rafmacalaba/datause-encoder-data huggingface dataset:
_Input tariffs and the availability of imported inputs – intensive and extensive margins_ Changes in input tariffs alter domestic prices of imported inputs and therefore their affordability. When input tariffs fall, the cost of accessing potentially higher quality intermediates also falls – for example, it becomes cheaper to import more of the synthetic fibers that Pakistani producers of dryfit sportswear were already importing, allowing them to improve their input mix. In addition, the number of varieties of imported intermediates increases. A reduction in the prohibitive tariff on meltblown fibers – the nanofiber used to produce N95 masks – could make the fibers available in Pakistan and make N95 production in-country feasible. Through both mechanisms, firms’ technological choices improve, and, with that, their productivity.

Our results are consistent with an increase in the imports of intermediate inputs following a reduction in tariffs. Table 9 shows the results of regressing imports of intermediate inputs (at the HS six-digit disaggregation, within the BEC classification for industrial supplies) on lagged tariffs. We find that lower input tariffs resulted in an increase in the volume of imported intermediate inputs. To give a sense of the magnitude of these effects, the average decrease in import tariffs experienced over the period 2014-2017 explains about 30 percent of the observed increase in the total volume of imported intermediates.

Table 9 – The association between tariffs and imports of intermediate products

|Dep. Var.: import of interme|(1)<br>(2)<br>diate imports (value)|(3)|
|---|---|---|
|Lagged Tariffs (6 digits)|-0.020***<br>-0.034***|-0.026***|
||(0.005)<br>(0.007)|(0.008)|
|Year FE|Yes<br>Yes|Yes|
|Sector (2 digits) FE|No<br>Yes|Yes|
|Sector (6 digits) FE|No<br>No|Yes|
|Observations|13833<br>13833|13833|
|Sectors (6 digits)|2584<br>2584|2584|

Note: The table reports the estimates of linear regressions. Standard errors clustered at the sector level are reported in parenthesis. Significant at *10%, **5%, ***1%. Products are classified as intermediates if they fall within the “Industrial supplies” category of the Broad Economic Categories (BEC) classification. Source: UN Comtrade.

Table 10 shows the results of regressing the number of varieties of imported intermediates at the sixdigit level of disaggregation (within a two-digit HS chapter) on import tariffs using both linear regression and Poisson (columns 1 and 2). We find that a decrease in tariffs is also associated with an _increase in the number of intermediate input varieties_ imported.

Table 10 – The association between tariffs and the variety of products imported

|OLS<br>Dep. Var.: number of products (at 6 digit level withi|Poisson<br>n a 2 digit chapter)|
|---|---|
|(1)|(2)|
|Lagged Tariffs (2 digits)<br>-0.262*<br>(0.152)|-0.010*<br>(0.006)|
|Year FE<br>Yes|Yes|
|Sector time trends<br>Yes|Yes|
|Observations<br>502|502|
|Sectors (2 digits)<br>84|84|

18
Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa<sup>*</sup> Cross-Country Comparisons Based on Investment Climate Surveys from 1999 to 2005 by Alvaro Escribano,<sup>†</sup> J. Luis Guasch<sup>‡</sup> and Jorge Pena<sup>§</sup>

**Key words:** Africa, Infrastructure, Total Factor Productivity, Investment Climate, Competitiveness.

> * This study is part of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world’s knowledge of physical infrastructure in Africa. Financing for AICD is provided by a multi-donor trust fund to which the main contributors are the Department for International Development (United Kingdom), the Public Private Infrastructure Advisory Facility, Agence Française de Développement, and the European Commission. Inquiries concerning the availability of datasets should be directed to vfoster@worldbank.org. The authors are indebted to Vivien Foster and her group of researchers at the World Bank for providing data sets and further insight into the African region. All errors in this report are the sole responsibility of the authors.

> † Telefónica-UC3M chair in the economics of telecommunications, Department of Economics, Universidad Carlos III de Madrid; alvaroe@eco.uc3m.es

> ‡The World Bank and the University of California, San Diego; jguasch@worldbank.org.

