rafmacalaba/datause-encoder-data huggingface dataset:
# **4. MODEL AND DATA**

The model used in this paper to identify the determinants of successful innovation activity is a probit model regression. This model analyzes the impact of: (i) firms’ absorption capability; (ii) human capital; and (iii) interaction with centers of knowledge production, on the probability that each firm introduces a successful innovation activity (after controlling for firms’ characteristics that vary according to the dataset used for the different countries). Innovation activity is defined here according to Rogers (1998), and measured by output and input innovation indicators.<sup>11</sup> These indicators refer, from the output side, to production of new products, introduction of new processes, patents applications or grants, and increase in sales from new or improved products or processes. From the input side the innovation indicator refers to the amount of investment in innovation and R&D activities.

Table 1 below reports the indicators as identified by Rogers (1998).

**Table 1 Measures of innovation**

|**Output measure**|**Input measure**|
|---|---|
|Introduction of new or improved products|R&D expenditure|
|Introduction of new or improved processes|Acquisition of technology from others (e.g. patents,<br>licenses)|
|Intellectual property statistics (patents, trade mark<br>and design applications and grants)|Expenditure associated with new products/processes|
|Innovation indicators linked to firm performance|Intangible assets (goodwill and capitalized R&D)|
|Percentage of sales from new/improved products or<br>processes|Marketing expenditure for new products|
||Training expenditures related to new/changed<br>products/processes|
||Managerial and organizational change|

Source: Rogers 1998 The model specifies the probability for each firm of having a positive outcome of the binary variable y, where y represents a firm undertaking innovation activity (and therefore defined as “innovative).

The probit model is generally defined as

# **Pr(y=1|x) = Φ(xb)**

where **Φ** is the standard cumulative normal probability distribution and **xb** is called the probit score or index.

The probit method fits discrete binary data by a maximum likelihood method to estimate the parameters. The Maximum Likelihood Estimator is an estimator for unknown vectors of parameters. The likelihood function is defined by _L_ (θ; _y_ ) = _f_ ( _y_ ;θ). Maximizing the

> 11 Rogers (1998) “The Definition and Measurement of Innovation”. We will use only some of these indicators in the analysis, according to data availability.

6
reasons were that the distance to the preschool was too great, that the child was too young (suggesting misinformation of eligibility rules or a perception that younger children are better off staying home) and that preschool was too expensive. Five percent of non-participating households in treatment areas reported applying to the preschool but not being accepted, while 13% applied but were not accepted in control areas.

# **4. Measuring Child Development**

The primary objective of the intervention was to improve children’s development along the domains of cognitive, social, emotional, and physical development, thus facilitating transition to primary school. We applied a set of standardized tests to measure cognitive ability (including problem-solving skills, memory, and early math skills), gross motor skills (e.g., running, jumping), fine motor skills (e.g., picking up objects, holding a pencil), language and communication (e.g., production and understanding of words, ability to identify letters), and socio-emotional development (e.g., getting along with peers and adults, following directions and cooperating, capacity to regulate emotions positively in stressful situations) (Fernald et al, 2009). We also collected children’s anthropometric measurements (height and weight) and caregiver-reported morbidity.

The specific child development tests were based on adapted versions of: (i) the “Ages & Stages Questionnaires®” (ASQ),<sup>23</sup> (ii) the “Teste de Vocabulario por Imagens Peabody” (TVIP);<sup>24</sup> and (iii) the Early Development Instrument (EDI). All tests were applied at baseline<sup>25</sup> and again at endline,<sup>26</sup> using age specific versions of the tests when appropriate. The adapted versions of the ASQ and TVIP were collected on the panel of target children. The adapted version of the EDI was collected on a repeated cross section of a random sample of 20 first graders in primary schools in treatment and control communities.

> 23 Ages & Stages Questionnaires (ASQ), Second Edition: A Parent-Completed, Child-Monitoring System, by Diane Bricker and Jane Squires. Copyright © 1999 by Paul H. Brookes Publishing Co., Inc. www.agesandstages.com. Used with permission of the publisher.

> 24 The TVIP is an adaptation of the Peabody Picture Vocabulary Test (PPVT).

> 25 See Bruns et al. (2010) and Naudeau et al. (2011) for a detailed discussion of findings at baseline. 26 A fourth test, the Strengths and Difficulties Questionnaire (SDQ) was added to the endline survey. We do not present the results due to a coding error present in the data which impaired the authors’ ability to carry out meaningful and reliable analysis.

