rafmacalaba/data-use-mentions-extended huggingface dataset:
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# **1.** Introduction

A previous paper used panel data from 50 countries and 35 urban areas to summarize trends in motorization and the provision of roads, and to examine the ratio of motor vehicles to roads in a production function framework at both the national and urban levels (Ingram and Liu, 1997). The countries and urban areas, which covered a wide range of income levels, exhibited strong regularities in cross section regressions across country and city characteristics. In particular, income was a strong determinant of both the level of vehicle ownership and road provision. Road provision was buoyant at the national level but not at the urban level, where congestion stimulated decentralized growth and urban road length increased mainly from the annexation of surrounding areas.

The current paper presents additional empirical results obtained using essentially the same national and urban data sets.' These new empirical analyses explore alternate specifications of the underlying relations between three dependent variables--vehicle ownership, road length, and the vehicle-road length ratio--and various independent variables. In particular, they investigate (i) whether income elasticities of motor vehicle ownership and road length vary or are essentially constant across a wide range of income, and (ii) how vehicle ownership and road provision respond to changes in income over time.

An income elasticity measures how responsive demand for a good or service is to a change in income, and income elasticities may vary across income levels. However, most empirical studies of motorization (including our previous paper) have estimated only constant income elasticities. Constant elasticities are usually estimated from regressions with a restricted functional form (typically a log-linear specification). One major weakness of a constant income elasticity is its limited predictive power for forecasting beyond the observed income range when the true elasticities vary with income. While constant elasticity estimates are often statistically very robust, their usefulness in forecasting depends on whether they are at least as robust as variable elasticity estimates. This paper reports estimates of variable elasticities and compares them with constant elasticities.

Our previous
in customs and port efficiency would further cut in the cost of these services.

Second, the EU Regular Report commends Bulgaria for having had made “good progress” in aligning standards with the EU, although the process of fully aligning them to European standards has not been completed as yet. There are still Bulgarian quality standards not conforming to generally accepted international standards, albeit these have been on the decline.

# _C. Concluding observation_

Both domestic and international dimension of the cost of doing business point, on the one hand, to a very significant improvement and, on the other hand, to continued weaknesses. Bulgaria ranks high in terms of overall ease of doing business. It is above Croatia, Slovenia and Romania. There are three areas where Bulgaria lags behind most of CEEC-11—starting a business, contract enforcement, and protection of investors’ rights. The latter is easy to address through appropriate legislative change. Considering, however, the limited confidence of firms in the effectiveness of the rule of law and the quality of judicial services, the contract enforcement strikes one as a barrier to business activity. This has negative implications for entry of de novo firms and for domestic firms’ ability to take advantage of international outsourcing.

The ability to participate in international outsourcing is further hampered by Bulgaria’s low level of performance in terms of efficiency of trade facilitation relative to CEEC-8 and Romania in port efficiency. Bulgaria’s exceeds benchmarks only in the quality of regulations. The quality of regulations is important. But as long as the capacity to enforce them is weak, it has rather limited impact on external transaction cost.

# **2.4. Conclusions**

Bulgaria has made large strides in implementing second generation, structural reforms considerably improving the quality of governance. Three different indexes—Corruption Perception Index (CPI), World Bank-based aggregate index of governance quality, and EBRD-based index of progress in second-generation reforms—all point to a very significant progress achieved over the last three-four years. But the values of these
exclusion of informal, or irregular workers is likely also be a function of banks’ preference for clients with steady employment, reflected in requirements for references and proof of work (see Chapter 2). At the same time, almost 50% of salaried workers are unbanked, suggesting that informality is not a primary reason for financial exclusion.

