rafmacalaba/data-use-mentions-extended huggingface dataset:
**Table 3: balance tests based on 2008 data**

||Non-<br>4P<br>obs.|Mean<br>for non-<br>4P|4P<br>obs.|Mean<br>for 4P|Diff.<br>(non-4P -<br>4P)|P-val|
|---|---|---|---|---|---|---|
|Household head female|465|0.1097|464|0.0927|0.017|0.391|
|Number of household members|465|6.3441|464|6.3987|-0.055|0.673|
|Number of children in the household (0-14 years)|465|2.9527|464|2.9806|-0.028|0.784|
|Number of adults in the household (15-64 years)|465|3.3011|464|3.3513|-0.050|0.581|
|Number of children in the household (65 years and above)|465|0.0903|464|0.0668|0.024|0.237|
|Household dependency ratio|461|1.0925|461|1.1146|-0.022|0.678|
|Household head no education|465|0.0452|464|0.0237|0.021*|0.073|
|Household head attended elementary school|465|0.4817|464|0.4375|0.044|0.177|
|Household head finished elementary school|465|0.2559|464|0.2823|-0.026|0.365|
|Household head attended high school|465|0.0753|464|0.0948|-0.020|0.
# **1 Introduction and Motivation**

Countries in South Asia have signed regional trade agreements (RTAs) within the region and with third countries in the rest of the world.<sup>1</sup> These RTAs are expected to contribute to increasing the region’s exports by reducing tariffs and non-tariff barriers, increasing productivity, facilitating technological transfers, and ultimately promoting economic growth and development. Unfortunately, the empirical evaluations of these agreements that take the most recent advances in the literature are limited. This limited evidence is not surprising given that the methodological advances and empirical analyses on RTAs’ effects have usually been conducted using data covering a small set of countries, usually advanced economies. These advances tend to be more demanding in terms of data and this might be the reason why the literature uses data sets with a relatively small number of countries.<sup>2</sup> This makes the evidence of the trade creation effect of trade agreements in developing countries remains not widely available.

South Asia is an interesting case study in terms of potentially heterogeneous effects of FTAs due to the variation in terms of size and economic development in the region. This paper focuses not only on the aggregate effect of trade agreements in South Asia but also pays attention to the heterogeneous effects by sectors, country, country-pairs, types of goods, and the difference between intra-regional RTAs and those with the rest of the world. To empirically identify the RTAs’ effect on South Asia, I use data covering 190 countries over a long period (1990-2015). I use an empirical strategy consistent with theory, in particular, I include domestic trade flows and explore different specifications to capture the heterogeneous effects of trade agreements over different dimensions.

Recent contributions to the literature show that RTAs’ effects can be disappointing and vary widely across different RTAs when these results are available (Baier et al., 2019). Among the recommendations in the literature to properly capture a trade agreement’s effect, the inclusion of domestic trade flows
# **7 Summary and Conclusion**

Using detailed routing data, large and accurate network graphs, and a rich economic geography including population, wealth/gdp, trade through ports, border frictions, road construction and upgrading costs, this paper rigorously examines Africa’s full continental road network and characterizes optimal investments into it in partial and general equilibrium (PE and GE).

Presently, Africa has 1.6 million km of non-residential roads, around 30% of which are paved. This fraction differs significantly by country and road type. Particularly in Central Africa, the Sahara, and the Horn of Africa region, most roads are unpaved and rough, severely limiting trans-African connectivity. The network’s overall route efficiency (RE) of 0.68 is acceptable but considerably lower than that of the US network at 0.84. The time efficiency and average travel speed along many roads is significantly worse than in other world regions. Particularly the aforementioned regions can only be traversed at speeds around 40km/h. In addition, cross-border frictions substantially impede continental market access (MA). Estimates suggest that crossing the median border in Africa is as costly as 1043km of road distance, and the time commitment is equivalent to 3.2 days of travel time. These frictions induce an aggregate MA loss between 26% and 78%. Locations across the border from major markets such as Nigeria suffer MA losses of up to 80%.

