rafmacalaba/datause-encoder-data huggingface dataset:
34 9 data would be the most helpful.1 If new modeling were to be undertaken, more thought would need to be given to the set of explanatory variables and to the interrelationshipa **s** sumed between endogenous variables. Perhaps exogenous variables for price and availability of public transport options could be used to make modal choice implicit.

Wharton (1977). This model represents a class of models that focus on predicting the demand for autos by applying econometric techniques to aggregate, rather than individual, data. Some of these models are based on a model of individual demand consistent with consumer choice theory; some are not. They may focus on the auto ownership decision only, or on the type of vehicles as well, but not on vehicle use. They apply time series analysis on a national or regional basis. Emissions characteristics are not incorporated. The most complete of these models is by Wharton (1977). Not only does it estimate new vehicle demand by type but it also estimates the number of used vehicles owned. This focus permits a crude analysis of changes in the age distribution of the vehicle fleet in response to policy or to technology changes in vehicles. It simplifies the vehicle choice problem by including only a term for cost-per-mile by vehicle type (which- embodies fuel economy, purchase price, and maintenanc **e** xpense) but omits non-price vehicle characteristics. As price and non-price characteristics can be correlated (i.e., fuel efficiency and size), this is a significant limitation, in general, biasing demand estimates.

As seen for the Uheaton-type models above, the Wharton-type models could be used to estimate welfare effects associated with a very limited set of 19. Time series analysis is unlikely to provide sufficient variation in variables to obtain useful estimates, even with perfect data.
# I. D4TRODUCTION

1. Over the past two decades, many countries of SubSaharan<sup>Africa have experienced a</sup> stagnation or decline in their traditional agricultural<sup>exports. I Given that most African countries</sup> have remained dependent upon such agricultural exports<sup>for the bulk of their foreign exchange</sup> earnings, such poor trade performance has been<sup>a major component of axid an important</sup> contributing factor to a wider economic crisis within<sup>the region.2This crisis has been evidenced</sup> by low or negative rates of economic growth,<sup>deepening debt-service problems, deteriorating</sup> physical infrastructure and public services, reduced<sup>food security, and rising unemployment.3</sup>

2. The reduced African earnings from agricultural exports<sup>can be partly attributed tc adverse</sup> trends in international prices for several of<sup>the region's most important export crops. For</sup> example, over the 1978 to 1987 period, the avernge<sup>annual price changes for cotton, coffee,</sup> tobacco and tea were all negative, ranging from<sup>-2.4% (for cotton) to -6.8% (for coffee).4</sup> However, unlike many developing countries of<sup>Asia and Latin America, most African countries</sup> have not been able to compensate for the declining<sup>world prices of 'traditional' primary</sup> commodities by either increasing export volumes<sup>of (and market shares for) these commodities,</sup> I World Bank (1981, Tables 7 and 14), (1989, Tables 12<sup>a7td 15).</sup> 2 In 1985, eighteen countries of sub-Saharan Africa<sup>relied upon food and agricultural products for 60% or</sup> more of their merchandise export earnings. For some<sup>countries, this share exceeded 80 % (World Bank (1987, Table</sup> 11).

3 For discussions of Africa's economic crisis,<sup>see Lofchie (1986), Ravenhill (1986), and the World Bank</sup> (1989,1990).

4 World Bank (1990) Price Prospects for Primary<sup>Commodities. international Trade Division. International</sup> Economic Department. An analysis by Macbean and<sup>Nguyen (1987) indicates that the average annual price changes</sup> for these commodities as well as other major African<sup>agricultural exports (eg. sisal, bananas, and sugar) were all</sup> negative over the 1960 to 1981 period
37 <!-- Start of picture text --> 6<br>5.8<br>5.6<br>5.4<br>5.2<br>5<br>4.8<br>4.6<br>4.4<br>4.2<br>4<br>1900 1920 1940 1960 1980<br>Birth-Year Cohort<br>ln(wage)<br><!-- End of picture text --> Note: The birth-year specific wages are the coefficients of Birth-Year dummies in the regression of log (wages) on birth-year dummies and survey dummies, where the survey dummies are taking care of across survey differences in exchange rates, inflation, unit of wages, differences in survey instruments.

