rafmacalaba/datause-encoder-data huggingface dataset:
|**Increase in Import Value**<br>|**Country with**|**Largest Increase**<br>**Country with Largest Decrease in**|
|---|---|---|
|**Product**<br>**Product Description**<br>**(US$, thousands)**|**in Impo**|**rt Value**<br>**Import Value**|
|901920<br>Ozone therapy/oxygen therapy/aerosol therapy/artificial respiration/other therapeutic respiration apparatus.<br>266.20|Germany|Russian Federation|
||Change in import value|315.25<br>-208.68|
||Import value in 2008|7,293.00<br>3,839.70|
||% Change in import value|4.32<br>-5.43|
|830210<br>Hinges of base metal.<br>252.27|China|Russian Federation|
||Change in import value|173.31<br>-22.86|
||Import value in 2008|5,504.48<br>269.73|
||% Change in import value|3.15<br>-8.48|
|320910<br>Paints & varnishes (incl. enamels & lacquers) based on acrylic/vinyl polymers.<br>228.60|Germany|Russian Federation|
||Change in import value|173.83<br>-245.31|
||Import value in 2008|2,186.00<br>4,497.97|
||% Change in import value|7.95<br>-5.45|
|848420<br>Mechanical seals.<br>206.52|UK|Russian Federation|
||Change in import value|173.63<br>-107.26|
||Import value in 2008|4,710.51<br>1,765.64|
||% Change in import value|3.69<br>-6.07|
|210210<br>Active yeasts.<br>190.05|Turkey|Russian Federation|
||Change in import value|277.41<br>-142.95|
||Import value in 2008|3,102.81<br>1,264.94|
||% Change in import value|8.94<br>-11.30|
|180632<br>Chocolate & other food preparations containing cocoa, in blocks/slabs/bars, weighing 2kg/less, not filled.<br>188.21|Turkey|Russian Federation|
||Change in import value|623.54<br>-478.04|
||Import value in 2008|3,782.29<br>15,515.80|
||% Change in import value|16.49<br>-3.08|
|391723<br>Tubes, pipes, & hoses, rigid, of polymers of vinyl chloride.<br>186.60|China|Russian Federation|
||Change in import value|127.41<br>-27.38|
||Import value in 2008|9,447.67<br>870.24|
||% Change in import value|1.35<br>-3.15|
|800700<br>Other articles of tin.<br>177.96|China|Russian Federation|
||Change in import value|178.62<br>-0.96|
||Import value in 2008|8,731.13<br>15.96|
||% Change in import value|2.05<br>-5.99|
|180631<br>Chocolate & other food preparations containing cocoa, in blocks/slabs/bars, weighing 2kg/less, filled.<br>177.39|Germany|Russian Federation|
||Change in import value|576.50<br>-516.66|
||Import value in 2008|3,509.00<br>41,083.14|
||% Change in import value|16.43<br>-1.26|
|640399<br>Other footwear without outer soles of leather, not covering the ankle.<br>160.12|China|Russian Federation|
||Change in import value|81.80<br>-8.65|
||Import value in 2008|16,228.41<br>667.85|
||% Change in import value|0.50<br>-1.29|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 51
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26
Income data have been adjusted for spatial price differences. We constructed a spatial price index that reflects prices of basic goods, services and housing at the state level, separately for rural and urban areas within the state (see Table A in the Appendix). The index for the years 2004-2016 was derived from the household-specific poverty lines that the Department of Statistics Malaysia calculates for each survey year, which depend on the location and demographic composition of the surveyed household.<sup>15</sup> For 2019 and 2022, we extrapolated the index based on the CPI of the corresponding area.

As explanatory variables in our income model, the method requires we only use variables that are time-invariant. We use three such categories. First, we employ dummy variables for the birth cohort of the household head, using five-year bins. The first bin starts in 1940; the cohort 1965-1969 is the omitted dummy, since it has members in every survey year. Second, we use six dummy variables for the highest level of education enjoyed by the household head. Those with primary education or less form the base category. Third, we use dummy variables for the ethnicity of the household head. The four categories are Bumiputera, Chinese, Indian and Other; Bumiputera is set as the base category. Fourth, we use a dummy variable for the gender of the household head.

In addition, we use two location dummies that, strictly speaking, are not time-invariant. These are one dummy for urban or rural location and another for being located in either Peninsular Malaysia or East Malaysia. This restricts the interpretation of results to households that did not migrate from one such classification to another. Table B in the Appendix provides the OLS regression results of our income models for each survey year, based on the subsample of households with heads in the age range of 25–60.