> § Department of Economics, Universidad Carlos III de Madrid; jpizquie@eco.uc3m.es.
feedstocks (e.g., cassava and sweet sorghum) also follow market prices, production costs of biofuels are sensitive to the current market and future demand for these crops. A relatively mature cassava market currently exists in China as shown in Table 3. If domestic demand for cassava increases following the current trends, its price would put pressure on the production of cassava-based ethanol. Besides this demand side factor, supply of cassava would be also a concern as China depends on import for cassava supply. In 2001, China became the largest cassava importer in the world, by 2006, China’s share of worldwide cassava (fresh or dry) imports reached its peak of 88.5%. Although the share decreased to 42.96% in 2008, China still remains as the largest cassava-importing country in the world (Zhan et al, 2010). One of the most important processed products of cassava, cassava starch, also relies heavily on import (Fig. 3). The higher dependence on imports might have discouraged Chinese famers to plant domestically (Chai et al, 2009).

<!-- Start of picture text --> 120.00%<br>100.00%<br>96.57% 97.00% 96.34%<br>93.09% 93.25%<br>89.02% 90.83%<br>84.59%<br>80.00%<br>76.66%<br>60.00%<br>45.40%<br>42.31%<br>40.00%<br>37.13%<br>32.87% 35.20% 33.53%<br>29.59%<br>20.00% 19.93%<br>5.73%<br>0.00%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Share of cassava import in cassava consumption Share of cassava starch import in starch import<br><!-- End of picture text --> Fig.3 Imports of cassava and cassava starch Source: the values of domestic Cassava Production are from Fang, et al (2010); the values of import cassava are from Zhan, et al.(2010); the cassava consumption is calculated by domestic cassava production plus cassava import; the values of starch are from DRST, et al. (2009).

In the case of sweet sorghum, domestic production is very limited due to lack of existing domestic market. Currently it is grown for the purpose of animal feed in Mongolia and alcohol in Heibei, Shandong, and Xinjiang provinces. Zeng et al. (2009) shows that plantation of sweet sorghum is very sensitive to perceived risk and comparative economic advantage. The storage and pretreatment of sweet sorghum also presents challenges. Sweet sorghum can be planted only once a year, and the traditional fermentation time is very long taking about 14 days (Li & Chan-Halbrendt, 2009). An increase in fermentation concentration and a decrease in fermentation time are necessary to lower the cost of producing ethanol from sweat sorghum (Li & Chan-Halbrendt, 2009).

9
<!-- Start of picture text --> Share of household overall transport expenditure by expenditure quintile<br>18%<br>16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>Poorest quintile Second poorest Middle quintile Second richest Richest quintile<br>——Allcountries © —<meHighincome ——Upper middle income ——Lower middle income <Low income<br>Share of overall transport expenditure by income group and rural/urban<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>Low income Lower middie income Upper middle income High income<br>mAllhouseholds mRuralhouseholds = Urban households<br><!-- End of picture text -->
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the Country level. All models are estimated via three methods: ordinary least squares (OLS) model, robust regression model and instrumental variable model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), status dummies (commercial bank, credit union / coop, NGO, non-bank financial institution, omitting rural bank), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. The details of instrumental variable model are shown as below.

*1 1st excluded IV is Supervisory Forbearance Discretion, and 2nd is Independence of Supervisory Authority - Political respectively

*2 1st excluded IV is Supervisory Forbearance Discretion, and 2nd is Percent of 10 Biggest Banks Rated by Domestic Credit Rating Agencies respectively

