rafmacalaba/datause-encoder-data huggingface dataset:
|Loans by government program|0.45|0.50|0|0|1|2,103|
|---|---|---|---|---|---|---|
|Loans by HEI|0.29|0.46|0|0|1|2,103|
|Other loans|0.34|0.47|0|0|1|2,103|
|Government scholarships|0.66|0.48|0|1|1|2,103|
|HEI scholarships|0.77|0.42|0|1|1|2,103|
|_Link with productive sector_|||||||
|Engagement with firms|||||||
|Somebody in charge of industry relations|0.82|0.38|0|1|1|2,103|
|Industry helps with student evaluation or curriculum design|0.70|0.46|0|1|1|2,103|
|Industry has agreements with HEI to hire program grads|0.39|0.49<br>|0|0|1|2,103|
|Industry has agreements to train faculty|0.35|0.48|0|0|1|2,103|
|Industry lends or provides equipment to program for student training|0.50|0.50|0|1|1|2,103|
|Collect data on employment or employers' satisfaction|0.56|0.50|0|1|1|2,103|
|Communicate with local firms about their needs|0.52|0.50|0|1|1|2,103|
|Program has staff assigned to collect grads' employment data|0.68|0.47|0|1|1|2,103|
|Job search assistance|||||||
|HEI coordinates job interviews with firms|0.60|0.49|0|1|1|2,103|
|HEI trains students for job interviews|0.69|0.46|0|1|1|2,103|
|HEI provides job market information|0.81|0.40|0|1|1|2,103|
|HEI has an employment center|0.60|0.49|0|1|1|2,103|
|_Faculty_|||||||
|Number of faculty|20.00|18.70|1|15|200|2,076|
|Percent of faculty|||||||
|<br>with BA degree|82.20|29.40|0|100|100|2,043|
|with graduate degree|48.60|32.60|0|43|100|1,998|
|with SCP degree|20.00|30.00|0|6|100|1,916|
|working full-time|38.40|30.30|0|31|100|1,987|
|<br>working in the industry|42.10|30.90|0|37|100|2,008|
|with 5yrs+ industry experience|55.70|33.10|0|56|100|1,988|
|<40 years old|40.00|29.20|0|34|100|1,961|
|that are women|34.00|23.50|0|30|100|2,021|
|Faculty are evaluated more than once per year|0.85|0.360|0|1|1|2,103|
|Important for faculty evaluation|||||||
|Classroom observation+|0.60|0.49|0|1|1|2,103|
|Class planning+|0.61|0.49|0|1|1|2,103|
|Student evaluation+|0.70|0.46|0|1|1|2,103|
|Students and peers' informal comments+|0.32|0.47|0|0|1|2,103|
|<br>Peer evaluation+|0.31|0.46|0|0|1|2,103|
|Almost all or all faculty participated in professional training last year|0.55|0.50|0|1|1|2,103|
|<br>Important for faculty hiring|||||||
|<br>Practical experience|0.88|0.32|0|1|1|2,103|
|Research skills|0.62|0.48|0|1|1|2,103|
|_Other Practices_|||||||
|Update or review admin data more than once per year|0.66|0.47|0|1|1|2,103|
|Admission requirements|||||||
|<br>General or specific knowledge test|0.59|0.49|0|1|1|2,103|
|Interview|0.50|0.50|0|1|1|2,103|

68
Policy Research Working Paper 8479

# **Abstract**

This paper attempts to study the impact of Brazilian Development Bank credit on resource misallocation in Brazil, using manufacturing firm-level data from 2003–14. The paper first estimates measures of resource misallocation based on Hsieh and Klenow (2009), documenting high variation in firms’ capital and output distortions. It then estimates the effect of financial frictions and access to Brazilian Development Bank loans on distortions and their dispersions. The analysis finds some preliminary evidence that the use of Brazilian Development Bank credit is not associated with a - more efficient allocation of resources. The lower cost of Bra zilian Development Bank loans reduces the marginal cost of capital, as it induces firms to reallocate inputs from labor to capital, and this effect is amplified for more financially dependent firms. The findings, together with extant evidence on the economic additionality of the Brazilian Development Bank, suggest that there is room for improving the allocative efficiency of the earmarked credit system in Brazil.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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://www.worldbank.org/research. The authors may be contacted at pcalice@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
nology first appeared in the earnings calls transcripts following the definition by Bloom et al. (2021).

