rafmacalaba/data-use-mentions-extended huggingface dataset:
decision-making unit of analysis. The above presented framework allows us to study graphically the optiifial structure of provision for road services. The concepts developed so far are applied in the next section.

# 4. **Data Definition and Sources**

The performance of roads is measured by the double cost concept introduced in the previous sections. Resource costs are defined in terms of unit cost of road works for the activities of construction, maintenance, and administration measured in $ per km of road. These are essentially the input costs. A measure for preference costs is derived using a framework developed by Ben-Akiva et al (1993) where condition of roads are substituted as a proxy for users costs while utilizing roads of a given condition. Decentralization is measured in terms of percentage local government contribution towards road works.

The analysis relies on data from three main sources: (a) cross-country and time series data on the unit costs of construction, maintenance and administration, the percent roads in bad condition (as measured by a proxy developed in the World Development Report of 1994 on Infrastructure and Development), and the contribution of local governments to financing road expenditures are obtained from the International Road Federation (IRF) statistics'° for eight countries: Argentina (1982-86), Brazil (1982-93), Colombia (1982-90), South Africa (1982-1989), Germany (1982-92), USA (1982-92), Philippines II ~~~~~~~~~12 (1982-86) , and Indonesia (1982-86) ; '° The quality of data from the International Road Federation may suffer from problems of precision. Many countries are unable to report with any precision the expenditures of local govermnent units of roads sicne their accounting systems do not record these data directly. Therefore, most of the local government data is based on estimates. This is the main reason why we used a case study from Germany where we conducted an original data collection and verified the results with supportive field work before using the definitions of data we used in the
This version: 15 Feb 2024 6 average income. Easterly (1999) looks at 81 indicators of well-being with four rounds of panel data spanning a 40-year period for a large sample of countries and finds when the data are analyzed without country fixed-effects, 61 of the 81 indicators show a strong positive relationship and 12 show a negative relationship. With country fixedeffects only 10 of the 81 indicators show a positive relationship with growth. He speculates that country effects such as “resource endowments, access to the sea, ethnic fragmentation, social infrastructure, climate, and legal systems fixed factors really could be the dominant determinant of a country’s income and quality of life indicators.” More recently there has been a growing body of research that provides a counterpoint to this literature by documenting several possible undersides to economic growth. Rising income can coincide with low levels of happiness in fast growing economies leading to “frustrated achievers” (Graham and Pettinato, 2006; Brockmann, Delhey, Welzel and Yuan, 2009; Knight, Ma and Gunatilaka, 2022). Affluent societies can also have citizens who face social exclusion and low living standards (Campbell, Converse, and Rodgers, 1976; Devarajan and Ianchovichina, 2018), and there is research that shows a weak relationship between GDP growth and social indicators (e.g., Victor 2019; Bleynat, Challú and Segal, 2021). An earlier line of research examines the social costs of growth (Crafts, 1997; Offer, 2006) and the macroeconomic cost of citizen discontent (e.g., Rodrik, 1999; Cerra, Lama and Loayza, 2021).

6
however, better-capitalized banks could better sustain lending and even lend more (Gambacorta and Shin 2018).

# **3. Conceptual Framework: Basel III Implementation and Transmission Mechanism**

This section provides a conceptual framework on how the Basel III implementation can affect SME access to financing. First, we describe the progress with Basel III implementation by country and how we classify whether a country is considered a Basel III implementer or not. Second, we focus on the potential transmission mechanisms from Basel III implementation to SME access to financing.

# <u>3.1 Basel III Implementation</u>

We use the BIS Financial Stability Institute’s survey on Basel II, 2.5, and III Implementation (FSI 2015) to determine whether a country implemented Basel III. The value of the variable _Def cap_ in the survey needs to be equal to the value of 3 or 4 for the country to be classified as a Basel III implementer for the purpose of our study.<sup>5</sup> _Def cap_ stands for a definition of capital. Table A.1, in the appendix, provides country-specific information on the implementation status—along with the list of countries and the availability of World Bank Enterprise Surveys (which we discuss in detail in the following data section).