# Panel E: Formation/operation of non-regulated microcredit institutions <u>(0-4)</u>

||||<br>Dep var:|<br>Portfolio atRisk(>|<br>30days)|||
|---|---|---|---|---|---|---|---|
||Robust Re|OLS Droin|Robust Re|OLS Droin|OLS w/ the|Instrumental|Instrumental|
|_Microscope variable_|g<br>w/o Lending<br>methodology<br>dummy|ppg<br>top 5% of dep<br>var w/o<br>Lending<br>methodology<br>dummy|g|ppg<br>top 5% of dep<br>var|<br>same<br>observations<br>to IV|<br>Variable 1*<sup>1</sup>|<br>Variable 2*<sup>2</sup>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Formation/operation of non-regulated microcredit institutions (0-4)|0.0006|0.0029|0.0005|0.003|-0.0023|-0.0319|-0.0191|
||[0.786]|[0.269]|[0.817]|[0.291]|[0.644]|[0.156]|[0.418]|
|Observations|741|702|627|598|547|547|525|
|Number of countries|40|40|39|39|29|29|28|
|R-squared|0.161|0.127|0.235|0.174|0.096|0.058|0.075|
|AdjustedR-squared|0.141|0.106|0.211|0.147|0.063|0.024|0.040|
|F statistic for weak identification||||||1.873|1.493|
|Shea partial R-squared||||||0.068|0.074|
|HansenJ statistics||||||0.424|0.807|

48
comparability of data across surveys occurs largely by chance. A recent summary of the financial information generated in the LSMS shows that only a handful of basic questions about accounts and loans are asked in most modules, and even those are often asked in different ways, making the validity of comparisons across surveys dubious (Gasparini, et al. 2004).

While the accounts-based and survey-based measures of usage of financial services are not substitutes for one another, recent research has shown that there is a robust statistical link between the two (Beck, Demirguc-Kunt, and Martinez Peria 2007, Honohan 2008). That is, a regression model can be constructed from the more readily available accounts-based information that can be used to generate reasonably accurate estimates of the harder-to-collect survey based data. Still, the fit of those regressions is not perfect. For example, Honohan (2007) estimates that 16% of Ghanaians have an account, whereas the information derived from the surveys we describe below places that figure at 25%. At best, it would appear that the usage estimates derived from accountsbased information could be used to monitor access between surveys of users.

Scaling up collection of data on usage of financial services to ensure accuracy and comparability across countries and over time would therefore require a survey-based approach. While there have been other stand-alone efforts to measure usage, the most advanced current one is that by the FinMark Trust, which has deployed its FinScope survey (www.finscope.co.za) in a number of developing countries, primarily in Africa.<sup>4</sup> Finscope surveys are designed to provide nationally representative information on individuals’ use of financial services. The questions on usage are similar to those that might be found in a marketing study, including detailed inquiries about specific types of

> 4 The FinScope website lists ongoing or completed surveys for fourteen African countries and Pakistan.

5
Third, the LCIR countries appear to be more unstable on several grounds. The coefficient of variability of the monthly inflation rate for the LCIRs is twice as large as that of the HCIR group. This difference is even greater when using the variance of the monthly inflation rate, which is computed after excluding the outliers in both groups (Argentina, Brazil, and Zaire). Likewise, the real exchange rate appears to be more volatile in the LCIR countries, probably adversely affecting their export industry.'<sup>0</sup> In addition, the political risk index (PRI) for the LCIR group is almost 11 points below that of the HCIR group. " Moreover, for the LCIR countries this index is well below the prohibitive risk level of 40.

Finally, countries in the HCIR group appear to be more creditworthy in that they maintained lower indebtedness and a higher reserves ratio during the early years of the high capital inflows episode and have better-performing export industries. In fact, prior to the surge the stock of international reserves for the HCIR countries was, on average, twice as high as that for the LCIR countries, while indebtedness was about half as great. In addition, the export ratio in the HCIR countries is four percentage points higher than that in the LCIR countries-10 percent points higher (32.8 percent compared with 23.2 percent) if the outliers in each group are excluded (Argentina among the HCIR group at 10.8 percent, and Jordan among the LCIR group at 86.8 percent). Furthermore, exports have also been growing at a higher rate in real terms in the HCIRs than in the LCIRs-the respective compound rates of growth are 8.5 percent and 4.8 percent annually-meaning that the repayment capacity of the HCIRs is improving faster than that of the LCIRs.

These facts provide a motivation for trying to assess more precisely the importance of pull factors. Although our empirical analysis in the next section looks similar to those carried out earlier by other authors, some important differences are worth noticing. First, we use a more comprehensive data set in that, instead of relying only on portfolio investment flows as the dependent variable, all sources of private

> 10 It is important to note that, because of missing data, the average group variance in the LCIR countries is computed with only six data points: Bolivia, Egypt, India, Jordan, Morocco, and Nigeria.

