rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: Alternative non-MNC Group in Manufacturing, 2013~2019

|||Depende|nt Variable:|Logof Em|ployment||
|---|---|---|---|---|---|---|
|||Full Sample||M|atched Sam|ple|
||(1)|(2)|(3)|(4)|(5)|(6)|
|MNC_i ×_Post_t_|-0.0242**|-0.0125*|-0.0118*|-0.0373**|-0.0187**|-0.0180**|
||(0.0103)|(0.0067)|(0.0066)|(0.0136)|(0.0086)|(0.0084)|
|Log sales||0.3294***|0.3292***||0.3571***|0.3571***|
|||(0.0108)|(0.0107)||(0.0124)|(0.0125)|
|Log purchase||-0.0018|-0.0022||-0.0029|-0.0032|
|||(0.0056)|(0.0056)||(0.0053)|(0.0054)|
|Exporter|||0.0082|||0.0026|
||||(0.0059)|||(0.0078)|
|Importer|||0.0080|||0.0081|
||||(0.0049)|||(0.0055)|
|Observations|38,730|37,358|37,358|26,448|25,585|25,585|
|R-squared|0.9553|0.9653|0.9653|0.9601|0.9699|0.9699|
|Fixed Effects:|||||||
|Firm<br>|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry-Region-Year|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Columns (1)-(3) use all of the alternative non-MNC firms as the comparison group, while columns (4)-(6) use a subset with the PS matching procedure described in 4.3.1 and run frequency-weighted regressions. MNC _i_ is an indicator variable that equals 1 if a firm _i_ has foreign affiliates in the years 2013~2016 and 0 otherwise. Post _t_ is an indicator variable that equals 1 if the time _t_ is after 2016 (i.e., 2017, 2018, and 2019) and 0 otherwise. Sales and purchases are in real terms. Exporter (Importer) dummy indicate whether the firm exports (imports) in each year. Standard errors are clustered at the industry level. The number of observations differs across columns because some observations of firm-level control variables are missing or zero. *** p<0.01, ** p<0.05, * p<0.1.

of the sample from a comparison of the number of observations in Table 5 and Table 6. Adding time-varying firm-level controls in columns (2) and (3) yields qualitatively identical results. Note that the alternative definition of the non-MNC group may produce a different PS matched sample. Columns (4) through (6) redo the PS matching with the alternative non-MNC group and show the frequency-weighted regression results. Again, all estimates are qualitatively the same.

# **5 MNCs’ Task Reallocation Abroad**

So far, we have confirmed robust findings that, in response to the abrupt labor market policy changes, Korean manufacturing firms with foreign affiliates reduced domestic employment more than those without foreign affiliates. We now investigate the specific mechanisms responsible for our findings. In particular, we hypothesize that the negative employment effects are associated with task substitution between domestic workers and 22
4 we can then assume that P can be determined independently of the other variables in the problem. Therefore. the fraction 1-P of the population does not receive the drug and suffers the loss L,. The overall expected los3 in the case with required prescri- eons is: It is no longer clear that this loss is smaller than that incurred in (1). b is presumablv higher than the expected loss due to self prescription. If this is not true, the problem is tri-ial in that self prescription is then worse than no access at all and should neNer be allowed. Therefore, the problem is simply 'o compare the values of the expected losses in equations I and 3 and to tollow the regime with the lower cost, given the functions F(e), G(e) and L(e ). the pattern of errors made by unaided consumers, and professionals and the related function indicating the cost of these errors, respectively.

In a practical application, the relevant information could come from a **variety of** methods which have been developed to look at the separate components of this problem. As for the specification of the loss function, much might he transferred from the literature on utilitv levels associated with different health states (see Torrance (1986) or Tor,2nce et. al. (1982) and references therein).

While not addressed to the effects of dosages of drugs this literature may be useful in tving one's physical condition to mistakes in drug prescription. Making the link between degrees of debility from misuse and the categories used in assessing health should not be neariv as difficult as determining the *arious utility levels of physical conditions in the first place. Modifications of these scales to incorporate utility from money (incorporating the differen. valuations of patient and prescriber) may be arbitrary but can be used in a sensitivity analysis when numerical calculations need to be made.