**Figure 3** : Log(wage) by 1905-1987 Birth-year Cohorts, World Averages
Policy Research Working Paper 6172

# **Abstract**

Farm-gate buying by small itinerant buyers is the dominant mode of primary marketing in Tanzania’s maize market. This paper estimates the effect of household distance to market on maize farm-gate prices, and the extent to which seasonally determined search costs can explain price variations between the lean and the harvest seasons using data from the most recent Tanzania Household Budget Survey (2007). The author observes that greater distance to market depresses farmgate prices but that it is a relatively modest effect, and that this effect is pro-cyclical in that it is stronger during the harvest season when prices are lowest. The paper discusses the latter result with reference to search costs as an explanatory factor. It also briefly places the findings in the context of Tanzania’s food security patterns, making a link between food insecurity and high search costs. The main policy conclusion is that coordinating mechanisms such as village market places (in parallel with farm-gate buying) may reduce transaction costs in rural markets.

This paper is a product of the Human Development Sector, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at smahdi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
46 Table 15. Countries and Econmlies Classified by Income Group (based on GNP per capita In 1986 U.S. dollars)

**Lou incrm CS42 ar leo** t)

|East Africa|West Africa|South Asia|
|---|---|---|
|Burundi|Benin|Afghanistan|
|Comoros|BurkinaFaso|Bangladesh|
|Ethiopia|Central African Rep.|Bhutan|
|Kenya|Chad|India|
|Lesotho|Equatorial<br>Guinea|Maldives|
|Hadagascar|Gambia,The|Nepal|
|Malaui|Ghana|Pakistan|
|Mozambique|Guinea|Sri Lanka|
|Rwanda|GuineaBlissau||
|Somalia|mali|East and Southeast Asia|
|Tanzania|Mauritania|Burma|
|Uganda|Niger|China (excluding<br>Taiwan)|
|Zaire|Soo Tome andPrincipe|Kampuchea,Dem.|
|Zambia|Senegal|Lao People's Dem.Rep.|
||Sierra Leone|Viet Nam|
|North Africa|Togo||
|Sudan||Oceania|
||Latin Ainrica andthe Caribbean<br>Haiti|Vanuatu|
|Lomm-mnddtlelntme(**42-$1,8JO)**<br>East Africa|Latin America and the Caribbean|Southwest Asia|
|Botswana|Belize|Gaza Strip|
|Djibouti|Bolivia|Jordan|
|Mauritius|Chile|Lebanon|
|Numibia|Colaobla|Syrian Arab Rep.|
|Swazitland|Costa Rica|Turkey|
|Ziebabwe|Dominica|Yemen Arab Rep.|
||Dominican Rep.|Yeaen People'sDem.Rep. of|
|Uest Africa|Ecuador||
|Cameroon|El Salvador|East and Southeast Asia|
|Cape Verde|Grenada|Irdoresia|
|Congo. People's Rep. of the|Guatemata|Philippines|
|C6te dlIvoire|Guyana|Thailand|
|Liberia|Honduras||
|Nigeria|Jamaice|Oceania|
||Nicaragua|Kiribati|
|North Africa|Paraguay|Other Micronesia|
|Egypt, Arab Rep. of|Peru|Pacific<br>Islands|
|Morocco|St. Kitts ard Nevis|Papua New Guinea|
|Tunisia|St. Lucia|SolotonIslands|
|Other North Africa|St. Vincentandthe Greradines|Tonga|
|||WesternSamoa|
auto assembling firms tends to be usually low In Japan.' The internal production rate of the Japanese auto producers is around 30 percent, whereas the figures are higher for U.S. producer General Motors (GM). The production structure of the Japanese motor vehicle industry is sometimes called a "pyramid" or "multi-tier structure." An assembler has direct transactional relations only with first tier subcontractors (suppliers); first tier subcontractors with second tier subcbntractors; second tier subcontractors with third tier subcontractors, and so on in a pyramid-like structure. A MITI study of the subcontracting relations of a Japanese automobile producer illustrates the resulting pattern (see Table 11.2).

