rafmacalaba/datause-encoder-data huggingface dataset:
administrative barriers to investment and customs delays that inhibit Russia participating in international production chains as well as explicit barriers to FDI. Despite these problems faced by foreign investors, there are foreign providers of business services operating in Russia in virtually all of the business services sectors (estimates by our specialist service sector institutes of the shares of the market captured by multinationals are in table 3.4), which shows interest in the Russian market by foreign service providers.<sup>21</sup> And the intensity with which the negotiations have been conducted in the business services sectors is due in most cases to lobbying interests by foreign providers of services. This suggests that multinational service providers have a desire to expand operations in Russia **.** Consequently, it is natural to assume then that a reduction of barriers against multinational providers of services will, at the margin, result in an increase in FDI in those sectors, at least with some time.<sup>22</sup>

**The impact of additional varieties on productivity.** We have discussed the evidence

on the productivity impacts of additional varieties of services in section 1. Here we focus on goods, as we employ the Dixit-Stiglitz mechanism in imperfectly competitive goods sectors as well. As Romer (1994) has argued, product variety is a crucial and often overlooked source of gains to the economy from trade liberalization. In our model, it is the greater availability of varieties that is the engine of productivity growth, but we believe there are other mechanisms as well through which trade may increase productivity.<sup>23</sup> Consequently, we take variety as a metaphor for the various ways increased trade can increase productivity. Winters et al. (2004) summarize the empirical literature by concluding that ―the recent empirical evidence seems to

> 21 See Tarr and Volchkova (2007, table 1) for data on foreign direct investment flows into Russia on an annual basis from 2000-2008.

> 22 More systematically, Alessina et al. (2005) find, for a sample of a large number of OECD countries, that regulatory reform, especially liberalization of the barriers against entry, plays a strong role in increasing investment in the sector. 23 Trade or services liberalization may increase growth indirectly through its positive impact on the development of institutions (see Rodrik, Subramananian and Trebbi, 2004).  It may also induce firms to move down their average cost curves, or import higher quality products or shift production to more efficient firms within an industry.  Tybout and Westbrook (1995) find evidence of this latter type of rationalization for Mexican manufacturing firms.

37
**Table 2. Regression Results: The Magnitude of Devaluations, FDI and Exports (whole sample)**

|VARIABLES|(1)<br>FDI|(2)<br>FDI|(3)<br>Exports|(4)<br>Exports|(5)<br>Ex-post<br>FDI|(6)<br>Ex-post<br>FDI|(7)<br>Ex-post<br>Exports|(8)<br>Ex-post<br>Exports|
|---|---|---|---|---|---|---|---|---|
|Lagged FDI||0.169***|0.0565***|0.0231*||-0.161***|0.0461**|0.0337|
|||(0.0355)|(0.0151)|(0.0126)||(0.0512)|(0.0224)|(0.0216)|
|Lagged REER|-0.0202|-0.0130|0.00721|-0.0108|-0.0556|-0.0861*|0.00336|-0.00300|
||(0.0407)|(0.0403)|(0.0172)|(0.0143)|(0.0517)|(0.0520)|(0.0234)|(0.0225)|
|Lagged Exports|0.289***|0.245***||0.580***|0.310***|0.326***||0.274***|
||(0.0864)|(0.0860)||(0.0297)|(0.115)|(0.114)||(0.0460)|
|Observations|886|882|901|901|445|445|464|464|
|R-squared|0.055|0.082|0.129|0.405|0.038|0.063|0.214|0.279|
|Number of<br>countries|60|60|60|60|60|60|60|60|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 All estimations include period fixed effects and country fixed effects (demeaning approach). Source: Author’s calculations based on data from the IMF. See text for details.
suppliers (Antras et al. 2012). Appendix C discusses the derivation of these indices and the procedure for calculating them. Appendix figure A4 (panel b) shows the change in sectoral input downstreamness between 2004 and 2013. The change in the downstreamness for mining is higher in the south (18 percentage points) than in the north (15 percentage points). Overall, regional variation in the change in downstreamness and the intermediate input use share point to mining’s stronger role as a downstream industry in the south than in the north. The relevance of mining in the supply chain has gradually increased over time. While the large-scale production of oil and gas in Ghana since 2010 has directly contributed to it, it is undeniable that a stronger intersectoral linkage between “other industry” and mining has also played a major role in growing importance of mining in the supply chain.

