rafmacalaba/datause-encoder-data huggingface dataset:
9

# 11.2 Measuring. the_DpXee_LfO_tns

Measurements on the restrictiveness of investment regulations of developing countries take the above mentioned issues into account. However, the perception of this restrictiveness varies from source country to source country. This is due to traditional and/or cultural links as well as to political factors. Germany never had colonies on a large scale and thus little particular cultural links to the developing world. It has on the other hand a strong competitive position in the classical sectors of manufacturing (for example chemistry) and thus might find it easier to operate in some developing countries than would other industrialized countries. Furthermore, Germany has a large number of bilateral investments treaties (63) and double taxation agreements (33) (see BMZ). In order to measure the degree of openness for German FDI these factors should be taken into account.

For the purpose of this study the degree of openness (OP) has to be quantified in order to be introduced in the regression analysis. This is of course a rather subjective task. One way to approach it is to summarize the information given in the synopsis in the appendix and classify countries into - - - very restrictive ( 1 ), restrictive ( 2 ), semi-open ( 3 ), and open ( 4 ) on the basis of the researcher's own judgement. Although no weighting index was quantified, the criteria of Part I of Annex II counted (in general) more than those of Part II, especially for "lender and ownership" restrictions. The approval process, although a very important criterion, is difficult to measure and was thus taken into account only in obvious cases. As it is often a composite of many rather harmless measures that form a severe restriction, no methodical procedure was applied in classifying countries according to the
|**Contents**||
|---|---|
|**1**<br>**Introduction**|**1**|
|**2**<br>**Brief background of the gravity model**|**3**|
|2.1<br>Advantages and disadvantages of gravity-based PTA analysis . . . . . . . . . . . . .|3|
|2.2<br>Review of the empirical literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|5|
|**3**<br>**PTA and trade variables revisited**|**9**|
|3.1<br>Documenting the rise in the number of PTAs . . . . . . . . . . . . . . . . . . . . . .|11|
|3.2<br>Estimating the share of world trade covered by PTAs<br>. . . . . . . . . . . . . . . . .|16|
|**4**<br>**PTAs and the gravity equation**|**28**|
|4.1<br>Model setup and baseline results<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . .|29|
|4.2<br>Application to exclusions and phase-in periods<br>. . . . . . . . . . . . . . . . . . . . .|38|
|4.3<br>Alternative specifcations of the PTA variable . . . . . . . . . . . . . . . . . . . . . .|43|
|**5**<br>**Conclusion**|**47**|
|**A Abbreviations and defnitions**|**52**|
|**B Theoretical foundations of the gravity equation**|**53**|
|**C Trade between PTA partners, regional detail**|**57**|
|**D Normality of regression residuals**|**58**|
|D.1 Signifcance of the normality assumption . . . . . . . . . . . . . . . . . . . . . . . . .|58|
|D.2 Analysis of the residuals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|59|
|**E Preferential Trade Agreements in force through 2004**|**62**|
|**F Data sources and data management**|**75**|
|F.1<br>Data source details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|75|
|F.2<br>Data transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|76|
|**List of Tables**||
|1<br>Distribution of PTAs by geographic region and income level . . . . . . . . . . . . . .|15|
|2<br>Number of countries in the trade and tarif database . . . . . . . . . . . . . . . . . .|17|
|3<br>Zero-duty MFN tarif lines as a share of all MFN ad-valorem tarif lines, percent<br>. .|21|
|4<br>Preferential imports as a share of world total under varying ROO thresholds, 2000-02|27|
|5<br>Gravity model estimation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|32|
|6<br>Wald test results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|37|
|7<br>Gravity model estimation results: preferential trade with exclusions and phase-in<br>periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|40|
|8<br>Simulation results: removing exclusions and phase-in periods<br>. . . . . . . . . . . . .|41|
|9<br>Alternative specifcation estimation results: preferential imports . . . . . . . . . . . .|44|
|10<br>Regional abbreviations and country membership<br>. . . . . . . . . . . . . . . . . . . .|52|
findings in Kusek and Silva (2018), where advocacy for improved business environment and business setup assistance ranked among the most valued IPA services.<sup>7</sup>