The value for most countries classified as Basel III implementors is 4, that is, the final rule on capital adequacy is already in force (FSI 2015). Basel III implementers that show the value of 3 published the final rule but the rule is not yet in force. The values of 1 and 2 in FSI (2015) denote that the regulation is published in draft form or is not published at all. Table A.1 shows that, according to our classification, Basel III has been implemented (for our sample of countries) mostly in 2013 (Argentina, Bolivia, Colombia, India, Indonesia, Kenya, Malaysia, the Philippines, Thailand, and Turkey) and 2012 (North Macedonia
**Table 3: IV estimations of labor productivity and exports**

||First-stage estimation|Reduced-form estimation|IV estimation|
|---|---|---|---|
||(1)|(2)|(3)|
||Panel A: Weighting bilate|ral routes by the interaction<br>trade in industry|of exporter and importer|
||DV: Δ Log country-|DV: Δ Log bilateral|DV: Δ Log bilateral|
||industry labor productivity|exports|exports|
|Δ Log estimator for technology|0.589**|0.938***||
|flows from the United States|(0.272)|(0.298)||
|Δ Log country-industry labor|||1.592**|
|productivity|||(0.637)|
|Observations|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|
||<br>Pan|<br>el B: Excluding sample weig|<br>hts|
||DV: Δ Log country-|DV: Δ Log bilateral|DV: Δ Log bilateral|
||industry labor productivity|exports|exports|
|Δ Log estimator for technology|0.267***|0.648***||
|flows from the United States|(0.078)|(0.112)||
|Δ Log country-industry labor|||2.429***|
|productivity|||(0.791)|
|Observations|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes:
60)|
|Mills||-0.405|-0.873|
|||(1.07)|(2.44)**|
|Observations|3920|5730|5730|

<!-- Start of picture text --> (OLS) (Heck) (Heck)<br>Lagged diasp -0.502 -0.514 -0.008<br>(5.87)*** (9.67)*** (16.05)***<br>Col. links -4.635 -4.619 -0.040<br>(4.68)*** (10.69)*** (9.93)***<br>Language 0.338 0.321 -0.004<br>(0.84) (1.09) (1.58)<br>Log(dist) 0.266 0.269 0.006<br>(1.24) (1.69)* (3.78)***<br>Schengen -0.193 -0.180 0.002<br>(0.50) (0.36) (0.49)<br>Constant 5.607 -3.240 -0.037<br><!-- End of picture text --> <!-- Start of picture text --> (0.29) (1.19) (1.60)<br>Mills -0.405 -0.873<br>(1.07) (2.44)**<br>Observations 3920 5730 5730<br><!-- End of picture text --> Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 18
# **Annex I. Description of Unified Land Tenure Questionnaire Modules**<sup>**16**</sup>

Due to the various survey designs in which this module could be integrated, multiple versions of the module have been designed to facilitate its inclusion in a number of different survey designs and types. The appropriate version selection for a given survey depends on: (i) whether one person responds on behalf of other household members (proxy respondent approach) or each individual is asked specifically about his or her land tenure rights (selfrespondent approach); and (ii) whether data is collected at the parcel level or the household/farm level. Five versions of the questionnaire module are provided, to account for the various combinations of respondent selection and level of data collection (refer to the attached questionnaire modules and guidance note for more detail). The versions are differentiated as follows:

- **Version 1:** Parcel-level data; self-respondent approach; no parcel roster elsewhere; assumes separate household member roster which records individual sex; administered to (a) one or more randomly selected individuals or (b) all adult household members.

- **Version 2:** Parcel-level data; self-respondent approach; assumes parcel roster elsewhere which can be fed forward into either (a) the interview of one or more randomly selected individuals or (b) the interviews of all adult household members; assumes separate household member roster with sex.

- **Version 3:** Individual-level data; self-respondent approach; not reported at parcel level; administered to (a) one or more randomly selected individuals or (b) all adult household members.

- **Version 4:** Parcel-level data; proxy respondent approach; no parcel roster elsewhere; assumes separate household member roster with sex.

