rafmacalaba/datause-encoder-data huggingface dataset:
spillovers from one country’s tax system to real investment elsewhere.<sup>7</sup> Against that backdrop, the core purpose of this paper is to make further progress on what thus remains, somewhat surprisingly, one of the least-understood aspects of international tax arrangements: the impact on real investment. The results then enable us to address a key but somewhat neglected aspect of the prospective impact of the global minimum tax: its effects on real investment, both by country and in aggregate.

The fundamental problems associated with the use of FDI data are overcome here by using a newly-constructed dataset on Foreign Affiliate Investment (FAI). These data explicitly record the acquisition of tangible assets (new and old) by foreign affiliates in each host country,<sup>8</sup> and see through conduit flows to identify the country of the ultimate parent. Consequently, neither of the main shortcomings of FDI data applies. Estimation can explore the effects on host-parent bilateral FAI flows of tax systems in the host country, parent country and other potential host countries. Our dataset covers inward investment from 187 parent countries into affiliates in 32 (advanced) host countries from 2003-2016, along with detail on a range of activity variables. The rich information in this dataset helps disentangle the effect of taxes from unobserved, confounding factors on inward foreign investment. The limitation of these data to tangible assets is a significant one, given the increasing importance of intangibles in multinational activity; but there can be little doubt as to the importance and salience of tangible investment for the formation of tax and other policies.

Armed with these data, the conceptual challenge is then to elucidate the precise channels by which taxation might affect inward FAI. Candidates explored in previous work, sometimes somewhat haphazardly, include statutory tax rates, average effective tax rates (along the lines of Devereux and Griffith (2003)) and marginal effective tax rates of the kind developed in closed economy contexts (classically, as in King and Fullerton (1984))<sup>9</sup> . The precise routes by

> 7The semi-elasticity on which the meta-analyses of De Mooij and Ederveen (2003) and Feld and Heckemeyer (2011) focus is that with respect to the host country rate. Some studies do construct simple tax differentials: B ́enassy-Qu ́er ́e, Fontagn ́e and Lahr`eche-R ́evil (2005), for example, examine the effect of tax differentials on bilateral FDI, though the differential is in this case simply that between host and parent statutory rates.

> 8They thus put to rest the lament with which the quotation from Slemrod (1990) continues: “Unfortunately, no data exist on real investment made by foreign branches and subsidiaries.”

> 9The King-Fullerton marginal effective tax rate is the wedge between the pre- and post-tax returns on an

4
Since we estimate the regressions using a linear probability model, we can interpret the coefficient on the Lerner index as an increase in the probability of access to finance. In our sample, one standard deviation of the Lerner index is about 0.07. Using the estimated coefficient of about 0.75 (from model (b) in Table 2, which corrects for the variance of the Lerner index by appropriate weighting), we obtain that a one standard deviation change in the Lerner index results in an approximately 5 percentage points change in the probability of having access to finance. In our sample, the average access to finance dummy equals one for about 60 percent of all firms, with a standard deviation of about 49 percent. A 5 percentage points change is modest for an average country, but it is more economically important for a country with low access to start with.

Most of the control variables have the predicted signs. Larger and older firms are more likely to have access to finance. Manufacturing firms are more likely than service and retail enterprises to have access to finance, because they have more collateral which helps them obtain financing. Exporters also are more likely to have access to bank finance, however, foreignowned firms are less likely. This might be because foreign firms can obtain financing from their parent company and, thus do not need to borrow from local banks. At the same time, government-owned firms appear less likely to have access to bank finance, which is a little surprising. We find that private credit, which is our measure of financial development, is associated with a higher likelihood of access to finance. The inflation rate has a negative association with our measure of access to finance, which is not surprising. Note that because of the country fixed effects, these variables capture the impact of the variation in inflation and private credit from the long-run country average.

In Table 3 we present our robustness checks with regressions weighted by the inverse of the square root of the number of firms in the country-year survey. Because there is a large variation in the number of firms in each survey and our variables of interest are measured at the country-year level, the regressions reported so far give more weight to surveys with a larger number of firms, which will have a disproportional impact on the estimated coefficients. We find that weighting by the inverse of the number of firms has no impact on the significance of the Lerner index. We continue to find that low competition has a negative impact on firms’ access to finance.