**Figure 4. Importance of IPA services**

<!-- Start of picture text --> How important are the following services offered by Investment Promotion Agencies to your<br>company in this country?<br>Most valued IPA service (composite) 61% 28%<br>Efforts to improve the business environment<br>35% 40%<br>in the country<br>Pre-investment information 30% 41%<br>Assistance in setting up 31% 39%<br>Assistance with operational issues and<br>29% 38%<br>grievances<br>Location marketing 29% 37%<br>Pre-investment assistance 25% 40%<br>Personalized contact/response to your<br>24% 40%<br>company<br>0% 20% 40% 60% 80% 100%<br>Critically important Important<br><!-- End of picture text --> _Source_ : Computation based on the 2019 GIC Survey.

# _Heterogeneity across investor types_

On average, importing a higher share of inputs is associated with placing greater importance on IPA services. When the importance of affiliates’ most-valued IPA service is used as the dependent variable, the coefficient on import share of inputs is positive and significant at the p<0.10 level in our preferred ordered logistic specification with country and subsector fixed effects (Table 41). This result is also robust to the exclusion of fixed effects and alternate specifications using OLS. Looking across specific services, we find similar results with respect to IPA assistance with business setup (Table 46, p<0.05) and with operational issues and grievances (Table 47, p<0.10). Both of these results are also robust to alternative specifications. Importers may value IPA services more insofar as IPAs help them navigate issues related to obtaining import licenses and addressing grievances concerning import processes, although further research is necessary to fully explain these results.

> 7 Assistance with resolving operational issues and grievances was the most valued IPA service in the 2017 GIC Survey, compared to the fourth-ranked service in the 2019 survey. Nevertheless, the 2019 survey continues to show a sizable majority (67 percent) valuing such services.

19
variation (standard deviation/mean). In Uganda and Rwanda, we also analyze a panel data set to assess the autocorrelation in outcomes and find substantially higher autocorrelation in the AAA measures compared to traditional estimates.

Next, as a direct test against an accepted benchmark, we compare the AAA measures to two sources of administrative data: bank loan officer evaluations in Ghana, and assessments by professional business coaches in Uganda. Note that administrative data on business performance for small firms are rarely available, but we carefully matched a small sample of administrative records to contemporaneous measures collected through traditional methods and the AAA tool for the purposes of this research. Across both settings, we first confirm findings from previous research that traditional measures underreport both sales and profits, with differences in profits being statistically significant. By contrast, the magnitude of AAA sales is statistically indistinguishable from administrative sales in either setting. Administrative profits are higher than AAA profits in Ghana, but the two measures are statistically indistinguishable in Uganda. The results also show that AAA measures are significantly more correlated with the administrative data, especially for profits. Hence, the AAA measures more closely match our best estimate of “true” performance values from administrative data.

Finally, we explicitly test the validity of the methodology in a field experiment in Ghana where we randomly assigned half of a small firm sample the electronic AAA tool and the other half an identical paper version of the AAA tool. We find that both electronic and paper versions of the tool significantly outperform traditional measures but are not different from each other, which indicates that the improvement in accuracy is due to the innovation in method rather than simply better survey technology.