Given a detailed network and economic geography, optimal road investments still depend on the geographic scale and magnitude of the investment and the planner’s objective (MA or welfare maximization with/without inequality aversion). Structural parameters such as the trade elasticity/elasticity of substitution and returns to scale in infrastructure also noticeably impact optimal spatial allocations, as do heterogeneous road construction costs and border frictions. The paper navigates this complexity via a graduated approach, starting with small-scale PE investments and gradually progressing towards GE simulations where social planners spend large amounts. This requires different network representations and methods
## Reported usual hours of work per week (reports from baseline of high frequency survey)

<!-- Start of picture text --> Males Females<br>wage employed self employed wage employed self employed<br>° °<br>0 50 100 0 50 100 0 50 100 150 O 50 100 150<br>Hours Hours<br><!-- End of picture text -->

# Reported hours of work last week (reports from 2013 GHUPS round)

<!-- Start of picture text --> Males Females<br><!-- End of picture text --> <!-- Start of picture text --> wage employed self employed wage employed self employed<br>e7 e<br>° °<br>0 20 40 60 84+ O 20 40 60 84+ O 20 40 60 84+ O 20 40 60 84+<br>Hours Hours<br><!-- End of picture text -->
these are important and there seems to be some substitutability between these and the “synergy effect”, nevertheless the inclusion of these variables does not alter our previous results.

# **4.3.2 Information and access to**

We now turn to an exploration of the mechanisms that could explain our results, primarily focusing on the hypothesis that the “synergy effect” is driven by the existence of some “information spillovers”.

First, this synnergy effect could indicate the presence of information externalities. For instance, when technical regulations or buyer policies change in the destination market, exporters may share information about upcoming changes, improving their ability to anticipate and adapt to these changes. Alternatively, buyers may take suppliers from a given country more seriously (and therefore share more information with them or show more flexibility in the face of glitches) when there is a critical mass of them and improve their reliability. If this conjecture is correct, we should expect a stronger synergy effect for products characterized by higher quality heterogeneity for which information asymmetries between buyers and producers are potentially more important. We proxy product _p_ ’s quality heterogeneity by _ρp_ , the coefficient of variation of its FOB unit value across exporters in 2000 (the initial value in our sample) using COMTRADE data, with a higher _ρp_ meaning more heterogeneous quality.<sup>14</sup> The results are presented in column (1) of Table 5. The coefficient on the interaction term _ρp ×_ ln _npdt_ is positive, although significant only at the 10% level, suggesting that the synnergy effect is stronger for products with a high unit-value dispersion, where information is more important.

Given the importance of finance, as shown by the survey discussed in Section 3, an alternative hypothesis could be that while information is still a key determinant of the synergy effect the mechanism behind it could be instead different. Consider the following scenario. A Senegalese firm is approached by a US buyer to provide a small trial order of t-shirts. Upon successful
WPS3431

# **FINANCIAL DEVELOPMENT, FINANCIAL FRAGILITY, AND GROWTH**<sup>*****</sup>

Norman Loayza World Bank Romain Rancière CREI and Universitat Pompeu Fabra

## October 2004

### **Abstract**

This paper studies the apparent contradiction between two strands of the literature on the effects of financial intermediation on economic activity. On the one hand, the empirical growth literature finds a positive effect of financial depth as measured by, for instance, private domestic credit and liquid liabilities (e.g., Levine, Loayza, and Beck 2000). On the other hand, the banking and currency crisis literature finds that monetary aggregates, such as domestic credit, are among the best predictors of crises and their related economic downturns (e.g., Kaminski and Reinhart 1999). The paper accounts for these contrasting effects based on the distinction between the short- and long-run impacts of financial intermediation. Working with a panel of cross-country and time-series observations, the paper estimates an encompassing model of short- and long-run effects using the Pooled Mean Group estimator developed by Pesaran, Shin, and Smith (1999). The conclusion from this analysis is that a positive long-run relationship between financial intermediation and output growth co-exists with a mostly negative short-run relationship. The paper further develops an explanation for these contrasting effects by relating them to recent theoretical models, by linking the estimated short-run effects to measures of financial fragility (namely, banking crises and financial volatility), and by jointly analyzing the effects of financial depth and fragility in classic panel growth regressions.