10
system.<sup>5</sup> However, we lack data for this variable for many countries that are included in our sample. Instead, we rely on the financial development index of Svirydzenka (2016). This index of financial development distinguishes between financial institutions and financial markets development. For both of these categories, the index further distinguishes between financial depth, access, and effectiveness. We express this conditioning variable as the _ratio of financial markets' depth index to financial institutions' depth index._ Because measures of financial depth might exhibit some volatility over time, this variable enters our regressions as a 3-year average. Fourth, we condition on the share of foreign-owned banks in the domestic banking sector. _Foreign bank ownership_ is expressed as the share of total banking assets held by foreign banks, and the data is taken from the dataset compiled by Panizza (2020).

# **2.2. Stylized facts**

Our data show that average CO2 emissions in kg per PPP $ of GDP for the countries in our sample declined from 0.29 to 0.19 between 1994 and 2019. Over the same period, the average credit-toGDP ratio increased from 33 percent to 57 percent.

Figure A.1 shows the change in CO2 emissions per dollar of GDP and the credit-to-GDP ratio between 1999 and 2019 for 88 out of 125 countries in our sample. Because our panel dataset is unbalanced, we plot the changes for the 20 years between 1999 and 2019 to maximize the number of countries in this figure. Countries fall into one of four categories depending on whether their CO2 emissions per dollar of GDP and credit-to-GDP ratio increased or decreased. Most countries (49) experienced financial deepening coupled with a decrease in CO2 emissions per dollar of GDP over this period. This group includes primarily advanced economies. Meanwhile, 14 countries experienced a decline in CO2 emissions per dollar of GDP and a decrease in financial deepening. At the same time, 22 countries experienced an increase in CO2 emissions per dollar of GDP and financial deepening. Most countries in this group are developing economies. Finally, in 3 countries, credit-to-GDP decreased while CO2 emissions per dollar of GDP increased.

> 5 The size of the entire financial system was calculated as the sum of the value of publicly traded shares (i.e., size of the equity market) and of the credit extended to the private sector by banks and other credit institutions.

8
40 Since the household survey contains information on which radio stations households listen to, it is possible to ask directly whether listening to stations that broadcast education programming influences household investment decisions. This is yet another link in the causal chain from radio access to education outcomes. Of households with children in school, 67 percent listen to community radio. Though this ratio is almost precisely the same (66 percent) for households that do not have children in school, it is nevertheless possible that the decision to listen to community radio is potentially endogenous to unobserved household characteristics that influence education investment. To address this possibility, radio access can be used as an instrument for listening behavior, assuming that radio access is exogenous to households and influences education investments only through its effect on household listening behavior.

The third column in Table 7 therefore asks whether households that report listening to at least one community radio station, using access to community, private commercial, public and religious radio stations as instruments for station preference. All types of stations are used as instruments since the decision to listen to any one type of station is dependent on the availability of other kinds. As the lower rows of Table 7 demonstrate, station access has a significant effect on whether households listen to community radio; the _F_ -statistic associated with the instruments implies that the probability that the instruments do not add explanatory power to the first-stage regression is less than two percent. Evidence from the Hansen‘s _J_ -statistic (with a _p_ -value of .92) is also supportive of the exclusion restriction: the effect of access variables on book-buying is only through household listening behavior. Using these instruments to control for the endogeneity of listening behavior, then, the estimated effects of listening to community radio are large: households that listen to community radio purchase of books for 1.8 additional children.

The fourth and fifth columns of Table 7 display the results for fee payments reported by parents. The fourth column shows the OLS estimates of the effect of living in a village with greater
- 23 -

# **REFERENCE**

- Alcazár, Lorena, Manuel Abdala, Mary Shirley. 2002. The Buenos Aires water concession, in _Thirsting for Efficiency: The Economics and Politics of Urban Water System Reform_ , eds. by Mary Shirley, pp. 65-102, Elsevier Science Ltd.

- Ausubel, Lawrence, Peter Cramton. 2002. Demand reduction and inefficiency in multi-unit auctions. Mimeograph.

- Baumol, William, John Panzar, Robert Willig. 1982. _Contestable Markets and the Theory of Industry Structure_ . Harcourt Brace and Jovanovich.

