rafmacalaba/data-use-mentions-extended huggingface dataset:
The problem with this approach is that aggregate data fails to capture heterogeneity across firms and non-linearities in investment decisions recently highlighted by a new streamn of works (Doms and Dunne, 1994; Caballero, et al, 1995; Cooper, et al, 1995).

The problems of an "aggregate" approach to the study of investment decisions are even more evident by comparing two recent studies on investment productivity and growth in Africa. The first - Devarajan, et al (1999) - focuses on investment productivity using cross-country data. Contrary to the aforementioned theoretical predictions, the authors find that neither public nor private investment significantly impacted growth and output in Africa over the past three decades. To reconcile the empirical findings with the theoretical predictions, the authors integrate cross-country exploration with firn-level data on investment in the mianufacturing sector in Tanzania. This analysis suggests that the low level of investment can be partially attributed to the low productivity of capital for African firms. The low level of productivity of capital then explains the absence of a cross-country correlation between growth performance and investment.

The second study - Gunning and Mengistae (1999) - on the other hand reaches the opposite conclusion: the productivity of capital for African firms has increasecl over the past ten years. In particular, the authors explore the rate and the productivity of investment using firmlevel panel data collected by the RPED in eight African countries. Their detailed data set contains information on capital stock and market structure and helps document low levels of investment as well as heterogeneity in firm and investment productivity in the African manufacturing industries. They conclude that manufacturing firms are profitable and that investment productivity has improved in the past ten years.<sup>1</sup> These results, in contrast with the findings of Devarajan et al. (1999), suggest that solving the puzzle of the low level of investment and the weak link between investment and growth in Africa may require exploring more closely other determinants of investment, such as the degree of reversibility or risk.

Micro panel data on characteristics
critical concern in light of the fact that non-farm enterprises account for from 35 percent to as high as 50 percent of rural household income in developing country contexts (Binswanger-Mkhize, 2013). In the very few studies that have been conducted, the relationships are complex. For example, Santangelo (2019) find that shocks to the productivity of agricultural activities can have widespread and consequential impacts on rural enterprises as a result of shifts in local demand and labor supply. Dercon (2014) similarly argues that identifying the wider effects of weather shocks, in addition to their impact on agricultural systems, is critical for the development of effective rural policies.

The current analysis addresses three key linkages associated with the rural economy in the state of Rajasthan. First is the impact of rainfall or weather variability on agricultural productivity wherein a farm productivity variable is constructed at the aggregate level which combines both the crop yields and the corresponding monetary values. The second is the impact of weather shocks on the non-farm enterprise in particular with respect to its total revenue, the value added by it and its distinctive operational characteristics. The third key link is how household consumption responds to agricultural productivity losses due to weather shocks, wherein this linkage is explored at the aggregate consumption level and at its individual expenditure variety. The analysis utilizes three key datasets, namely, the district-level ICRISAT database for 14 crops from 1990 to 2015, two rounds of the nationally-representative survey on unincorporated enterprises from the National Sample Survey Organisation (2010-11 and 2015-16), and the Consumer Pyramids survey datasets 3
1993.

Data on Data on Central Government, Non central government, Education and Health employment are taken from Public Management: OECD Country rofiles, OECD 1992 and relate to 1992. Public education is provided by State schools operated by local authorities, a mixed sector which includes church schools with substantial public funding from local authorities, and a small but expanding fee-paying sector. Local education authorities employ teachers as well as administer the schools. Health care is provided by a National Health Service. It employs health employees. Over 80% of health service costs are paid out of general taxation.

Average Government wages is taken from Trends in Public Sector Pay in OECD Countries, 1995 edition and relates to 1993. Consolidated Central Government wages and salaries are for 1993 and are taken from IMF Government Finance Statistics Yearbook, 1995. Data on wages in manufacturing (monthly basis) are taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1993.

# United States

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1994.

Data on Data on Central Government, Non central government, Education and Health employment are from Public Management: OECD Country profiles, OECD 1992 and relate to 1985. Data on military employment include certain paramilitary units, e.g., the Border Guard, and exclude certain paramilitary units, e.g., the Civil Air Patrol (51,000), and the Coast Guard.

Average Government wages is taken from Trends in Public Sector Pay in OECD Countries, 1995 edition and relates to 1993. Data on wages in manufacturing (monthly basis) are taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1994.
Annual Business Survey (provided by the ONS). Finally, data on firm control variables, age, multi– establishment status and foreign ownership are sourced from the UK business registry the annual Business Structure Database.

