rafmacalaba/datause-encoder-data huggingface dataset:
38. The national development plans as well as the University's<sup>Higher</sup> Council policies have consistently emphasized the Yemenization<sup>of the academic</sup> staff. Therefore, since the University's inception a number<sup>of qualified</sup> Yemenis graduating both from the University of Sana'a and<sup>from overseas</sup> institutions, have been included for overseas fellowships under the<sup>University</sup> staff development program for masters and doctoral degrees<sup>(see Appendix,</sup> Table 5) to upgrade teaching and administrative capability. In 1988, there were 223 Yemeni staff training for higher degrees<sup>in various foreign countries</sup> such as Egypt (109), the United States of America (63)<sup>and the United Kingdom</sup> (30) (see Appendix, Table 6). However, few women are in this<sup>program because</sup> Yemeni tradition prevents them from studying overseas,<sup>unless accompanied by a</sup> male member of the family. Like in many other Arab countries,<sup>this custom of</sup> severely limiting women's access to overseas higher education<sup>is creating a</sup> two-tier staff composition. The majority of staff are men overseas<sup>trained,</sup> while the handful of women staff are largely trained<sup>at the University of</sup> Sana'a. Until a change in this tradition occurs, the staff<sup>will continue to</sup> be predominantly male and overseas trained. Many of the<sup>overseas training</sup> fellowships have been provided by donor agencies and by joint<sup>donor and</sup> university contributions. On completion of the program, university<sup>sponsored</sup> fellowship holders must serve the University a minimum of twice<sup>the supported</sup> years.

39. In spite of competition from neighboring oil-rich<sup>Arab countries for</sup> professionals since 1976, the Universities has successfully<sup>recruited faculty</sup> members, through which it has attracted qualified Yemenis<sup>from overseas to</sup> take up academic and administrative positions. In the 1988/89 academic session, out of a total staff of 470, 273 or 58% were Yemenis,<sup>and over 90% of</sup> whom were men. In June 1989, of the 111 academic staff members<sup>in the then</sup> only four Faculties of Education, nine were women (8%).

Table 4: Number of Instructional Staff in Selected<sup>Faculties</sup>

||Ye|meni|Non-|Yemeni|
|---|---|---|---|---|
|Faculty|1982/83|1988/89|1982/83|1988/89|
|Law<br>Arts|5<br>22|20<br>52|13<br>46|8<br>39|
|Science|13|40|72|62|
|Commerce&Economics|26|39|18|14|
|Education|11|50|5|21|
|Total|77|201|154|144|

Source: University of Sana'a

- 14 -
Figure 6-A

# Real Exchange Rate and Trade Balance

~~~~~115 .

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argue that the use of a services FDI linkage measure based on FDI stocks and lagged two periods is a reasonable way to mitigate potential reverse causality problems.

The considerations above lead to our final empirical specification: where _FDIsl it_ − 2 is the two-period lag of the services FDI linkage, _FDIl jt_ − 2 and _FDIres jt_ − 2 are the two-period lag of the manufacturing and the mining FDI linkages, respectively.26 In the estimation, the coefficients on input growth are allowed to differ _j_ across 2-digit industries. Finally, recall that the vector _Z it_ − 1 includes dummies for foreign ownership, export status, and size, that _ind * year_ and _reg * year_ are industry-year and region-year interaction fixed effects, and _u itj_ is an i.i.d. disturbance.

# **6. Results**

# **6.1 Main Results**

Our main results are shown in Table 1. Column 1 presents the results from a plant fixed-effects specification that follows previous studies in weighing services FDI penetration by industry-level services usage based on coefficients from the Chilean 1996 input-output table, as detailed in the Appendix. We find insignificant effects of that measure of weighted services FDI penetration on plant productivity growth. In Column 2, we estimate a variant of (5) including only the one-period lag of the services FDI linkage.

We find a strong positive effect of services FDI on the productivity growth of

> 26 These variables are indexed by the industry subscript _j_ since they are calculated based on coefficients from the 1996 Chilean input-output table, as described in the Appendix.