# **Final Remarks on Intersectoral Linkages and Structural Transformation**

As a final step, we estimate sectoral TFP using the I-O tables. This technique is based on Jorgenson et al. (1987), a standard reference in the literature for the estimation of TFPs. Since we only have I-O tables for two points in time, 2004 and 2013, we can only calculate TFP for one period (2004 to 2013). As reported in appendix table A2, the TFP ratio between mining and agriculture is much larger in the south (12.088) than in the north (–9.594). The south experienced a much larger productivity shock in mining than in the north—the TFP ratio between mining and “other industry” is larger in the south (0.532) than in the north (0.204). On the other hand, the TFP ratio between mining and WRT services in the south (–1.785) is much smaller than that in the north (21.434).

Table 3 summarizes the main findings on regional differences in intersectoral linkages between 2004 and 2013 using the I-O tables for the north and the south. Simple difference-indifferences calculations (between 2004 and 2013, between the north and the south) for valueadded share, Domar weights, and the TFP, suggest a larger mining output and productivity shock in the south than in the north. Correspondingly, the TFP in “other industry” is also larger in the south than in the north. In addition, the difference-in-differences outcomes on the intermediate use share, and input downstreamness index point to the prominence of mining in supply chain growing at a faster rate in the south than in the north, which in turn produces differences in intersectoral linkages with mining between the south and the north. To recapitulate, we find support for the link between the mining productivity shock and differences in intersectoral linkages between the south and the north. Several factors could explain this result, including a weak productivity shock in mining and limited intermediate use by other sectors in the north.

12
Essentially, there is no scale for measuring a pupil’s scholastic ability, so that some reference levels have to be fixed for the mean and standard deviation of achievement measured by a test. International achievement tests thus fix these reference levels of mean and standard deviation for a given set of country and year, and the subsequent evaluations conducted with the same test are reported using the same arbitrary scale. In the TIMSS procedure, test -scores were normalized by fixing the mean and standard deviation of achievements of pupils who participated to the first 1995 international TIMSS survey at, respectively, 500 and 100. This standardization must be dealt with in inequality measurement.

The inequality measures to be used for documenting the distributions of test scores must account for several methodological issues. In particular they must be robust to the standardization of these TIMSS variables. However, as noted by Ferreira and Gignoux (2011a), no inequality index satisfying a set of basic desirable properties (symmetry, continuity and the transfer principle) can be both scale- and translation invariant, and thus robust to the standardization. A number of wellknown inequality indices, such as the Gini and Generalized Entropy indexes, are not even ordinally equivalent, so that the arbitrary standardization could affect the ranking of different population by inequality levels.

For handling this standardization issue, we follow Ferreira and Gignoux (2011a), and base our inequality analysis on the variance (and standard deviation). Consider a post-standardized distribution of test-scores (yij), obtained as a linear function of the pre-standardization distribution (xij): The variance (or standard deviation) of a post-standardized distribution (Vj<sup>y</sup> ), for example, is a monotonic (proportional) function of the pre-standardization variance (Vj<sup>x</sup> ), and does not depend on any other moment of the pre-standardization distribution; for the variance: 14
construct (given that infrastructure services are often provided free of charge) and that errors in estimating initial stocks (a common problem with this methodology) tend to become less significant over time. In addition, the alternative of using actual stocks of infrastructure (such as roads, electricity production, or water supply) was not feasible due to lack of data; the only complete series that we had at our disposal was that of electricity production, but it was felt that using it as a the sole indicator of quality of the overall stock of infrastructure was not warranted.

# **3.  Quality Indicators**

In assessing the impact of the public capital stock in infrastructure on private investment, it is important to account not only for the absolute amount of that stock, but also for the quality (or efficiency) with which public capital is used.<sup>10</sup> A common procedure to estimating the quality of public infrastructure capital is to calculate the index proposed by Hulten (1996). His composite measure of public capital efficiency is based on four basic indicators: mainline faults per 100 telephone calls for telecommunications; electricity generation losses as a percent of total electricity output; the percentage of paved roads in good condition; and diesel locomotive utilization as a percentage of the total rolling stock. In practice, researchers have found that these individual quality indicators tend to be highly correlated with the quantities of each type of infrastructure.<sup>11</sup> Thus, much of the variation in infrastructure quality may be well captured by variations in its quantity.