As for the distribution of errors in drug use, there have been a number of studies made to assess the prescribing behavior of doctors in developed countries (see ). With some refinement. it should be possibl to assign probabilitv densities for varying types of errors. If the categories for the
# **Primary Commodity Prices: Co-movements, Common Factors and Fundamentals**

Joseph P. Byrne,<sup>§,*</sup> Giorgio Fazio,<sup>§,†</sup> and Norbert Fiess<sup>§,‡</sup> § _Department of Economics, University of Glasgow, Glasgow, UK_ † _DSEAF, Facoltà di Economia, University of Palermo, Palermo, Italy_ ‡ _The World Bank, Washington, USA_

**Keywords** : Commodity Prices, Panel Estimation, Factor Models.

**JEL Classification Numbers** : E30, F00.

> * Corresponding Author. The authors wish to thank Serena Ng for her MATLAB codes.  Byrne: Department of Economics, Adam Smith Building, University of Glasgow, G12 8RT, Glasgow, UK, Email: <j.byrne@lbss.gla.ac.uk>. Fazio: DSEAF, Facoltà di Economia, University of Palermo, Viale delle Scienze, 90128, Palermo, Italy, Email: <giorgio.fazio@unipa.it>. Fiess: The World Bank, Buenos Aires, Argentina Email: <nfiess@worldbank.org>.
# **3 Data**

Our sample includes data on 699 young children in 552 households from 73 rural communities in western Kenya. Data were collected during the baseline survey that preceded a pre-literacy intervention (Jakiela, Ozier, Fernald and Knauer 2020). Households living within 750 meters of the local government primary school were included in the sample if they had children between three and six years old. Here, we restrict attention to those households which also had exactly one older child between the ages of seven and 14. Our treatment of interest is an indicator for having an older child who is female. In this restricted sample of households, having an older child who is female is uncorrelated with a range of covariates, as we discuss further below.

Our data set includes information on household and parental characteristics (e.g. household assets and mother’s education) as well as multiple measures of child development and familial investments in young children. We consider two main developmental outcomes that can be measured in preschool-aged children: vocabulary and fine motor skills. Both are measured through direct child assessment.

Our vocabulary index combines includes three sub-scales: expressive vocabulary and receptive vocabulary in English (one of Kenya’s national language and the primary language of instruction at upper levels of primary school) and Luo (a Nilotic language that is the mother tongue of all of the children in our sample). Receptive vocabulary is the ability to understand words, while expressive vocabulary is the ability to produce words — for example, to identify familiar objects. Children begin developing receptive vocabulary before they begin to express themselves through speech (Fernald, Prado, Kariger and Raikes 2017). To measure receptive vocabulary in English and Luo, we adapted items from the British Picture Vocabulary Scale, a version of the Peabody Picture Vocabulary Test suitable for speakers of British or Commonwealth English (Dunn and Dunn 1997; Dunn, Dunn and Styles 2009; Knauer et al. 2019b). We assessed expressive vocabulary through a 37-item assessment developed and validated as part of an ongoing evaluation of an early literacy 18
Table 4 shows the IV estimation result, where sales growth from new products is instrumented using indicators for whether the product is completely new to the firm and whether the firm invests in R&D. The HansenThe new IV estimates Sargen Overid test p-value confirms the validity of the instruments used. suggest that the previous OLS estimates on the innovation elasticities were biased downwards. The new estimates are also statistically significant but much larger, especially in Africa and to a lesser extent in low and middle income countries. This suggests that the employment growth associated to product innovations is larger in these regions, likely as a result of having lower efficiency gains on innovation in new products from being further away from the technological frontier, which makes the elasticity of employment changes to innovative sales changes larger in these countries. The elasticity is farther from and below unity only in the ECA region and high-income countries, suggesting that innovation in countries closer to the technological frontier increases employment less than proportionally given the larger impact of innovation on efficiency.

ployment growth is never statistically significant. In addition, regarding the trend productivity parameter of all products, in the instrumented specifications the parameters are not statisticaly significant, with the exception of the ECA region where it is positive and marginally significantly negative for South Asia, MENA and low income countries. This result for the last three regions is slightly puzzling since we would expect to find a positive or not statitiscally significant trend in labor productivity.

Since these results could be explained by the different sector composition in these regions, we examine how these elasticities vary across aggregate sectors, technological intensity levels and skill intensity. Table 5 examines the employment effects of product innovation according to whether the firm operates in the manufacturing or services sector. We find that β is on average higher in manufacturing than in services, but in both cases we cannot reject that the elasticity is equal to one, suggesting that product innovations are not labor displacing in both sectors. When we disaggregate manufacturing into its degree of technological intensity, measured according to a sector’s intensity of R&D expenditures, we find that contrary to intuition the effect increases when comparing low-tech firms to medium-high tech firms, from 0.917 to 17
on these indicators, we generate five mutually exclusive and exhaustive binary outcome variables: (1) DI & no employment; (2) DI & employment; (3) employment & no DI; (4) public work & no DI; (5) no DI & no employment & no public work.

In addition, we look at four quarterly indicators of health care use: GP visits, outpatient specialist care visits, hospital days, and total spending (social security plus out-of-pocket spending) on prescription drugs. Indicators of health care use are included in our data from 2009.

