rafmacalaba/data-use-mentions-extended huggingface dataset:
24 the national tariff line level so the two sources of information are comparable. Given that patterns of protection change only relatively slowly, the SMART and IDB records will, together, afford reasonable insight into the patterns ruling over the early 1990s.

As far as intra-block preferences are concerned, Article 5 of the Treaty of Asuncion, which created Mercosur, required "progressive, linear and automatic tariff reductions" be implemented starting in 1991 "with a view to arriving at zero tariffs for the entire tariff area by 31 December 1994." Although there was some slippage in this objective, the World Trade Organization (October 1996) reports "that by the end of 1994 intra-regional trade between Brazil and its Mercosur partners has been duty-free since 1 January 1995 except for 29 tariff line items" (out of 9,107).'6 The same report quotes (p. 23) Brazilian authorities as stating that duty free trade covered close to 95 percent of intra-regional (Mercosur) trade in 1994. From these observations we conclude that the reported external tariffs will be highly correlated with the degree of preferences offered on intra-Mercosur trade.<sup>17</sup> As such, the average levels of current "applied" tariffs were computed and these were taken as a measure of intra-block preferences. 18 A second attempt at identifying discriminatory trade barriers focuses on nontariff barriers. Two measures of the potential importance of NTBs are available. The first, a NTB trade coverage ratio shows the share of all imports (measured in current values) subject to nontariff barriers. The second, often 16Behar (1995) reports that the first preferential reduction in Mercosur's tariffs took place in June of 1991. A 1994 background study for a World Bank country economic memorandum gave the schedule along which tariffs on intra-trade were then being reduced. This schedule set a minimum margin of preference of 47 percent and augmented the margins of preference which already exceeded this minimum (due to previous arrangements) by an additional 7 percent. A 7 percent cut
economies and weak benefits for TFP from the diversity of local economic activity. Henderson‟s specification relates plant-level time-varying output controlling for inputs (i.e., TFP levels) to time-varying agglomeration indices at the industry-location level hence is at a more disaggregated level than those in earlier papers. Cingano and Schivardi (2004) estimate production functions for Italian industries using firm-level survey data and following the Olley and Pakes (1996) methodology. Based on growth in industryregion averages of their firm-level TFP estimates, they find evidence of MAR externalities but no effects of sector competition or diversity.<sup>6</sup> Our paper‟s contribution is threefold. First, we examine the importance of the local economic structure - concentration, specialization, and competition - for long-run TFP growth at the sector-location level using manufacturing census data for an emerging economy. We differ from Henderson (2003) and Lopez and Südekum (2009) since we focus on _long-run,_ rather than on yearly effects of the local economic structure. Furthermore, we focus on TFP _growth_ , rather than TFP _levels_ , as our outcome variable. By considering long-run effects, our paper mitigates the potential concern of a spurious relationship between agglomeration and TFP growth that could arise from the correlation between unobserved determinants of TFP growth across sectors and regions and the agglomeration measures (Martin et al., 2009). Our inclusion of sectoral and regional fixed effects in some specifications explicitly addresses this concern. Second, our estimates of TFP are unbiased, obtained as the residuals from flexible translog production

> 6 Lopez and Südekum (2009) examine a related question using Chilean plant-level data following Henderson (2003)‟s approach. Their study differs from ours in several important respects. First, their empirical specification relates plant-level time-varying TFP levels to time-varying indices of concentration and diversity at the regional and sector level. Second, their indices of concentration and diversity are based on a simple count of the numbers of plants in particular sectors in the region. Third, their study covers an
# **1.2 Trade, Production and PTAs**

A fully-fledged general equilibrium structural gravity approach relies on a trade matrix that includes both intranational and inter-national manufacturing and agricultural trade flows. The inclusion of intra-national sales is critical, as the correct benchmark for trade integration is the domestic economy (Head & Mayer 2014, Yotov 2021). The trade database used for the empirical analysis includes both domestic and international sales of agricultural and manufacturing products. Domestic sales are calculated as the difference between the value of production and total exports (both measured in gross terms).<sup>18</sup> Manufacturing output and trade come from UNIDO INDSTAT2 (2022) and UN-ComTrade, respectively.<sup>19</sup> We follow standard practice for filling gaps in intra-national flows if output is missing or implies negative domestic sales. Specifically, we linearly interpolate production values between non-missing data points and extrapolate the remaining missing values using the ratio of gross output to value added as in Head & Mayer (2021).

