rafmacalaba/data-use-mentions-extended huggingface dataset:
Djibouti is a small country in East Africa with limited economic diversification. In the last two decades, economic growth in Djibouti has been driven by direct foreign investment and public sector-led infrastructure investment, both of which have had limited trickle down effects on job creation and poverty reduction among large portions of the population. Based on the international poverty line for lower-middle-income economies with a daily wage of $3.20 (in USD 2011 PPPs; World Bank, 2019), approximately one-third of Djibouti’s population lives in poverty. The country also suffers from a high level of food insecurity (World Food Program, 2022) and very low human development outcomes compared to other lower middle-income countries. One-third of Djiboutian children are underweight or stunted, and maternal mortality and child mortality are higher than in neighboring and economically comparable countries (World Bank, 2018). Only 60 percent of the population aged 15 years and older is literate, and net enrollment in primary school is only 66 percent (from the 2019 World Development Indicators).

In Djibouti, both women and men have extremely low literacy levels, but the former are less literate than the latter (43 and 60 percent, respectively; World Bank, 2019). Although safety nets exist, their coverage is limited. According to the 2017 Djiboutian Household Survey (Enquête Djiboutienne Auprès des Ménages [EDAM]), only 11 percent of all adult women are employed. Women with some post-primary education are marginally more likely (13 percent) to be working than their peers with less schooling. Working women with more education are also much more likely to be employed within the public sector (73 percent with any post-primary education and 19 percent with only primary education). The same is also true for men. Overall, the public sector employs 78 percent of working adults with at least some senior secondary education (EDAM 2017, authors’ calculation).

Within the poor and highly food insecure context described above, a drought that occurred in late 2011 and into 2012 resulted in significant loss
**Real effective exchange rate:** The nominal effective exchange rate divided by a price deflator or index of costs, where 2007=100. The real effective exchange rate was collected from the IMF’s International Financial Statistics.

# **4. Summary statistics**

In Georgia, 8 percent of firms in the sample were exporters in any year, and 2 percent of total sales were represented by exports. The data however suggest that of firms that export, a significant and increasing share of their sales tend to come from exports. On average for Georgian exporting firms, 13 percent of sales came from exports. For firms that export to the EU or high-income countries, exports tend to represent a larger share of sales (see Table 1).

**Table 1: Share of exports and exporting firms**

||All firms|Exporters|EU exporters|HIC exporters|
|---|---|---|---|---|
|Exported in at least one year|8.4|--|--|--|
|Share of exports in total sales|1.9|12.9|19.5|18.4|
|Number of firms|61,849|5,090|1,447|1,713|
|Number of observations|132,028|18,296|7,138|8,151|

Source: GEOSTAT Statistics Survey of Enterprises for 2006-2017. Note: HIC = high income country.

Exporting firms tend to be bigger and more productive than firms that sell exclusively to the domestic market (Bernard and Jensen 1995, Bernard and Jensen 1999, Bernard, Jensen, Redding and Schott 2007, Clerides, Lach and Tybout 1998). This holds true in our Georgia sample with respect to firm size in terms of employees, where exporters are four times as large as non-exporters. For example, the average number of employees in Georgian exporting firms was
**Table 6: Separate Effects of Round 1 and 2 Support on Round 3 Performance**

||Percentage<br>change in sales|Percentage<br>change in<br>employment|Decreased<br>employment|Anticipate<br>falling into<br>arrears|
|---|---|---|---|---|
|Received in Round 1 only dummy|8.415**|4.972|-1.915|3.757|
||(3.821)|(3.445)|(3.197)|(5.190)|
|Received in Rounds 1 and 2 dummy|0.655|-4.233|8.670**|3.017|
||(4.475)|(3.947)|(3.605)|(3.009)|
|Received in Round 2 only dummy|-0.849|-1.776|-0.192|-0.599|
||(7.075)|(4.657)|(4.313)|(4.826)|
|R<sup>2</sup>|0.055|0.104|0.065|0.230|
|Number of observations|3,507|3,546|3,546|3,247|

_Sources_ : Enterprise Surveys COVID-19 Follow-up Surveys and Enterprise Surveys. _Notes_ : All regressions are ordinary least squares and include sector and country fixed effects, as well as the control variables listed in Table 5. Standard errors are clustered at the country level. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1.