The automobile industry in Ohta is structured in pyramid-style, with Fuji Heavy Industries on top as an assembler. Although there is no one-to-one correspondence between the size of a firm and the type of a subcontractor, higher-tier subcontractors are generally larger than lower-tier subcontractors. A survey of subcontractors for Fuji Heavy Industries conducted by Ohta city found that the average number of employees for the first-tier, secondtier, and third-tier subcontractors are, respectively, 258 (range: 35 to 1850), 24 (5 to 78), and 6 (11 to 30).8 In 1989 there were 73 establishments in Ohta's transport machinery sector.<sup>9</sup> (Table II3). Among them are two assemblers, Fuji Heavy Industries and Nissan Diesel. Assuming 100 employees as a cut-off point for first- and second-tier subcontractors, 11 establishments, or around 15 percent of the total, are first-tier subcontractors. This makes the number of second- and third-tier subcontractors around 60, or 82 percent of the total.<sup>10</sup> Among the 60 second-tier and third-tier subcontractors, it may be plausible to assume that 10 to 20 are second-tier subcontractors and 40 to 50 are third-tier subcontractors, assuming 30 employees as a cut-off point. Despite the large number of second and third-tier subcontractors, they account for only 5 percent of total value added for the industry.

7 y the ratio of intemal production we mean the share of value added **to** which assembling firms contribute, compared **to** the total value added for the automobile industry.

' Ohta city office, intemral document.

Lack of detailed statistics precludes one from isolating the automobile **and** automobile parts industry from the rest of the transport machinery sector.

**1** The **cut-off** point of 100 employees for the first-tier and second-tier subcontractors and 30 employees for the second-tier and third-tier subcontractors is based on a survey conducted **by** the Ohta city office of subcontractors for Fuji Heavy Industries.

6
# **Figure 3: EBRD indicator of progress in large-scale privatization**

<!-- Start of picture text --> Progress in large-scale privatization<br>4,5<br>Albania<br>4,0<br>Bosnia<br>3,5 Bulgaria<br>Croatia<br>3,0<br>Czech Rep.<br>2,5 Estonia<br>Hungary<br>2,0 Latvia<br>Lithuania<br>1,5<br>Poland<br>1,0 Serbia<br>Slovakia<br>0,5<br>Slovenia<br>0,0<br>1989199019911992199319941995199619971998199920002001200220032004<br><!-- End of picture text --> Source: EBRD, 2007.

31
and firms’ outcomes are also covered, including firms’ age, legal status, international markets’ engagement, workforce composition, gender, innovation, and performance. The gender section includes information on whether the firm has a female owner. The 16 ES conducted in Sub-Saharan Africa included a section on mobile money adoption and use. To the extent of our knowledge this is the most extensive firm-level data on mobile money adoption in Sub-Saharan Africa. Summary statistics for the sample are provided in table 1.

# **3.1 Mobile Money Adoption**

Table 1 shows that 27 percent of firms in the sample have adopted mobile money. About 15 percent of firms used mobile money to receive payments from customers, which is the most common use of mobile money by firms in the sample. This is followed by paying utility bills (10 percent), paying suppliers (8 percent) and finally paying employees (4 percent). Table A1 provides the breakdown by whether or not the firm has at least one woman among the owners (female-owned firms). About 29 percent of female-owned firms and 26 percent of fully male-owned firms use mobile money for financial transactions. The difference is not statistically significant. With regards the use of mobile money, 76 percent of female-owned firms use mobile money to receive payments from customers in contrast to 60 percent of fully male-owned firms. The difference is statistically significant at the 5 percent level. The other uses of mobile money do not show any statistically significant difference between female and male-owned firms.

There is considerable heterogeneity in the adoption of mobile money by firms across economies. Kenya (2017) had the highest penetration with 77 percent of firms using mobile money (Table A2). This is followed by Zimbabwe in 2015 (68 percent) and Uganda in 2012 (54 percent). At the other end, Guinea (6.7 percent) in 2015, and Ghana (4.2 percent) and Zambia (3.7 percent) in 2012 have the lowest rates of mobile money penetration. The time differences in the surveys could explain some of the penetration rates given that penetration may be higher for more recent years due to technological or regulatory advancements. This is empirically accounted for in the estimations.

In table 2 we regress mobile money use on a number of firm-level variables, accounting for location (within country) and sector fixed effects. We explore these correlations by female-owned 9
From equations [4] and [6] it is clear that the coefficient of the technology gap measures the speed of (conditional) convergence to the long-run steady state level of technological efficiency. Moreover, in the presence of technological convergence, the technological distance between each country/industry and the leader converges to a constant value. This implies that the vector of covariates as well as the country and industry fixed effects translate only into differences in efficiency levels, and not into permanent differences in growth rates of technological efficiency.