# 3. Results

We discuss the results of our analysis of economic mobility in three parts. First, we focus on absolute mobility at the lower end of the income distribution by investigating poverty dynamics. Then, we take a broader perspective by studying upward and downward absolute mobility between three income groups, which takes into account considerations of vulnerability and economic security. These first two analyses are based on fixed income thresholds. The third part studies relative mobility by analyzing movements between income quintiles. This allows us to analyze what is happening in the richer parts of the income distribution too.

> 15 We are aware that the index is thus based on expenditure patterns at the very low end of the income distribution. However, this is the best we could do in the absence of spatially disaggregated consumer price data.

9
# **Financial Development: Structure and Dynamics**

Augusto de la Torre, Erik Feyen and Alain Ize<sup>1</sup>

# **JEL classification codes:** G2, G38, O16, O54

**Keywords:** Financial development, financial stability, finance and growth, benchmarking

1 The authors work for the World Bank as, respectively, Chief Economist for Latin America and the Caribbean (adelatorre@worldbank.org), Senior Financial Specialist (efeijen@worldbank.org), and Senior Consultant (aize@worldbank.org). The paper benefited from valuable comments by Martin Cihak, Eduardo Fernandez-Arias, Roberto Rocha, and participants in the December 2010 Authors’ Workshop for the LAC Flagship Study on financial Development. We thank Francisco Ceballos for excellent research assistance. The views in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank, its executive directors and the countries they represent.
25 Figure 1: Nominal Rates of Assistance for import-competing, exportable and all covered products, 19 African countries, 1961 to 2004 <!-- Start of picture text --> (percent)<br>200<br>150<br>100<br>50<br>0<br>‐50<br>‐100<br>1961 1966 1971 1976 1981 1986 1991 1996 2001<br>Import‐competing products Exportables Nontradables All covered tradables<br><!-- End of picture text --> Source: Anderson and Valenzuela (2008)
Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Uttar Pradesh, and West Bengal.<sup>8</sup> Although the survey is not nationally representative, it is representative for these states, which make up about 70 percent of the country's population according to the latest government census conducted in 2011. Gallup conducted the survey between January-March of 2016. It included our module, plus a wide range of questions regarding demographic, employment, and income characteristics. The target population is the entire civilian, noninstitutionalized adult population (age 15 and above) living in the 13 states.<sup>9</sup> We include four dimensions of public delivery. The first three services are administered at the state level:

- “Goods” refers to government run schools and health services.

- “Services” refers to registration of land/property and issuance of driver’s licenses.

- “Utilities” refers to utilities such as electricity, gas and water.

The fourth category, “identity cards” — voter ID cards and Aadhaar biometric identification cards — are provided by the federal government. For the sake of brevity, “goods and services” is used to refer to all state and federal services collectively.

# **3. Access to Government Delivered Goods and Services**

This paper begins by measuring “access” — the percentage of the population that have access to government provided goods and services that they need.

> 8 The state of Karnataka is excluded from the analysis in this paper due to data inconsistencies.

> 9 To ensure that the sample is representative of the adult population of the 13 states surveyed in India, weights based on available population demographics were used. Final weights consist of the base sampling weight, which corrects for unequal probability of selection based on household size, and the post-stratification weights which corrects for age, gender, education, caste and urban/rural to correct for nonresponse error. Additional information on survey methodology is shown in Appendix 2.

8
**Table B7: Baseline Differences by Treatment Status: Wage Employed**

||In-<br>person<br>Weekly|**means**<br>Phone<br>Weekly|Phone<br>Triweekly|vs in-p<br>we|**p-valu**<br>erson<br>ekly|**es**<br>vs<br>phone<br>weekly|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(2=1)|(3=1)|(3=2)|
|**A. Variables used in randomi**|**zation**||||||
|Male|0.56|0.59|0.55|0.72|0.89|0.63|
|Age|33.53|31.60|32.83|0.21|0.69|0.45|
|Education|9.40|10.03|9.51|0.31|0.87|0.41|
|Married|0.35|0.34|0.29|0.93|0.46|0.48|
|Self-employed|0.00|0.00|0.00||||
|Wage-employed|1.00|1.00|1.00||||
|Accra|0.63|0.61|0.57|0.85|0.60|0.73|
|Cape Coast|0.04|0.07|0.09|0.40|0.37|0.81|
|Dependency ratio|0.44|0.40|0.47|0.27|0.67|0.14|
|Log hh assets|7.13|7.53|7.59|0.09|0.05|0.83|
|p-valuejoint F test all variable|s used in A|||0.32|0.47|0.18|
|**B. Variables not targeted in t**|**he randomi**|**zation**|||||
|Days worked per week|5.69|5.51|5.51|0.22|0.23|0.96|
|Hours worked per week|50.49|49.96|47.02|0.87|0.26|0.25|
|Manufacturing|0.08|0.10|0.11|0.66|0.52|0.9|
|Trade|0.16|0.10|0.15|0.31|0.82|0.36|
|Services|0.67|0.62|0.66|0.56|0.92|0.61|
|Public sector|0.09|0.14|0.09|0.28|0.86|0.18|
|Pay per week|139.95|146.23|103.97|0.85|0.25|0.07|
|p-value joint F test all variable|s used in A|and B||0.19|0.45|0.19|
|Total number of individuals|75|97|82||||

