rafmacalaba/data-use-mentions-extended huggingface dataset:
13 mortality rate, lowering infant mortality from IMRO to IMR2, rather than leaving it unchanged.

14. The distinction between the direct (structural form) effect and the total (reduced form) effect is of more than theoretical interest. Differences between the two effects can be substantial and if fully understood by policy makers can make the difference between acceptance or rejection of selected health and family planning programs. The remainder of the paper elucidates the differences between these two effects for the Indonesian case.

# Data

15. The data used to estimate the structural equations is derived from aggregated province level information for 26 of the 27 provinces in Indonesia. The 27th province, Timor Timor, was only recently formed and does not have complete data, but it compri3es less than one half of one percent of the population. The demographic and socioeconomic information, with the exception of government health expenditures and household income, is for 1980 to 1983 and was collected by the divisions of statistics in the Ministry of Health and the Department of Planning and published in a summary report by ESCAP (1984). Because detailed budget data is not available for earlier years, expenditure information is based on analysis of MOH regular and development budget data for 1984 (March 1984 to February 1985) and includes budgeted recurrent expenditures at the Central and Provincial levels. The budget headings allowed the identification of hospital expenditures. Relating 1984 government expenditures to 1980 demographic
dummies||yes|Yes||yes|Yes|
|Material dummies||Yes|Yes||Yes|Yes|
|District FE|||yes|||yes|
|Observations|2,960|2,943|2,943|2,959|2,942|2,942|
|R-squared (within)|0.037|0.027|0.028|0.005|0.005|0.005|
|R-squared (overall)|0.072|0.375|0.394|0.015|0.094|0.107|

_Notes_ : Difference-in-difference regressions with robust standard errors. * p < 0.10, ** p < 0.05, *** p < 0.01. p-values in parentheses. Full sets of control dummies are included for household size and the type of construction material used for the dwelling (permanent, semi-permanent or traditional).

_Source:_ Authors’ analysis using data from the Living Standards Measurement Study (LSMS).

12
|47.4|55.4|52.1|41.7|
|1 child|23.4|24.4|22.9|23.8|25.7|28.7|31.8|34.0|32.4|36.6|41.4|49.4|41.3|48.6|46.9|35.9|
|2 children|28.7|32.3|26.7|30.1|36.5|37.9|41.7|43.7|44.1|44.9|53.3|60.7|48.9|58.4|52.5|40.1|
|3 and more|||||||||||||||||
|children|97.7|88.4|86.4|80.8|86.1|86.3|88.3|86.5|88.9|96.3|88.3|90.6|89.1|96.0|89.8|95.8|
|singleparent|60.2|62.2|64.9|60.6|60.9|64.4|68.0|64.7|67.1|66.5|68.1|72.7|67.0|65.0|67.8|64.3|

Source: Authors’ estimates based on HSS data.

31
when explaining the region’s high inequality or low productivity. It also requires that policymakers and academics recognize that the emphasis on superstar firms currently dominating the inequality debate in the developed world directs the spotlight away from the segment of the economy where the incomes of a vast majority of Latin Americans originate.

In terms of the business size distribution, our work is closest to Poschke’s (2018), which departs from the use of firm-level datasets by using information at the entrepreneur level from the Global Entrepreneurship Monitor and Amadeus. Our use of the official employment/household surveys, the most comprehensive data sources on workforces at the national level, permits a full coverage of businesses of all sizes and motivations –including survival entrepreneurship lacking employment generation potential–. It also enables us to tie business size to the earnings of individuals associated with different-sized businesses. The prevalence of self-employment without employees as a defining characteristic of less developed economies, and its relationship with survival –rather than transformational– entrepreneurship, has been documented, among others, by Schultz (1990); Pietrobelli et al. (2004); Gindling and Newhouse (2014). Our analysis merges that fact into a more comprehensive view of the business landscape in LDCs versus AEs, and of how its characteristics are reflected in individual incomes.