18
villages ( _N_<sup>_NE_</sup> ). Note that we still have to control for the number of neighboring evaluation villages ( _N_<sup>_E_</sup> ) in order to account for the fact that the identifying variation is non-zero for 42 percent of the evaluation villages (i.e., those that have at least another evaluation village in their radius of zero to five kilometers) and in order to assure that the exclusion restriction is valid (because of the correlation across village neighborhoods between _N_<sup>_E_</sup> and _N_<sup>_NE_</sup> ).

The corresponding estimation results are reported in table 6. The point estimate of the first-stage parameter for the effect of an additional treatment group village on neighborhood treatment density is estimated at 1.43 (column 1). The corresponding t- statistic is 1.97 and the F test of the excluded instrument is 3.9, which is statistically significant at the five percent level. The reduced form coefficient for the effect of the number of treated villages in the zero- to five-kilometer radius on secondary school enrollment is 0.028 (column 2), which is very much in line with the corresponding estimate reported in column 1 of table 3. Column 3 reports the IV point estimate of the relationship between the frequency of neighboring program localities and secondary school enrollment, which is positive but not significantly different from zero. The IV point estimate has a smaller magnitude than the OLS one reported in column 2, which reflects the larger-thanone first-stage relationship and is estimated with more noise due to the reduced statistical power in this specification.

As a last robustness exercise, we consider alternative measures of program take-up based on the health component of the program. We use household-level information from the post-program survey round of October 1998 on the uptake of three screening tests that form part of the health requirements of the _Progresa_ program: hypertension (blood pressure test), diabetes (blood sugar test),
vis the UNPS 2015/16 sampling weights. The base weights are those defined in Section 3 at the household level. The UNPS sampling weights are those defined at the individual level and are currently used for the survey estimates by UBOS (2011). They are obtained according to a complex process, based on performing preliminary adjustments for non-response based on noresponse propensity scores. Specifically, the final individual-level weights are obtained through the following six methodological steps: i) definition of the base weights as the reciprocal of the probability of inclusion, ii) calculation of the fair share correction factor, iii) pooling of the weights defined in step 3, iv) computation of the correction factors for attrition, v) trimming of the weights defined up to step 4, and vi) post-stratification.

Table 6.1. Population projection by age and gender (2015)

|Age group|Male|Female|
|---|---|---|
|0-4|3,220,300|3,028,800|
|5-9|2,870,800|2,730,500|
|10-14|2,528,000|2,500,100|
|15-19|2,008,700|2,097,500|
|20-24|1,509,200|1,790,100|
|25-29|1,181,200|1,403,800|
|30-34|938,700|1,082,800|
|35-39|743,900|836,500|
|40-44|630,600|677,500|
|45-49|470,800|486,800|
|50-54|382,300|443,200|
|55-59|241,700|277,900|
|60-64|194,600|241,600|
|65-69|140,400|171,100|
|70-74|114,900|158,600|
|75-79|71,500|90,
large part by greater flows of FDI from industrialized, high-income countries to developing nations.

A key difference between the Petri (1997) and the Dee and Hanslow (2001) approach is that Petri modelled customers as first choosing among firms (suppliers) on the basis of their ownership, and then among the locations that the firms were based in. This treatment assumed that in any given sector, Japanese-owned firms were closer substitutes for each other than for Australian firms in the relevant sector, irrespective of location. By contrast, Dee and Hanslow (2001) assumed that customers choose between phyisical location first (where the services are produced) and then select between suppliers in a given geographic location on the basis of ownership (nationality). As noted by Dee (2003), from an Australian perspective, a US multinational located in Australia is assumed to be a closer substitute for an Australian-owned firm than it is for a US firm located in the United States.

