1968 and 1969 should also be mutually comparable, apparently covering all conglomerations. 1967, finally, covered only cities with 50,000 inhabitants and abov3 with a population about 55 percent of that of urban areas in the just mentioned sense. Assuming unemployment to increase with city size, not a self-evident assumption, 1967 might be upward biased as compared with 1982-85 which in turn might be upward biased as compared with 1968-69 which in so far as size is concerned should be unbiased. For 1967-69 the source refers to November as the time of the surveys. For 1982-85, October-November appear to be the time of the surveys. We have finally the problem that 1967-69 cover persons age 15 and above, 1982-85 persons age 12 and above. High youth unemployment could create an upward bias for 1982-85 as compared with 1967-69. What these possible biases add up to is hard to say. Assuming the biases not to be seriously distorting, and disregarding the

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|**Table S6.1.**<br>σ<sup>2</sup>(uc)<br>/σ<sup>2</sup>(lnYh)|**: Simulated inequ**<br>Per capita<br>household<br>consumption|**ality measure**|**s in the presence**<br>Per capita cell c|**of measurement**<br>onsumption|**error**|
|---|---|---|---|---|---|
||Gini|Gini|Theil|Theil within|Share of<br>within<br>inequality|
|10%|48.83<br>(0.39)|49.61<br>(0.39)|0.50<br>(0.01)|0.01<br>(0.00)|0.03|
|20%|49.65<br>(0.52)|51.16<br>(0.50)|0.53<br>(0.02)|0.03<br>(0.00)|0.06|
|30%|50.60<br>(0.66)|52.78<br>(0.63)|0.57<br>(0.03)|0.04<br>(0.00)|0.08|
|40%|51.37<br>(0.82)|54.12<br>(0.75)|0.60<br>(0.03)|0.06<br>(0.00)|0.10|
|50%|52.10<br>(0.79)|55.41<br>(0.73)|0.63<br>(0.03)|0.07<br>(0.00)|0.11|
|60%|52.79<br>(0.82)|56.57<br>(0.77)|0.65<br>(0.03)|0.08<br>(0.00)|0.13|
|70%|53.61<br>(0.90)|57.86<br>(0.82)|0.69<br>(0.04)|0.10<br>(0.01)|0.14|
|80%|54.40|59.04|0.72|0.11|0.15|
||(1.13)|(1.01)|(0.06)|(0.01)||

Source: « Pauvreté et Structure Familiale » (PSF) survey, authors’ simulated distributions, 100 replications, standard errors between parentheses.

Note: Decomposition of inequality for simulated distributions of individual consumption, with varying cell level measurement errors added to an original distribution with no intra-household inequality.

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# **II. Country cases**

32. Over-indebtedness resulting from the crisis and the adoption of the euro in the context of a weak policy and regulatory environment are common themes for all European countries currently affected by the euro crisis. There are, however, considerable differences among various countries.

# **Ireland**

33. The crisis in Ireland is essentially one of a boom and bust of a real estate bubble. Encouraged by the fall in interest rates that went along with the adoption of the euro, banks obtained funding from British, German and US banks, usually in the form of short-term debt, foreign-owned bank deposits, or foreign-owned portfolio equity, to expand credit to the private sector. Figures 11 and 12 below indicate the degree of Irish indebtedness.

<!-- Start of picture text --> Figure 11: Foreign bank claims in Ireland<br>1200<br>1000<br>US banks<br>800<br>British banks<br>German banks<br>600<br>French banks<br>Belgian banks<br>400<br>Other Europe<br>200 Rest of World<br>0<br>Dec.1999 Dec.2001 Dec.2003 Dec.2005 Dec.2007 Dec.2009 Dec.2011<br>Source: Bank for International Settlements.<br>US$ (millions)<br><!-- End of picture text --> 17
resource sector are predominantly from countries with strong institutions and investors in such countries are more sensitive to instability, then this might be reflected in a spurious correlation between greenfield FDI into resource sectors and instability. To test this hypothesis we separately examine investment flows to different sectors from countries with strong versus weak institutions. The results, presented in Table 6, however, reject the notion that the responsiveness of investments differs between the two country groupings; differences in coefficient estimates between different country groupings are consistently very small.

Another potential concern is that the results are driven by differences in adjustment dynamics and investment gestation periodicity across sectors. Some extraction activities require years of planning and perhaps investors financing such projects are less concerned with shortterm risks but instead have longer term planning horizons. To assess whether this might help explain the patterns of results, we re-estimated our sectoral models presented in Table 4 using both 1-year and 2-year moving averages of the political instability variable as proxies for political risk instead. The results, which are presented in Table 7 below, remain robust to taking a medium-term perspective; we continue to find a negative association between instability and FDI into non-resource tradable manufacturing and commercial services, but fail to find a significant association for FDI into the resources and non-tradable sectors.

# **4.  Conclusions**

Political instability is often alleged to undermine incentives to invest, yet empirical studies have resulted in widely varying estimates of the relationship between political instability and FDI inflows. One possible explanation for the ambiguity in empirical findings is that the relationship between political instability and FDI varies across sectors. Our analysis of quarterly greenfield FDI investments into the countries of the Middle East and North Africa attests to the 19
# **<u>Appendix C.  Returns to education and experience</u>**

The estimated returns to education and experience used in the cross-country regressions were derived from Montenegro and Patrinos (2009). They use an earnings function where the natural logarithm of earnings (hourly, weekly, monthly, etc.) is a function of schooling and experience in the labor market. The specification they use is the following: where _LnW_ is the natural log (of hourly or annual, depending on data) earnings for the _i_ th individual;<sup>_S_</sup> _i_<sup>is years of schooling (as a continuous variable);</sup> _X i_ is labor market experience (estimated as: agei - Si - 6); Xi2 is experience-squared; DGi is a dummy variable that takes the value 1 for females;  _j_  _Pji_ is a set of control dummy variables (Pj) to take into account the periodicity of the earnings; and<sup></sup> _i_<sup>is a random disturbance term reflecting unobserved abilities.</sup> Therefore,  1 can be viewed as the average rate of return to years of schooling and  2 2  3 _X_ as the average rate of returns to years of experience.

Montenegro and Patrinos (2009) estimated this Mincerian specification equation for men and women jointly and separately, and also for urban versus rural. Their sample includes all workers 14-65 years of age with positive employment earnings, and positive labor market experience. Their estimates include all the countries with appropriate information that were available in the sample described in detail in Montenegro and Hirn (2008).

Appendix Table C1 presents the Montenegro and Patrinos (2009) calculations: 35
# **Figure 18. Origin of methodological weakness in studies done in general populations and youth, by country. Andean Region; 2009**

<!-- Start of picture text --> Defines objective<br>Inclusion criteria<br>Sample size<br>Sampling technique<br>Technique for reducing data collection errors<br>Analytical technique<br>Describes limitations<br>Calculation of statistical errors and/or p<br>value<br>0 20 40 60 80 100<br>Percentage of studies<br>Peru n= 21 Ecuador n= 19 Bolivia n= 12<br><!-- End of picture text -->

**Source:** Authors’ calculation using information from _Database and References for an Evidence-based Strategy_ in Peru, Ecuador and Bolivia.

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