rafmacalaba/datause-encoder-data huggingface dataset:
Figure 18. Shapley Decomposition of the Dissimiliarity in Credit Card use

## (Individual level, FINDEX)

<!-- Start of picture text --> 2011 2014<br>100% 100%<br>90% 12.9 20.8 17.6 24.7 13.4 3.1 90% 29.5 26.8 20.4 25.8 15.5 0.6<br>80% 17.5 4.9 80%<br>70% 31.0 70% 5.3 32.8<br>60% 27.5 44.4 22.8 60% 16.0 9.3 31.7<br>33.2<br>50% 42.0 50%<br>13.6<br>40% 40% 15.0<br>36.4<br>15.7<br>30% 17.9 30% 58.7<br>52.4 51.2<br>46.8<br>20% 38.9 20%<br>32.9<br>10% 27.6 20.1 10% 18.1 25.3<br>0% 0%<br>Netherlands Spain Turkey Denmark Taiwan Netherlands Spain Turkey Denmark Taiwan<br>Age Education Gender Income<br><!-- End of picture text --> (Household level, LITS) <!-- Start of picture text --> 100% 2.2 4.6 3.8<br>90% 15.3 20.9<br>22.1<br>80% 4.7<br>37.3<br>70% 19.0 10.8 56.7 23.9<br>60%<br>10.2<br>50% 16.8 12.7<br>40% 10.6<br>30% 58.8 3.2 25.6<br>52.3<br>20% 38.0 15.3<br>10% 14.3 16.9<br>0% 4.1<br>Latvia Czech Republic Turkey France Croatia<br>Age Education Gender Income Urban<br><!-- End of picture text --> _Notes:._ LITS is household level. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. See Appendix for all countries. _Sources_ : FINDEX, LITS

# **iv. BORROWING**

In terms of borrowing, informal sources are more heavily used than formal ones in Turkey (Figure 19). Less than 5 percent of adults borrow from financial institutions in Turkey which is lower than the formal borrowing levels at both the BRICS and the rest of ECA. On the other hand, over 40 percent of adults use store credit and family/friends as an informal borrowing source. These levels indicate that individuals in Turkey are more than 8 times likely to use store credit than a financial institution whereas no other country groups uses store credit more than financial institutions as a lending source.<sup>4</sup> Individuals in Turkey do not seem to trust formal sources as viable borrowing source.

4 In fact, Turkey has the highest coverage rate in having store credit amongst the surveyed FINDEX countries. See appendix for further information on the EAC decomposition for borrowing form formal and informal institutions.

20
United States ( _i_ ) so that ln _τzt_<sup>_DD_</sup> = ln( _τUS,Mex,z,t/τUS,RoW,z,t_ ). However, the choice of EU12 restricts our sample period to 1990 to 1999, since in 2000 the European Union and Mexico signed an FTA. The large group of reference exporting and importing countries allows to circumvent the lumpiness in monthly HS-6 trade flows. Because our identification of the anticipatory effects relies on the variation in monthly trade flows, the relevant observations are those HS-6 with non-zero trade flows between sub-periods of the 4 directions to obtain growth rates. This reduces the sample size while also selecting the sample towards those goods that are more frequently traded. In Section 2.5 we dig deeper into this sample selection. We find that anticipatory effects are stronger for less frequently traded goods suggesting that the sample selection is more likely to undermine our results.

We obtain monthly US trade flows and applied tariff rates from the US Census Bureau and European trade flows are obtained from Eurostat. Trade flows are aggregate at the most disaggregate HS-6 product level available and measured as the CIF (Customs, Insurance and Freight) value of imports for consumption, which disregards imports for re-export purpose and includes insurance and freight charges. Tariffs are defined as the applied tariff rates, that is, the ratio of duties over the freight-on-board (FOB) value of imports.<sup>19</sup> We obtain the classification of HS-8 goods into their corresponding phaseout category from the original NAFTA text. To classify HS-6 products into their phaseout category we take the trade weight of its HS-8 products and pick the largest category to be the one corresponding to the HS-6 classification. The characteristics of the resulting sample by HS-6 phaseout category are very similar to the original at HS-8 level and reported in Table 2.