- **Version 5:** Individual-level data; proxy respondent approach; not reported at parcel level.

Each version offers advantages and disadvantages. The use of proxy respondents has the potential
8 coordinating tax reforms: can lost tariffs be recouped?

SITC Section 3 and Division 27, 28 and 68).<sup>12</sup> Exports, however, could be misleading as a measure of natural resource abundance because a country that is too poor to consume its own natural resources exports much of its output, compared with a richer country which exports less but produces just as much. Therefore, I also use a second measure – oil and gas rents per capita – taken from the World Bank’s Adjusted Net Savings data center.<sup>13</sup>

# 1.2.3 _Estimation Method_

I use four different estimation methods. The first method uses the fixed effects “within” estimator in equation 1.1 where the dependent variable – domestic taxes (net of trade taxes) – is regressed on a set of explanatory variables explained earlier. The fixed effects model removes the correlation between time-invariant unobserved effects and the explanatory variables. The main explanatory variable – tax revenue as a share of GDP – is, however, possibly endogenous. Both the collection of non-trade tax and trade tax revenues could, for example, be driven by a reformed customs administration.

The second method, therefore, addresses the potential endogeneity of trade tax by using instrumental variables which are its own first and second lags. Despite these corrections, a bigger problem in the first two models as specified in 1.1 equation is that the presence of the lagged dependent variable as one of the explanatory variables regressor (DTit−1) renders the estimates inconsistent because of its correlation with the fixed effect, causing a dynamic panel bias (Nickell 1981). There could also be serial correlation in the error term. Roodman (2009) offers a useful guide on the use of dynamic panel estimators in these situations.<sup>14</sup>

> 12 These are primarily fuel, metals, and ores, whose total export values for the years 1982-2006 I obtained from partner country records in
Kikkawa, 2021; Kee and Tang, 2016; Puzzello, 2012).4 Recent micro approaches to GVC measurement use detailed firm-level transactions. Tang et al. (2016) and Bems and Kikkawa (2021) infuse firm information in IO tables, while Kee and Tang (2016) directly estimate DVAR from the customs transaction data. However, these approaches rely on complex firm-to-firm data or matching customs records with census data when firms do not use all their imported inputs for exporting, therefore limiting their scope to specific countries or industries and raising concerns about broader applicability.5 This paper builds on Kee and Tang (2016) by incorporating firm-level heterogeneity through customs data, reducing aggregation bias and complementing IO-based macro approaches. With fewer data requirements, the export revenue function approach applies to a wider range of developed and developing countries, enhancing the external validity of micro-level GVC measures. It also enables bootstrapped standard errors for statistical testing on DVAR estimates. From a policy perspective, this paper emphasizes the critical role of domestic transportation infrastructure—beyond trade and FDI liberalization—in advancing China’s position in 4Moreover, the infrequent updates of IO tables, coupled with significant time lags and discrepancies in benchmark years across countries, pose challenges for researchers attempting to capture the dynamic shifts in global value chains. For a comprehensive review of the GVC literature, including the advantages and limitations of both macro and micro approaches, see Antràs and Chor (2022) and Johnson (2018).

5Even in countries where both accounting-type firm records and transaction-based customs information are available, combining these datasets can be challenging and often leads to matching errors. For a detailed discussion on matching efficiency, see Egger et al. (2021).