Agricultural product flows come from the Food and Agriculture Organization of the United Nations Statistics Division (FAOSTAT).<sup>20</sup> The FAOSTAT reports a Detailed Trade Matrix in both quantities (in tons) and values (in thousands of US dollars) from 1986 to 2018 and a disaggregated agricultural production data. As done for the manufacturing sector, after filling in some missing values following the same procedure mentioned earlier, we combined production and trade data into a dataset containing both domestic and international sales.

Finally, we combine manufacturing and agricultural data and aggregate, for each year, the underlying flows by country of origin and destination. The final dataset includes, for each year, only countries with non-missing (or nonnegative) domestic sales. Overall, the estimation dataset covers 137 countries (34 of which in the African continent) on 1-year intervals from 1986 to 2018. The counterfactual exercises are computed for year 2018 and 2014.<sup>21</sup> Trade
The third data set contains the monthly balance sheet information of the 18 commercial banks in our sample, representing more than 98 percent of commercial bank lending.<sup>7</sup> We merge this data set with information from Bloomberg on the stock prices and CDS spreads of the banks.

Overall, our data contain a total of 1,718,740 loans to firms in the energy and non-energyrelated sectors. We classify firms as belonging to an energy-related sector according to their 5- digit North American Industry Classification System (NAICS) codes.<sup>8</sup> The summary statistics for our sample are shown in Table 1, grouped in five panels: (i) bank-month-level indicators, (ii) loanlevel variables of firms in energy-related sectors, (iii) loan-level variables of firms in non-energy sectors, (iv) real outcomes at the firm-year level, and (v) GDP measures at the state-quarter level. Table A1 in the appendix presents the definitions of all the variables.

The first variable in Table 1, panel A, captures the banks’ exposure to borrowers in energyrelated sectors as a share of their tier 1 capital. On average, the ratio of exposure to capital is 9.9 percent, with banks in the bottom decile having no exposure to the energy sector, while banks in the top decile have exposure above 25 percent. Our measure of exposure follows the Basel Committee’s assessment of exposure to related entities, which is defined as the credit volume of a bank to related entities as a share of its tier 1 capital.<sup>9</sup> The next variables in panel A correspond to different elements of the banks’ balance sheets, including their tier 1 capital ratios, lending portfolio, and delinquency rates, along with statistics on the banks’ stock prices and CDS spreads. The average tier 1 capital ratio of the banks is 15.4 percent, with the banks in the bottom decile having a capital ratio of 12.5 percent
the Urban Household Sector," _Journal of Transport Economics and Policy,_ vol. 17, no. 3 (1990), pp. 11937.

10 Robert Pindyck, _The Structure of World Energy Demand_ (Cambridge, Mass: MIT Press, 1979); Kenneth Button, Ndoh Ngoe, and John Hine, "Modeling Vehicle Ownership and Use in Low-Income Countries," _Journal of Transport Economic and Policy,_ vol. 27 (January 1993), pp.51-67; Olof Johansson and Lee Schipper, "Measuring the Long-Run Fuel Demand of Cars," _Journal of Transport Economics and Policy,_ vol. 31 (September 1997), pp. 277-92; Gregory K. Ingram and Zhi Liu, "Motorization and the Provision of Roads in Countries and Cities," Policy Research Working Paper 1842 (Washington, D.C.: World Bank, 1997); and Gregory K. Ingram and Zhi Liu, "Vehicles, Roads, and Road Use: Altemative Empirical Specifications," Policy Research Working Paper 2036 (Washington, D.C.: World Bank, 1998).