Table 2: Sample Characteristics at HS6 Level

|Categories|HS6 Goods|HS6-Year|M<br>%|exico’s Import Share<br>1990-1993|Median Duty<br>1993|
|---|---|---|---|---|---|
|Phased-out|451|5,283|16.5%|12.3%|7.7 %|
|Class A|129|1,541|4.8%|14.0%|6.5%|
|Non NAFTA|2,274|15,182|78.7%|73.6%|0.2 %|
|TOTAL|2,854|32,006|100%|0.8%||

> 19We drop observations at HS10, month, country of origin, district of entry level - the most disaggregate level available in census data - when applied duties are larger than 100%. These are outliers that were not subject MFN nor NAFTA rate provisions. Results are not sensitive to this choice.

10
a problem. As we shall see below, firms in the trading sector perform rather well compared to nontraders.

# **2.2 Enterprise Characteristics and Performance**

# _Size, age and growth_

Table 6 shows basic summary statistics for the nonfarm enterprises in the RICS-AgSS sample. The key fact documented here is that most enterprises are young (six years on average) and very small. About 73% of all enterprises are one-person firms while another 26% employ only 2 or 3 workers. Only 1% of all enterprises employ more than 3 workers. In terms of employment within enterprises, there is very little growth: only 8% of firms have expanded their labor force since startup, while about 3% have shed workers. Few enterprises operate on a full-time basis. Furthermore, enterprise activity is highly seasonal and countercyclical with agriculture, as shown in Figure 3. Thus few households appear to specialize in nonfarm enterprise activities. Instead, nonfarm enterprises, it seems, are set up primarily as a complement to agriculture, providing an alternative source of income in periods when the level of activity in agriculture is low.

# _Profitability_

How profitable are nonfarm enterprises? Table 6 shows that the average of the log of profits per day from operating a nonfarm enterprise is 1.72, which is equivalent to 5.6 Birr per day, or less than a dollar per day.<sup>2</sup> Profits are highest in Tigray, where the daily return to working in a nonfarm enterprise is 8.8 Birr, and lowest in Amhara, where the corresponding return is 5.1 Birr, even though the average reported contribution of nonfarm enterprise profits to household income does not vary markedly across these regions. The average log of profits per month is 4.00, which corresponds to 55 Birr, or approximately $6, in levels. Firms operate on average 14 days per active month, thus the monthly data confirm earnings are well below a dollar a day. The average log of profits per year, averaging across inactive and active periods, is 5.83, which corresponds to 340 Birr, or approximately $37, in levels. These numbers indicate that enterprises are dormant over very long periods. For example, comparing profits per day to profits per year suggests the average firm operates for about 60 days a year, on average.<sup>3</sup> We now model the returns to nonfarm enterprise employment, measured by the log of profits per day, as a function of the characteristics of the household head<sup>4</sup> , distance from the nearest markets, seasonality, the base of the enterprise, and the age of the firm. We also include controls for type of activity (sub-sector). The results, shown in Table 7, indicate that enterprises owned by female headed

> 2 The Birr to the US dollar interbank exchange rate is approximately 9.47 in February 2008. 3 Exp(5.83-1.72).

> 4 The RICS-AgSS data do not contain information on the gender of the manager of the enterprise, but instead contain information on the gender of the household head. The RICS-Amhara data suggest that the head of the household is also typically the manager of the nonfarm enterprise.

-5-
<!-- Start of picture text --> Figure 4.6: Growth rate of the Quality of Life, 1991-2000<br>40.0%<br>35.0%<br>30.0%<br>25.0%<br>20.0%<br>15.0%<br>10.0%<br>5.0%<br>0.0%<br>Source: Calculations based on IPLANCE data.<br>36.9%<br>35.2%<br>33.7%<br>31.3%<br>30.1%<br>29.2%29.0%<br>27.7%27.6%<br>27.0%<br>21.5%<br>19.6%<br>17.4%<br>16.8%16.3%16.3%<br>15.0%14.7%14.7%14.6%<br>13.5%13.3%13.1%13.0%12.5%12.4%<br>10.6%<br>9.1%<br>8.4%<br>7.7%<br>6.9%<br>6.0%<br>4.7%<br>Alagoas NordesteTocantins PernambucoBahia RG Norte SergipeRoraimaMinas Gerais Mato GrossoBrasil Norte Santa CatarinaSul AcreC Oeste RG do SulMS AmazonasRio de Janeiro SudesteDistrito Federal<br><!-- End of picture text -->