> 22The E-commerce survey defines big data and big data analytics as the following. Big data typically have characteristics such as: (1) vast amounts of data generated over time, (2) variety in terms of different formats of complex data, either structured or unstructured (for example text, video, images, voice, docs, sensor data, activity logs, click streams, coordinates). (3) velocity in terms of the high speed at which data are generated, become available and change over time. Big data analysis refers to the use of techniques, technologies and software tools for analyzing big data from our own business or other data sources.

> 23Given that half of workers do not spend any time on data tasks, see Table A2, we use non-zero time use on data tasks to determine these groups

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20 another 21 percent planning to open electricity markets to them (World Bank 2004). State ownership is most prevalent in low-income countries with weak institutional and regulatory capacities. Of the 47 Sub-Saharan African countries, for example, power utilities in 32 remain fully state-owned and operated, while only eight countries have had concession contracts and another seven have management or lease contracts (Gökgür 2004). But state ownership is not prevalent in Africa alone: a recent survey of nearly 20 low-income countries in different regions--including 11 in South Asia, East Asia, and Europe and Central Asia--shows that while many have corporatized their state-owned firms and/or brought in the private sector through IPPs, only one country (Mali) had private participation in distribution (while four are in advanced stages of the process), and only four countries had private participation in generation (Briceño-Garmendia et al 2004).

37. Similarly telecoms utilities are still owned and operated by the state in 60 percent of the developing countries included in the PPI database. State-ownership exists in virtually all regions. While Sub-Saharan Africa has had some successes with PPI (Mauritania, Uganda for example), the incumbent operator is still fully state-owned in 27 countries, seventeen of which have not initiated reform while the remaining ten are either stalled or delayed for political and/or market reasons (World Bank 2005). The remaining 20 countries in the region have had varying levels of private participation: one (Somalia) is fully private, 13 have majority private participation, and six involve minority share sales. In Latin America, telecom operators in 40 percent of the countries are still fully state-owned and operated, while in the Middle East, South Asia, and Europe and Central Asia the numbers are at 65, 60 and 30 percent respectively (ITU 2003).

38. The numbers for transport and water are substantially higher, with nearly 60 and 70 percent of developing countries respectively not having any form of private participation in these sectors. In transport, concessions have
; Gupta, Hasan, and Kumar 2009; Ahsan and Pagés 2009). In general, these studies have shown that states with more protective regulations have significantly lower (formal) employment that states with more flexible rules (World Bank 2010, 2012). To some extent, this research has been complicated by disagreements about the characterization of Indian states in terms of their EPL and, also, by the interaction between EPL and other labor market regulations.<sup>34</sup> Nonetheless, as Freeman (2010: 4673) concludes, “Indian labor institutions ... do more harm than good than the institutions in other countries, though their impact is limited ... due to the huge size of the Indian informal sector.” Some studies discover that employment impacts are significantly affected by interactions between job security rules and other labor market institutions. Unions and collective bargaining appear to be particularly relevant, at least in the OECD. However, there is some disagreement about the mediating effects of unionization. On the one hand, based on aggregate data for 20 OECD countries from 1960-98, the IMF (2003) found that EPL increases unemployment but that effect decreases with increased union density. On the other hand, Belot and van Ours (2000) concluded that EPL increased structural unemployment in OECD countries only when union density was above average. Elmeskov, Martin, and Scarpetta (1998) find that EPL increases structural unemployment in the context of “intermediate” bargaining structures (i.e., in terms of centralization and coordination).

Effects of interactions between institutions are also evident in research on developing countries. For example, Ahsan and Pagés (2009) find that EPL reduces formal sector employment in India 34 See Bhattachargea (2006) for a critique of the Besley-Burgess index.
# **1. Introduction**

Micro and small enterprises (MSEs) are widely recognized as a major source of employment and income in developing countries. If they grow in size, they would contribute more to economic growth and poverty reduction. In reality, however, their productivity remains low and their sizes remain small (e.g., Mead and Liedholm, 1998, Tybout, 2000). While their low performances may be attributed to the unfavorable circumstances surrounding them, recent empirical studies have identified problems within firms, especially problems regarding management (e.g., Bloom et al., 2010; Bruhn, Karlan, and Schoar, 2010).

Management has been increasingly recognized as a major determinant of productivity in the recent economics literature (e.g., Syverson, 2010). Bloom and Van Reenen (2007, 2010) collected data on management practices from a number of mediumsized firms in developed and fast-growing countries to establish a close association between management and productivity. Using unique data, Ichinowski, Shaw, and Prennushi (1997), Lazear (2000), and Bertrand and Schoar (2003), among others, show that human resource management and top executives’ management style are important determinants of productivity in the U.S.