Ultimately, this research reconciles the recommendations of de Mel, McKenzie, and Woodruff (2009) for increased attention to survey design, with those of Fafchamps et al (2012) for identifying ways to leverage an electronic survey technology beyond _ex-post_ consistency checks. This research is also closely related to focused work on improving the accuracy of consumption expenditure surveys at the household level (e.g. Beegle et al., 2012; Gibson, 2006; Deaton and Grosh, 2000). The findings from this research and the resulting survey tool are a public good that 6
|**Table 3c: Characteristics of Home-**|**based Workers in**<br>**Home-based**<br>_Informal_<br>_Entrepreneurs_|**Mexico(1999), **<br>**Workers**<br>_Contract_|**inpercent**<br>**Rest of the**<br>**Economy* **|
|---|---|---|---|
|_Sample size_|_4322_|_93_|_89683_|
|Female|64.8|41.9|35.5|
|Age 15-25|8.5|48.4|31.7|
|Age 26-45|51.6|40.9|48.6|
|Age 46+|39.9|10.8|19.6|
|No school|13.05|11.0|5.05|
|Primaryeducation|71.7|75.3|73.5|
|Secondaryeducation|15.2|13.7|21.4|
|Household head|46.3|20.4|47.5|
|Husband|23.7|14.0|37.3|
|Wife|35.5|20.4|13.8|
|Single Mother|3.6|2.2|3.01|
|Unmarried Female,no children|5.04|12.9|11.0|
|Unmarried Male|5.8|34.4|19.0|
|Child age 0-5 in household|27.8|37.6|29.4|
|Child age 6-12 in household|41.4|62.4|40.03|
|Child age 13-18 in households|42.2|59.1|43.02|
|Indigenous**|N/A|N/A|N/A|
|% Household income from this job?|47.0|28.0|54.0|

*Rest of the economy are those who are working, but not in their homes

** Ethnicity was not reported in the survey

29
Table 3: Earnings

||(1)<br>Earnings from<br>wage employment<br>(monthly)|(2)<br>Earnings from<br>self employment<br>(Profts, monthly)|(3)<br>Revenues from<br>farming<br>activities<br>(last 12 mths)|(4)<br>Revenues from<br>livestock<br>activities<br>(last 12 mths)|
|---|---|---|---|---|
|Panel A. Pooled Estimates|||||
|Treatment (ITT)|-154.47|1726.78|19909.81|-2091.02|
||(700.90)|(1777.12)|(17808.79)|(1544.53)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|
|Mean in Control|3057.36|24050.42|124989.51|8013.67|
|Observations|2,615|2,620|2,620|2,620|
|Panel B. Treatment Arm Estimates|||||
|VSLA (T1) (ITT)|230.73|859.10|26657.37|-685.07|
||(920.89)|(2372.50)|(24159.71)|(1990.21)|
|Cash Grant with repayment (T2) (ITT)|-170.29|2393.22|12659.68|-2162.26|
||(818.13)|(2217.70)|(19719.88)|(1609.86)|
|Cash Grant (T3) (ITT)|-905.88|2000.59|22505.26|-4839.52***|
||(807.37)|(3098.32)|(28995.56)|(1546.04)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|
|Mean in Control|3057.36|24050.42|124989.51|8013.67|
|p-value T1=T2|0.67|0.57|0.55|0.36|
|p-value T2=T3|0.34|0.91|0.74|0.02|
|p-value T1=T3|0.19|0.74|0.89|0.01|
|p-value T1=T2=T3|0.38|0.85|0.83|0.01|
|Observations|2,615|2,620|2,620|2,620|

Robust standard errors clustered at locality level. Earnings and revenues are in CFA franc and winsorized at 99%. Earnings are computed over the three main activities of the respondent. The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A5.

> _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
There is some suggestion of program impacts on large-scale deforestation. Based on the graphs, large-scale deforestation declines more rapidly, especially in 0-2 kilometer road distance, than small-scale deforestation. This pattern disappears when moving further than 5 kilometers from roads. Again, fragmentation of the forest within 2 km of roads will eventually result in a decline of large-patch deforestation relative to small. However, overall near-road deforestation appears to decrease relative to deforestation distant from roads.