Consistent with expectations, hiring related to these technologies has become more dispersed across countries and regions over the past decade. The negative coefficients (hence the decline in the values of Gini and CV) across both panels, displayed in Figure 1, demonstrate that technology hiring has become more dispersed over time both across countries and country-region pairs. The stronger coefficients for the country-region pair regressions suggests greater diffusion across country-regions over time than across countries alone. In terms of magnitude, for the country-level regression, an increase in 1 year since the emergence of the technology is associated with a 4.9% decline in the coefficient of variation, and a 3.4% decline in the Gini.

Table 1. Dispersion of technology hiring across countries and regions

|Coeffcient of Variati|on<br>Gini|
|---|---|
|(1)|(2)|
|Panel(a): Acros|s countries|
|Years since emergence<br>-0.0160*|-0.0279*|
|(0.0106)|(0.0048)|
|Adjusted R-squared<br>0.8408|0.7933|
|Observations<br>232|232|
|Tech FE<br>Yes|Yes|
|Year FE<br>Yes|Yes|
|Panel(b): Across c|ountry-regions|
|Years since emergence<br>-0.1747***|-0.0872***|
|(0.0506)|(0.0190)|
|Adjusted R-squared<br>0.8168|0.8365|
|Observations<br>232|232|
|Tech FE<br>Yes|Yes|
|Year FE<br>Yes|Yes|

Notes: This table displays regression results of the two concentration measures in columns 1-2 on the years since the technology’s first emergence across 17 countries and 156 regions, controlling for technology and year fixed effects. Standard errors are clustered by technology. The coefficient of variation is defined as the standard deviation of normalized technology share of job adverts divided by the mean. The Gini is defined as the inequality of the distribution of tech share of job adverts, computed using Stata’s ineqdeco command. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# **3.2 Technology hiring is highly concentrated in multinational firms and their supply chains**

We proceed by using the FactSet-Lightcast matched dataset to explore the role of multinationals and their supply chains in technology hiring. While our matched dataset of 107,981 firms accounts for only just above 20 percent of all online job postings in these 17 countries, they account for nearly 40 percent of all technology postings. In addition, Fortune 500 firms alone account for around 7 percent of all technology job postings, while Fortune 14
**The result of the growth accounting decomposition shows that output growth within the manufacturing sector has been driven primarily by TFP gains** : TFP accounted for an average 63 percent of the annual growth in output (value-added) from 2004 to 2008 as shown in Figure 3. The contribution of labor and capital accumulation to Russia’s manufacturing output remains low, in line with evidence from Alam et al (2008).

<!-- Start of picture text --> Figure 3: Decomposition of Average Value Figure 4: Aggregate and Average Productivity in<br>Added Growth in Russia's Manufacturing Sector: Russia's Manufacturing Sector<br>2004 to 2008.<br>Labour Capital TFP Aggregate TFP Average TFP Efficiency<br>40% TFP Efficiency<br>35% 25 8<br>7<br>30%<br>20<br>6<br>25%<br>5<br>20% 15<br>4<br>15%<br>10<br>3<br>10%<br>2<br>5% 5<br>1<br>0%<br>0 0<br>2004 2005 2006 2007 2008<br>2003 2004 2005 2006 2007 2008<br><!-- End of picture text -->

# **(ii) Productivity Decomposition**

**The most productive manufacturing firms are found to have a larger market share.** The result of the Olley-Pakes decomposition shows, in the first instance, that efficiency is positive in the aggregate (see Figure 4). Moreover, the efficiency increased during the period of observation, slowly at first (between 2003 and 2005), then sharply from 2005, peaking in 2007 – meaning that there was a persistent increase in the share of output from the most productive firms in the entire manufacturing sector until the economic crisis that started in 2008. However, at the industry level (2-digit), the efficiency dynamics varied slightly (see Table 2). While efficiency estimates in all industries are positive through the years, indicating a higher productivity of market-dominant firms, the scale of efficiency in a number of industries –

9
Table 3: The results for each series using the ETS smoothed data and the elastic net estimator. Relative MSE (1) is the MSE for the expanding window ARMA model results versus the benchmark model given in columns 6. Relative MSE (2) is the MSE for the elastic net model versus the benchmark in column 6. A relative MSE less than 1 indicates that the proposed model beat the benchmark.