> *   This paper was written while Loayza and Rancière worked in the research department of the Central Bank of Chile.  Research assistance from Megumi Kubota and Guillermo Vuletin is gratefully acknowledged.  We benefited from comments and advice by J. Benhabib, S. Bond, J. Cummings, F. Gallego, A. Gaytán, P.O. Gourinchas, O. Jeanne, B. Jovanovic, P. Martin, D.
and the capital adequacy ratio, since high capital requirements are likely to deter entry. We include an index of bank auditing requirements and an index of bank disclosure requirements as measures of bank transparency. All the variables discussed so far come from the Barth, Caprio, and Levine (2001) database. We control for the impact of macroeconomic conditions by including the inflation rate and the real GDP growth. These variables come from the _World Development Indicators_ . As measures of institutional development, we include GDP per capita from the _World Development Indicators_ and, alternatively, an index of property rights from the _Heritage Foundation._

- 8 -
literacy among researchers, low trust in data from the national statistical system, and lack of infrastructure to access and use the data, which may be particularly an issue of low- and middleincome countries.

The policies needed to boost data supply differ from those needed to boost data demand. Boosting the data supply of the national statistical system requires more financing for national statistical offices, better technical capacity of its staff, and at times better statistical laws to ensure their independence from other government bodies. Policies to boost data demand, on the other hand, involve financially cheap (but not necessarily politically cheap) wins, such as making data more accessible for researchers. It also involves increasing the data literacy of academics. Given the non-overlapping policy responses, it is useful for countries to know whether data demand or data supply is their weakest link in the data value chain. Below, we classify countries by whether they are primarily constrained by data supply, data demand, neither or both.

Building on the semantics introduced by Porteous (2020) who classified countries into research deserts and research oases, we classify countries into four groups: _Data deserts_ have little data demand and little data supply, _data swamps_ have high data supply but little data demand, _data oases_ have high data demand but little data supply, and _data lakes_ have high data demand and high data supply. For assessing data supply, we again turn to the availability of various data sources, such as recent population and business censuses, household and health surveys, Civil Registration and Vital Statistics (CRVS), and sub-national geospatial data (for details see Dang et al., 2023). We measure data use as the residual from the regression of number of articles on the country using data on log population, log GDP per capita, and the average number of qualitative papers. The control for the number of qualitative papers once again serves as a proxy for general research interest and research environment unrelated to data.

We classify countries by whether they are below/above the
# **5. Conclusions**

The last few years have seen the attention of policy makers and the general public to be increasingly focused on Ninis. The perception is that Ninis are associated with other social problems in the region, such as shortcomings in the quality and relevance of education, which may lead to early dropout, and high levels of youth unemployment, crime, and violence. A related concern is that being Ninis will prohibit a sizeable part of today’s youth population from accumulating the human capital necessary for their own future success in the labor market, as well as for the economic prosperity of their countries.

This paper contributes to our understanding of the decision process behind being a Nini and the factors with which it is associated, by testing the implications of the theoretical model developed by Berhman et al. (2014). To achieve this, the analysis employs almost a decade of information from the quarterly ENOE labor survey in Mexico, exploiting the longitudinal nature of the data, and verifying the robustness of the main results using data from Argentina and Brazil.

Our initial results show that nearly a fifth of Mexican youth are Ninis, with a majority of this group, 76.3 percent, being composed of women and, in particular, women aged 19 to 24. Descriptive statistics also show large difference by gender, rural/urban status, and household income. Moreover, Nini status is also much more persistent among youth from poor families, especially when they are younger, and among women living in rural areas. Approximately a fifth of male Ninis and a tenth of female Ninis aged 15-24 years old were found to be studying a year earlier, composed mostly of younger individuals who are disproportionally from disadvantaged socioeconomic backgrounds and urban areas.