12
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27
Table 5: Determinants of probability of participation in conservancy

||_Kunene_|_Caprivi_|
|---|---|---|
|Constant|-2.38**|-2.89 **|
||(0.36)|(0.45)|
|Grade 7 to 9|0.56**|0.52|
||(0.19)|(0.45)|
|Grade 10 and above|0.73**|0.68|
||(0.21)|(0.37)|
|Formal employment|0.01|0.31|
||(0.33)|(0.45)|
|Informal employment|-0.28|-0.06|
||(0.33)|(0.45)|
|Cash crop farming||0.02|
|||(0.37)|
|Retired|0.09|0.07|
||(0.33)|(0.42)|
|Self employment||-0.33|
|||(0.51)|
|Young adults:16-35|-0.07|0.02|
||(0.05)|(0.05)|
|Village Participation ratio|3.40**|3.49 **|
||(0.33)|(0.46)|
|Livestock: Principal Components|0.07|-0.06|
||(0.04)|(0.27)|
|Assets: Principal Components|0.07|0.29 **|
||(0.21)|(0.07)|
|Access to Electricity|-0.20|0.00|
||(0.19)|(0.29)|
|Months Fuelwood Harvested|0.02|0.04|
||(0.03)|(0.03)|
|Crop/Livestock damaged by wild|0.42*|-0.14|
||(0.19)|(0.23)|
|Female Headed Households|0.09|0.04|
||(0.16)|(0.21)|
|N|236|189|
|Log likelihood|-123.94|-77.59|
|Pseudo R squared|0.24|0.29|

Note:

1.      In Kunene some variables did not have sufficient variation between control and treatment and were dropped.

2. Numbers in the parentheses are standard errors.

3. Statistical significance of the coefficients are indicated by * for 5 percent and ** for 1 percent.

24
_Credit Growth =_ _T_ in our case is 17 since 1991 is the first year for which a growth rate can be computed. _Credit Growth_ is used as a dependent variable that captures banking development. The data on private credit is obtained from the World Bank’s Financial Structure Database, while GDP is from the IMF’s _International Financial Statistics._

# <u>B. Related Lending</u>

Because regulatory authorities in many countries do not require banks to report the percentage of loans made to related parties, and because even those that do have such requirements do not employ uniform definitions of a related party, a key challenge is measuring the extent of related lending across countries. It seems highly likely, however, that related lending is more prevalent in countries where regulators are more tolerant of cross-ownership between banks and nonfinancial firms and where restrictions on the ownership of bank capital by related parties or a single owner are less binding. Indeed, in nineteenth century New England, which is perhaps the quintessential case of the rapid development of a banking system on the basis of related lending, state regulators were quite tolerant of the fact that the owners of the major industrial firms were also the owners of the banks, and that the ownership of bank stock tended to be highly concentrated among a small group of individuals. Indeed, the banks were essentially the treasury arms of family-run manufacturing companies (Lamoreaux 1994). We therefore construct a proxy for the prevalence of related lending drawing on the database of bank regulation and supervision created by Barth, Caprio, and Levine (hereafter BCL, 2000, 2003).<sup>3</sup> Box 1 reports the questions that underlie our proxy. The first two questions refer to the ownership structure of banks (the maximum share that a single entity or individual may own, and the maximum that a related party may own). The second two refer to the degree to which banks are restricted from owning non-financial firms, and vice versa. Survey responses come from the regulatory and supervisory authorities in each country.

> 3See Barth, Caprio, and Levine (2001) for a description of the database. The survey and data are available at: <u>http://go.worldbank.org/SNUSW978P0.</u>

7
<!-- Start of picture text --> 7.0<br>6.4 6.5 6.3 6.3 6.3<br>5.9 5.8 6.0 6.0 6.0 6.0 5.8<br>5.4 5.5<br>5.0 5.0 5.0<br>EU CE & BC WB SC CA Russia Turkey EE RoW<br>2010 2016<br>8<br>6<br>4<br>Tier 1 stringency ratio<br>2<br>0<br><!-- End of picture text --> Source: Bank Regulation and Supervision Survey conducted by the World Bank.

Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe; **ROW** for the rest of the world **.** Having made a distinction between regulatory and economic capital, we examine how these capital ratios have changed in the region over the past 10 years using bank-level information on over 20,000 banks. In the analyses that follow, regulatory capital (RC/RWA) is calculated as the sum of Tier 1 and Tier 2 capital divided by risk weighted assets. The equity ratio (Equity/TA), also referred to as the simple leverage ratio, is calculated as total common equity divided by total assets. We also examine how risk-weighted assets have changed over time by calculating risk-weighted assets as a percentage of total assets (RWA/TA).<sup>10</sup> Figure 8 shows the evolution of regulatory capital ratios in the ECA subregions. Because many countries are dominated by a few large banks, we show changes in capital ratios for both large and small banks. Large banks are defined as banks that are in top 20th percentile in terms of total assets in a given country for each year. Although there is variation across countries, overall there has been an increase in regulatory capital ratios especially for larger banks. Excluding EU countries, on average in ECA regulatory capital has increased 5 percentage points between 2007 and 2017.