|Country|Series|Relative MSE (1)<br>Relative|MSE (2)|N|Benchmark|
|---|---|---|---|---|---|
|cr|food01|1.04644|1.09103|94|ar|
|cr|food02|1.00313|1.09517|94|median|
|cr|food03|1.08606|0.977452|94|mean|
|cr|food04|1.0755|1.06406|94|median|
|cr|food05|1.32068|1.32068|38|median|
|cr|food06|1.01586|1.04028|94|ar|
|cr|food07|1.08315|1.1684|94|mean|
|cr|food08|1.08143|1.17251|94|mean|
|cr|food09|0.886634|1.20473|57|median|
|cr|inf01|0.980986|1.09914|94|ar|
|cr|inf02|1|1.00755|63|mean|
|cr|inf03|0.993006|2.13919|63|median|
|cr|inf04|1.08064|1.08917|63|mean|
|cr|inf05|0.981035|1.11166|95|ets|
|cr|inf06|0.840378|0.808711|63|ar|
|cr|inf07|1.03188|1.00261|63|ar|
|cr|inf08|1.03867|0.860798|63|ar|
|cr|inf09|1.0767|1.01002|63|median|
|cr|inf10|1.11333|1.23137|63|ets|
|cr|inf11|1.10306|1.15871|63|median|
|cr|inf12|1.17467|1.18608|63|ets|
|cr|inf13|1.14892|1.35561|63|ets|
|cr|inf14|1.0491|1.04081|63|ar|
|hn|food01|1.11477|1.23652|94|median|
|hn|food02|1.06082|1.02454|94|mean|

Continued on next page 26
**Table** S ANALYSIS OF INSTABIlITY AND lO(NG TFRM CHANIGFS IN PRIMARY AND PROCESSED<sup>CO4IMODiTYPRICFS,</sup> 1965 1987 (Cont d)

||**A**<br>1l|i<br>**h**|**_--_**<br>**_easrre,_**<br>_of_<br>Acnira-|**PriLes**<br>Stabilxy<br>Cof JS|ierir<br>**_of_**||Secul-<br>r<br>pic-e|<br>ch..&.'|**Trend**|
|---|---|---|---|---|---|---|---|---|---|
|Proc-ssing<br>Chaln|**verage**<br>a--a<br>1965<br>to<br>_1_<br>Primry<br>**stage**|pr-ce<br>**canges**<br>_98l_<br>_(%)_<br>**Fia1**<br>**st-R.e**|Stability<br>(il<br>o<br>**less**<br>(|<br>**detecmirr.**<br>Puirsry<br>**sta-g**|<br> <br>**JRore**<br>**rion**<br>**(i**<br>**2**<br>**)**<br>**Final**<br>**s-8g**|5t-biliy<br>-rnre<br>i)<br>**or**<br>**1e-.**<br>(|<br> <br> <br>1965 68**Io**<br>1<br>PrIma,<br>**-sragr**|<br> <br>985-8r<br>(1)<br>**Finl1**<br>**st.ge**<br>.|**gc-sth-re**<br>(01<br>or<br>1-s.|
|**Trmgsten**|24<br>|iS<br>||-<br>04<br>|0 44<br>||_32?_<br>|_64_<br>||
|Coppe-|33|13<br>|*|009<br>|0 64<br>|*<br>|92<br>|96<br>||
|**Pi-spha-e**|Is|1. -||II 52<br>|Ii 91<br>|r|!98<br>|619<br>||
|S lph,r|In|IS<br>|r|I)18<br>|082<br>|**r**|**42'**<br>|223<br>||
|**Asb-,-rrs**|9<br>|11<br>||1 10<br>|0 lb<br>|.|156<br>|228<br>||
|(<br>**..ss**|14|15||( 61|O3 1||133|09||
|IV<br>PETROLEUM|21|_20_||ao<br>80|50||_828_|168||

**The** prte -nstability **and pci,e -rge m relate -easure** - t1-o te fi rd **I-a, tages** ci **Ite pr Irg stage** i **r p-1tcessii *t-ges otl ned** i-, Appeid I Table I **Stliar data** cor ltrretrsedl **are stag,s (taiete** they exist,/ **are ava** lIable from 1ire sitho tori 1es