4
||||29||||
|---|---|---|---|---|---|---|
|Topic|Year|Title|First author|Journal / Edited volume|Citations|Rank|
|Determinants of health and ill-health|1999|The Long-Run Growth in Obesity as a Function<br>of Technological Change|Tomas J. Philipson||279|226|
|Determinants of health and ill-health<br>|2004<br>|<br>Addiction and Cue-Triggered Decision Processes<br>|B. Douglas Bernheim<br>|American Economic Review<br>|275<br>|230<br>|
|Determinants of health and ill-health|1995|Evaluation of the Subjective Probabilities of<br>Survival in the Health and Retirement Study|Michael D. Hurd|Journal of Human Resources|274|232|
|Determinants of health and ill-health|1986|<br>Nutrition and the Decline in Mortality since<br>1700: Some Preliminary Findings|Robert W. Fogel|Long-term factors in American economic<br>growth|273|235|
|Determinants of health and ill-health|1996|The Demand for Food and Calories|Shankar Subramanian|Journal of Political Economy|271|241|
|Determinants of health and ill-health|2001|Early Childhood Nutrition and Academic<br>Achievement: A Longitudinal Analysis|Paul Glewwe|Journal of Public Economics|269|245|
|Determinants of health and ill-health|1987|<br>Will Developing Country Nutrition Improve with<br>Income? A Case Study for Rural South India|Jere R. Behrman|Journal of Political Economy|269|245|
|Determinants of health and ill-health<br>|2003<br>|Maternal Employment and Overweight Children<br>|Patricia M. Anderson<br>|Journal of Health Economics<br>|266<br>|249
personal consumption experience. Clark and Senik (2009) made a systematic analysis of the types of income comparisons that individuals make by looking at the data of Wave 3 of the European Social Survey. They concluded that self-employed are less likely to compare in general and that employees tend to compare to their colleagues. They also observed that "men compare less to family members than do women. Comparisons to colleagues increase after the age of 25, whereas the opposite is true of comparisons to family members and friends. The married compare more to family members and friends, and individuals with children compare more to family members..." The approach taken in the present paper is somehow more akin to that of Ferrer-iCarbonell (2005) who, in a study of income and well-being, assumed that the individual well-being depends on one's income, on the income of the reference group and on other variables such as age, education, the number of children living in the household, and whether the individual works. Ferrer-i-Carbonell (2005) defined the reference income of an individual as the average income of the reference group, the latter including all the individuals with a similar education level, inside the same age bracket, and living in the same region.

We similarly assumed that the relative deprivation of an individual, which is obviously negatively related to his/her well-being, depends on the comparison of the actual income of the individual with that of his/her reference group, the latter being the income he/she could have expected on the basis of his/her personal characteristics.

Applying expression (8) to such a comparison of predicted and actual income shares could amount in fact to defining a population weighted distributional change measure _D PW_ of relative deprivation with  8
# **7. Cross-Country Comparisons**

With their differences in factor market closures, elasticities of substitution, methodologies for grouping households and modeling labor markets, etc., the country case studies described up to this point have been non-comparable. This makes it difficult to generalize on the basis of cross-country comparisons. Therefore we included two cross-country studies in the program: first, Maros Ivanic (2005) which provides a crosscountry comparison for 15 countries – each of which is treated in a symmetric manner. While this approach is somewhat stylized, and therefore less definitive for any given country, each of the focus country data bases has been built up from the same types of individual household surveys as the single country case studies. Another virtue of this paper is that is offers a fully integrated, global/national/micro modeling approach. In particular, Ivanic has augmented the GTAP global CGE model with reconciled data on 140 disaggregated household groups for each of the 15 focus countries. His grouping is based on income specialization, e.g., agriculture-specialized households rely almost entirely on agricultural self-employment for their income, and similarly for a wagespecialized stratum, etc. Because his is a global framework, he can simulate all of the trade reform scenarios directly in his model, which also facilitates further decomposition of the elements of trade reform and their poverty impacts.

Ivanic’s findings with respect to the poverty impacts of the Doha Agenda are particularly interesting. Specifically, he finds that the Doha trade reform scenarios are not as poverty-friendly as the global liberalization scenario. Now, if Doha represented the same mix of policy reforms as full liberalization, we would expect both simulations to have the same pattern of poverty reduction but with larger cuts under Full-Lib because of 27
place of study, or obtaining a weapon for personal security. For this analysis, “Fear Any” is a binary variable which indicates whether the respondents answered any of these six questions affirmatively. “Fear Count” is a 0-6 count variable summing how many of the items the respondent selected. Finally, as a robustness check, we use a measure of perceived threat of violence to the respondent and their family (“Violence”), which is measured as low, medium, or high.