**Figure 3. 4  Employment profile of the unbanked**

<!-- Start of picture text --> Banked and unbanked by type of activity Banked and unbanked by type of job<br>100% Banked 50% 50% Banked<br>90% 48% Unbanked<br>80% 78% 76% 73% Unbanked<br>70% 60% 40% 36%<br>60% 52%<br>50% 40% 48% 30% 24%<br>40%<br>30% 22% 24% 27% 20%<br>14%<br>20%<br>9%<br>10% 10% 7%<br>0% 2% 4% 1% 3% 3% 0% 0%<br>0%<br>%<br>%<br>Housing chores Students Unemployed Other Employed salaried worker in private sector self-employed domestic worker employer family worker without pay salaried worker in public sector other<br><!-- End of picture text --> _Source: DANE household study of Bogotá, 2003_ 9. **The unbanked have lower educational attainment levels than those with bank accounts.** Figure 3.5 shows a clear relation of education to the use of formal banking products. As educational levels increase, the percentage of unbanked falls and the percentage of banked persons rises. Regression analysis shows a high correlation between educational levels and likelihood of being banked or unbanked. At the same time, it is noteworthy that approximately 15% and 25% of the group with less than elementary and
(2007) and Keefer et al. (2010) find that earthquake fatalities increase with corruption, while Anbarci et al. (2005) and Kahn (2005) find that fatalities are associated with ethnic fragmentation. Wheeler (2010) uses a variable capturing democratic accountability and citizens‘ political freedoms as a measure of transparency in disaster reporting.

Table 4 shows that the coefficient of variation for the income variable used in our models was larger within countries than between countries. Although it was smaller within countries than between countries for the governance variable, it remained relatively large in samples that included all the countries or just developing countries. The country effects apparently risk eliminating too much of the variation needed to identify the effects of the income and governance variables only for governance in developed countries.

# _3.4 Other controls_

A country‘s total land area (km<sup>2</sup> ), urban population percentage, and mean annual national precipitation come from WDI (2010). Latitude (in absolute value), mean elevation (meters above sea level), and proximity to coast (measured by the percentage of land area within 100 km of ice-free coast) come from Gallup et al. (1999). Data on total forest area (km<sup>2</sup> ), encompassing both natural forests and plantations, are available for 1990, 2000, and 2005 from FAO (2001, 2005, 2007). We interpolated estimates for other years by calibrating an exponential curve to the three observations for each country and expressed forest area as a percentage of total land area. Descriptive statistics for these variables are presented in Table 3. Aside from urban population percentage and forest percentage, all are timeinvariant and appear in the models only if we exclude the country effects.

The WDI provides data on mean national precipitation for just 2008. Due to this limitation, our FE regressions do not include precipitation, but we doubt this omission 13
being surveyed in larger economies and is chosen to make each country’s sample representative of the non-agricultural private economy.

From the Enterprise Survey we construct the following four access to and use of financial services indicators: (i) _access to finance_ is an indicator variable ranging from 1-5 with 1 indicating access to finance is “no obstacle” to the operation of firm to 5 indicating a “very severe obstacle”; (ii) _account_ is a dummy variable equal to one if the firm has an account at the time of the survey and zero otherwise; (iii) _overdraft_ is a dummy variable equal to one if the firm has an overdraft facility at the time of the survey and zero otherwise; (iv) _loan_ is a dummy variable equal to one if the firm has a line of credit or loan from a financial institution at the time of survey and zero otherwise.

We match the two samples by building a cross-sectional dataset that matches the firm characteristics with the average of the available data from the FSAP reports. Maximum country overlap between the two data sources is 54 countries with over 25,000 firm level observations. Appendix Table A1 lists the countries in our sample, a breakdown of the firm distribution by country, and by-country summary statistics of the FSAP variables we will use in the subsequent analysis. Table 1 provides descriptive statistics and Table 2 correlations on the country-level.