The individual quality indicators proposed by Hulten (1996) are subject to limitations. For instance, electric power losses include both “technical” losses, reflecting the quality of the power grid, and theft; in general, the breakdown between

> 10According to the World Bank (1994, p. 1), technical inefficiencies in roads, railways, power, and water in developing countries caused losses equivalent to a quarter of their annual investment in infrastructure in the early 1990s. See Estache (2004) for a further discussion.

> 11 Calderón and Servén (2004 _a_ , p. 19) found a high degree of correlation between the individual quality indicators listed above with the related quantities of infrastructure (that is, between power generation capacity and power losses, or between road density and road quality, the latter measured by the proportion of paved roads in total). In a companion study (Calderón and Servén (2994 _b_ , p. 11)) they obtain the same result with their two synthetic indicators of quantity and quality of infrastructure.

15
Although weak and often not comparable, available data show that access of households to banking services varies greatly. In developing countries, many households do not have a bank account. Table 1 provides data on the degree to which households use a basic financial service provided by a formal financial institution, e.g., have a checking or savings bank account, across a number of countries.<sup>4</sup> It shows that usage in most of the OECD countries is nearly universal, with many percentages above 95% and with an average of 90%; in developing countries, though, usage is much less and the average is only 26%. The highest usage of financial services from formal financial institutions is 59% percent in Jamaica. Some other high numbers may not be representative of the whole country as they apply to the population of the capital city only (Mexico) or some specific cities or regions (China, Colombia, India) or urban areas (Brazil). For most of the other developing countries, use of a basic bank account does not exceed 30 percent, and in the lowest-income countries, use is less than 10% of households.

Individuals obtain financial services through other means, though, including through nonfinancial institutions, as the comparison for some Latin American countries shows (Table 2). The Microcredit data also show that there is often use of other forms of financial services, outside the banking system. As such, these numbers underestimate the degree of access to financial services, but they do show the large differences between developed and developing countries in terms of usage of financial services from formal financial institutions.

The next question then is who are then the unbanked households, and how do they differ, if at all, between developed and developing countries? To the extent we know, the

> 4 The data for Table 1 come from a variety of sources.  The main sources are households’ surveys, the LSMS (Living Standard Measurement Surveys). Here individual households responses on questions of usage of financial services are averaged for each country.  Second main sources are the surveys conducted by FINMARK and genesis in a number of Southern African countries.  Again, these are household surveys, but more specifically aimed at usage of financial services. The source for the EU -countries is Pesaresi and Pilley, 2003.  For the US, the source is the Survey of Consumer Finance, 2004. For Brazil, Colombia, India and Mexico the recent estimates are from Kumar et al. 2004, Basu et al. 2004, and Solo et al. 2004.  The earlier India numbers are from the regular Indian household survey. The Kenya, Tanzania and Uganda estimates are from Peachey and Roe, 2004.  Questions on financial services usage do vary across the households survey, numbers are thus not necessarily comparable and some of the numbers are only rough estimates.

9
of projects in Jordanian schools. The new entity also leverages years of experience in integrating technology into the education process to provide global consulting services. Figure 1 shows the current programming ―tracks‖ of JEI’s second phase of operations.

**Figure 1: Programming Tracks of JEI Phase II Operations**

<!-- Start of picture text --> Phase II tracks<br>Discovery Expansion Global Outreach<br>Pilots new, Provides services<br>Rolls out<br>innovative solutions to local and global<br>projects<br>& creates private sector firms<br>through Madrasati<br>new partnerships and governments<br><!-- End of picture text --> Source: Authors The original project management office maintains the vision of JEI, tracks progress and project implementation, and holds partners accountable for their commitments. In addition, it has put standard technical and maintenance procedures in place to improve the initiative’s ability to monitor and maintain ICT infrastructure in Jordanian schools. The PMO also conducts site surveys to identify when and where assistance is needed in schools and its staff facilitates communications between the MoE and the various partners that develop content. The PMO sits in between the public and private entities—it is neither inside any of the key ministries involved, nor attached to any of the private companies. This neutrality offers the PMO flexibility in its operations, allowing it to experiment and pilot new activities, and the independence needed to facilitate and support complex discussions between all stakeholders.