Note: *** p<0.01, ** p<0.05, * p<0.10. Standard errors are clustered at treatment level (Household).
# FOOTNOTES

# 1. See Eichengreen (1987, 1988) and Eichengreen and Portes (1986, 1987, 1988a,b).

2. For two accounts of the literature on loan pushing in the 1920s, the first of which is more agnostic than the second, see Skiles (1988) and Darity and Horn (1988).

3. The quote is from de Cecco (1985), p.57. See also the evidence on the lending practices of different banks in Mintz (1951).

4. See the discussion in U.S. Senate (1932).

5. See for example Schuker (1988).

6. The two samples of foreign bonds are described in Eichengreen and Portes (1988b). For Britain we constructed estimates for all 125 overseas bonds offered for subscription and listed by the Stock Exchange Yearbook, while for the U.S. we drew a stratified sample of 250 foreign bonds from the list of more than 1400 such bonds published in Young (1931). After adjusting for the actual price paid by the purchaser, we then tracked interest and amortization payments until the bond issue was extinguished and calculated the nominal own-currencv internal rate of return. The number of bonds included in the regression analysis was limited by the availability of explanatory variables. For a similar analysis utilizing grouped data which yields results generally consistent with those reported below, see Eichengreen (1988).

7. Edwards's results for bank loans are basically consistent. In addition, see Ozler (1988a), who suggests that spreads decline with the amount of time the borrower his been in the market.

8. See for example Madden and Nadler (1929). Other variables to which investment advisors referred included the natural resource endowment of the country and the position of the central bank.

9. For both sterling and dollar bonds, the coefficients have signs consistent with this hypothesis, but it is impossible to reject the hypothesis that each individual year effect is zero. This contrasts with the analysis of grouped data for the U.S. in Eichengreen (1988), where there was some apparent tendency of the spread to rise over the course of the 1920s, suggesting that investors recognized the increasingly risky nature of foreign loans.

10. We discuss this evidence on ex post returns in Section VI below.

11. Under the provisions of the Hay-Bunau Varilla Treaty of 1904, the U.S. was permitted to intervene in Panama to preserve order and to supervise the expenditure of government loans placed in the U.S. Under the Platt Amendment, the U.S. was entitled to object to "improvident or otherwise objectionable fiscal policy" in Cuba. The Dominican Republic was under U.S. military administration until 1924, and thereafter the U.S. retained the right to object to changes in Dominican tariffs and public debt. Haiti was under U.S. martial law from 1916 to 1931. See Angell (1933), pp. 8-27.

50
# **Table 4: Demand and credit sensitivity on capacity**

||||Chan|ges in C|apacity||||||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Demand Sensitivity<br>-|188.567***||||-200.054***|-201.714***|-154.513***|-142.214**|-132.498***||
||(33.60)||||(34.85)|(40.48)|(44.25)|(55.07)|(39.93)||
|Dependence on External Finance||-6.969|||22.459|||14.001|||
|||(12.37)|||(23.75)|||(31.67)|||
|Cash Conversion Cycle|||-19.487**|||3.691||-12.129||-13.604|
||||(8.66)|||(9.10)||(13.86)||(8.77)|
|% of investment from own fund 20||||-0.080|||-0.097|-0.074|||
|||||(0.06)|||(0.07)|(0.09)|||
|Firm age||||||||-0.276<br>(0.30)|||
|Small firm||||||||-4.280<br>(6.08)|||
|Foreign owned||||||||-4.658<br>(9.01)|||
|Small*DemandSensitivity|||||||||-69.334*<br>(38.25)||
|Small*DependExtFin|||||||||||
|Small*CashConCycle<br>Small*Ownfund07||||||||||-11.080**<br>(4.94)|
|Constant|-12.562|-20.368***|-6.031|-7.496|-14.173*|-13.915|-6.225|11.106|-13.053|-6.348|
||(9.66)|(5.77)|(9.94)|(9.63)|(7.67)|(9.24)|(8.51)|(16.59)|(9.69)|(10.23)|
|Country fixed effects|√|√|√|√|√|√|√|√|√|√|
|Observations|444|709|539|474|377|391|267|205|412|499|
|R-squared|0.18|0.04|0.07|0.08|0.18|0.18|0.19|0.23|0.20|0.09|

With capacity we see differential impacts of demand sensitivity and credit needs on small and large firms. For a 1 percent decline in the stock price, small firms see capacity falls by an additional 0.69 percent compared to large firms. The impact of external finance needs (via the proxy Cash Conversion Cycle) is also steeper for small firms than for large firms. Other interactions between small firm dummy and other proxies are not significant; and therefore not shown here.