Given the translog production function, the rate of growth of technological efficiency (∆ _Ln A_ ) can be proxied by the so-called Solow residual, or multifactor productivity, as follows: while the following superlative index is used as a measure of the level of MFP: where a _bar_ denotes a geometric average over all the countries for a given industry _j_ and year _t_ and ~ α _ijt_ = (α _ijt_ + ~~α~~ _j_ )/ 2 where α _ijt_ is the labor's share in country _i_ and industry _j_ and ~~α~~ _j_ is the cross-country average for industry _j._ The MFP index has the desirable properties of transitiveness that makes it possible to compare national productivity levels (see Caves _et al._ 1982). However, the comparison of productivity levels also requires the conversion of underlying data into a common currency, while also taking into account of differences in purchasing powers across countries. These issues are discussed in the next section.

# **4. The empirical analysis**

# **_The data_**

Our main source of data is the OECD STAN-2002 database (see the appendix for further details). We have also compiled data on occupational skills (employment and wages) and hours worked by sector and country from various sources.

Given data availability, we adopt a value-added measure of MFP. Labor input is based on total hours worked using industry-level data, and the capital stock series had to be constructed for a few country/industries using the perpetual inventory method (see appendix _)_ . The calculation of MFP also requires estimates of the partial elasticity of output to labor. Under perfect competition, the partial elasticity can be proxied by the share of labor compensation in total costs. The latter, however, is 9
actual credit advanced. These findings corroborate our analysis where we, in fact, document a decline in aggregate loans.

The CBK Credit Surveys further analyze the effect of the interest rate caps on lending to SMEs in the second and third quarters of 2017, and find that banks report tightening of risk mitigation measures and a corresponding decline in lending to SMEs, with clients below an elevated threshold being rationed out of the formal credit market. In fact, 54% of the respondents revealed that interest rate capping had negatively affected their lending to SMEs. These findings have severe implications on financial inclusion.

# **_Sector Level Analysis_**

Next, we turn to sector-by-sector analysis. Figures 2(a)-(k) replicate lending trends across the 11 sectors of the economy. Corresponding regression tables are presented in Appendix D.

While the growth in normal loans varied across sectors with some increasing lending and others decreasing, the trend in abnormal loans is consistent. Virtually every sector (except for mining and quarrying) exhibited a significant increase in loans classified as abnormal, which is indicative of the downturn in the economy. As discussed above and as the regression tables in Appendix D confirm, we cannot attribute this downturn to the interest rate caps alone, as the negative effects were already present after the banking crisis in early 2016.

There are differential effects across sectors when we analyze changes in normal lending. The largest loan sector, personal and household loans, saw a 2.5% increase in lending in the post crisis period, and a further 5.3% increase after the caps. The abnormal portfolio in this sector also increased substantially. This increase in lending could signify a general shift in lending preferences by banks and a move towards personal and mobile lending platforms. Conversely, it could indicate a shift to larger loans to established individual clients. The next section will analyze how different types of banks (tier 1, 2, and 3) responded differently to the crisis and caps, and provides further insight on shifts in lending portfolios.

For other sectors, loan growth picked up slightly in Energy and Water after the caps, while it declined significantly in Agriculture, Building and Construction, Manufacturing, Tourism, and Trade. Overall, the trend in the major lending sectors of the economy tended towards a slowdown in loan growth.

# **_Bank Level Analysis: Loan Portfolios_**

The most disaggregated data we analyze come directly from commercial banks. The data were originally received at the bank-sector-period level but were difficult to analyze due to a high level of missing observations and other statistical discrepancies. Hence, the data were converted to the bank-period level, which is the basis of our analysis.

First, we analyze changes in loan portfolios across banks and across time. Figure 3 presents the average shares of corporate, consumer, asset finance, project, SME, cooperatives, micro and mobile, and other unsecured loans across all banks.