# **4.12 Effects on size of deforestation patches**

The Environmental Control Program explicitly focused on large landholdings. The argument was that large-holders contribute relatively more to deforestation in Mato Grosso than smallholders. Thus we would expect the program to depress large-scale deforestation (i.e. related to large individual clearings) relative to small-scale deforestation. Indeed, the proportion of largescale deforestation, especially above 1000 hectares, decreased drastically in 2000-01 according to FEMA’s statewide figures (Figure 16).<sup>13</sup> There was an increase in the proportion of deforestation occurring in smaller patches (50-200 has). This suggests an effect on large-scale deforestation which might be connected to the advent of the control system. But arguably it could be connected with changes in the nature or motivation of forest conversion.

> 13 In Figure 16, the value on each bar expresses the per-cent proportion of deforestation per class of clearing size for a given year.

28
- 9 -

institutional indexes from the same source, such as lack of corruption, contract enforcement, etc., and we obtain similar results using these alternative indexes, an average of the indexes, or GDP per capita.

Bank soundness is also affected by the macroeconomic outlook, as slow output growth, high and volatile inflation, rapid exchange rate depreciation, high real interest rates, and rapid credit expansion have been found to be associated with bank instability (see, for instance, Demirgüç-Kunt and Detragiache, 1998). Thus, in robustness tests we employ various combinations of these macroeconomic variables in alternative specifications. We also use S&P’s sovereign rating as a comprehensive indicator of the quality of macroeconomic policies and institutions which might affect bank stability in a country. Macroeconomic variables are mainly from the IMF’s International Financial Statistics.

The sample covers 86 countries and over 3,000 banks, including commercial banks, cooperative banks, real estate and mortgage banks, and savings banks (Table A1). We also work with different subsamples of banks; from smallest to largest, these are: rated banks (those with a Moody’s rating), commercial banks, and all financial institutions, including investment banks/security houses, medium and long-term credit banks, nonbank credit institutions, specialized government credit institutions.

Since countries in the sample have economies and banking systems of vastly different size, the sample is very unbalanced, with some countries represented by only a handful of banks, and others with hundreds. In particular, Japanese banks account for 23.4 percent of this sample (721 banks). To ensure that regression results are not overly influenced by Japan, we examine results both with and without Japanese banks. Finally, we also estimate specifications including only the top 10 banks in each country and the top 20 percent of banks in the sample, to explore whether the relationship between bank soundness and compliance may differ for the largest banks.<sup>10</sup> 10 For more details on the various subsambles, see Table A6.
from household surveys. Points (i)-(iii) are jointly needed to derive income levels at all points of the assumed distribution. According to Anand and Segal (2008), Milanovic (2002, 2005, 2012) is the only author who works directly with the household survey data without scaling to national accounts and we follow this approach in our baseline specification.

Anand and Segal (2008) offer a detailed overview of the literature on global interpersonal inequality to date. All studies agree that the level of inequality is very high, with a Gini index between 63.0% and 68.6% in the 1990s. Because methodologies and data sources differ substantially (e.g. the use of national accounts aggregates, the estimation of within country distributions, the use of different and often inconsistent PPP exchange rates), there exists substantial uncertainty over the direction of change in global inequality. Hence “there is insufficient evidence to reject the null hypothesis of no change in global interpersonal inequality over 1970–2000” (p. 91), to which Pinkovskiy (2013) agrees using a very different methodology (see above). In our results section we compare our estimates with these previous results.

# **1.2** **<u>PPP exchange rates</u>**

Comparing incomes in different countries requires the use of exchange rates. If the law of one price held and there were no non-tradables, we could simply use market exchange rates (Deaton and Heston, 2010). This, however, is clearly not the case and market exchange rates would understate the real standard of living in poor countries, thus overstating global inequality (Anand and Segal, 2008).<sup>11</sup> We use PPP exchange rates in order to account for differences in the cost of living across countries. PPP exchange rates convert a given local currency into US$, the numeraire. Because we are dealing with household income or consumption, we use the PPP exchange rates for private consumption rather than the GDP conversion factors.