Others have followed the example of the Dee and Hanslow/FTAP effort, including the Central Planning Bureau in the Hague, which extended its WorldScan model to include FDI in services (Lejour, Rojas-Romagosa and Verweij, 2008), and Brown and Stern (2001) and Brown, Deardorff and Stern (2001), who extended the Michigan model to include FDI along Petri lines. A major limiting factor here is the lack of detailed bilateral FDI data by sector. In addition, an issue rarely discussed is the use of FDI data as a proxy for actual (unobserved) sales of services by foreign affiliates when representing the operations of multinational enterprises.

While the inclusion of FDI further increases the data challenges confronting modelers (Stone et al., 1999; Hanslow et al., 1999), extending CGE models to include FDI is critical as it is a major channel for the international exchange of services. As discussed subsequently, the extension of CGE models to include FDI has been shown to be very important from a welfare (real income) and growth (productivity
. SS contains all persons of SS Current time tracked, along with all members of their households at time tt<sup>∗∗</sup> are tracked and surveyed. The rules of tracking and data collection are the same as those tt<sup>∗∗</sup> tt<sup>∗∗</sup> ←tt<sup>∗</sup> tt. No new sample is selected. The SS and SS samples formed at the time tt<sup>∗∗</sup> sample. tt<sup>∗∗</sup> ←tt<sup>∗</sup> according to the specific survey’s tt<sup>∗</sup> already described for the formation of the SS longitudinal sample from the SS tt<sup>∗∗</sup> tt<sup>∗∗</sup> Thus, the enumerators track all nn individuals of sample SS ∩ tt←tt<sup>∗∗</sup> tt←tt<sup>∗∗</sup> tracking rules. This way, the enumerators form longitudinal sample SS , with mm and observed in longitudinal sample tt←tt<sup>∗∗</sup> tt<sup>∗</sup> households and nn people. Furthermore, the enumerators track all the people of SS tt<sup>∗∗</sup> ←tt<sup>∗</sup> tt<sup>∗</sup> SS who are the people selected in original sample SS people. tt<sup>∗∗</sup> ←tt<sup>∗</sup> tt←tt<sup>∗</sup> tt←tt<sup>∗</sup> tt←tt<sup>∗</sup> SS . So, the enumerators form longitudinal sample SS , with mm households and nn . tt←tt<sup>
to engage in seasonal work. The analysis on DHS shows 5.5 percentage points less likelihood of seasonal employment with one unit increase in log distance from a good quality road (approximately 2.7 fold increase). These results could be explained by the outcome that people living nearby improved roads are more likely to work outside the village. Better quality road infrastructure stimulates mobility and commuting through two main channels: increased productivity, and reduced job search and commuting costs. Therefore, people living closer to better roads might be more likely to find additional jobs during less 30
conflict event, where estimates using the alternative weights are higher and therefore farther away from the PPCS estimates.<sup>12</sup>

## An unweighted approach

As an alternative way to try to control the distribution of observable characteristics, we sequentially restrict both survey samples to three groups that are overrepresented in the full Facebook sample: people with secondary or higher education, men, and people who report working in the past week. We then reestimate the main indicators from Table 2 using each pair of restricted samples, now without weights. We compare these estimates to those generated using the full (unweighted) samples for each survey. None of the three sample restrictions significantly alter the between-survey differences estimated for the main indicators using the full (unweighted) samples. Estimates of the between-survey differences in the main indicators for all four specifications are included in Appendix C.