20
## PESTICIDE EXTDRNALXTIZS, CONPIBTR8NT **ADVANTPAG,**

# AND CONMODITY TRADES

## COTTON IN ANDNR 132DZSN, INDIA1

## I. INTRODUCTION AND MOTIVATION

Modern agriculture is highly dependent on a large number of chemical inputs such as fertilizers and pesticides. In the<sup>initial</sup> flush of the technological revolution, we were bowled over by<sup>the</sup> yield increasing potential of these inputs and their untrammelled use was freely advocated by policy makers. rt was visualized that increasing recourse to these inputs was the only way to feed<sup>the</sup> world's hungry millions. Developments during the last thirty<sup>years</sup> have, however, uade us realise that the use of chemical inputs is not an unmixed blessing. There have been disquieting<sup>developments</sup> such as contamination of groundwater through leaching of pesticides through the soil, complete crop devastation arising<sup>from pest</sup> resistance in several parts of the world, soils<sup>being made</sup> physically and chemically unfit for further cultivation through I This research has been supported by World Bank RPO# 676-92. The empirical part of this paper is based on a preliminary<sup>field</sup> visit to Andhra Pradesh, India. Financial support for<sup>the visit was</sup> provided by FERRO (USDA) and IOPXR. The views expressed in this paper are the author's own and should not be attributed<sup>to the</sup> World Bank or any of its affiliated agencies. Special thanks<sup>to</sup> Garry Pursell for sowing the seeds of the idea and<sup>for patiently</sup> going through several drafts. Thanks also to John English and to Geoff Jackson for their support for this project. I<sup>would like to</sup> acknowledge the helpful discussions I have<sup>had with the</sup> participants of the ICAR/IOPRN/USDA Joint Project Development Group Meeting on Managing Insecticide Resistance with Focus<sup>on Heliothis</sup> Management in India, Hyderabad, India, Oct. 16-17, 1991.<sup>The usual</sup> disclaimer applies.

I
|**Table 7: Return on Labor**||LEVEL|OF PROFITS||LO|G OF PRO|FITS|
|---|---|---|---|---|---|---|---|
||Associ<br>Contro|ations in<br>l Group|Treatment IV|estimates|Associa<br>Contro|tions in<br>l Group|IV<br>Treatment|
||Cross<br>Section|Panel<br>Data|Unconditional<br>Profits|Conditional<br>Profits|Cross<br>Section|Panel<br>Data|Effect|
|**Panel A: Using Wage subsid**|**y Only Treatme**|**nt for IV**||||||
|Number of paid workers|6674***|4844***|2403|3054|0.216***|0.122***|0.138|
||(557)|(575)|(6055)|(5697)|(0.015)|(0.017)|(0.279)|
|Sample Size|4959|4959|951|905|4429|4429|884|
|<br>**Panel B: Using all groups wh**|**ich received a**|**wage subsidy f**|**or IV**|||||
|Number of paid workers|||897|1151|||0.140|
||||(3530)|(3297)|||(0.155)|
|Sample Size|||1993|1888|||1851|
|Notes: robust standard erro<br>*, **, *** indicate significan<br>Regressions control for time<br>Columns 1, 2, 5, and 6 use c<br>and all groups receiving a w|rs in parenthes<br>ce at the 10, 5,<br>fixed effects, r<br>ontrol group on<br>age subsidy in p|es, clustered a<br>and 1 percent<br>andomization<br>ly. Columns 3,<br>anel B.|t the firm level.<br>levels respectively<br>strata, and contro<br>4 and 7 use wage|.<br>ls used in re‐ran<br>subsidy only an|domization.<br>d control gro|ups in pan|el A,|

43
# Table A7: Restricted <u>samples — Impacts of hosting refugees on household welfare and poverty</u>

||||||Household<br>|s within th<br>|e respective|buffer<br>|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||C|onsumption||Cons|umption po|verty||Wealth||W|ealth pove|rty|
|||||||Buffer|||||||
||70km|60km|50km|70km|60km|50km|70km|60km|50km|70km|60km|50km|
|Refugee Intensity|-0.162***|-0.154**|-0.131**|0.145**|0.131**|0.130**|-0.001|0.008|0.008|-0.018|-0.033|-0.011|
||(0.060)|(0.060)|(0.057)|(0.061)|(0.060)|(0.059)|(0.048)|(0.043)|(0.045)|(0.045)|(0.037)|(0.033)|
|Constant|9.875***|10.069***|9.874***|-0.450|-0.556|-0.426|1.233***|1.253***|1.209***|0.595|0.354|0.138|
||(0.560)|(0.621)|(0.613)|(0.480)|(0.471)|(0.495)|(0.342)|(0.349)|(0.349)|(0.414)|(0.371)|(0.322)|
|Round FEs|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|FEs|HH|HH|HH|HH|HH|HH|HH|HH|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|R-squared|0.075|0.082|0.091|0.048|0.043|0.049|0.194|0.175|0.160|0.085|0.067|0.074|
|# of EAs|107|95|86|107|95|86|107|95|86|107|95|86|
|N|3049|2679|2404|3049|2679|2404|3185|2803|2515|3185|2803|2515|

_Notes:_ This table provides estimates of the impacts of logarithm of refugee intensity on logarithm of total household consumption expenditure per capita (the first three columns), a binary measure of household consumption poverty status (the second three columns), “started log” of household wealth score (last third three columns), and a binary measure of household wealth poverty status (the last three columns). I use samples restricted to buffers of 50-, 60-, and 70-kilometer from refugee settlement sites. Household sample weights from ESS2 applied. All columns control for household (HH) fixed effects (FEs), round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 characteristics are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

57
21

- Gunning, Marie. (2003). “Managing Small and Medium Size Enterprise Investment Funds in Latin America and the Caribbean: Lessons Learned and Recommended Best Practices.” Report submitted in conjunction with the Multilateral Investment Fund’s (MIF) workshop on Small and Medium Sized Enterprise Investment Funds in LAC.