The PMO is accordingly perceived as the interface between the initiative’s global, local, private, and public sector partners. It manages projects to achieve the results desired by the Ministry of Education by continuously coordinating, facilitating, and following up on matters related to projects that seek to achieve education reform through technology projects. JEI therefore engages various partners in a dynamic relationship and provides guidance and feedback to stakeholders. Its continuous contact and interaction with global partners, moreover, keep it abreast of the latest developments in education, which help JEI operate as an agent of innovation and research.

4
We first discuss the data sources and definitions. We then present some descriptive statistics of relevance for the empirical analyses that follow.

# 3.a. _Data definitions and sources_

The present study characterizes the role of the investment climate within which firms operate, and how it affects product innovation. This is done with data from the World Bank’s numerous Investment Climate Surveys (ICS) and Business Environment and Enterprise Performance Surveys (BEEPS). There is substantial overlap between the ICS and BEEPS questionnaires, but there are, however, some differences in their sampling approaches. The ICS tend to focus on manufacturing firms; the BEEPS are drawn from a broad range of economic activities including services (actually the BEEPS database is slightly skew towards services firms). We restricted the coverage of the BEEPS data to firms in the manufacturing sectors.

Three sets of variables are used in our regression analyses discussed below, namely firm-, sector- and country-level variables. The first set includes our product innovation proxy that is also de dependent variable: the introduction of a new product. The surveys asked managers whether the firm had introduced a new product during the past two years. Hence our dependent variable is dichotomous.

Regarding explanatory variables, firm characteristics that may affect a firm’s proclivity to innovate include firm size, measured by the natural logarithm of the average number of permanent and temporary workers and its squared term (to test for a nonlinear relationship); a firm’s exporter status, measured by a dummy variable equal to 1 when a firm exports at least 10 percent of its sales; firm ownership, measured by a dummy variable equal to 1 for foreign ownership (when more than 10 percent of assets of the firm are owned by foreigners); and capacity utilization, measured as the average utilization of its productive capacity over the year preceding the survey. The surveys also provide information about the value of R&D expenditures and firm sales. From those data we calculated the share of R&D in sales. Since the literature on innovation has paid much attention to the adoption of foreign technologies, we also use data derived from a question in the surveys that asked managers whether the firm had paid licensing fees during the past two years. This variable is also dichotomous.

7
|**Country Group-**<br>**Asset**<br>Note: This table prese<br>assets for three periods|**Period**<br>**Ta**<br>**EM/AM assets and their**<br>nts correlation coefficients of <br>during the 2000s. Source: Bloo|**S&P**<br>**ble 5**<br>**sensitivity to glob**<br>the PC1 of EM/AM<br>mberg, MSCI, GEM|**US HY**<br>**al factors**<br> Equity/Spreads<br>World Bank, U|**Broad Dollar**<br>**Index**<br>/FX against global<br>S Treasury.|
|---|---|---|---|---|
||2000-2004|0.723|-0.654|-0.358|
|_PC1-EM-Equity_|2005-2008M02|0.677|-0.504|-0.007|
||2005-2010|0.826|-0.694|-0.584|
||2000-2004|-0.396|0.660|0.451|
|_PC1-EM-Spreads_|2005-2008M02|-0.576|0.739|0.039|
||2005-2010|-0.572|0.767|0.589|
||2000-2004|-0.453|0.427|0.610|
|_PC1-EM-FX_|2005-2008M02|-0.262|0.235|0.495|
||2005-2010|-0.740|0.499|0.770|
||2000-2004|0.888|-0.608|-0.063|
|_PC1-AM-Equity_|2005-2008M02|0.792|-0.654|0.197|
||2005-2010|0.890|-0.751|-0.497|
||2000-2004|-0.134|0.085|0.584|
|_PC1-AM-FX_|2005-2008M02|0.029|0.017|0.676|
||2005-2010|-0.602|0.406|0.748|
**Table S11. Relationship between the Environmental Performance Index and SPI scores, 2016-2022**