**Figure 4.7:                         Human Development Index 2000 and its Growth Rate (Percent) during 1991-2000**

<!-- Start of picture text --> 0.900<br>0.800<br>0.700<br>0.600<br>0.500<br>HDI-M<br>0.400<br>Var 1991-2000<br>0.300<br>0.200<br>0.100<br>0.000<br>-2 3 8 13 18 23 28<br>Growth of HDI-M during 1991-2000 (percent)<br>Source: Calculations based on UNDP data.<br>HDI (2000)<br><!-- End of picture text --> 26
shape of the commune units in relation to district boundaries. Because communes are too small to sensibly plot them for the whole country, two map inlets display the heterogeneity of their boundaries. While in rural areas, boundaries can be large with diameters of more than ten kilometers, they can be very small in urban areas.

This study uses geocoded economic data for communes from the Enterprise Survey conducted annually by the Vietnam General Statistics Office.<sup>2</sup> This survey covers characteristics of firms such as employment, revenue, profits and industry classification. The firms are geographically identified at the commune level. This data is aggregated to the commune in order to work with the total count of enterprises and counts of employment at this small geographic scale. Economic activity of each commune is then approximated by the number of workers and the number of enterprises employing them. The data comes with several quality concerns which are discussed here. At first, it is limited by the fact that only formal employment is recorded. In a country with a potentially very high share of the informal economy (Cling et al., 2011), these numbers can severely underestimate the actual employment numbers and results should therefore be interpreted as measures of the formal economy. Second, it excludes self-operating farmers and only accounts for employment in agricultural enterprises. Further, the sampling scheme is designed to fully survey enterprises with more than 10 employees only, while a random sample is used for smaller firms, and is thus not fully representative. There are also inconsistencies over time as boundaries occasionally change.<sup>3</sup> Lastly, an issue with the geocoding arises due to the fact that the enterprise data is not based on plant location but rather on firm location. This potentially exaggerates measures of economic activity at the headquarter location and underestimates it at the branch location.

> 2The authors thank Klaus Deininger at the World Bank and Fang Xia at the University of International Business and Economics, Beijing for providing access and invaluable guidance relating to the data to us.

> 3This study deals with this issue by aggregating up communes that have split or merged to create boundaries consistent over time.

6
Table 2 Statistical distribution: firms according to country and sector (year 2005).

|**Country/industry**|**Beverages**|**Food**|**Garments**|**Metals and**<br>**machinery**|
|---|---|---|---|---|
|Bulgaria|10|2|5|9|
|Czech Republic|7|3|3|29|
|Estonia|4|1|5|8|
|Hungary|7|28|41|153|
|Latvia|2||4|5|
|Lithuania|4||3|8|
|Moldova|40|4|14|20|
|Poland|15|46|97|178|
|Romania|56|36|75|86|
|Slovak Republic||1|1|12|
|Slovenia|1|1|2|23|
|Total|146|122|250|531|

Source: Authors‘ calculation from the Investment Climate Survey Databank, World Bank.

The previous table 2 comprises the sectors analyzed in this research paper respectively and includes secondary activity (industry) sectors.

# 4.2 Variable Selection

In order to estimate technical efficiency with DEA, there exists considerable disagreement in empirical literature on the definition of outputs (in the context of multiple-outputs firms) of an industrial institution. Nevertheless, according to Coelli et al. (2005), a commonly-used classification of inputs involves five categories: capital (K), labour (L), energy (E), material inputs (M), and purchase services (S). The use of data according to these categories in productivity measurement is sometimes referred to as KLEMS approach<sup>19</sup> .

To ensure the validity of the DEA model specification, an isotonicity test (according to Golany and Roll, 1989) was conducted. This isotonicity test involves the calculation of all intercorrelations between inputs and outputs for identifying whether increasing amounts of inputs lead to greater outputs. Finally, according to Golany and Roll (1989), the number of DMUs should be at least twice of the total number of input and output factors considered when applying the DEA model. In this study the number of DMUs was 146 (for Beverages sector), 531 (for Metals and Machinery sector), 250 (Garments sector) and 122 (for Food sector), at least ten times of the selected four factors. According to these facts, in this study, the proposed DEA model has good construct validity.