The paper proceeds as follows. Section 2 presents the data. Section 3 shows our results, first characterizing the overall business size distribution (Section 3.1.) and then the relationship between personal earnings and the size of businesses where those earnings originate (Section 3.2.). The implications of our results are discussed in Section 4. Throughout the paper, we maintain a comparative perspective, contrasting LATAM with European economies, the US, and some Asian economies, including both those more and less advanced. The sample of comparators is dictated by data availability.

# **2 Data**

The use of comprehensive data on the productive activities where individuals obtain their income, either as self-employed or as salaried workers
Transparency International, the Political Risk Service, the International Risk Guide, and the Business International. Focusing only on the corruption ratings one should note a higher values go in line with lower corruption. We observe a highly positive correlation between the corruption ratings and the tax morale, including 19 countries (r=0.610, significant at the 0.01 level). In other words, a higher level of tax morale is correlated with lower corruption. This result is consistent with Uslaner (2007) in showing that high levels of perceived corruption are associated with high levels of tax evasion.

Torgler (2007b) using a multivariate analysis indicates that there is a significant negative correlation between tax morale and the perceived size of corruption. The advantage of this study is that perceived corruption was collected at the individual level. The use of ̳perceived corruption‘ is in line with other indexes employing measures of perceptions (such as the Transparency International index). However, perceptions are not objective, nor are they quantitative measures of the actual degree of corruption. Rather, perceptions are an indirect mechanism for measuring corruption (Tanzi, 2002). However Treisman (2000, pp. 410-411), in his analysis of the Transparency International index, was able to present valid arguments as to why data based on perceptions should be taken seriously. Components of the surveys and ratings are highly correlated among themselves, even though they have been conducted with different methodologies, different inputs and in different time periods. Such consistency allows us to conclude that factors are almost free of biases such as a ―temporal mood‖ or guesses. A practical method by which one can test whether the World Values Survey question about the perceived corruption is through the use of a useful proxy to check whether the variable is correlated with other well-known indexes on corruption. Thus, comparing the WVS variable with the corruption indexes TI (Transparency International), International Country Risk Guide (ICRG) and Quality of Government (Control of Corruption) developed by Kaufmann, Kraay,
# **5. Summary and Conclusion**

In this paper we tested two opposing hypotheses regarding the effect of aid fragmentation on aid tying. Results offer no support for the hypothesis that more concentrated aid confers monopoly power on donors that they exploit by tying more of their aid. Instead, we find strong evidence that lower fragmentation is associated with less aid tying. This result is predicted by a model of donor behavior adapted from Knack and Rahman (2007) and based on collective action theory. In countries where aid is less fragmented across donors, the typical donor has stronger incentives to pursue development objectives rather than commercial (or other) objectives. A given donor operating in multiple recipient countries has a stronger incentive to pursue development objectives where it has a larger share of the total aid market in a country. Our findings are robust to alternative definitions of aid tying, and to a strong set of controls (including donor and recipient fixed effects). Moreover, instrumental variables tests in our panel data support the interpretation that higher donor aid shares reduce aid tying. In our cross-sectional tests, aid tying rises initially as the number of donors increases, but eventually begins to fall. This result - that aid tying is lower not only when there are very few but also very many donors - is not predicted by our theoretical approach, but nor is it consistent with concerns that fewer donors operating in a country implies enhanced leverage on their part to tie more aid. Moreover, in our panel data tests, higher donor aid shares are monotonically associated with lower aid tying.

Aid tying also varies inversely with the costs of aid tying (proxied empirically by donor country size and per capita income), as predicted by the model. We also find that aid to the LDCs is less likely to be tied, indicating that the DAC recommendation on aid untying to least developed countries has influenced donor behavior.