1 lThese income elasticities measure the percentage change in vehicle ownership or use when income changes by one percent. An elasticity greater than one means that vehicle ownership or use changes more rapidly than income.

12 Cross-country per capita income can be measured on the basis of market exchange rates or purchasing power parity (PPP) exchange rates. Income measured at market exchange rates may be more relevant for cross-country studies of motorization because vehicles are traded goods, and market exchange rate income measures the ability of an economy to purchase traded goods. Income at PPP exchange rates spans a narrower range than income defined at market exchange rates, because PPP-based income substantially exceeds market-rate based income for low-income countries. Thus income elasticities estimated using income at market exchange rates tend to be smaller than those using income at PPP exchange rates. For a discussion of purchasing power parity exchange rates, see Robert Summers and Alan Heston, "The Penn World Table (Mark
attributed to two or more GTAP categories. To calculate the correct sharing coefficients, input output tables of GTAP are used. In the household data, there are various categories that cannot be matched with those of GTAP. These consist mainly of transfers and negative savings, whose average income flow are assumed not to vary with the simulation. Another issue is that income is usually underreported in the household surveys, and total expenditures usually exceed total income. This factor, together with consumption smoothing issues prompted me to use total expenditure as a proxy for total income. Nevertheless, the income structure of the household data is still maintained. It is likely that different income categories have different degrees of underreporting. A final issue is that the income composition of the survey data is very different from the share of GTAP income categories. Because of the misreporting issues mentioned above, I relied on the GTAP endowment structure, nevertheless still maintaining the distribution of the endowments across households.

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explain how key dimensions of personality traits are measured and how they differ among individuals in our sample before discussing in detail the channels through which they might affect technology adoption and technical efficiency.

# **3.1 Study setting, data sources, and key household characteristics**

We use data from the Kpong and Weta irrigation schemes, located in Ghana’s Eastern and Volta Regions. Cultivators of rice plots were selected randomly from a listing of all cultivators in the schemes during the 2013 major season. With the sample stratified by gender and scheme,<sup>3</sup> data were collected on agricultural production, household income, livestock and other assets, credit, decision making, and expenditures in the 2012 major season (April-September). After dropping non-useable data,<sup>4</sup> we have 1,194 cultivators with 1,778 rice parcels.

> 2 An analogy may be the shift from a focus on school enrollment to a focus on improving school quality (e.g. Hanushek and Woessmann 2008). 3 All female farmers were included to allow sufficient statistical power for analysis by gender sub-group.

> 4 Of the 1,600 farmers in the sample, 159 were dropped because they did not cultivate irrigated rice parcels, 80 because accurate harvest information was not available, and another 184 due to missing control variables.

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of land for agriculture, oil mining and gas mining is simply set fixed for all periods equal to the base year value.

Tax rates are set equal to those for 2010 derived from the SAM. These are summarized in Table A4. For the government deficit,  _G_ , we set it at the base year 1.69% of GDP initially, declining steadily towards zero in the long run. These deficits are cumulated into the stocks of domestic and foreign debt, _Bt G_ * and<sup>_B_</sup> _t_ , assuming a constant division between domestic and foreign borrowing. Data for the stock of debt and interest paid on it comes from the China Statistical Yearbook (NBS 2012, Table 8-13), IMF’s _World Economic Outlook 2012_ online database and the 2010 Social Accounting Matrix. Government transfers, _G_ transfer_ , are set to rise in proportion with population and average wage. The nontax fees paid by enterprises are set to be a fixed share of GDP equal to the base year’s share (Table A4).