The first step in the computation of PPPs involves the collection of price data around the world by the International Comparison Program, which in its most recent round has been coordinated by the World Bank. The latest round of price comparisons refers to year 2005. This round has the

> 11 Alessandria and Kaboski (2011) explain the failure of the law of one price even for tradables by a model which assumes that poor country consumers have a comparative advantage in search activities.

8
**Figure 3.7: Time to Obtain Tuberculosis Treatment in a Public Health Facility (calendar days)**

<!-- Start of picture text --> Time to obtain tuberculosis testing and treatment in public<br>health facility<br>6<br>5<br>5<br>4 4 4<br>4<br>3 3 3<br>3<br>2<br>2<br>1<br>0<br>Tuberculosis testing Tuberculosis treatment<br>Chile Ghana India Vietnam<br>Calendar days<br><!-- End of picture text --> _Source: Living Life data_

# **3.5. Access to Education**

Education can contribute to an individual’s long-term social and financial opportunities. Inclusive education systems are the foundations of more equitable societies (OECD 2008). _Living Life’s_ Access to Education focus area aims to understand bureaucratic challenges for accessing each level of education including enrollment procedures, requirements, and costs.

Access to Education data found that primary school enrollment is free and compulsory in all surveyed economies. Secondary school enrollment is free of charge in all economies except Vietnam. Public tertiary education is only available for a fee in all economies. While in India and Vietnam students are assigned to specific primary and secondary schools based on their residential address, this criterion is not imposed in Chile and Ghana, where children can be enrolled in any public primary or secondary school.

## **3.5.1. Procedures and requirements**

The minimum requirement to be enrolled into primary education in all four economies is the attainment of the age established by legislation to start compulsory basic education (Table 3.5). In order to move to secondary education students must have completed primary school as proven by relevant records or certificates. Enrollment into tertiary education is conditional upon the successful completion of secondary education as proven by relevant examinations or certificates.

**Table 3.5: Compulsory School Enrollment by Age**

|**Country**|**From what age**|**Until what age**|
|---|---|---|
|**Chile**|5|18|
|**Ghana**|4|18|
|**India**|6|14|
|**Vietnam**|6|11|

_Source: Living Life data_ 24 | P a g e
<!-- Start of picture text --> 107<br>x assets by education,<br>3 ©<br>[e)) eae<br>—2 ~~" migration duration<br>0<br>2 o by education<br>5 10 andsharesshares borrowingself-employed for migration {a5 (e) and destination<br>= each by education 2<br>fe)<br>=<br>_@) CO<br>2 <<br>& 1 02 6 emigration rates by education and destination<br>O<br>aD<br>10° 107 10°" 10° 10" 10?<br>observed moments (log scale)<br><!-- End of picture text -->
# Can Facebook Ads Prevent Malaria? Two Field Experiments in India<sup>∗</sup>

Dante Donati<sup>†</sup> Nandan Rao<sup>‡</sup> Victor Orozco-Olvera<sup>§</sup> Ana Maria Muñoz Boudet<sup>¶</sup>

> ∗This research is part of the entertainment-education program of the World Bank’s Development Impact Department (DIME). Computational replicability was verified by DIME Analytics. We thank our study partners at Facebook’s Campaigns for a Healthier World initiative and Upswell, in particular Nisha Deolalikar, Sarah Francis, and Drew Bernard. We also thank Malaria No More for their collaboration. We are grateful to attendees at CEGA-DIME Measuring Development Conference, Centro de Investigacion y Docencia Economicas seminar, Columbia Business School seminar, Marketing Science Annual Conference, Marketing Science DEI Conference, MIT’s Annual Conference on Digital Experimentation, Quantitative Marketing and Economics Conference, SSRC Workshop on the Economics of Social Media, Universitat Autonoma de Barcelona seminar, and Yale School of Management seminar. The research received ethical clearance from Columbia University IRB (AAAU6317). All errors are our own.

> †Columbia Business School and CESifo; dd3137@gsb.columbia.edu;

> ‡Virtual Lab and UAB

> §DIME, The World Bank

> ¶Gender Group, The World Bank