- Branco, Fernando. 1997. The design of multidimensional auctions. _RAND Journal of Economics_ , Vol. 28, pp. 63-81.

- Brannman, Eric, Douglass Klein, Leonard Weiss. 1987. The price effect of increased competition in auction markets. _The Review of Economics and Statistics_ , Vol. 69, pp. 24-32.

- Chakraborty, Indranil. 2006. Bundle and separate sales in auctions with entry. _Games and Economic Behavior_ , Vol. 54, pp. 31-46.

- Cho, In-Koo, Kevin Jewell, Rajiv Vohra. 2002. A simple model of coalitional bidding. _Economic Theory_ , Vol. 19, pp. 435-57.

- Christensen, Laurtis, William Greene. 1976. Economies of scale in U.S. electric power generation. _The Journal of Political Economy_ , Vol. 84, pp. 655-676.

- Clark, Robert, Richard Stevie. 1981. A water supply cost model incorporating spatial variables. _Land Economics_ , Vol. 57, pp. 18-32.

- Cripps, Martin, Norman Ireland. 1994. The design of auctions and tenders with quality thresholds: the symmetric case. _Economic Journal_ , Vol. 104, pp. 316-326.

- Garcia, Serge, Alban Thomas. 2001. The structure of municipal water supply costs: application to a panel of French local communities. _Journal of Productivity Analysis_ , Vol. 16, pp. 5-29.

- Gracía-Valiñas, M.A. and M.A. Muñiz. 2007. Is DEA useful in the regulation of water utilities? A dynamic efficiency evaluation (a dynamic efficiency evaluation of water utilities). _Applied Economics_ , Vol. 39, pp. 245-252.

- Guasch, Luis. 2004. _Granting and Renegotiating Infrastructure Concessions: Doing It Right_ . The World Bank.

- Gupta, Srabana. 2002. Competition and collusion in a government procurement auction market. _Atlantic Economic Journal_ , Vol. 30, pp. 13-25.

- Foster, Vivien. 2005. Ten years of water service reforms in Latin America: toward an AngloFrench model. Water Supply and Sanitation Sector Board Discussion Paper Series No. 3, The World Bank.
as measured by test scores at a point in time (Todd and Wolpin, 2003). Let _Ait_ be student _i_ ’s academic achievement (or test scores), and **S** _it_ and **X** _it_ denote vectors of school-supplied inputs (e.g. student _i_ ’s class size, school infrastructure, quality of teachers, etc) and family-supplied inputs at time _t_ , respectively. Allowing for the idiosyncratic error, which includes all omitted inputs (observed and unobserved) and measurement error, _εit_ , and inherited ability, _ai_ 0, the general achievement production function is given as: In this framework, a student’s performance at the university level is influenced not only by her current individual and household characteristics, but also by current and previous school/universitysupplied inputs. Because skill begets skill, the influence of past school-supplied inputs is partly reflected in the child’s performance in JC and COSC/LGCSE examinations.

# **4 Data and Descriptive Statistics**

This paper uses the National University of Lesotho’s students’ administrative records data for years 2010 up to 2014. It makes use of first-year and second-year students’ data. As the largest and oldest undergraduate degree-awarding university in Lesotho, the NUL admits the largest share of all students who achieve the minimum score for university entry in the senior high school exit examinations. It therefore admits the largest proportion of students who have gone through the FPE system. Given that the first cohort that has gone through the FPE program first entered university level in 2012, the second-year students are a control group, while the first years are a treatment group. I discuss this group categorization in detail in Section 5.

The data contains information on each student’s gender, date of birth, academic year, the senior high school exit (or Cambridge Overseas School Certificate (COSC)) examination pass grade, the NUL Admission Point System (APS) score<sup>7</sup> for each student, the high school attended, faculty, department or program of study, the overall weighted mean or year mean performance, and whether a student is local or international. The overall weighted mean (OWM) is the main outcome variable. It is calculated as the weighted sum of the final scores of core, pre-requisite, and/or elective subject courses that make a maximum of 36 credit hours, where the weights are

> 7 APS score (commonly known as the aggregate score) is the sum of grade points/ranks for 6 COSC subjects, including English language. For example, grade A<sup>*</sup> (or A<sup>+</sup> ) is given point/rank 1, grade A is equivalent to 2 points, grade B is equivalent to 3 points, etc. Therefore, the lower the APS score the higher the performance in COSC exams. The lowest achievable APS score is 6 and indicates the highest level of performance in all six subjects.