> 10 The time trends for the ratios in Figures 8, 9 and 10 are given in the appendix (Figures A1, A2, and A3, respectively).

9
6

**As a first** step, annual value and quan..ity data were drawn from UN Series D trade tapes for French, German, Italian and Japanese exports to Madagascar over the 1979-1988 period and unit values were computed. This provided a "benchmark" on average prices (unit values) paid for the four years (1979-82) prior to the adoption of PSI ai well as five full years (1984-1988) afterwards. In general, the data were drawn at the very detailed five-digit SITC (Revision 2) level although some four-digit products were included in the Italian, German and Japanese statistics when more disaggregated data were not available.<sup>9</sup> Value **and** quantity data were also drawn for exports of these products to other develop g and industrial countries so Madagascar's relative import prices could be

9The selection generally included every five-digit product exported to Madagascar over most of the 1979-88 period for which both quantity and value data were available. Certain products, such as those traded irregularly, or which clearly had diverse characteristics (i.e., f4ve-digit items with "not elsewhere classified" or "not elsewhere specified" headings) were excluded. Data on United States exports were not used since this country generally did not compile quantity information required for computation of unit values. A poLnt to note concerning the unit value information is that quality or product-mix variations may make price comparisons unreliable for some soecific products, but their influence should cancel out in the large number of products included in this study (i.e., there is no reason to believe that Madagascar is generally a purchaser of relatively high- or low-priced goods). See Appendix 1 for separate price comparisons for goods with homogenoub characteristics.
# Summary

This paper reports on the findings from a major international research project investigating the poverty impacts of a potential Doha Development Agenda. It combines in a novel way the results from several strands of research. Firstly, it draws on an intensive analysis of the DDA Framework Agreement, with particularly close attention paid to potential reforms in agriculture. The scenarios are built up using newly available tariff line data and their implications for world markets are established using a global modeling framework. These world trade impacts, in turn, form the basis for twelve country case studies of the national poverty impacts of these DDA scenarios. The focus countries include: Bangladesh, Brazil (2 studies), Cameroon, China (2 studies), Indonesia, Mexico, Mozambique, Philippines, Russia, and Zambia. The diversity of approaches taken in these studies allows them to stress local conditions and priorities and illustrates a wide range of facets of the trade-poverty links. It does, however, limit the ability to draw broader conclusions. Thus an additional study which provides a 15 country cross-section analysis is aimed at this objective, and a global analysis provides estimates for the world as a whole.

# A few of the main findings follow:

- The liberalization targets under the DDA have to quite ambitious if the round is to have a measurable impact on world markets and hence poverty.

- Assuming an ambitious DDA, we find the near-term poverty impacts to be mixed; some countries experience small poverty rises and others more substantial poverty declines. On balance, poverty is reduced under this DDA, and this reduction is more pronounced in the longer run.

- Allowing minimal tariff cuts for just a small percentage of special and sensitive products virtually eliminates the global poverty reduction due to the DDA.

- Deeper cuts in developing country tariffs would make the DDA more poverty friendly.

- Key determinants of the national poverty impacts include: the incomplete transmission of world prices to rural households, barriers to the mobility of workers between sectors of the economy, as well as the incidence of national tax instruments used to replace lost tariff revenue.

- In order to generate significant poverty reductions in the near term, complementary domestic reforms are required to enable households to take advantage of new market opportunities made available through the DDA.

- Sustained long term poverty reductions depend on stimulating economic growth. Here, the impact of the DDA (and trade policy more generally) on productivity is critical. In order to fully realize their growth potential, trade reforms need to be far reaching, addressing barriers to services trade and investment in addition to merchandise tariffs.

ii
Impact of International Migration on Labor Supply in Nepal<sup>_∗_</sup> Lokendra Phadera<sup>_†_</sup> Poverty and Equity Group, World Bank

# September 2019

Keywords: Migration for employment, Labor supply, Well-being of left-behind JEL Classication: F220, O150, J220

> _∗_ Acknowledgments: Earlier draft of the paper was circulated under the title International Migration and its Eect on Labor Supply of the Left-Behind Household Members: Evidence from Nepal. I gratefully acknowledge the nancial support provided by the ACES Oce of International Programs, Arlys Conrad Fund and the ACES Education Abroad Oce. I would like to thank participants from the 2015 Advance Research and Scholarly Communication (ACE 561) and International Policy and Development (IPAD) seminars at the University of Illinois Urbana-Champaign. In particular, I would like to thank Mary Paula Arends-Kuenning, Alex E. Winter-Nelson, Benjamin Crost, Rebecca Thornton, Hope Michelson, Kathy Baylis, Craig Gundersen, Charles H. Nelson, Philip Garcia, Mark Borgschulte, and the two anonymous proposal referees for their comments and advice. Any mistakes are my own.