To measure political behavior, we ask respondents their likelihood of taking different types of action in the coming year (5-point scale). We focus on four key types of behavior indicative of civic engagement: protest, community meetings, and volunteering. We analyze these measures separately and as a 3-item “Civic Index.” Voting, a more conventional measure of political behavior, is not included in the index because national elections would not occur until 2023. Therefore, asking about voting intent within the coming year was not a meaningful question for respondents. In addition to our civic action items, we also measure

> 2Thus, we cannot include in our analysis those respondents who did not use a coyote to enter the U.S., since we should not uniformly assume that they did not experience extortion from some other criminal actor during the journey.

> 3Our sample is larger for this analysis since this dependent variable was asked at the arrival baseline survey.

13
Figure 4: Most adults living in agricultural households use informal ways to save Adults living in a household where growing crops or raising livestock is the main source of household income (%), 2017 <!-- Start of picture text --> Central African Rep. hm<br>Congo, Dem. Rep. |<br>Voands<br>0% 10% 20% 30% 40% 50% 60%<br>@ Saved formally @ Saved semiformally<br>@ Saved using other methods only Did not save<br><!-- End of picture text -->

Source: Global Findex database. Note: The total length of the bar represents the share of adults living in a household where growing crops or raising livestock is the main source of income. People can save in multiple ways, but categories are constructed to be mutually exclusive. Saved formally includes all adults who saved any money formally. Saved semiformally includes all adults who saved any money semiformally but not formally.
“Keqiang Index”), are highly correlated with real economic activity but less manipulatable, and thus suitable as additional predictors of GDP. Column 10 controls all these three variables. In column 11, following Clark, Pinkovskiy, and Sala-i-Martin (2020), we combine nighttime light and the “Keqiang Index” to predict the true GDP, which is then used to construct the over-reporting measure. All of the above results remain very similar to the baseline, revealing a significant inverse U-shaped relationship between the PS’s years in office and over-reporting. Importantly, the over-reporting always peaks around the 5th year.

# **5.2.3 Alternative specifications, estimation techniques and placebo tests**

We consider alternative specifications and estimation techniques of our baseline regression in Table A.2, and conduct several placebo tests. In columns 1 and 2, we adopt a one-step approach that uses a single regression to estimate the effect of political cycles on over-reporting while at the same time controlling for the relationship between night lights and GDP.<sup>38</sup> Since our baseline measure of overreporting is the difference between reported growth and light-predicted growth (scaled by the optimal weight on the light-predicted growth), we use the two components of this measure, i.e., reported

> 38 Magee and Doces (2015) use a similar specification to show that dictators exaggerate their true growth rates relative to the democratic regimes.

20
<!-- Start of picture text --> Mean regression Median regression Average elasticity<br>OPER OPER OPER<br>N OPHL N OPHL N OPHL<br>OMEXBRA aa OMEXaRA oo OMRRRA _<br>22 gir U7OZAF © 22 QR 7OZAFE- 22 oo OZAE-~ -<br>8 ~ captor _— s ~ oapoor —— 8 ~ a<br>Ba OvNMO— QECUROU w OVNMOOEcU ROU B orou ~- own?Occ<br>a Ocri aze Ocri a> Ocri<br>2 ba] bac}<br>OBGR OBGR OBGR<br>-10 o 10 20 30 -10 o 10 20 30 10 0 10 20<br>% diff. with actual data % diff. with actual data % diff. with actual data<br>Avg. without outliers Including total GDP Including GDP*Informality<br>OPER OPER OPER<br>N OPHL N OPHL N<br>OME%aRA _- OMEXBRA - OBRA<br>=ge6 ~ oan,grrft Seok< AOzar— 226 ed aapQtrStor- _Ozar7 225 = Ce° IDI ~ —_-—- _OZAF<br>ro)> ~~OVNKD OEcuROU ro=> OVNMO—~ QecuROU =w> OVNM<br>aor ocri aor oOcri aor Ocri<br>Li] iQ ac<br>OBGR OBGR<br>-10 o 10 20 30 -10 o 10 20 30 5 0 5 10 15 20<br>% diff. with actual data % diff. with actual data % diff. with actual data<br><!-- End of picture text -->