The current account balance was in a huge surplus in the mid-2000s but has since declined. There is no consensus about the future evolution of this variable, for simplicity, after setting it as a share of GDP at the observed sample period values, we set it to decline rapidly to zero. This _CAt_ deficit is also the assumed rate of borrowing from the world. Import prices, _PM_<sup>_i*_</sup> , are assumed fixed at the base year value for every period with one important exception. World oil price forecasts are taken from the U.S. Energy Information Administration and shown in Figure A7.<sup>24</sup> The model also requires projections of the export share; while this has been rising rapidly in the past, it fell with during the 2008 Global Financial Crisis and never recovered. Given the aim to rebalance the economy away from exports and investment towards consumption, we simply project a constant
.3|11.2|12.5|11.1|18.5|35.9|-|20.8|33.8|
|**2007**|A|35.2|-|12.2|11.6|12.8|11.6|18.6|37.0|-|21.3|34.2|
|**2006**|A|34.1|-|13.2|10.2|13.1|11.6|20.5|39.2|-|20.7|37.5|
|**2005**|A|32.1|-|12.9|12.4|13.2|10.1|20.3|37.0|-|19.6|38.1|
|**2004**|A|31.41|-|10.9|12.2|14.1|10.4|17.0|35.3|-|14.3|41.6|

Source: State of Industry Reports, various years. A= actual; T = target; - indicates no data.

The T&D loss data provide an interesting case where a state-owned utility had been unbundled but that no further reform step has been taken. The parallel existence of KE, that was privatized during the reform period, also provides a valuable contrast. On this variable the data strongly support the hypothesis that unbundling by itself does not improve performance, even when a price structure is used that makes inefficiencies explicit and visible. The profit motive, introduced by privatization, appears to be linked to limited efficiency improvement.

An indicator of the degree of **commercial efficiency** is rate of “recovery” (percent of billing collected), which is available for all the DISCOs for several years.
specified in the survey. These problems call for prudence in the use of the econometric results, but it seems warranted to conclude that financial aid matters for university access.

# **6. Impact of Loans on Student Behavior**

The second channel through which financial support can contribute to human capital accumulation is by changing the behavior of students. According to the survey results, almost 60% of the students indicated that they increased their effort because of the loan, and 22% said that the loan affected their career choice. In this section we first discuss the proposed methodology to investigate the relationship between loans and student behavior, and then turn to a discussion of our findings for student performance, part-time employment decisions, and career choice.

# _6.1 Research strategy_

We want to investigate the effect of the SOFES loan on the behavior of students in terms of study choice, decision to work on the side, and student performance. To estimate the impact of SOFES on student behavior we need to have a control group. In case of the survey data the group of students that did not receive a credit from SOFES can be used as a control group. The general regression model is given by where _Y_ is the endogenous variable of interest, and the other symbols have the same meaning as above. The treatment effect, i.e. the impact of the intervention on the outcome variable, is measured by γ. Ideally, assignment to the treatment group and to the control group is random (a so-called controlled experiment; this is the standard research method in for instance pharmaceutical studies where the effects of new drugs are tested). In our case, assignment to the treatment group and the control group is not random, but linked to a set of observed and unobserved factors. Whereas we can correct for the observed factors (the most important being the student’s socio-economic background), the influence of unobserved factors (such as the student’s motivation) cannot be ruled out. When the incidence
line are slightly higher but show the same gap.

> 18 Figures based on National Commission for Nomadic Education estimates retrieved 29<sup>th</sup> May 2019: - <u>http://www.ncne.gov.ng/about us/</u> 19 th - Figures based on Internationally Displaced Monitoring Center estimates retrieved 29 May 2019: <u>http://www.internal displacement.org/events/workshops-on-internal-displacement-data-in-nigeria</u> and UNHCR website, retrieved 29<sup>th</sup> May 2019: - <u>https://www.unhcr.org/nigeria emergency.html</u> 20 As the consumption aggregate for wave 4 contains slightly different components than that of waves 1 to 3, due to the change in the components comprising the new national poverty line, monetary poverty estimates are only presented for wave 1 to 3. Given the break in series, monetary poverty estimates for wave 4 are considered not fully comparable to those of waves 1 to 3.