The results of the multinomial logit models also support several of the predictions of the theoretical model: (i) the history of past decisions matters for future decisions, at least in the periods in which youths are observed; (ii) the relative welfare position of
consensus about the sign (or shape) of the ETR-firm-size relationship.<sup>6</sup> Our evidence instead is largely consistent across countries, pointing to an inverse U-shaped pattern in the ETR-firm-size relationship.

Finally, we connect to the literature on tax capacity in LMICs. Corporate taxes are a larger source of revenue for LMICs than for high-income countries (Besley and Persson, 2013). Governments also rely on firms to collect other taxes (Kopczuk and Slemrod, 2006; Garriga and Tortarolo, 2022). Thus, an erosion of the corporate tax base can be costly, and a distortionary corporate tax may be particularly detrimental to growth in LMICs. We show that LMICs forgo a sizable share of revenue in tax benefits to the largest firms. Conversely, our simulations suggest that a minimum tax could raise tax revenue substantially.

# **2 Data and Methodology**

## **2.1 Data**

We use corporate tax records from 13 countries, listed in Table 1. Each dataset captures the universe of corporations filing tax returns in the country over a five to ten-year span. In our main analysis, we focus on the latest cross-section which occurs between 2015 and 2019, depending on the country. Administrative tax data contain precise information on firms’ taxable income, costs, and all tax exemptions which allows for a breakdown of the tax burden. Unincorporated firms are excluded, as their tax treatment differs across countries. Appendix B details each country’s tax system and how we deal with special tax regimes which apply in some countries. Although administrative tax data are increasingly used by researchers (Pomeranz and Vila-Belda, 2019; Mascagni et al., 2016), our study is the first to use micro tax data from a large number of countries.

The data include countries in Africa, Latin America, and Eastern Europe, and cover a wide range of income levels (from Ethiopia with a GDP per capita of 500 USD to Costa Rica with a GDP per capita of 10,
market or time fixed effects leads to collinearity issues and lack of power and thus they are not included. We note, however that in Table 4, the inclusion of fixed effects does not significantly affect the results.

The odds ratio reported in Column 1 of Table 9 is 5.83 (p-value=0.016) This odds ratio is substantially larger than the one in the Democratic Republic of Congo in Table 4. The point estimate for the female client dummy implies a marginal effect of 12 additional percentage points in the probability of transacting with a female agent for female clients (marginal effects are reported at the bottom of the table and evaluated at the average probability of a client being female). In Column 2, we include as regressors the type of transaction (an indicator for withdrawals) and the log of the transaction amount. The logit point estimates and marginal effects are virtually unchanged. The marginal effects estimated for the data in Senegal are about 12 percentage points, a bit larger than our estimates for the Democratic Republic of Congo using similar specifications, which range from 8.1 percentage points in Column 1 to 8.4 percentage points in Column 3 of Table 4.<sup>9</sup> Table 10 displays the results from the dyadic regressions in equation (2) using all of the transaction-level data from Senegal (instead of the sample used in the case of the Democratic Republic of Congo). Our findings are remarkably similar to those obtained for the Democratic Republic of Congo, suggesting again that both males and females have gender homophily. Transactions between clients and agents of the same gender are 1.6 times more likely than transactions between individuals of different gender. Similarly, we also find that clients are more likely to make transactions with those agents located in their same area. As in the case of the Democratic Republic of Congo, there is no gender differential effect, and this is reflected in the fact that the coefficient associated with the binary variable that indicates that both the client
|2.187901|0.0294|
|R-squared<br>0.966|303|Mean|dependent var|657.8306|
|Adjusted R-squared 0.965|653|S.D. d|ependent var|282.6279|
|S.E. of regression<br>52.37|935|Akaik|e info criterion|7.938791|
|Sum squared resid<br>8532|58.4|Schwa|rtz criterion|8.021604|
|Log likelihood<br>-1706|.490|F-stati|stic|1486.385|
|Durbin-Watson stat<br>1.97|1090|Prob(F|-statistic)|0.000000|

The drift, the trend and the dummy _dl_ are very significant. An ADF based on this model suggests the rejection of the null of unit root. **_15_**

# _ii) POLDIS_ (sample: 7/29/94 to 7/12/96).

I) The first model is a simple random walk, that is an AR(]) with one unit root. The evidence for unit root is very strong.16 The value of the Dickey-Fuller Test Statistic is -1.264866, while the 10% critical value is -1.6174.