# Conclusion

Given the opportunity to compare a Facebook survey to an in-person survey covering conflict exposure and potentially associated socio-economic and mental health outcomes in the West Bank and Gaza over a similar time period, we found that the Facebook survey consistently estimated more severe outcomes for a variety of indicators. Multiple mechanisms may have contributed to this difference in estimates, including overrepresentation in the Facebook sample of respondents who were interested in the survey topics, reduced sensitivity bias in the context of a self-administered online questionnaire, and reporting more severe outcomes than personally experienced to encourage resource flows to perceived needs.

Estimates were more similar between the surveys in Gaza, where the population is more homogeneous in relevant characteristics and more likely to be interested in responding to a survey on the effects of the May 2021 violence. The overall pattern of more severe outcomes estimated from the Facebook survey is robust to different ways of controlling the distribution of observable characteristics.

### **To what extent might these results generalize to other internet surveys?**

Most internet surveys are likely to produce results that differ meaningfully from those estimated by an inperson survey. Most are also likely to find that their
thermore, qualitative research finds that the classes were held infrequently, were generally of low quality, and were not taken seriously by participants, suggesting that the classes did not likely impart new knowledge on program recipients that would impact their food consumption decisions (Rodriguez Herrero, 2005). As shown below, our main empirical results are robust to separating the in-kind villages into the group with classes and the group without. Using data from the experimental sample, Cunha (2014) documents that both cash and inkind transfers led to equally sized increases in total consumption (food plus non-food) and food consumption between in-kind and cash communities. However, several of the in-kind food items were extra-marginal, as evidenced by greater increases in consumption of those goods in in-kind communities compared to cash communities. Some of these extra-marginal foods were nutrient rich, such as fortified powdered milk and vitamin enhanced corn flour, and thus children in in-kind communities were observed to consume more iron, zinc and vitamin C than children in cash communities.

# **3 Data and Sample**

## **3.1 Data**

Our data come from several sources: pre- and post-intervention surveys of individuals and households in PAL villages, student-level ENLACE test scores, student surveys from a subset of ENLACE test takers, and school-level data collected by the SEP.

In each of the experimental PAL villages, approximately 33 households were selected to be surveyed pre- and post-intervention. The pre-intervention survey was administered in the last quarter of 2003 and the first quarter of 2004, and the post-intervention follow-up survey was conducted in the final quarter of 2005.<sup>9</sup> The pre-intervention data allow us to confirm the randomization was successful, as well as segment the population by various socio-economic characteristics. From the post-intervention survey, we use data from a 24-hour food recall for children and a time allocation module for both children and adults. The food recall was completed by the survey respondent, usually the female household head, for all children aged 2 to
for the average manufacturing firm and 7.93 percent for the average service firm. A one standard deviation improvement in the tax regulations index is correlated with 4.77 percent and 7.10 percent increases in labor productivity for manufacturing and service firms, respectively. A one standard deviation increase in the trade regulation index is associated with a 7.48 percent increase for the average service firm, but is not statistically significant in the manufacturing industry.

> 39 For a second method of evaluating the relative impact of firm-level variables, see Appendix XII.

11
of 3.2 and 0.4 percent, respectively. These results provide further evidence that lending to local public governments in the pre-reform period was crowding out lending to private firms.

Fourth, we further test whether private-sector borrowers operating in sectors less dependent on public spending (i.e., less affected by the contraction in government spending) benefit relatively more from the reform. Following Belo, Gala, and Li (2013), we use the Mexican Input-Output table at the 4-digit NAICS level to calculate the share of revenues that comes from sales to the government for each sector. The results of this exercise are displayed in Table A7. They suggest that private borrowers operating in sectors less dependent on government spending benefit relatively more from the reform curtailing local public debt. More concretely, a one-standarddeviation decrease in the level of government exposure is associated with a 0.1 percent increase in total loan volume, and a 0.7 percent increase in loans destined to investment.