This paper adds to a growing body of empirical evidence on the adverse impact of fragmented aid (Djankov et al.,
Survey, the adjusted version shows good targeting performance on the entire wealth distribution ∂f<sup>∗</sup> <u>(x)</u> with < 0, ∀x∈[1,100].<sup>18</sup> However, testing for robustness with respect to α�, a flat area ∂x appears among the 10% poorest. Using NSS Bihar data we also find high targeting performance ∂f<sup>∗</sup> <u>(x)</u> for FCi∗(x) ∈[20,40], but we also see a deterioration among the poorest 20% ~~�~~ ∂x ≥0, ∀x∈ . [1,20]� The definition of Li is not exactly the same in the NSS and in the World Bank’s Bihar surveys. This implies some discrepancies in the impact of the proposed adjustment on targeting performance assessments. Nevertheless, we see consistently better targeting performance among the 50% poorest when using Ci∗ instead of Ci, with less impact of the adjustment among the richest 30%.

We test if involuntary unemployment matters by estimating f<sup>∗</sup> and f for un-rationed households only using the information available in the World Bank’s Bihar survey. Redoing our analysis on this subsample, we get an estimated f<sup>∗</sup> that is clearly steeper around the bottom of the distribution. The participation rate among the poorest is more than double using adjusted consumption. This pattern is robust when changing α�, as observed in Figure 6.

It will be recalled that the above calculations only adjust for the disutility of casual manual wage labor. As we note in the introduction, this is a type of work in this setting that is very likely to yield disutility—far more so than self-employment on one’s own farm or regular salaried work. But that is an assumption on
Figure 1 presents the scatter plot between each of a set of measures in the Database at the country level and the stage of development measured by GDP per capita along with the R- squared from the corresponding regression among the plotted pair of variables. Appendix 3 presents analogous scatter plots between each of a set of measures in the Database and either the economic size of the economy measured by GDP or the importance of the export sector measured by the ratio of exports to GDP. The Database measures as well as the GDP per capita, GDP, and the ratio of exports to GDP values are taken as averages over the period 2006-2008.<sup>37</sup>

**Figure 1: Correlations of Selected Database Measures with GDP per Capita (averages over 2006-2008)**

<!-- Start of picture text --> Number of Exporters - GDPpc Mean Exports per Exporter - GDPpc<br>ESP<br>TUR BEL<br>MEX SWE<br>PAK IRNBGRZAFBRA PRT NZL BEL NOR BRAMEXCHL<br>TZABGD KEN EGY MARGTMJORSLVECUALBPERMKDDOMCOLCRIMUSBWALBN CHL EST KWT MLIKHM MAR PER ZAFCRIBWA ESP SWE<br>MWI NERUGABFAMLIKHMLAO SENNICYEMCMR MWI NERUGATZABFABGD KENLAOPAKSENNICYEMCMR EGY GTMJORSLVECUIRNMKDBGRDOMCOLMUS TUR ESTPRT NZL KWT NOR<br>LBN<br>R2=0.72 ALB R2=0.34<br>6 7 8 9 10 11 6 7 8 9 10 11<br>Ln GDPpc Ln GDPpc<br>Share of Top 5% Exporters - GDPpc Number of Products per Exporter - GDPpc<br>BWA<br>MWI NERUGATZABFAMLI KENLAOPAKSENYEMNICCMR EGY MARGTMSLVJORECUALBPERIRNMKDDOMBGRCOLZAFCRIMUSBRALBNTURMEXCHL ESTPRT NZLESPKWTBELSWE NOR MWI NERUGATZABFABGDMLIKHMKENPAKSENNICYEMCMR MARGTMSLVECUPERIRNMKDBGRDOMCOLZAFCRIMUSBWALBNTURMEXCHL ESTPRT NZLESPKWTBELSWE NOR<br>BGD ALB<br>KHM LAO JOR R2=0.40<br>6 7 8 9 10 11 6 7 8 9 10 11<br>Ln GDPpc Ln GDPpc<br>12 17<br>10 16<br>8 15<br>Ln Number of Exporters 6 14<br>Ln Mean Exports per Exporter<br>4 13<br>
Our dataset includes 110 developing and developed countries and covers the period of 19942009.<sup>4</sup> Countries are selected based on data availability.<sup>5</sup> For the purpose of consistency, all series are extracted from World Bank’s World Development Indicator (WDI) Database and they are all for central government only. The average values of the tax rate for each country are reported in column (2) of Table A1 in the Annex.<sup>6</sup> Given that differences in income levels and across regions can be important factors in determining tax revenues of countries, tax rates are investigated across income groups and regions. Simple averages are calculated for each group. The data points for the years of 1994, 1998, 2003, and 2009 are reported in the following figures. The results confirm the ones presented in Bird (2007), Fox, et al. (2005), and Le, Moreno-Dodson, and Rojchaichaninthorn (2008).