To establish causality more directly, Karlan and Valdivia (2011), Drexler, Fischer, and Schoar (2010), and Bruhn, Karlan, and Schoar (2010) have carried out randomized control trials in which management training or a consulting service is provided for MSEs in their study sites in Latin America. The most clear-cut result of these experiments is that rudimentary, as opposed to standard, management training improves business practices. This indicates that many MSE entrepreneurs in developing countries know little about management. A somewhat discouraging result of the experiments, however, 2
# **Appendix B. Validation of Final Database**

We validate the representativeness of the final database by calculating the ratio of the sum of employment and gross output in the database to their corresponding aggregates, in the same manner as Gopinath et al. (2017). Aggregates for employment and gross output are obtained from Eurostat’s Structural Business Statistics Database (SBS). Table B.5 compares the coverage of our final database to that of Gopinath et al. (2017) for the Spanish manufacturing sector. Our coverage is smaller than theirs because not all firms have the appropriate information in the historical ownership module to determine their linkage to the state or not, resulting in additional attrition in our sample. Tables B.6 and B.7 show the coverage of our sample by country, separately for manufacturing and non-manufacturing sectors.

Table B.5: Coverage of Final Database Relative to Gopinath et. al. (2017) - Spain Manufacturing

|Y|Empl|oyment|Gross|Output|
|---|---|---|---|---|
|ear|Final Database|Gopinath et. al.|Final Database|Gopinath et. al.|
|2010|41.9%|68.0%|37.3%|74.0%|
|2011|43.5%|69.0%|35.6%|75.0%|
|2012|39.8%|65.0%|31.5%|72.0%|

Note: This table only compares the years for which our database overlaps that of Gopinath et al. (2017), 2010 - 2012.

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information asymmetry between buyers and sellers. We conjecture that this would in turn impact the distribution of product unit values across different woredas in Ethiopia. However, we were not able to obtain unit values from the Ethiopian manufacturing panel data to conduct the price dispersion analysis for a substantial number of products. In future research, we plan to also explore whether household survey data analysis could provide evidence on the variation in price information access across years and districts.

One open question is whether the potential competition, driven by the increased internet access of buyers, which helps them find the best alternative prices at their fingertips, yields Bertrand competition (sellers underprice each other to capture higher market share) or vertical differentiation (sellers upgrade product quality to capture higher market share). For example, Kugler and Verhoogen (2012) provide evidence of the role of quality upgrading for manufacturing competition in Colombia. Our prior is that price competition would be more dominant for relatively homogenous goods (e.g., cement, concrete, flour, bricks, cloth, etc.), and that quality upgrading competition would be more salient for heterogenous goods. Indeed, we use CPI survey data to compare the standard deviations of retail prices for selected commodities based on surveys in 2008 and 2010 for the month of September. We find that the introduction of 3G is associated with the reduction in dispersion in more homogeneous products like cement, mobile phone apparatus, imported rice and some varieties of barley. By contrast, the price dispersions of items such as beer, coffee beans, cotton and wheat have increased. See Table A.1 in the appendix.

Although it is standard in the trade literature to emphasize the Rauch (1999) Index as a measure of horizontal differentiation, its focus on product homogeneity is better interpreted as reflecting differentiation more generally (i.e., both horizontal and vertical). Indeed, homogenous goods are those traded on a commodity exchange or those that have a quoted price in industry trade publications. Interestingly, the degree of
National Statistic Institute (INE) provides calculations different from those of the Ministry of Work, both of which date back to 1980. Based on census and survey data, estimates for selected years have also been provided by the United Nations Development Programme (UNDP) for Guatemala. The labor force is usually defined as the working and job-seeking population, but the different calculations do not always reveal what underlies the specific assumptions and age definitions used for calculations. To develop a consistent time series of the economically active population, the International Labor Organisation (ILO) has used information on age specific labor force participation rates and population statistics. Unfortunately, for the reasons clarified below, these estimates are unreliable.

_(1) Data discrepancies._ First, there is no agreement either on the level or on the growth rates of the labor force. Virtually all data is different from each other. For example, UNDP (2003a) reports a total labor force estimate of about 2.84 million for 1989, as compared to 2.54 million from INE or 2.95 million from ILO. Second, as typical for estimates in other countries, labor force data should show some cyclical fluctuations as labor responds to higher output growth. Official estimates for Guatemala, however, are remarkably free of _any_ fluctuations and follow a

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most countries. Other members of the committee include directors of departments of the MOF, such as budget, financial market, guarantee department and macroeconomic forecasting unit (see graph 7).