||**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|
|---|---|---|---|---|---|---|
|Overall SPI Score|0.498***|-0.032|-0.038||||
||(0.05)|(0.07)|(0.07)||||
|SPI Pillar 1 Score (Data use)||||0.011|0.040|0.044|
|||||(0.04)|(0.03)|(0.03)|
|SPI Pillar 2 Score (Data services)||||0.073**|0.012|0.010|
|||||(0.03)|(0.02)|(0.02)|
|SPI Pillar 3 Score (Data products)||||-0.542***|-0.003|0.005|
|||||(0.04)|(0.06)|(0.06)|
|SPI Pillar 4 Score (Data sources)||||0.309***|-0.082|-0.093|
|||||(0.04)|(0.06)|(0.06)|
|SPI Pillar 5 Score (Data infrastructure)||||0.227***|-0.036|-0.034|
|||||(0.03)|(0.03)|(0.03)|
|Log GDP per capita (constant 2015 US$)|||3.595|||4.844|
||||(4.43)|||(4.43)|
|Trade (% of GDP)|||0.030|||0.030|
||||(0.02)|||(0.03)|
|Agriculture, forestry, fishing value added (% of<br>GDP)|||0.053|||0.071|
||||(0.15)|||(0.16)|
|Manufacturing value added (% of GDP)|||-0.011|||-0.015|
||||(0.21)|||(0.23)|
|School Enrollment, Primary (% gross)|||-0.019|||-0.017|
||||(0.07)|||(0.06)|
|Year 2017||0.076|-0.060||-0.155|-0.340|
|||(0.17)|(0.21)||(0.28)|(0.32)|
|Year 2018||0.156|-0.083||-0.211|-0.496|
|||(0.35)|(0.42)||(0.47)|(0.54)|
|Year 2019||0.165|-0.137||-0.070|-0.436|
|||(0.37)|(0.49)||(0.46)|(0.59)|
|Year 2020||-8.941***|-8.849***||-9.035***|-8.996***|
|||(0.70)|(0.69)||(0.81)|(0.79)|
|Year 2021||-8.747***|-8.983***||-8.477***|-8.903***|
|||(0.91)|(0.93)||(1.26)|(1.28)|
|Year 2022||-12.403***|-12.978***||-12.188***|-12.988***|
|||(0.98)|(1.10)||(1.26)|(1.37)|
|Constant|19.196***|||54.307***|||
||(3.03)|||(3.11)|||
|Sigmau||8.91|5.23||6.8|5.08|
|_<br>Sigma_e||5.91|5.9||5.28|5.25|
|R2|0.299|0.925|0.926|0.577|0.926|0.927|
|Country FE|No|Yes|Yes|No|Yes|Yes|
|No of countries|158|158|158|158|158|158|
|No of observations<br>**Note**: * p<0.10, ** p<0.05, *** p<0.01. Standard e<br>Development Indicators (WDI) and SPI. In cases wh<br>the nearest available value.|1085<br>rrors are clust<br>ere data are m|1085<br>ered at the co<br>issing for a pa|1085<br>untry level.<br>rticular covar|1085<br>Data are from<br>iate, the data|1085<br>the World<br>are imputed f|1085<br>Bank's World<br>orward using|

60
<mark>The independent variables included vary by country and round, since the sampling frame and survey instrument vary by country and round. The basic information document for each phone survey provides specifics of what variables are included.</mark> <mark>5. We predict the probability of response (i.e. propensity score) and create ten equal groups (deciles) for this variable. Within each decile d, we compute the mean value and take the reciprocal as the phone survey attrition correction factor for observations in each decile</mark> (acD=d): <mark>where</mark> phonei is the predicted value <mark>(i.e. propensity score) for each observation i. N is the total number of individuals in each decile. We then apply the attrition correction factor to the adjusted household sampling weight as derived in step #3.</mark> <mark>6. We winsorize the resulting weight (</mark> wi,ac <mark>) by replacing the top two percent of observations, with the value at the 98</mark><sup>th</sup> <mark>percentile cut-off point.</mark> <mark>7. Finally, we post-stratify weights to reduce standard errors and to match the projected population totals at the highest spatial resolution possible, ranging from region to district, based on the data availability in each country. Population projections come from national statistic bureaus, often as part of census data. The post-stratification weights (</mark> wps <mark>) are calculated as the weighted total number of observations from the data divided by the census projections:</mark> <mark>where</mark> Mr <mark>is the total population in the region, or whatever is the highest spatial resolution available, and</mark> population is the national statistic bureau’s projected population for that region. The final weight is then calculated as: <mark>In our case, post-stratification is not a major adjustment to the weights but a fine tuning of their values.</mark> <mark>In the case of Nigeria, to obtain a nationally representative sample for the phone survey, a sample size of approximately 1,800 successfully interviewed households was targeted. Based on the</mark> 19