> **15** In order to perform a proper test, we split the sample as before: 4/06/90 to 12/16/94 and 12/23/94 to 7/12/96. For the first sub-period the drift and the trend are not significant and there is strong evidence of unit root. For the second period, drift and trend are significant and we can reject the null of unit root at the 5% critical level.

> **16** The evidence of unit root is maintained if we split the sanple at 12/16/94 as for MEXDIS.
# **1. Introduction**

Recent years have seen the study of intra-generational mobility increasingly capture the attention of policy makers and researchers. The type of policies needed to attack persistent poverty may be quite different from those required to address transient poverty or movements in higher parts of the income distribution. When measuring mobility, it is desirable to work with panel datasets that follow individuals or households over time. Unfortunately, such surveys pose substantial empirical challenges. First, because they are typically costly and complex to administer panel datasets (especially in developing countries) that track individuals or households over time are rare. This is particularly true for longer term panel data (that track the same unit of analysis for more than 5-10 year). Second, and connected to the previous point, it is usually complicated to revisit households who move physically or dropout from panel data surveys. As such, nonrandom attrition may significantly bias results, leading to an underestimation of the actual mobility in the general population (Antman and McKenzie 2007). Finally, classical measurement error will also introduce bias in the mobility estimates.

Because of growing concern to assess evaluate transitions into and out of poverty, an emerging body of research exists to develop techniques to overcome the major limitations of panel data sets by using cross-sectional surveys. Most of the literature has focused on what is commonly referred to as a pseudo-panel approach, which tracks cohorts of individuals over several periods of time. Recent developments on pseudo-panel analysis include Bourguignon, Goh, and Kim (2004) and Artman and McKenzie (2007). However, these studies usually impose significant data demands and structural assumptions in order to yield mobility measures out of repeated cross-sectional surveys. For example, more than two cross sections are often needed and specific functional forms have to be assumed for earnings dynamics (Lanjouw, Louto, and 2
# **Appendix D: Border Costs**

# **Estimate of the impact of a Deep and Comprehensive Free Trade Agreement with the European Union on Armenian Border Costs**

For the estimate of Armenian border costs associated with exporting and importing we begin with the survey of border costs undertaken by Maliszewska _et al_ ., (2008) in late 2007. They report that import and export procedures are regarded by Armenian businessmen as one of the most corrupt areas of public administration. Customs procedures are riddled by rent seeking and corruption. The results of their survey (see Maliszeswska _et al_ ., (2008, table 9.2) reveal that the average costs of exporting to the European Union are higher by 10.4 percent due to customs procedures. Although they report the results of their survey by sector, and in some sectors the reported costs are considerably higher.(as high as 60 percent), they argue that the size of the sample is too small to distinguish exporting costs by sector. We thus take the average costs of customs procedures of 10.4 percent as applying to all sectors in 2007, both for exports and imports.

To update the estimate to 2010, we employ data from the Cost of Doing Business Survey of the World Bank.<sup>32</sup> According to the Doing Business Survey, in 2007, the cost of exporting a container from Armenia was $1600, but had risen to $1731 in 2010. Prices in Armenia have risen by 27 percent between 2007 and 2010 (January to September average).<sup>33</sup> Converting the $1731 estimate for 2010 to 2007 prices, we estimate the cost of exporting a container from Armenia in 2010 are $1365 in 2007 prices (which are $1731/1.27) This suggests that the costs of exporting a container in Armenia in 2010 are .854 times the costs in 2007 ($1365/1600). Finally, we estimate that the border costs