<!-- Start of picture text --> Graph 7 ‐ Composition of the SALM committee<br>12 11<br>10<br>8<br>6<br>6 5<br>4 3 3 3<br>2 1<br>0<br><!-- End of picture text --> Source: Responses to SALM survey

# 3. Challenges and priorities of debt managers for sovereign ALM

The responses to the survey confirm that there is no international consensus on the specific role of the debt management offices in sovereign ALM. One argument in favor of the involvement of government debt managers is that they usually have the stronger technical expertise and capacity in financial management among government units. They already use cost‐risk models to develop their issuance strategy and assess trade‐offs between financial returns and risks. On the other hand, as often mentioned by debt managers, there is a risk that expanding their role to sovereign ALM could lead to overstretching their (usually) limited staff and losing their focus on their primary mission which is managing the debt of the central government. The characteristics and history of each country appear to guide the decision to task the government debt manager with a larger ALM mandate or not.

This section examines the challenges debt managers reported and their priorities in developing SALM practices.

Among all, 82 percent of the respondents mentioned one or several challenges, which is consistent with the information collected in the rest of the survey as no country reports implementation of a fully integrated SALM framework.

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We use simple linear regression to further investigate the relationship between changes in school attendance, the F'ONCODES index, and FONCODES expenditures. Table 6.1 shows results for four regressions. The change in the district-level school attendance rate is the dependent variable in all of the specifications. The independent variables are the FONCODES index only (row 1), per capita FONCODES expenditures on education only (row 2), both the index and expenditures (row 3), and per capita expenditures instrumented with the index (row 4). An identifying assumption of the instrumental variables specification is that initial conditions, as measured by the modified FONCODES index, affect _changes_ in school attendance only through their effect on the amount of funding received by a district.

The results in Table 6.1 suggest that changes in school attendance are consistently related to the FONCODES index: when only the index is included in the regression, a one-standard deviation increase in the index is associated with a non-trivial increase in the attendance rate of 0.043 points. Changes in school attendance are also related to differences in per capita FONCODES expenditures: when only per capita expenditures are included in the regression, a one-standard deviation increase in expenditures is associated with an increase of 0.043 in the attendance rate.l1 When both the index and expenditures are included in the regression, the effect of school expenditure decreases substantially. This is perhaps not surprising, since the FONCODES index is the main determninant of FONCODES expenditures. Finally, when per capita expenditure is instrumented with the index, the effect of education expenditures is significant and very large indeed: a one-standard deviation in expenditure is associated with an increase in the school attendance rate of 0.141 points.

attendance rates in the later years. Moreover, if attrition is negatively correlated with income, so that children in poorer areas are more likely to drop out during the school year than their counterparts in rich: r areas, our estimates could _underestimate_ the effect of FONCODES expenditures on attendance. The LSMS surveys provide
loss is 6%, then provisions should be set at 6% and the appropriate capital requirement would be 12%.

However, this may be felt by some not to comply with Basel II’s IRB as the rating scale is centralized. Moreover some developing country jurisdictions may find it difficult to obtain the necessary authority from Congress or other relevant authority to implement such a complex system or give the supervisor the necessary autonomy to set capital in that way. An alternative approach, as outlined in the previous section, is for the country to adopt the Basel II’s SA or even SSA for Pillar 1. For most loans this will give a capital requirement of 8%, except in the unlikely event that the loan has a rating. Then, the above technique can be used to set forward looking provisions. In the example cited if the Basel SA gave a requirement of 8%, then the forward looking provisions would have to equal 10%. Although it is often difficult to reconcile logically, while many developing country supervisors do not have the autonomy to set capital, they tend to have more autonomy to set provisions. This would allow a country to be compliant with Basel II by the letter and yet the total bank reserving policy would still be that equal to a Basel II (CP3), IRB type rule of Expected plus Unexpected Loss.

This process may sound simple. However, the data and other requirements are large and there is also a tremendous amount of work that is required to ensure that the data is of reasonable quality and that each step in the process is carefully checked. In the case of Argentina, to develop a public credit registry with a reasonable standard in terms of data quality and of a useful size to cover most of the banks’ loan portfolios took a process of some 5-6 to years. And the data that was then available to truly test the Basel calibration was only for about a two year period before the crisis began - when the database suffered once more from serious