The descriptive statistics in Table 1 show that over 90% of firms in our sample have an account. This percentage, however, varies significantly across countries. While in the Slovak Republic, 20.8% of firms have an account, 99.8% do so in Croatia. Almost 50% of firms have an overdraft facility and 45% have a loan. Behind this average, however, are again large crosscountry variations. While only 1.3% of firms have an overdraft facility and 3.1% a loan in Guinea-Bissau, 87.5% and 74.5%, respectively, do so in Chile
|
|Planted maize area harvested (%)||6.385***<br>(1.642)|||||||
|Constant|784.3***|783.3|359.7***|-1,836|894.4***|10,025|1,252***|3,271***|
||(115.0)|(1,684)|(70.56)|(1,344)|(264.2)|(7,343)|(86.41)|(934.1)|
|Observations|568|544|493|490|442|442|842|842|
|Adjusted R-squared|0.373|0.509|0.385|0.448|0.468|0.556|0.090|0.333|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|

Note: Regression of self-reported realized maize yield on expected maize yield, planted area loss (only IHPS19), and a number of plot, manager-, and household controls as well as inputs and district fixed effects. Controls: Inputs - Plot area, total labor p. ha, any hired labor dummy, seed quantity p. ha, improved variety dummy (only IHPS19 and IHPS15), inorganic fertilizer quantity p. ha, any organic fertilizer dummy; Household - dependency ratio, head employed for wage dummy, any HH member owns non-ag enterprise dummy, any HH member has primary education dummy, agricultural asset index; Manager - female dummy, age, primary education dummy; Plot - intercropped dummy, precipitation of wettest month, soil fertility index; Zone fixed effects. Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
forward looking concept, the basis for the valuation of an asset should be current cost accounting (CCA). CCA evaluates the replacement cost of an asset with the lowest cost asset which serves the same function (this is called modem equivalent asset, and it should incorporate the latest technology available), rather than the historic cost at the purchase time followed by HCA.

An historical accounting basis may distort decisions when technology is evolving rapidly. Moreover, inefficient past decisions will have effects on future choices if a HCA basis is adopted. Take the example of the replacement of BT's ducts: the current cost is likely to exceed the historic cost. The reverse is true for switches costs. If access prices are calculated with HCA, then inefficient entrants may enter access services and efficient firms may not find it convenient to enter into conveyance. On the other hand, a CCA basis sends signals to all players that reflect current market conditions and therefore a CCA basis is helpful both to the incumbent and to the entrants in their investment and pricing decisions.

Despite its flaws, HCA is deeply rooted into many firms' accounting systems and it may be the only option available before better accounting procedures are introduced. Oftel, anticipating that it would have taken time to switch between the systems, decided to keep the old one in place while working on two interrelated projects. One methodology, known as "topdown", was developed by BT to estimate the incremental cost of different network components in both historic and current cost terms, starting from BT's management and financial accounting data. The other model, based on a "bottom-up" methodology, involved the construction of an engineering model of the plants and other parts needed to provide an inland Public Switched Telephone Network.

The bottom-up model attempts to determine the cost of network with a topology similar to BT's existing network, and to compute the average incremental cost of providing a particular service. The model is rather simple and transparent and it explicitly focuses on the parameters that
endowments thanks to higher investments in productivity-enhancing technological and process improvements, diversification away from maize, and ability to tap into global value chains and export markets. Formal agricultural firms’ strong performance also underscores the potential for increased commercialization and adoption of improved technology to drive jobs and economic transformation in Zambia despite the headwinds to overall agricultural productivity growth highlighted in World Bank (2024). Nevertheless, formal agricultural firms account for an as yet marginal portion of the total agricultural labor force (only an estimated 119,000 workers). In general, large-scale commercialized farms—of which the firms in the CIT database are a subset—only farm about 0.7 percent of Zambia total cropped area and represent less than 0.08 percent of all farming entities, highlighting the scale of both the potential opportunity and need to drive increased commercialization (World Bank 2024).