- International Finance Corporation. (2006). _Scaling Up Innovation and Entrepreneurship in Development Countries: The Role of Private Sector Finance_ . Washington, DC, April.

- (2008a). _Financing Technology Entrepreneurs & SMEs in Development Countries: Challenges and Opportunities_ . Washington, DC, June.

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- (2008d). _Financing Technology Entrepreneurs & SMEs in Development Countries: Challenges and Opportunities Peru Country Study_ . Washington, DC, June.

- (2012). _Interpretation Note on Small and Medium Enterprises and Environmental and Social Risk Management_ . Washington, DC, January.

- McKinsey & Company. (2010). _Two Trillion and Counting: Assessing the Credit Gap for Micro, Small, and Medium-Size Enterprises in the Developing World_ . October.

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- (2012). _World Economic Outlook: Growth Resuming, Dangers Remain._ April.

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James, Barbara. (2007). _Private Equity and Venture Capital in Africa_ . April 24.

Kaberuka, Donald. (2010). “Capturing Africa’s Business Opportunity.” _McKinsey Quarterly_ , June.

- Latin American Law & Business Report. (2011). _The Multilateral Investment Fund: Lessons Learned Building a Local Venture Capital Industry_ . June.

- LAVCA (Latin America Venture Capital Association). (2012a). _2012 Scorecard: The Private Equity and Venture Capital Environment in Latin America_ . New York.

- (2012b). _LAVCA Annual Review 2012_ . New York, March.

Milken Institute. (2009). _Stimulating Investment in Emerging-Market SMEs_ . October.

Multilateral Investment Fund. (2006). _Innovation Support Program (ISP)_ . Washington, DC, October.

Palacios, Juan. (2012). _MSME’s in Latin America—Innovation in MSME’s: Applying Digital Tech_ .
of reproductive age who consume FAFH at lunch and the average household expenditure are calculated based on data of the ENAHO household survey. In addition, information on the socioeconomic strata of each neighborhood from the ENAPREF survey is used to determine typical household expenditures. For ease of interpretation, the five original strata are collapsed into four groups for the analysis by socioeconomic strata.<sup>9</sup>

# **3 Diet quality index**

Several diet quality measures are commonly used in the nutrition literature. To exploit the detailed information available on nutrients in our restaurant survey more effectively, we propose a new index inspired by two existing indexes: the Ratio of Recommended to Restricted Food Components (RRR) and the Mean Adequacy Ratio (MAR). Most indexes in the nutrition literature are based on food groups or a combination of the RRR and the MAR (Drewnowski 2005; Scheidt and Daniel 2004). The underlying approach in both indexes is to examine the daily intake of nutrients relative to a recommended nutrient measure. The main difference between the two is that the RRR divides food components into six recommended nutrients (protein, calcium, iron, vitamin A, vitamin C, and fiber) and five restricted nutrients (energy, saturated fat, cholesterol, sugar, and sodium) (Scheidt and Daniel 2004), whereas the MAR does not differentiate between these two.<sup>10</sup> The RRR can be written as follows: The daily value (DV) expresses the share of the real to the required nutrient intake. This value is truncated at a maximum of 100 percent. The interpretation of the RRR is based on a standard of 1.0 (equal to 100 percent) that represents an equal proportion of recommended and restricted nutrients. Values below 1.0 show a higher percentage daily value for restricted values than for recommended values and are therefore an indicator of less nutritious food.

Following Drewnowski (2005), the MAR is an alternative, but similar diet quality measure. It is defined as the sum of the real and recommended nutrient intake (RNI) per day for various nutrients, _k_ , divided by the total amount of nutrients, N, as follows: One important shortcoming of both diet quality indexes is that they do not account for overnutrition by capping values above the ideal value of 1. This truncation leads to a biased picture of diet quality if the focus is on restricted nutrients (saturated fat, cholesterol, sugar, and sodium), which are the key relevant nutrients in terms of

> 9 The high and mid-high socioeconomic groups have been collapsed because of the low number of observations in each of the socioeconomic groups.

> 10 Recommended nutrients can be readily consumed and require a minimum amount of consumption. Restricted nutrients should ideally only be consumed up to a certain limit. Overconsumption of these nutrients over longer periods may be harmful to health.

7
of adult beneficiaries in the shorter-run (generally two- to five-year follow-ups), followed by effects on future outcomes of adolescents in the longer-run (typically five- to 10-year follow-ups).