> 11 The PRI was developed by Business Environment Risk Intelligence (BERI). This index measures the sociopolitical conditions in a country using a 0-100 scale. An increase in this index means lower political risk. A prohibitive risk (index below 40) means that political conditions severely restrict business operations: asset losses are possible and disturbances are part of daily life.

9
surveys—are high quality cross-sectional data sources that offer at best a snapshot of living conditions at specific moments of time.

In order to overcome this limitation, we implement in this paper a methodology for converting the NSS cross-sectional surveys into synthetic panels. The approach we follow has been recently introduced into the literature (Dang, Lanjouw, Luoto and McKenzie, 2014a; Dang and Lanjouw, 2013) and a number of studies which validate the method have generally yielded encouraging findings (Dang et al., 2014a; Dang and Lanjouw, 2013; Cruces et al., 2014; Martinez, 2015). We highlight the assumptions imposed by the methodology and discuss their applicability to the Indian context.<sup>4</sup> The methodology for constructing synthetic panels is predicated on strict comparability of the underlying cross-section surveys. It has already been noted that India’s NSS surveys are generally regarded as high-quality data sources. We also focus our attention here on the “thick” rounds that involve larger sample sizes and that are designed to be representative at the rural/urban and state level. Nonetheless we investigate whether the 2009/10 and 2011/12 rounds are strictly comparable, since the possibility of a breakdown in comparability is prompted by the remarkable rate of poverty decline as well as evidence that there are some noticeable changes in the design of the consumption questionnaire between these two years. We note that there had been intensive and contentious debate around the comparability of the 1999/00 round of the NSS survey with earlier NSS rounds, after a certain number of changes and adjustments had been made to the

> 4 Synthetic panels constructed using the Dang et al. (2014a) and Dang and Lanjouw (2013) methods have been applied to study poverty dynamics in various settings including multi-country analysis for Latin America (Ferreira et al., 2013; Vakis, Rigolini, and Lucchetti, 2015), South Asia (Rama et al., 2015), and Europe and Central Asia (Cancho et al., 2015). Specific country case studies using synthetic panels investigate countries including the Kyrgyz Republic (Bierbaum and Gassmann, 2012), Bhutan (World Bank, 2014), and Senegal (Dang, Lanjouw, and Swinkels, forthcoming). Another promising use of synthetic panels is to evaluate program impacts (Garbero, 2014).

4
<!-- Start of picture text --> 60<br>Services exporters<br>58 @ PAN<br>= DoM ®<br>S CRI<br>2<br>%S 40 Services & manufactures exporters<br>x<br>=”oc .<br>2=o 30 URY<br>a<br>Fre} COL<br>& 20 e<br>wZ RG More diversified commodity exporters<br>a>=o @ BOLECu PER‘e e BRA®<br>10 c<br>More basic commodities exporters MEX<br>Manufactures exporter<br>0<br>i) 10 20 30 40 50 60 70 80<br>Manufactures Exports (as % of Total)<br><!-- End of picture text -->
**Figure 15. Business Licenses as a Problem for Doing Business (percent of firms indicating business licenses and permits as a problem for doing business)**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Slovakia Turkey Hungary Bulgaria Poland Czech Rep.<br>2002 2005<br><!-- End of picture text --> _Source:_ EBRD-WB BEEPS, 2005.

Bulgaria also fares well in terms of communication and simplification of rules and procedures (Figure 14b) compared to OECD countries. The indicator captures aspects of the government’s communication strategy and its efforts to reduce and simplify the administrative burden of interacting with government.

Bulgaria has implemented a number of reforms to improve the communication of rules and procedures to affected parties. The _Law on Normative Acts_ was amended in 2003 to require that all affected parties be notified through mass media or representative associations prior to the approval of new regulation by Parliament. In addition, the _Law on Administrative Regulation and Administrative Control on Economic Activities requires_ that the rationale and the regulatory impact evaluation for a draft legislation that introduces licensing or registration regime, is posted on the web or released publicly. Affected parties have the right to comment on the proposed regulation. Consultations on new legislation are organized with business associations or interested groups and some of the new regulations are discussed at the sessions of the Council for Economic Growth<sup>12</sup> . The Law on Normative Acts also requires the use of plain language when drafting of rules and regulations.