At the same time, the analyses also highlight key climate change-related vulnerabilities faced by formal agricultural firms—as well as farmers in Zambia write large—that drive significant year-on-year volatility. A notable exception to the general upward trend in labor productivity was a sharp drop in 2016 (see Figure 24). This sudden deterioration was driven by an intense drought in 2015-2016, attributable to El Niño and climate change, which translated into steep within-firm productivity declines (Alfani, et al. 2019). Looking forward, climate change is likely to exacerbate the likelihood and intensity of such extreme climactic shocks, highlighting the need to increase resilience of the sector (Thorton and Lipper 2014). Interestingly, although the impact of the 2015-2016 drought is evident in the data, there is no similarly steep drop in productivity in 2019, when Zambia experienced another drought. Formal agricultural firms’ relatively strong performance it attributable to the 2019 drought being relatively less intense and the relative concentration of large commercial farms in the Copperbelt and Easter provinces, which were less affected by drought.

Figure 23. Cumulative change in weighted average log labor productivity, 2014-2021 (agriculture, forestry, and fishing; constant
_kjt_ that gives the lowest value of the poverty measure is chosen.

**_Data:_** The data for implementing these measures come from the World Bank’s well-known LSMS.<sup>38</sup> Table 2 lists the countries, years of survey, and numbers of households surveyed. In keeping

> 36 Glewwe (1992) recommended this in his formulation of the optimal targeting problem.

> 37 <mark>We use the “fmincon” program in Matlab.</mark> 38 The LSMS has designed and implemented household surveys across many countries since the 1980s. These are nationally representative multi-purpose surveys spanning a quite wide range of topics. Further information can be found at the <u>LSMS website. All surveys except for Ghana are LSMS-ISA surveys.</u>

15
confirms the hypothesis that bank branches are underrepresented in low income neighborhoods in Bogotá, while the hypothesis is not valid for financial credit cooperatives. When taking financial credit cooperatives into account, Medina’s analysis finds that lower income areas in Bogotá are significantly better served in terms of branch presence.

25. **However, within an urban area, bank branch presence is driven by other factors in addition to current income levels** . Medina’s analysis finds that the number of bank branches in Bogotá is positively correlated with the level of permanent household income and welfare. In addition, positive correlations are found between the number of bank branches and the security of the neighborhood, the health of its infrastructure and the presence of religious and cultural institutions. On the other hand, the presence of bank branches is found to be negatively correlated with population density, an area’s unemployment rate and the percentage of an area occupied by industrial activity. The particular importance of security in access to finance is highlighted by analysis which shows that the cost of handling cash is a key factor in bank branch profitability<sup>39</sup> .

26. Focus groups also revealed that remote bank locations are a key factor in financial exclusion for people in income strata 1, while people in income strata 2 and 3 did not mention location as a key reason for lack of financial access<sup>40</sup> . For Bogotá, the demand survey confirms that 62% of the unbanked are over 10 blocks away from the next bank branch (see Chapter 3). Greater distance to bank branches increases transaction costs for low income clients and the unbanked.

27. **Alternative access mechanisms are limited.** ATMs provide a costeffective alternative of physical access to formal financial services, such as payment services, check deposits or savings deposits. While the number of ATMs in Colombia has remained broadly stable over the past few years, the number of ATM transactions increased, pointing to increasing acceptance of ATMs among
Table 4. Effects of the PEP Program on Labor Market Access

|_Dep Variable:_|Employed [=1]<br>(1)|Formal Job [=1]<br>(2)|
|---|---|---|
|_Panel A:_All sample|||
|PEP [=1]|0.032<br>(0.011)|0.075***<br>(0.037)|
|Observations|6,339|4,104|
|_Panel B:_Women|||
|PEP [=1]|0.061*<br>(0.026)|0.066***<br>(0.017)|
|Observations|3,591|1,437|
|Wave FE|Yes|Yes|
|HH FE|Yes|Yes|
|Department_×_wave|Yes|Yes|
|Geographic Sampling_×_wave|Yes|Yes|
|Pre-migration controls_×_wave|Yes|Yes|