> _†_ E-mail: lphadera@worldbank.org
30 Table S. 7: Alternative estimates of Total Energy Expenditure (TEE) and components over time

||M|ale 15+||Fe|male 15|+|Child|14 and|under|
|---|---|---|---|---|---|---|---|---|---|
|Year|TEE|REE|AL|TEE|REE|AL|TEE|REE|AL|
|||||Pane|l A: All|India||||
|1983|2767|1424|1.95|2166|1128|1.93|1444|937|1.51|
|1988|2782|1437|1.94|2175|1139|1.92|1431|943|1.49|
|1994|2804|1457|1.93|2198|1154|1.91|1432|953|1.48|
|2000|2808|1475|1.91|2205|1170|1.89|1462|976|1.47|
|2005|2820|1490|1.90|2213|1184|1.88|1467|987|1.47|
|2009|2758|1510|1.84|2199|1202|1.84|1474|1011|1.44|
|2012|2729|1517|1.81|2183|1210|1.81|1481|1020|1.43|
||||Pa|nel B:|Rural s|ector o|nly|||
|1983|2858|1404|2.04|2216|1121|1.98|1451|933|1.52|
|1988|2867|1418|2.02|2219|1131|1.97|1433|939|1.50|
|1994|2895|1436|2.02|2248|1144|1.97|1429|946|1.48|
|2000|2901|1454|2.00|2255|1158|1.95|1458|969|1.48|
|2005|2920|1469|1.99|2261|1170|1.94|1462|981|1.47|
|2009|2842|1490|1.91|2243|1188|1.89|1471|1008|1.44|
|2012|2807|1496|1.88|2223|1194|1.87|1475|1016|1.43|

_Source_ : Authors’ calculations using India Time-Use Survey 1998-1999, National Family Health Survey rounds 2, 3, and 4 and National Sample Survey rounds 38, 43, 50, 55, 61, 66 and 68.

_Note_ : Alternative estimates of Total Energy Expenditure (TEE), Rest Energy Expenditure (REE) and Activity Level (AL). For REE we include a linear time-trend in the regression equation and predictions. For AL we consider a specification pooling all men or all women (regardless of age or sector) and including three way interactions of dummies for primary status, 1-digit primary industry, and education level.
appearing in the data in each country. It is important to note that the respondents are categorizing the support they received or expected into these types, creating room for several complications. First, one support measure may be characterized as taking more than one form. For example, some respondents could potentially consider a grant to be access to new credit as well as a cash transfer; or wage subsidy could also be interpreted as cash transfer depending on the details of its implementation. A further complication arises from the potential miscategorization of support measures. For example, we know that the Government of Georgia did not provide wage subsidies as an explicit form of support to businesses. Still, many firms in the Georgia survey reported receiving such support. We think that cash assistance provided to newly or temporarily unemployed was interpreted this way, but it is impossible to know exactly which support measure did each respondent mean when providing Yes/No responses to this battery. We thus urge readers to interpret our findings into the effectiveness of these types with additional caution.

# **4.4. Firm Characteristics Used for Matching Estimation**

Table 2 presents the summary statistics for a long list of variables from the baseline ES datasets that we use in matching estimation. For each country, means are reported separately for two subgroups of respondents: those that received government support as of the second round (as defined above), the Support group, and those that did not, the No-Support group. The table also reports t-tests comparing means across the two sub-groups. In this subsection, we briefly describe each of these variables.

The firm size is determined based on the total number of full-time equivalent permanent workers of firms as of the reference period used in the baseline ES (i.e., December 2015 in El Salvador and December 2018 in Georgia). Following the standard ES stratification categories, small firms are those employing 5-19 workers. Medium-sized firms employ 20-99 workers, and large firms employ 100 or more workers. The firm age shows the number of years the firm has been operating, which we break into three categories, 0-10 years, 11-20 years, and 20+ years. The variables on sectors, namely, manufacturing, construction, transportation, hotels and restaurants, and retail, are dummy variables indicating the firms’ sector of operation.

We use a long list of dummy variables, in particular, whether the firm is exporting at least 10% of their sales, whether the firm is at least 10% foreign-owned, whether the firm introduced new or significantly improved product or service or process innovation over the last three years 14