On the other hand, Smith (2000) argues that, in order to obtain correct and robust estimates of program effects using PSM, one need to use a broad set of observed firm characteristics in the estimation of the propensity score. The propensity score is in general estimated using probit or logistic regression, from a set of possibly abundant observable variables which are supposed to

> 19 In our sample, Capital (K) and Purchased Services (S) are not available.

14
frequently have exclusive relationships to resolve information problems and/or avoid duplicative costs of multiple relationships.

Regarding the second motive, there is evidence that large U.S. firms not only engage in multiple banking relationships in order to offset the hold up problem, but they also reduce the control of their main banks and lending costs by issuing public debt at higher costs (Houston and James 1996 and Santos and Winton 2005, respectively).<sup>9</sup> A cross-country study finds that firms maintain a higher number of banking relationships in countries with unconcentrated banking sectors, contrary to the theoretical predictions (Ongena and Smith 2000).<sup>10</sup> Regarding the third motive, there is information on the effects of bank fragility and the costs to firms of losing their main banking relationship, but some of the evidence is also conflicting. One study using German data finds that borrower quality – which should be a proxy for the ability of the firm to recover in the event of the cutoff of bank funds – has no impact on the number of banking relationships (Machauer and Weber 2000).<sup>11</sup> However, a study using Italian data finds that the number of banking relationships is higher for firms with more leverage, suggesting lower quality, consistent with the third motive (Detragiache, Garella, and Guiso 2000). This study also finds that those borrowing from banks that are prone to financial distress – measured in terms of the bank’s size, volatility of liquidity, and nonperforming loans – are likely to maintain a larger number of banking relationships, consistent with the motive for multiple banking as insurance against the premature termination of services. A study of Argentine banks similarly finds that bank fragility has a positive impact on the likelihood that a firm will borrow from multiple banks (Berger, Klapper, and Udell 2001). However, Ongena and Smith (2000) find mixed support in this regard. They find that firms maintain a higher number of banking relationships in countries with inefficient judicial systems and poor enforcement of property rights, consistent with the theory, but also find more banking relationships in nations with more stable banking systems (i.e., with better bank credit ratings), contrary to the predictions.

> 9 A related empirical study finds that banks charge higher rates to customers with limited financing options during economic recessions (Santos and Winton 2005).

> 10 One potential explanation for this result is that banks in unconcentrated sectors are smaller and firms might need many relationships to satisfy their needs.

> 11 A second study using German survey data finds that multiple lending relationships increase collateral requirements (Harhoff and Korting 1998).

8
to the question of whether financial development was necessary for the real effects to be manifested.

We find a strong response in lending activity in districts adjacent to the GQ highway network, manifested in terms of both loan counts and larger loan sizes. Our results are strongest in districts where there was new construction (as opposed to upgrades) and dynamic specifications support the effect taking hold shortly after construction. Moreover, our results hold in straight-line IV frameworks and are also not present in 'placebo tests' with a second highway that was planned to be upgraded at the same time as GQ but subsequently delayed. Our results point to bank lending responding to the increase in real activity that arose from improved transportation infrastructure. Importantly, how- ever, we find our results are entirely concentrated in regions with strong initial financial development. Lending activity did not increase and in some specifications is seen to fall slightly in regions with initially low financial development, suggesting that while finance responded to help support increased real activity, the level of financial development played a critical role in determining where real economic activity grew. These results suggest that the initial level of financial development might be critical in shaping how (and where) infrastructure investment can jumpstart real economic activity.

Our study is the first to connect micro-level financial development with plausibly exogenous infrastructure development. This is not possible for the United States, where most research has traditionally focused, due to the older nature of the Eisenhower highway system. The later timing of the Indian investment and better collection of financial data over recent decades provide unprecedented platforms. Moreover, prior work mostly identifies how the existence of transportation networks impacts activity, but we can quantify the impact from investments into improving road networks compared to placebo networks that are not enhanced. This provides powerful empirical identification, and the comparisons are informative for the economic impact of road upgrade investments, which are very large and growing.<sup>3</sup> This project also contributes to important questions facing India as it seeks to build 3 Through 2006 and including the GQ upgrades, India invested USD 71 billion for the National Highways Development Program to upgrade, rehabilitate, and widen India's major highways to international standards. A recent Committee on Estimates report for the Ministry of Roads, Transport and Highways suggests an ongoing investment need for Indian highways of about USD 15 billion annually for the next 15 to 20 years (The Economic Times, April 29, 2012).