Lastly, we examine the impact of demand and credit sensitivity on the change in full-time permanent workers between June 2009 and at the end of 2007 fiscal year. Table 5 shows that none of credit or demand proxies is robustly associated with the change in full-time labor<sup>9</sup> .

> 9 We also examine the change in full-time temporary workers, and do not see an impact of demand sensitivity either. However the small sample of firms declaring temporary workers in both periods is very small (85 firms total), preventing us from reaching a meaningful conclusion.

15
food processing industry) and that often refer to sector-specific production processes.

For each general and sector-specific business functions, we measure the technologies used ( _extensive margin_ ) and the most frequently used ( _intensive margin_ ). The distinction between these margins provides a very granular measure of technology adoption to inform policy decisions. For example, when asked about the adoption of particularly technology such as digital payment methods, many firms may report that they are already using it ( _extensive margin_ ), but very few of them may be using this technology intensively ( _intensive margin_ ). By disentangling the differences between the extensive and intensive margins we can have more clarity to inform policy, such as if lack of adoption this is a problem of infrastructure, or if this is more related to lack of capabilities of firms. To our knowledge, this is the first study providing a comprehensive analysis of technology adoption at the firm-level using a standard methodology across different sectors and based on nationally representative data covering formal and informal firms in Senegal.<sup>3</sup> To advance some of the key results, most firms in Senegal are performing general business functions with pre-digital technologies. On a scale from 1 to 5, where 1 represents the most basic technology to perform a general business function, 2 is the usual threshold to use computers (e.g. using a computer with standard software for production planning) and 5, the most sophisticated technology, usually advanced digital technologies, firms in Senegal score on average 1.92 on the extensive margin and 1.29 on the intensive margin.<sup>4</sup> These results suggest that on average firms are not yet adopting basic digital technologies to perform general business functions.

For sector-specific technologies, firms in Senegal are also far from the frontier. On a scale from 1 to 5, the sector-specific technology index is 1.59 for the extensive margin, and 1.27 for the intensive margin. Senegalese firms are less likely to adopt advanced sector-specific technologies, compared to general technologies. In addition, the intensive margin being close to 1 implies that even if firms adopt slightly advanced technologies, they do not fully use such technologies and remain using basic technologies in their production processes.

Senegal faces relevant challenges to improve the adoption of technologies by local firms. Our analysis highlights three groups of these challenges, based on perceived obstacles for the firms, as well as the facts associated with these obstacles: i) access to finance; ii) lack of capabilities; and iii) access to markets and uncertainty.

> The paper is structured as follows. Section 2 describes the level of adoption using standard measures of general-purpose technologies, such as access to electricity and Internet,

> 3The coverage of technology questions in standard firm level datasets is almost negligible, even for highincome economies.

> 4The FAT index is described by Cirera et al. (2020)

3
creation across sectors in each country is given in Table 4. These tables show the variation in both percentage of exporters and net job creation rates across sectors.

In Figure 1, I look at the relation between the percentage of exporters and net job creation rate for all countries in 2002 and 2005. The correlation coefficient between these two variables is -0.52. Quite a similar picture emerges when I plot the same graph separately for each survey year as well as for the industries in manufacturing sector. The graph shows in a crude way that the more firms are constrained by EPL in creating new jobs, the smaller fraction of them chooses to export. The empirical analysis will refine this relation by controlling for the factors that might affect this negative relation.

# **Figure 1 Net Job Creation and Fraction of Exporters**

<!-- Start of picture text --> .1 .2 .3 .4 .5 .6<br>Fraction of Exporters<br>Net Job Creation Fitted values<br>.1<br>0<br>Percent<br>-.1<br>-.2<br><!-- End of picture text --> The broad scope of the survey conducted to firms allows me to observe a rich set of variables on firm performance and investment climate. The questions about export ask the percentage of revenues obtained from direct and indirect exports. In analyzing the relation between restrictive labor regulations and exporting, I can control for factors like innovative capacity, skill level in the firm, wages paid, use of external finance, population of the city where firm is located, and being part of a multi-plant firm. Description of all variables used in the analysis is given in Table 5 and descriptive statistics for these variables are presented in Table 6. In one of the specifications, I use labor productivity as a control of firm performance. In calculation of productivity, I use real sales which are calculated as deflating nominal sales by GDP deflators. GDP deflators are obtained from World Bank Development Indicators database.

10