# **_Income Classification and Taxation_**

One important factor determining tax revenue of countries is their income levels. When countries are classified based on the share of tax revenues in percentage of GDP across income groups, it can be seen that differences across groups are sharp (see Figure 1).<sup>7</sup> The low-income group has the lowest tax-to-GDP ratio, but it has been improving since 1998. The improvement is clearer especially in recent years. For this group of countries, the average share of taxes in GDP increased to 13.6 percent of GDP in 2009 from 10.5 percent in 2003 and 10 percent in 1998. Throughout the years, each group managed to increase their average tax-to-GDP ratio, but this increase is much higher in the low-income group. Even there is still a large room for further improvement, recent developments are very promising, given the fact that this group of countries always finds it difficult to raise enough public funds to finance enormous development
Z is the ratio of imports of goods and nonfactor services to CNP, Y is CUP its real terms at 1974 dollars, as adjusted for cross-country purchasing power differences, P is crude oil production per capita, L is arable land per capita, I is a dummy variable for presence of significant iron ore reserves (1 if present, zero if not), T is the index of transportation costs discussed above, D. is a dummy variable for Japan (1 for Japan, zero otherwise), and log is ihe natural logarithm; t- statistics are in parentheses. This equation is from annual data for 1974 through 1984, for the pool of 11 countries plus the EEC, giving 132 observations. See Bergsten and Cline (1985), p. 77.
of the business cycle and short term fluctuations, as expected. Results based on the longer sample are reported in the last three columns of Table 5: Permanent component (column III), supercycle (column IV), and long term trend (column V). A summary of the results is also depicted in Figure 7. A number of important findings emerge from the supercycle analysis. First, the decomposition revealed four supercycles, consistent with the literature summarized in Table 2 (also shown in Figure 8). Second, the share of the permanent component based on the 1900-2020 sample was much larger compared to the 1970-2020 sample for all commodities, in some cases considerably so—the average share increased from 60 percent to 80 percent. Third, longterm trends (with frequency of more than 50 years) account for twice as much price variability compared to supercycles (with frequency between 20 and 50 years). Fourth, metals revealed a high degree of heterogeneity while annual agriculture is a highly homogenous group, further confirming results from the earlier analysis. Lastly, while the permanent component of agriculture was dominated by long-term trends, that of metals was

> **14** The sample based on 1970-2020 data is not long enough to capture supercycles which could last up to 50 years. Thus, the sample extension to 1900-2020.

> **15** The annual data used for analysis came from the World Bank’s database (since 1960), complemented by Grilli and Yang (1988) during 1900-60.

_— 13 —_
-42 -

Hiring lesser qualified teachers and encouraging cheaper types of schools are obviously feasible in many of the countries where public sector teacher pay is relatively high. A direct consequence would be to allow coverage to expand; at the same time the policy does not necessarily compromise student learning. A recent evaluation suggests that students in Mali's "ecoles de base" in fact outperform their counterparts in regular public schools; and that students taught by the "volontaires" in Senegal outperform pupils taught by the more qualified teachers.<sup>37</sup> Thus, although gains in coverage and improvement in classroom conditions generally materialize as part of the process of economic development, countries achieve better or worse results than other countries at comparable levels of economic development according to the policies they adopt, particularly with respect to budget priorities, teacher salaries, and the relative emphasis on expansion of coverage and decreases in pupil-teacher ratios.