We extend the analysis with job quality indicators derived from the administrative panel database. We generate a binary indicator of earnings above the minimum wage, after adjusting the monthly wage to hours worked. We define a binary indicator of full-time job, which equals one if the weekly hours of work are at least 40. We generate a binary indicator of working in a skilled job, which includes all occupations except for elementary occupations, with elementary occupations corresponding to International Standard Classification of Occupations (ISCO) code 9. Finally, using the entire sample in the administrative database, we calculate the year-specific median of the total factor productivity (TFP) of firms, weighted by firm size.<sup>8</sup> Based on this indicator, we define a binary indicator of above-median employer TFP.

# **4 Empirical Framework**

## **4.1 Control and Treatment Groups**

In our empirical analysis, we estimate the impact of the 2012 reform on DI recipients subject to the compulsory health reassessment—partially disabled individuals with health impairment below 80%, who were under age 57 at the end of 2011. A limitation of our data is that we do not have information on the reassessment procedure itself; we observe exits from the DI system, but not the reason for leaving the system. Consequently, it is not possible to isolate those who lost their benefit as a results of the revision from those who would have exited DI even in the absence of the reform. For this reason, to identify the impact of the reform, we focus on a narrow age group around the age cut-off of the policy, assuming that

> stated functions: to reintegrate participants into the primary labor market and to exclude people not willing to participate in public works from receiving benefits and social assistance (Molnár, Bazsalya, Bódis and Kálmán, 2019). However, the vast majority of Hungarian public workers – especially the unskilled and those in depressed areas – worked in separated public works units (Köllő, 2015) and received very low pay. Between 2011 and 2015, both the net and gross basic public work wage ranged between 70% and 80% of the statutory minimum wage.

> 8We calculate the value added-based TFP. When doing so, we apply the estimation procedure of Wooldridge (2009) and use the _prodest_ Stata package of Rovigatti and Mollisi (2020).

8
the adults is also correlated with poverty. Except in Poland, households consisting only of elderly have the highest poverty incidence and poverty gap. We return to this later when discussing the age-dimension of poverty.

**Table 3: Poverty and Aggregate Family Composition**

|_Family Composition_|_Bulgaria_|_Hungary_|_Poland_|_Estonia_|_Kyrgyz_<br>_Republic_|_Russia_|
|---|---|---|---|---|---|---|
|||**H**|**eadcount**|**(P0, in pe**|**rcent)**||
|No Children, No Elderly|18.3|13.0|13.3|23.8|37.6|31.9|
|Child(ren), No Elderly|25.0|23.9|28.1|32.8|43.1|40.5|
|No Children, Elder(ly),|39.0|26.1|18.1|39.4|43.8|43.8|
|Child(ren), Elder(ly)|28.0|22.2|32.7|35.2|42.9|_51.5_|
|All|26.1|20.6|23.0|30.5|42.5|39.4|
||**Poverty G**|**ap (in pe**|**rcent)**_V1_||||
|No Children, No Elderly|18.8|13.6|13.6|22.0|29.4|32.2|
|Child(ren), No Elderly|19.4|14.1|13.2|18.3|24.2|27.1|
|No Children, Elder(ly)|21.1|15.1|15.5|22.8|34.0|**_35.5_**|
|Child(ren), Elder(ly)|19.5|10.5|11.9|19.2|23.9|27.0|
|All|19.8|14.1|13.3|20.2|25.0|29.8|

_Source:_ Household Expenditure and Income Date for Transition Economies Data Set (HEIDE). _Notes:_ I/ The poverty gap is the poor's average shortfall in expenditures from the poverty line, expressed as a percentage of the poverty line (this measure is also known as the expenditure gap ratio).

29
POLICY RESEARCH WORKING PAPER 1403

# Summary findings

Itoh and Urata examine how Japan's public and private sectors support small and medium-size enrerprises (SMEs). They focus on technical, financial, and marketing assistance.

Their findings are based on a survey of 107 firms: 40 that produce silverware, 33 engaged in synthetic textile weaving, and 34 that manufacture auto parts. Each sector represents a distinctive form of industrial organization, but they also share several characteristics: Each industry is concentrated in a particular region, each involves-close subcontracting relationships, and each has overcome difficulties and achieved a cerrain measurc of industrial success.

Technical support for these SMEs came largely through private channels, including parent firms, equipment suppliers, and other firms in the same line of business. Public institutions played only a subordinate role. Marketing support also came largely from parent firms, trading companies, and other private sources. Producers of intermediate goods in particular - such as - auto parts and synthetic textiles relied heavily on subcontractors.

Most loans for Japan's SMEs were provided under competitive market conditions bat three sources of directed credit - loans from specialized parastatals, loans channeled through local governments, and loan - guazantees accountcd for about 20 percent of all SME borrowing and 35 to 60 percent of investment borrowing. Default rates averaged less than one-half of one percent of outstanding loans, and real interest rates were positive. The majority of firms surveyed used and valued directed credits. The smallest firms in particular valued them.