6 **The averages** shodt- r i a- beeco- tp rtr-t isin I ievelnph rg r- rtr ry **t rade** I f Itght s i tir- -h1, -vt'1 or- nodities i }e grrp
testinal distress that could impede nutrient absorption and potentially raise TEE. Column 9 of Table 4 reports the level of infant mortality (per 1,000 live births), which declined from 106 to 42 over this period. This can be interpreted as a mid-level estimate of the total caloric burden of disease using the slope coefficient of 1 and linear specification reported in (Duh and Spears 2017), and low (column 8) and high (column 10) end estimates are also reported based on coefficients reported in their paper. Quantitatively, the decline in infant mortality mirrors the fall in caloric intake between 1983 and 2005, although the timing of the decline does not fit as well with the between-round variation (e.g. the huge drop in caloric intake between 2000 and 2005). If these estimates capture the true caloric burden of disease, they also imply that caloric intake measures (official, or the independent estimates presented here) using the 30-day recall period must almost surely be too low, particularly in recent years, but the estimated caloric burden of disease is of the correct magnitude to reconcile the TEE estimates with the official 7-day recall period estimates.

A final piece of evidence on net nutrition comes from anthropometric data. (Deaton and Dr`eze 2009) recognize that there has been some increase in average height in India during the 1983-2005 period, a trend that has continued since although the level of height and the rate of increase remain low compared to other countries with similar GDP per capita and economic growth trajectories. (Smith 2015) cites additional evidence of improving nutritional outcomes for children. A rise in average body mass for individuals of a given age and sex is one of the factors contributing to the increase in population REE estimated in this paper.<sup>9</sup> Although changes in height undoubtedly reflect nutrition, the quantitative relationship between net caloric surplus (intake minus TEE and any caloric cost of disease) and height is not well known. Changes in population height may be more reflective of micro-nutrients, macro-nutrients composition, and the timing of life-cycle nutrition from conception to adolescence rather than changes in the average caloric surplus of the population. However, the NFHS allows for measurement of weight gain over time, which does have a well known quantitative relationship with caloric surplus. While a single cross-section

> 9In Appendix Table S.7, even larger increases in REE for adults and children and hence smaller decreases (or even small increases) in TEE over the 1983-2012 period are found when including a linear time trend in the REE prediction equation. This may be reasonable in later period but is likely to be a heroic assumption when extrapolating a linear trend observed between 2005 to 2015 back 22 years.

16
- 24 -

- Haggarty, Luke, Penelope Brook, Ana Maria Zuluaga. 2002. Water sector service contrasts in Mexico City, Mexico, in _Thirsting for Efficiency: The Economics and Politics of Urban Water System Reform_ , eds. by Mary Shirley, pp. 139-187, Elsevier Science Ltd.

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Ménard, Claude, George Clarke. 2002. A transitory regime: water supply in Conakry, Guinea, in _Thirsting for Efficiency: The Economics and Politics of Urban Water System Reform_ , eds. by Mary Shirley, pp. 273-315, Elsevier Science Ltd.

Nauges, Céline, Caroline van den Berg. 2007. How “natural” are natural monopolies in the water supply and sewerage sector? Case studies from developing and transition economies. Policy Research Working Paper No. 4137, The World Bank.

Nerlove, Marc. 1963. Returns to scale in electricity supply, in _Measurement in Economics: Studies in Mathematical Economics and Econometrics in Memory of Yehuda Grunfeld_ , eds. by Carl Christ, Stanford University Press.

NOA. 2007. _Improving the PFI Tendering Process_ , The National Audit Office.

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# Appendix F – Reweighting and Attrition

Attrition is a crucial aspect to consider in any panel survey. Attrition introduces a bias in survey estimates if respondents who choose to withdraw from the survey are systematically different from the other ones that remain in the survey in the subsequent rounds (Fitzgerald et al. 1998). This issue is particularly relevant in high frequency phone panel surveys because of the large number of times that respondents are asked to undertake an interview.