10
**Figure 8: Female labor share, global vs. non‐global firms, by country**

<!-- Start of picture text --> GVC participants vs. non‐participants<br>60%<br>50% lvabgr ukr mng<br>bih rus lka mdatjk mmr<br>40% col hrvnic mar<br>30% ecupolcritto chnmexkazgtmpannamperjam uzbkgzazeidnsrb geo slvtunarm blr mkdrou eth<br>zarbolpry zwechllbn uga bgd vnm<br>20% nga ghaargtza ken<br>10% wbgindegynpl zmbsen jor<br>irq pakyem<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%<br>Female labor share (%), GVC participants<br>FDI vs. non‐FDI firms<br>60%<br>mngbgrblr ukr<br>50% mar lka lva rou tjkmkd mdammr<br>rus<br>kgz ethtun<br>40% nam col idn srbgeojamvnmarmbih bgdslv hrv<br>30% ecu pangtmmex peruzb chnnic<br>20% pry tza arglbnzwechl ugapolkenttocri kaz<br>gha bol jor<br>10% wbgpak indegynpl zmbnga senyemzar<br>irq<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%<br>Female labor share (%), FDI<br>Female labor share (%), non‐participants<br>Female labor share (%), non‐FDI<br><!-- End of picture text --> Source: The female labor shares are averages using firm employment as weights. For country abbreviations, see Organization for Standardization (ISO), https://www.iso.org/obp/ui/#search.

# **4. Regression Results**

## **4.1 Baseline Results**

The following section shows the results of the regression analysis following the specification of equation (1). We include the following _additional control variables_ in all regressions:

- Output, ln _Y_ = total sales in natural logarithms.

- Capital intensity _,_ ln _k_ = capital stock per output in natural logarithms.

- Total factor productivity, ln _tfp_ = total factor productivity in natural logarithms.

16
# **Table** **<u>6</u>** <u>: Descriptive Statistics</u>

|**Variables**||**Ru**|**ral**|||**Urb**|**an**||
|---|---|---|---|---|---|---|---|---|
||199/|01|1995|/96|1990|/91|199|5/96|
||Mean|SD|Mean|SD|Mean|SD|Mean|SD|
|Head Sex (male)|0.62|0.48|0.64|0.48|0.82|0.38|0.80|0.40|
|Head Education, none (yes)|0.25|0.43|0.21|0.41|0.05|0.22|0.04|0.20|
|<br>Head Education, primary (yes)<br>|0.62|0.48|0.60|0.49|0.48|0.50|0.42|0.49|
|Head Education, secondary or<br>higher (yes)|0.13|0.34|0.19|0.40|0.47|0.50|0.54|0.50|
|Head age (years)|45.31|15.30|44.91|15.78|39.81|12.10|39.54|12.65|
|Household size (#)|5.28|3.07|4.88|2.83|4.23|2.73|4.09|2.54|
|# Children<sup>a</sup>|0.99|1.07|0.89|0.99|0.67|0.87|0.61|0.79|
|# Boys|0.88|1.07|0.79|1.01|0.50|0.84|0.48|0.82|
|# Girls|0.87|1.07|0.79|1.01|0.53|0.86|0.52|0.85|
|# Male adult with no education|0.11|0.32|0.08|0.27|0.03|0.19|0.02|0.13|
|# Male adults with primary<br>education|0.51|0.67|0.49|0.65|0.43|0.58|0.34|0.54|
|# Male adult with secondary or|||||||||
|higher education|0.38|0.68|0.38|0.65|0.82|0.91|0.86|0.87|
|# Female adults with no education|0.25|0.49|0.19|0.42|0.06|0.26|0.04|0.19|
|# Female adult with primary|||||||||
|education|0.69|0.72|0.66|0.70|0.45|0.60|0.39|0.57|
|# Female adult with secondary or|||||||||
|<br>higher education|0.29|0.61|0.32|0.60|0.64|0.82|0.72|0.84|
|# Elderly<br>|0.30|0.57|0.29|0.56|0.09|0.33|0.10|0.35|
|Access to credit (yes)<sup>b</sup>|0.20|0.22|0.30|0.19|0.48|0.15|0.37|0.16|
|Non-farm income share|0.56|0.38|0.55|0.41|0.97|0.12|0.96|0.16|
|# of income sources|3.88|1.67|2.96|1.41|2.32|1.15|2.20|1.12|
|# of income sources per capita|0.96|0.73|0.87|0.72|0.77|0.66|0.72|0.62|
|# of income earners|2.11|1.21|2.18|1.29|1.78|0.92|1.69|1.02|
|# of income earners per capita|0.50|0.28|0.54|0.29|0.55|0.31|0.50|0.31|
|% with formal wage income|0.31|0.46|0.30|0.46|0.72|0.45|0.64|0.48|
|% with informal wage income|0.11|0.31|0.16|0.37|0.19|0.40|0.28|0.45|
|<br>% with farming income|0.86|0.35|0.84|0.37|0.40|0.49|0.42|<br>0.49|
|% with non-labor income|0.69|0.46|0.46|0.50|0.47|0.50|0.36|0.48|
|Formal wage income share|0.27|0.42|0.26|0.41|0.66|0.43|0.58|0.46|
|<br>Informal wage income share|0.04|0.16|0.07|0.21|0.13|0.31|0.17|0.33|
|<br>Agricultural income share|0.42|0.38|0.44|0.40|0.02|0.11|0.03|0.14|
|Non-labor income share|0.23|0.30|0.20|0.31|0.13|0.28|0.14|0.30|
|Per capita real consumption<sup>c</sup>|78.5|111.8|59.64|97.18|243.5|438.2|146.9|278.4|
|Per capita asset holding|0.66|0.70|0.56|0.61|0.82|1.29|0.80|0.99|
|Home ownership (yes)|0.71|0.45|0.67|0.47|0.34|0.47|0.35|0.48|