Public institutions complemented the private marketplace in all three areas. Public technical and marketing support helped create and maintain private networks. In finance, Japan successfully embedded directed credit in a well-functioning, predominantly private, competitive, and prudential financial system. Through partnerships, public support continues to play both a direct and an indirect role in supporLing the development of SMEs in Japan.

- This paper a product of the Finance and Private Sector Development Division, Policy Research Departmentis part of a larger effort in the department to examine the impact of proactive intervention on SME performance. Copies of the paper are available free from the World Bank, 1818 H StreetNW, Washington, DC 20433. Please contact Daniele Evans, room N9-055, extension 38526 (82 pages). December 1994.

_Thc Policy Research Workig Paper Series dissemsnates the findings of_ **_work_** _in prog;ress to ancowage te exchtage of ideas about deaelopment isses. An objective of th series is to get rhe fidings out qikly czein if thb prentatio are lss than lidly polished Tbe papers arry the nmes of the atahrs and shod be sued and cited accordingly. The fidngsnt Werpretations. and conlusions ar the awubors'_ **_ownmand_** _should_ **_nat_** _be_ **_attributed_** _to the World Bank_ **_its_** _Eeutive_ **_Boad_** _ofDirectors, or any of its member contries_

## Produced by the Policy Research Dissemination Center
between 1929 and 1935.<sup>21</sup> We use the following additional data in our robustness analysis.

_GDP Per Capita_ **.** The country-specific GDP per capita data are obtained from PWT (rgdpch) for the years 1985 and 2000.

_Schooling_ **.** The country-specific measure of human capital is proxied by the total years of schooling obtained from Barro and Lee (2013). We use the log initial version of the data in the first-stage growth regression.

_Institutions_ **.** The country-specific institutional quality is measured by the EFW legal and property rights score (variable area 2). We use the log initial version of the data in the first-stage growth regression.

_Capital City Dummy_ **.** The capital city dummy takes a value of 1 when the largest city in a country is also the capital city of the country, as in the studies of Ades and Glaeser (1995) and Storeygard (2012).

_Port Dummy_ **.** The port dummy takes a value of 1 when the largest city in a country has a seaport. This dummy variable has been constructed by the authors by checking the existence of a port in the largest city of each country in the sample. If this is the case, the Rauch (1991) argument suggests that trade liberalization would shift resources and population to the largest city because it benefits from increased access to foreign markets as a port city. Consequently, we would expect that the urban concentration as we measure it will rise with trade liberalization.

_Initial Domestic Transportation Infrastructure._ We use the percentage of roads paved

> faculty.haas.berkeley.edu/arose/.

> 21Historical GDP data from Angus Maddison has been obtained from http://www.ggdc.net/MADDISON/.
subsidy. Another question of interest is how program benefits are distributed across people. For example: What fraction of people experience a positive benefit from the intervention and how do the benefits vary according to the demographic characteristics of the participant? A third question that usually merits consideration is whether the program benefits outweigh the costs. A program that generates benefits that are less than its costs might be deemed unsuccessful, unless there are other compensating factors.<sup>1</sup> A fourth question is how program impacts and costs would differ if some features of the program were changed. For example, if a policy intervention provides childcare subsidies, we may want to know how an increase in the level of the subsidy would affect mothers‟ labor supply. Answering these types of questions requires assessing the effects of programs that have never been tried, by extrapolating from previous experience with an existing program. Relatedly, it might be of interest to explore how program benefits would vary if the program were extended to new segments of the population. For example, the program eligibility criteria might be relaxed to be more inclusive or the program may be introduced to new regions. Answering questions about the impacts of hypothetical treatments, such as changes in a subsidy level, usually requires a more fully specified behavioral model.

Most program evaluation studies compare the performance of a group that participates in a program to that of a group that does not participate. It is common, however, to target social programs selectively at families or individuals that are deemed most in need of them or that are likely to get the largest benefit from participating. For example, a family planning program might be targeted at high-fertility regions or an unemployment program at areas with high unemployment rates. In addition, individuals cannot usually be coerced to participate in programs and typically self-select into them, raising the possibility that only more motivated individuals or those that expect to benefit most from the programs participate. Selective targeting and self-selection into programs can promote efficient use of program resources; but these mechanisms also pose challenges for evaluating the impact of the program, because they generate differences between the groups that participate and do not participate in the program. Such differences need to be taken into account in evaluating the program‟s impact through a comparison of program participants and nonparticipants.

> 1 For example, some training programs in the U.S. that are targeted at older displaced workers generate income benefits that are less than the cost of the program, but it may still be desirable to provide training and employment services for such workers if a high value is placed on their employment per se.

2