Reforms aimed at simplification of rules and procedures also took off in 2003 with a completed count of all centrally mandated licenses and permits and clearly defined rules and responsibilities for their optimization. Further steps could include the simplification of sector-specific regimes and the extension to the municipal level of rules such as ‘silence is consent’ for business registration and licensing.

# _2.1.3 Administrative Burdens on Start-Ups_

> 12 A consultative body established in 2002 under the Council of Ministers that consists of representatives of businesses, and ministers of economy and energy, finance, transport, regional development, and labor and social policy.

20
# [INSERT TABLES 2a AND 2b AROUND HERE]

Once the estimation is complete, we use the estimated parameter values from the equations above to predict the missing values in the bilateral migration matrices for both years, genders and educations levels.

One critical issue is that of zero observations where two basic options are available. The first would be to use PPML estimates. It is worth noting, however, that our predicted numbers are almost never integers and so our estimates should be interpreted as the expected numbers of migrants. Although PPML predictions cannot produce true zeroes, many of our estimates are extremely close to zero, such that we could postulate having predicted a zero, e.g. if the expected number of migrants generated by the PPML model is smaller than one (or indeed below any other arbitrarily small threshold). The second option which we implement in this paper, instead involves splitting the aggregate figures provided by OPSW by using our PPML regressions to estimate the shares of males and females and the skilled and unskilled. Since we have access to the totals, predicting these shares is no doubt more accurate than attempting to impute unrestricted totals. In following this methodology, we necessarily take all zero observations from OPSW as opposed to imputing them with our PPML estimation. Indeed, there are a very few cases where OPSW is greater than zero but where a PPML estimate would be close is zero, but these observations constitute are less than one percent of the total.

Before analyzing the predictions and their implications for the global migration patterns by skill level, we evaluate the out-of-sample performance of our predictions. To do so, we 24
Education levels are strongly related to poverty. That is, having incomplete or some complete education is important in determining the likelihood of being poor. In Mexico, the P0 is 45 percent for household heads that have received no education in dispersed rural areas and 28 percent in semi-urban areas in 2002.

<!-- Start of picture text --> Figure 4.1: Poverty headcount (P0) and education level attained,<br>rural Mexico (<15,000 people), 2002<br>60.00<br>No education<br>50.00 Primary incomplete<br>Primary complete<br>Secondary complete<br>Higher education<br>40.00<br>30.00<br>20.00<br>10.00<br>-<br>Total Norte Capital Golfo Pacifico Sur C-norte Centro<br>Source: Calculations based on ENIGH 2002.<br><!-- End of picture text --> There appears to be a relatively large difference in extreme poverty between household heads with no education (45 percent) and household heads with incomplete primary education (36 percent) in dispersed rural areas. Surprisingly, there seems to be very little difference in extreme poverty between household heads with incomplete primary education and household heads with complete primary education (36 percent), the latter up from 31 percent in 1992. Nevertheless, household heads that have completed secondary education are much better off (18 percent are poor) than those with only primary education. Extreme poverty for high-school graduates decreased rapidly during 1992-2002, down from 22 percent in 1992. Of the household heads with some or complete tertiary education 13 percent were extremely poor in 2002, up from 1 percent in 1992. These findings indicate that education is a very important key to poverty reduction in rural Mexico. It is not a silver bullet, however, as the more educated are increasingly experiencing poverty, something that was rare a decade ago.It is useful to compare the characteristics of the extreme poor across rural areas to gain information about the spatial dimension of poverty. Geographic factors are important when analyzing poverty in rural Mexico. Table 4.1 and Figure 4.1 show the variation in P0 across the rural areas with less than 15,000 inhabitants by region and education level attained. The Center, Gulf, and South regions have the highest headcount poverty rates for all levels of education in rural Mexico and, in some cases, it is more than 10 times higher than in the North or Capital regions such as the case of completed primary 19