**Figure 16: Initial Refusal (First Round) and Panel Attrition**

<!-- Start of picture text --> 40%<br>37%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>6%<br>5%<br>5% 3% 2% 3% 3% 2% 3% 2%<br>1% 1%<br>0%<br>1 2 3 4 5 6 7 8 9 10 11 12<br>Total attrition rate (%) Attrition rate excluding no phones (%)<br><!-- End of picture text --> _Note: the total participation rate of randomly selected households was approximately 75 percent in the first round. However, initially selected replacements also occasionally refused, leading to a total refusal rate of 37% of attempted interviews in the first round._ In the L2CU survey, attrition rates (or nonresponse rates) tend to be low and stable across rounds, ranging from 1 and 6 percent (Figure 16). The households that stop participating are replaced after each round from the same neighborhood as the household that left the sample. As attrition might be selective in terms of observable households’ characteristics, a set of probit models is estimate for the probability of attrition in comparison to the baseline characteristics of the sample frame. This indicates whether some variables predict attrition and to use the predicted likelihood of a given household dropping out to reweight the households that remain in the survey. The results show that for L2CU there are some differences in participation rates by location (Tashkent City has higher than average attrition, Ferghana and Qaraqalpaqstan lower than average). Attrition is also slightly (but significantly) more likely for households with higher total consumption per capita (as well as food consumption per capita).

60
-26-

# V. INSTITUTIONALIZING ENTERPRISE REFORMS

# BASED ON A PRINCIPAL-AGENT RELATIONSHIP

This paper attempts to reform state owned enterprises by leasing them to entrepreneurs. It specifies lease payments to the state based on fee schedules from the principal-agent literature. The aim of the principal (the state) in this literature is to get his agents to act in his interest. He does so by rewarding the lessees according to observed actions and/or outcomes. However, since the reform process is based on the fact that the state is unable to manage state owned enterprises efficiently, the principal (state) must rely only on observed performance as measured by the pure profit level of the firm. To implement the sharing rules between the lessee and the lessor, auction theory is used. More specifically, a first-price sealed bid auction with reservation prices is recommended.

The reason for using principal-agent theory in the setting of enterprise reform is because it has several desirable qualities. Among these is the ability to delegate responsibility in an efficient way. As mentioned in the introduction to this paper, some problems seem to be more burdensome in the context of enterprise reform than others. In the following, we will discuss these explicitly within a principal-agent framework.

The problem of ownership of assets. Experience makes it clear that, because public enterprises do not own their own assets, they have no incentive to accumulate and manage those assets as they are not rewarded
# **1 Introduction**

The international fragmentation of production processes has challenged the capability of standard trade statistics to truly represent supply and demand linkages between economies. In general, bilateral exports differ from the portion of a country’s GDP related to the production of goods and services shipped to a certain foreign market. On the one hand, exports also embed imported intermediate inputs and on the other, the directly importing country often differs from the product’s ultimate destination. Whenever production is organized in sequential processing stages in different countries, trade statistics repeatedly double count the same value-added. The diffusion of global value chains (GVC) has therefore deepened the divergence between gross flows, as recorded by traditional trade statistics, and the data on production and final demand as accounted for in statistics based on value-added (above all GDP).

In this new context, we must reconsider how to track production-demand linkages, assess the spillovers of macroeconomic shocks, and evaluate the impact of trade policies. We also need to assess countries’ participation in the international sharing of production. New, specific empirical questions have also emerged such as: What part of a country’s exports can be ascribed to the value-added produced at home or abroad? How can we allocate this value-added across the different bilateral and sectoral trade flows? Which markets absorb this production as final demand? How important is GVC-oriented trade for a country’s exports and imports? To what extent is a country’s production affected by macroeconomic shocks emanating from other countries? How can it be affected by trade policies implemented in a given country on a given sector, and/or vis-à-vis certain partners? This is just a subset of all the key questions raised by the prevalence of GVCs.

This paper develops a new toolkit of methodologies for value-added accounting of trade flows at the country-, bilateral- and sectoral-levels by using InterCountry Input-Output (ICIO) tables. The measures we propose can be used to properly address important empirical questions, such as those mentioned above, and to evaluate countries’ and sectors’ participation in GVCs. We argue that different empirical issues require distinct methodological approaches, and provide a mapping of the questions under investigation and the accounting methods needed to address them.

ICIO tables, like the WIOD, the OECD-WTO TiVA or the EORA, combine national input-output data with detailed trade statistics to trace cross-country and 2