Source: Zimbabwe ICES 1990/91 and 1995/96 a Children are those with age <=5 years; boys and girls are those between 6 and 15 years; adults are those between 16 and 59 years of age, and are further sub-divided by education level; and finally elderly are those over 59 years of age.<sup>b</sup> Credit access, an indicator of whether a household had access to a bank or other credit source, is measured at the community level.<sup>c</sup> Normalized to real terms by July 1990 Zimbabwe dollar using consumer price index that takes into account variations in survey month and regions.

19
Multiple studies document instances where climate mitigation policies have spurred innovation and efficiency. Albrizio, Kozluk, and Zipperer (2017) use a cross-country panel to document that climate policies can increase productivity in the most technologicallyadvanced economies, though the effect is only temporary. Though not focused on climate mitigation policies, Berman and Bui (2001) find that oil refineries subject to more stringent environmental regulations increase productivity relative to those subject to less stringent regulation in a historical study of U.S. refineries. Greenstone, List, and Syverson (2012) find that regulations on certain pollutants lead to higher firm productivity, though others generate reductions. These results point to the potential for climate mitigation policies to complement strategies for economic growth, but it is unclear to what extent similar mechanisms may work in LMICs.

A related strand of literature does not directly document innovation as a result of climate mitigation policies, but provides evidence that such policies do not reduce economic output and growth. In India, Garg and Shenoy (2021) document that explicitly excluding environmentally damaging industries preserved local forests while still generating high economic returns. In the U.K., R. Martin, Preux, and Wagner (2014) show that a carbon tax on manufacturing plants generates environmental benefits without reducing employment or plant revenues or increasing firm exit. While this evidence is promising, it is important to consider what factors enable firms to innovate in response to environmental policies and which types of firms may benefit. The sparse empirical evidence for firms in LMICs leaves open questions about how governments can best design climate mitigation policies that protect economic growth.

Despite evidence of complementary effects between climate mitigation policies and firm productivity, a large body of evidence also documents stark trade-offs. In China, He, Shaoda Wang, and B. Zhang (2020) find substantial reductions in TFP as a result of stricter environmental regulations and H. Fan et al. (2019) show sharp reductions in firm profits, capital, and labor. Greenstone, List, and Syverson (2012), Greenstone (2002), and Walker (2011) 32