12
Table A4: Equivalent Years of Schooling based on the regression results

||Pooled with<br>countryFE|Pooled|Bolivia|Colombia|Ghana|Kenya|Vietnam|
|---|---|---|---|---|---|---|---|
|Panel A|||Learning|gains(standard de|viation)|||
|1year|0.16|0.23|0.17|0.12|0.26|0.09|0.14|
|2year|0.16|0.23|0.16|0.12|0.25|0.09|0.14|
|3year|0.16|0.22|0.16|0.11|0.24|0.09|0.14|
|4year|0.16|0.22|0.16|0.11|0.24|0.09|0.14|
|5year|0.16|0.22|0.15|0.11|0.23|0.10|0.14|
|6year|0.15|0.21|0.15|0.11|0.23|0.10|0.14|
|7year|0.15|0.21|0.14|0.11|0.22|0.10|0.14|
|8year|0.15|0.20|0.14|0.10|0.21|0.10|0.14|
|9year|0.15|0.20|0.14|0.10|0.21|0.10|0.14|
|10year|0.14|0.19|0.13|0.10|0.20|0.11|0.14|
|11year|0.14|0.19|0.13|0.10|0.20|0.11|0.14|
|12year|0.14|0.19|0.12|0.10|0.19|0.11|0.14|
|Panel B||||EYOS||||
|1year|6.1|4.3|6.0|8.5|3.9|11.5|7.3|
|2year|6.2|4.4|6.1|8.6|4.0|11.2|7.3|
|3year|6.3|4.5|6.3|8.8|4.1|11.0|7.3|
|4year|6.3|4.5|6.5|8.9|4.2|10.8|7.3|
|5year|6.4|4.6|6.6|9.1|4.3|10.5|7.3|
|6year|6.5|4.7|6.8|9.3|4.4|10.3|7.3|
|7year|6.6|4.8|7.0|9.4|4.6|10.1|7.3|
|8year|6.7|4.9|7.2|9.6|4.7|9.9|7.3|
|9year|6.8|5.0|7.4|9.8|4.8|9.7|7.3|
|10year|6.9|5.2|7.9|10.2|5.1|9.3|7.3|
|11year|6.9|5.2|7.9|10.2|5.1|9.3|7.3|
|12year|7.0|5.3|8.1|10.4|5.3|9.2|7.3|
|EYOS|6.5|4.7|6.8|9.3|4.4|10.3|7.3|

35
skill jobs are filled through informal channels. Job postings require on average 2.5 years of work experience and tertiary education, are mostly full-time, and exhibit strong regional focus on Pristina. Foreign language, extraversion and computer skills are the most demanded skills across all industries and occupations, which confirms that they are “horizontal” skills requirements. Some industries such as the IT sector express high demand for particular software skills, such as programming skills, thereby signaling specific industry needs. From an applicant’s perspective, it points at the importance of more specific abilities that need to be trained beyond these transversal skills before entering the job market.

The approach presented in this paper provides valuable insights for policy makers and other stakeholders on priority skills demanded by employers and this information should be useful in aligning curricula with labor market needs in general education and training programs. The findings on skill needs and education requirements by sectors and occupations are very detailed and directly actionable. Such findings can also benefit parents and youth looking for more information on career and education choices.

Nevertheless, skill needs are dynamic and thereby evolve quickly over time, especially technical skills in rapidly evolving sectors (for example IT and computer science), often linked to technological progress and overall economic development. The construction of time series of skill requirements to follow skill demand over time would be a key extension of this analysis. Several years of such data could allow us to monitor the changes in demand for skills, for policy use.<sup>45</sup> Another main avenue for analysis is the inclusion of data from public platforms, which would improve the coverage of the labor market. Discussions with the public employment agency of Kosovo suggest that the public platform includes more middle and low-skilled jobs than private platforms and has a better coverage of secondary cities in the country. This suggests that private and public platforms are complementary and serve different markets. We encourage further applications of this approach to other Western Balkan countries to compare skills requirements across borders and to account for enhanced regional mobility of predominantly young labor market participants.

_This paper was written prior to the outbreak of the COVID-19 pandemic in Europe. While the severity and duration of the pandemic and the resulting impact on labor markets are uncertain, analysis of online vacancy data provides real-time insight into firms’ demand and severe distress in the spring of 2020. Kahn et. al. (2020) for example show that job vacancies in the United States collapsed by 30 percent in late March compared with early 2020. The continued monitoring of job vacancy data will signal when labor markets are starting to recover._

> 45 For a recent example of an analysis on skills evolution in the context of the Great Recession, see Hershbein and Kahn (2019).

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