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# **4.2 Simple Model Specification.**

A very simple model specification to analyze what emerges from these hypotheses tests is advanced as follows:

# (1)  Y = f(X, Z, c, C) + ε

Y = city performance as measured by a vector of qualitative service variables which include access to water supply, sewerage, electricity, and telephones from the UN dataset, as well as the overall quality of infrastructure services and electricity supply, access to telephones and/or cell phones and access to internet in schools, using the EOS database; X = measure of globalization at the city level, which we specify using the Taylor dataset as the total number of offices of major international advertising, accounting, and financial firms in a city; Z = vector of governance indicators, which includes control of corruption, bribery in utility, and state capture, largely drawn from the EOS database; c = other city characteristics, such as city size, whether it is the capital city, and/or a port, as well as availability of websites and availability of information on city budget and business regulations in such websites; C=country characteristics, such as income level, size, extent of urbanization, and degree of globalization at the country level, and, ε = error term.

# **4.3 Data description and Mapping of Variables**

The KLM database, which integrates existing data with newly collected data and indicators, covering a total of 412 cities in 134 countries, was constructed by drawing from a variety of sources. Each variable we utilize in this paper is listed in Table 1 below. From the UN Observatory (1998), we downloaded 4 service access/performance variables, covering about 80 cities in 60 countries. From the enterprise-specific EOS survey database (2003), we construct city-specific averages for 12 governance indicators, as well as 5 service access/performance variables, covering 271 cities in 101 countries (see Table 1 below for details)
rates using minimization criteria of the sum of the squares of the residual adjustment results.

h. _Deferred Life annuities._ The parameters _( )_ were calibrated by the Chilean mortality tables [Pension Superintendent of Chile (2004)] using the same criteria as above.

i. _Individual Account Contributions_ . The dynamics for the contributions were characterized by the following equation: , where is the volatility of the process, is a random shock with standard normal distribution . The parameters _(_ were calibrated by adjusting the trajectory of the expected contributions used by Viceira (2010, pg. 223) for the Chilean case utilizing the previously mentioned criteria, while utilized the figures used by Munk and Sørensen (2010) for the U.S. economy. _Minimum Wage._ The minimum wage was characterized by: _._ . Other parameters. R = 2,5 y T = 35.

# **c.** **<u>Optimal portfolio calculation</u>**

161. The calculation of the optimal portfolio is preceded by the implementation of the proposed analytic solution in Appendix A, through the simulation of the conditional hopes involved. Based on the developed expression (that is, ), which makes the theoretical model solution numerically implementable, the calculation of the optimal portfolio requires computing the quantities: , , , , , , , , problems that are possible to solve by means of simulations, according to the work of Detemple et al. (2003).

162. The quantities in question can be written as _(_ with _,_ and _):_ where 45
factors within and across regions can be expected to generate substantial differences in water blockage and methane intensity per hectare. Research on the effects of local production techniques remains limited to some areas (e.g. Nguyen et al. 2022; Devkota et al. 2019; Stuart et al. 2018). For rice production areas more generally, we would expect S5P CH4 observations to capture part of the interarea variation produced by different techniques.

# **5.3 Identifying Outlier Areas from Regression Residuals**

Figure 6 displays residuals from the regression results reported in Table 1. They have potential interest for policy analysis because they identify areas where variations in irrigation water use and field conditions may be associated with unusually high or low CH4 anomalies, after controlling for field scale. In Asia the areas with much higher-than-expected values are in northeastern China and Western India, while areas with much lower-than-expected values are in western China, southwestern China, Hainan (China), central Vietnam, southern Thailand, southern Philippines, Sri Lanka and the entirety of Indonesia. Elsewhere, areas with higher-thanexpected values include northern Italy, southern Nigeria, southeastern Brazil, central Bolivia and northern Colombia. Regions with notably lower-than-expected values include southeastern Australia, central Islamic Republic of Iran, the Nile Delta, northern Madagascar, southern Mali and eastern Guinea, northern Senegal, western Spain, Uruguay, western República Bolivariana de Venezuela, Costa Rica, Cuba, the Dominican Republic, western Mexico and California.

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