The most well-known government CCT program is Mexico’s PROGRESA, which later became Oportunidades. Parker and Todd (2017) provide a review of its effects on labor market outcomes for adult beneficiaries and find no effects on work or leisure (citing Parker and Skoufias 2000; Rubio-Codina 2010). They note some effect on women substituting for children’s time within the household, because child labor (including paid, domestic, and household agricultural) goes down under PROGRESA/Oportunidades. Bianchi and Bobba (2013) find that expected future transfers increase the probability of entrepreneurship (self-employment) in the short-run. However, this effect disappears in the medium-run, remaining only in areas with low levels of selfemployment at baseline, (canceled by a negative effect in areas with high self-employment at baseline). Finally, Gertler et al. (2012) observe a positive _short-term impact_ on _non-agricultural microenterprises_ and total agricultural income among treated households. However, in the medium-term, i.e. 5-6 years after the start of the program, they find no effects on outside wages. In summary, the effect of Mexico’s CCT program on the labor market outcomes of its adult beneficiaries is small at best.

Banerjee et al. (2017) re-analyze the results of seven randomized controlled trials of government-run CCT programs from six countries to examine impacts on labor supply.<sup>3</sup> The authors do not observe a significant effect either individually or pooled on employment or hours of work. Similarly, they find no pooled effect on whether work is self-employed/within family vs. outside the household. They do find a decrease in outside work and an associated increase in within household work in PROGRESA, but the opposite pattern holds for RPS (which has a similar transfer size). Similarly, there are no overall pooled effects on any outcomes when disaggregating by gender.<sup>4</sup>

> 3 These programs include Honduras’ PRAF II, Morocco’s Tayssir, Mexico’s PROGRESA and PAL, Philippines’ PPPP, Indonesia’s PKH, and Nicaragua’s RPS. Two were not entirely conditional. Mexico’s PAL program, where benefits were not conditioned on behaviors and Morocco’s Tayssir program, which had two treatment arms consisting of a CCT and a “labeled cash transfer”.

> 4 There is a related parallel literature, mostly from high-income countries, on the effects of universal cash transfer programs or universal basic income. Examining lottery winners in Sweden and the Netherlands, beneficiaries of negative income tax experiments in Canada, and recipients of fund dividend programs in the United States, studies consistently find small reductions in earnings and hours worked (Cesarini et al. 2017; Picchio et al. 2017; Marinescu 2018). Using a large and universal cash transfer program in the Islamic Republic of Iran that was implemented to offset large increases in prices of energy and bread (because of removal of large subsidies), Isfahani and Mostafavi-

7
comparison to what others have found, it is only half the share of “gung-ho” entrepreneurs (30.4 percent) identified by Banerjee et al. (2019); these entrepreneurs are those whose businesses benefitted from access to microfinance in India. Following Lewis (1979), we describe this group of firms as “the in-between firms” to signal their status and performance as somewhere between formal (most productive) and informal (least productive) manufacturing firms.

How can policy makers support the productive small firms while not encouraging overinvestment in unproductive activities? The importance of this question cannot be overstated. These small firms are considerably more likely to use labor-intensive technologies and will thus could be an important source of employment in African countries (and elsewhere) for years to come.<sup>5</sup> One popular strategy for spurring growth in promising ventures in both the developed and developing world is business plan competitions, which seek to identify and encourage entrepreneurs with growth aspirations by helping them to develop a detailed plan and then providing financing or in-kind benefits (such as training) to those deemed most likely to succeed.

YouWin! is an example of a successful, large-scale nationwide business plan competition initiated in Nigeria in 2011. The top-scoring plans overall and in each region won awards of roughly $50,000, and then out of the 1,900 plans that were semifinalists in the competition, 700 were selected at random as winners. An evaluation of this competition using the underlying random variation by McKenzie (2017) tracked winners over five years and shows that winning firms had higher survival, profits, sales and employment: winning also increased the likelihood that a firm has more than 10 employees by 20 percent. For present purposes, what is important about the business competition is that manufacturing was the second most common sector for new firms, comprising 13 percent, and third most common for existing firms, comprising 14 percent of winners. The types of products being manufactured by firms in the competition are very heterogeneous, and include processed food products, books and media, metal products, chemicals and detergents, and a range of other items.

> 5 A related question which we do not take up here is the extent to which support for small firms has encouraged the proliferation of unproductive businesses (for example, Martin et al. 2017). However, in the context of SubSaharan Africa where wage employment is scarce, it is difficult to think of this as a misallocation of resources. Instead, we might think of indiscriminate programs targeted at small businesses as a kind of social safety net. A final consideration is the extent to which support in the form of financial resources displaces or substitutes for alternative sources of financing (Fafchamps et al. 2014).

17