# **5. Conclusion**

In this study we have used aggregate data to explore the relation between country wealth and various aspects of educational development; examine the underlying sources of differences in education among rich and poor countries; and raise questions about promising directions for policy development in the sector, particularly in the context of lower income countries. As countries become richer, they generally spend more per pupil, expand coverage of the school-aged population, lower the pupil teacher ratio, achieve improvements in student learning, enhance the efficiency of student flow patterns and sector operations, and improve equity in access to schooling and the distribution of public spending on education. On some dimensions, such as coverage, access and per pupil spending, the improvement follows a relatively steep rise with per capita GNP, whereas on other indicators, such as student learning, the rise is less steep, and on yet other indicators, such as the share of enrollments in private schools, there is hardly any relationship to the per capita GNP.

An appealing explanation for the generally superior outcomes in richer countries is that they have more
extent to which firms have a foreign share. In our case, information on both variables are hard to find. Ideally, we would need tariffs at 2‐digit NACE level, but this information is not directly reported at Eurostat or WITS. On the other hand, since a long time tariffs within the EU have been set at community level encompassing all member countries. Even though these EU‐wide tariffs are available in WITS, they only have a time‐varying dimension and would therefore drop out from the regressions due to our sector‐year fixed effects.

Similarly, although input tariffs are multiplied with country‐sector varying input‐output coefficients and should therefore not be dropped from our regressions, since tariffs are measured at EU‐level what we would measure otherwise is precisely these coefficients and not any country‐sector variation stemming from restrictiveness. Furthermore, regarding the foreign share of firms, unfortunately the Amadeus database does not report time‐varying information on this variable, which means that this variable remains constant throughout our period selection. It therefore does not allow for any variation over time at the level of the firm, country and sector and so this variable would be dropped from our regressions as well. Taken together, all our control variables are regrettably omitted in our specification.

# **3.3 Baseline Extension**

In a next step, we would also like to expand our baseline specification to take into account any differential impact of services reform on TFP of firms located in a country that is institutionally strong. The recent empirical services literature has found that domestic institutions matter as a type of governance for services reform because of a variety of reasons. First, Van der Marel (2016) shows that domestic institutions in terms of strong regulators are essential in terms of “shaping” domestic competitive markets after services liberalization has taken place. Merely opening up a services sector would not suffice due to the many market failures from which services sectors suffer and which therefore requires additional design of regulations in order to
example, in the case of signal strength). However, only 13 respondents identified a religious station as their most preferred broadcaster.

The number of community radios to which villages have access ranges from zero to seven stations, averaging 2.4 and with a standard deviation of 1.5. All but 20 villages in the sample receive strong signals from one or more relayers of the national broadcaster and nearly all villages (175) have access to both a community radio station and national public radio. About 23 percent of villages have access to private commercial and 34 percent to religious radio stations.

Of the 3828 households that listen to some radio, 64 percent report listening to at least one community radio station and 45 percent report listening to national public radio.<sup>11</sup> Only 176 report listening to private commercial radio.<sup>12</sup> These preferences are consistent with availability (few households have access to private commercial radio), and with claims of broadcast media experts that the programming of national broadcasters conforms less well to the tastes and linguistic preferences of poor households in rural Africa compared to local radio stations (Buckley et al, 2008).

The observations drawn from the survey are not contaminated by noise from other mass media. Especially in the study area in northern Benin, radio is essentially the only media to which most citizens have access. Twenty-five percent of respondents to the nationally representative Afrobarometer (2005) survey of Benin report that their household owns a television. In the sample here, of 4200 households from northern Benin, only eight percent own a television, but 84 percent

> 11 ―Listenership‖ data was gathered by simply asking households to name the station they listen to and then having our investigating team post-code the response for type of radio. We did not directly prompt the household to categorize what _type_ of radio they like listening to.

> 12 Even among these 176 that report listening to some private commercial radio, only 67 report listening _only_ to commercial radio