Fifth, along with the debt ceilings, the Law also introduced procurement rules requiring state-owned entities to carry out a competitive procurement process with multiple lenders and select the offer with the lowest cost. One potential threat to our identification is that by potentially reducing the cost of credit of local governments, the new procurement rules might have induced local governments with ex ante lower debt (i.e., the states that belong to our control group) to increase their indebtedness. In such case, the gap in public debt that widened across states of varying indebtedness after the Law would be driven not exclusively by the debt contraction of ex ante more indebted governments (due to the new debt ceilings), but also by the rise in debt of ex ante less indebted governments (due to the procurement rules). We rule out this concern by examining changes in bank lending of ex ante less indebted local governments before and after the Law. As Figure 2 shows, while local governments with ex ante higher public debt contracted considerably their bank liabilities ex-post,
use R&D spending as a potentially less noisy dependent variable capturing the key input into innovation outcomes, the significance of the state ownership ( _patient_ capital) disappears. When we change our baseline measure of innovation "firm introduced a new or improved product or service in the past three years" ( _innovation diffusion or new to the firm_ ) to “firm introduced a new/improved product or service that is also new to the market in the past three years” (a subset measure of more _radical innovation_ ) and adjust for a possible selection bias using the Heckman model, the benefits of foreign-state ownership remain significant at 10 percent level with a large economic effect, similar to the one on innovation diffusion. This result complements the literature on financing radical innovation, which suggests that external firm financing for radical innovation may be best provided by equity investment intermediaries such as the venture capital industry in suitable economic environments.

The paper is organized as follows. Section 2 describes the data and methodology. Section 3 discusses the estimation results. Section 4 concludes.

# **2. Data and Estimation Methodology**

We rely on several data sources for our analysis. First, the World Bank Enterprise Surveys provide information on firms' characteristics, such as the firm's age, ownership, size, whether the firm has an active line of credit and the name of the bank the firm borrows from. We focus on the last year for which this banking information is available.

Our sample consists of over 22,000 firms from 49 countries.<sup>1</sup> Firms tend to be small, with fewer than 20 employees, and relatively old, on average 20 years old. Less than one-third of the firms have access to a line of credit, and less than one-fifth has introduced a new product or service over the past 3 years.

Second, FitchConnect provides information on banks and financial institutions. We follow Micco et al. (2007) and Panizza (2022) to assess whether a bank
# **1 Introduction**

Small businesses are the main source of income for households across Sub-Saharan Africa (outside agriculture) (International Labour Organization, 2019), however, they contribute a very modest fraction of total tax revenue. Many governments have tried to address this by registering large numbers of small businesses (Benhassine _et al._ , 2018, Moore _et al._ , 2018) and using simplified tax regimes (STRs) for registered, small businesses with low revenue levels (Mas-Montserrat _et al._ , 2023). These regimes, often called turnover or presumptive tax regimes, are meant to provide an ‘on-ramp’ for small businesses to meet their tax obligations as they are easier to comply with than more complex corporate or personal income tax regimes.<sup>1</sup> Beyond simplifying the makers aim for these STRs to collect taxpaying process, policy typically meaningful amounts of revenue from small businesses that were otherwise likely to have remained informal. In addition, there is often a desire to ensure that the tax burden is distributed equitably among small businesses and across tax types. Despite the widespread use of STRs, relatively little is known about how well, in practice, these regimes actually meet these objectives of revenue collection, simplicity, and equity.

We present novel empirical insights about the trade-offs that policy makers face when setting the design features of STRs in Sub-Saharan Africa by conducting a comprehensive cross-country review of these regimes and a detailed examination of the turnover tax (TOT) regime in Kenya. We systematically document the specific design features of STRs in each Sub-Saharan African country (when one exists) by reviewing the most recent tax legislation and policy guidance notes. Building on this review, we conduct detailed analysis of ten years of tax administrative data from the TOT regime in Kenya. Kenya provides an insightful case study of the challenges governments face when taxing small businesses, as despite there being more than 1 million potential TOTpayers, the regime contributes less than 1 percent of total tax revenue. In