3
# **<mark>Table 2: Determinants of Attrition between R1 and R2/R3</mark>**

<mark>Coefficients (marginal effects), standard errors in parentheses</mark>

||**Respon**<br>**201**|**dent attrite**<br>**0 and 2011 [**|**d between**<br>**yes=1]**|**Respon**<br>**201**|**dent attrited**<br>**0 and 2014 [**|**between**<br>**yes=1]**|
|---|---|---|---|---|---|---|
||O|LS|Probit|O|LS|Probit|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Treatment|-.01**|-.02**|-.02**|.030**|.030***|.030**|
||(.007)|(.007)|(.007)|(.010)|(.010)|(.010)|
|Number of household<br>||-.005|-.005||-.020***|-.020***|
|members||(.003)|(.004)||(.006)|(.006)|
|Number of female<br>||.0008|.0008||.009|.009|
|household members||(.005)|(.005)||(.008)|(.008)|
|Number of household<br>||.0004|.0001||.007|.008|
|members (below 5years)||(.006)|(.006)||(.009)|(.009)|
|Male household head<br>||.004|.003||-.001|-.001|
|[yes=1]||(.009)|(.009)||(.010)|(.010)|
|Age of household head||.0006**|.0006**||-.002***|-.002***|
|||(.0003)|(.0002)||(.0004)|(.0004)|
|Household head attended<br>||.010|.010||-.040***|-.040***|
|any school [yes=1]||(.008)|(.008)||(.010)|(.01)|
|Constant|.06***|.04***||.200***|.300***||
||(.005)|(.02)||(.008)|(0.03)||
|Observations|4,240|4,234|4,234|4,240|4,234|4,234|
|Sample|T, ST|T, ST|T, ST|T, ST|T, ST|T, ST|
|R-squared|.006|0.004||.001|.015||

Notes: *** denotes significance at 1% level; ** at 5%; and * at 10%. The dependent variable is a dummy that is equal to 1 if a household attrit between the baseline and follow-up surveys and 0 otherwise. We report marginal effects for the Probit model. The standard errors in parenthesis are clustered by community.

24
11 -

-

by steam raising electric utility plants. Table 2.4 gives data on these for the widest span of years available (1973 and 1988) and also for the intermediate year 1981 when the oil prices were at their peak.

Table 2.4: Co.t of Fossil Fuels at Steam Electric Utility Plants in Cents/BTU for the U.S.A.

||Coal|Heavy Fuel<br>Oil|Gas|All Fuels|IndustrLal<br>Good Price.<br>index|
|---|---|---|---|---|---|
|1973<br>|40.5<br>|78.5<br>|33.8|47.6|100.0|
|1981<br>|153.2<br>|533.4<br>|280.5|225.6|241.5|
|1988|46.6|240.5|226.3|164.3|263.9|

Source: (EZIA Monthly Energy Statistic.).

This table shows firstly that fuel priceas rose very sharply relative to other prices in the period 1973 to 1981 but fell back some of the way by 1988. The price of heavy fuel oil relative to coal nearly doubled in the first period but fell to a low level by 1988, while the price of heavy fuel oil relative to gas fell throughout the period (the price of coal relative to gas rose slightly ln the second period). Although the U.S.A. is not representative of all countries in the O.E.C.D. it is by far the largest user of energy so that these figures are suggestive. There was a sharp increase in the relative cost of fuel associated with the two oil 'shocks', but by 1988 much of this had been reversed. However, with investment taking tlme to plan and Lnstall, the reaction to the fuel price decline may not yet be fully felt. There were also large shifts in relative fuel prices. The degree of fuel switching that this induced will again be difficult to assess because of the need to take into account lags in adjustment. This has important implicatlons for econometric modelling. Total industry DLaaaareqgation bv Country withln the O.3.C.D.

The picture so far established ie for the O.E.C.D. as a whole. This grouping of countries certainly includes a considerable degree of heterogeneity.