_Notes:_ The table presents the estimates of the specification described in equation (1) using variables on labor market access as main outcomes. Panel A presents results for the whole sample and panel B for women only. Department corresponds to the five departments in which the sample was collected and geographic sampling corresponds to the four geographic levels at which the sample is representative, including three main cities and a fourth group that accounts for nine smaller urban centers with prevalent migration from Venezuela. Pre-migration control variables include: (i) individual controls for the head of household (gender, age, and education); (ii) labor history for the head of household (probability of being employed, type of job, probability of having a written contract, and the time gap between the last job and the migration episode); (iii) household characteristics (number of children, household size, access to public services, owning
selected developing East Asian countries is conducted. The exercise draws from the 2017 Global Innovation Index (GII) data set<sup>2</sup> which covers 127 countries; the only developing East Asian countries covered are the following: Cambodia, China, Indonesia, Malaysia, the Philippines, Thailand, and Vietnam. This data set captures overall innovation capacity through a mix of indicators encompassing both innovation enabling factors (called as “inputs”) as well as innovation outputs. It has a multi-level structure with two sub-indices and 7 pillars which are then divided into three sub-pillars, each of which is composed of individual indicators of different types (composite indicators, survey questions and hard data indicators),<sup>3</sup> totaling 81 indicators. Scores (of index, sub-index, pillars and sub-pillars) are normalized in the 0-100 range. Figure 4 below displays the indicator schemata. Despite its drawbacks – particularly regarding the lack of comparability over time – this database provides a single source of information that allows the comparison of performance of national innovation systems across economies in a given year.

According to this data set structure, the innovation enabling factors encompass five main pillars. The _Institutions_ pillar captures the institutional framework of a country and measures the extent to which this framework is conducive to attract private sector business by providing good governance and the correct levels of investment protection. The _Human capital and research_ pillar reflects both the standard and level of education and research activity, which are prime determinants of a country’s capacity to innovate. The _Infrastructure_ pillar measures the extent to which a country is served with good and ecologically friendly communication, transport and energy infrastructures; all these factors are key facilitators to production and exchange of goods and services and ideas. The _Market sophistication_ pillar reflects the underlying market conditions that are relevant for business operation and innovation activities; it includes availability of credit; conditions to support investment and access to international markets. The _Business sophistication_ pillar measures the extent to which firms are conducive to innovation
<!-- Start of picture text --> x<br>xSI = 0 0<br>kf wi pj +wi p r (18)<br>j=1 j i nf h<br>[∑ pj pr ]<br>xf<br>xMI = 0 + xnf<br>kf wi pj (19)<br>[∑j=1 j pji ] [p p r rh0 ]<br><!-- End of picture text --> In this application, the non-food deflator is a single commodity index (based on rent only): this is tantamount to assuming that the spatial price dynamics of all non-food items is the same as the one captured by housing prices. Reflecting the inherent non-tradable nature of housing services and typical tradeable nature of food items, this deflation approach is expected to maximize the relative spatial variability of the non-food price index compared to the food price index, and it is hence well suited to showcase empirical implications of proposition 2.<sup>11</sup> Regarding the temporal dimension of price adjustments, the _aggregate_ monthly CPI index, as estimated by the Statistical Center of Iran (SCI), is used throughout the analysis.<sup>12</sup>

# **6.2 Results**

Proposition 2 delivers a prediction as to the sign of the difference between a welfare aggregate deflated using a single index and one deflated using a multiple index approach. According to proposition 2, this difference would depend on the relative level of food and non-food price indexes as well as on observed consumption patterns (implicit differences in the level of welfare), i.e. on the food share level. What makes the analysis of data for the Islamic Republic of Iran particularly useful for an empirical illustration of Proposition 2 is the significant spatial variation observed in the Islamic 11 In practice, this assumption may seem too extreme, and welfare analysists may be reluctant to adopt it. A