rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2: Percentage of Female Firm Owners by Industry**

<!-- Start of picture text --> 60<br>48<br>50<br>40<br>32 32<br>28<br>30<br>21 22<br>20 16 16 13 15 13 14<br>10 8 5 11 6 10 7 10 11 9 7 10 11<br>2 0 1 1 0 1 0 2 0 2 0<br>0<br>Argentina Bolivia Brazil Ecuador Honduras Mexico Peru<br>Agriculture Manufacturing Construction Trade Services<br><!-- End of picture text --> _Source_ : Author’s calculations based on household surveys from local statistical agencies.

# **3. Characteristics and Performance of Female-Owned Firms vs. Male-Owned Firms**

# **3.a. Data Description**

This section relies on four different firm level data sets from a number of Latin American countries data to compare characteristics and performance of female-owned and male-owned firms. The first dataset is a 2002 nationally representative micro firm survey from Mexico, the _Encuesta Nacional de Micronegocios_ (ENAMIN). The second and third datasets are World Bank surveys of micro and small firms in Bolivia and Peru, from the year 2007 and 2008, respectively. These surveys cover only a small number of industries and are not designed to be representative of these industries. The fourth dataset encompasses the 2003 World Bank Enterprise Surveys for Brazil, Ecuador, El Salvador, Guatemala, Honduras, and Nicaragua. Unlike the other three datasets, the Enterprise Surveys focus on larger firms and include only few firms with less than 5 employees. The Enterprise Surveys cover only the manufacturing industry and are nationally representative for manufacturing firms. Firms in the Enterprise Survey are all formal, whereas the three micro firm surveys interview formal and informal firms.

All these datasets were chosen because they allow me to identify whether the principal owner of the firm is male or female. The three micro firm surveys interview the owner and also include several owner characteristics, such as education and family background. The Enterprise Surveys do not necessarily interview the firm owner, but they include a question about the gender of the firm’s principal owner<sup>4</sup> . They do not provide other owner characteristics. All surveys also provide information on the firm performance, financing, as well as obstacles the firm faces.

> 4 Another round of World Bank Enterprise Surveys was conducted in a large set of Latin American countries in 2006. However, the 2006 surveys do not ask for the gender of the principal owner. Instead, they ask whether any of the principal owners is female, which could also be the case for family firms. There is no way of distinguishing

6
**Table 6. Correlations with steady-state employment ratios and country characteristics (continued)**

|<br>Correlate|Number of<br>observations|I<br>Total|II<br>Women|III<br>Men|IV<br>Non-<br>agriculture|V<br>Women in<br>non-<br>agriculture|
|---|---|---|---|---|---|---|
|Trade-related variables|||||||
|Total exports (percent of GDP)|101|0.0065|-0.0017|0.0123*|0.0249***|0.0166***|
|Goods exports (percent of GDP)|100|0.0073|-0.0003|0.0127|0.0232***|0.0153***|
|Services exports (percent of GDP)|100|-0.0172|-0.0197|-0.0138|0.0250**|0.0212|
|Finance-related variables|||||||
|Credit to the private sector (percent of<br>GDP)|103|-0.0026|-0.0066|0.0005|0.0110***|0.0069***|
|<br>Number of commercial bank branches<br>(per 100000 adults)|102|-0.0114|-0.0193*|-0.0045|0.0224***|0.0155***|

28
This effect was stronger for men. For women, the size of the effect varies by family size and residential tenure; while increased tenure security did not produce a significant labor supply effect for women in the average household, it did have a large and significant positive effect on women who reside in small households with long residential tenures.

# **7. Agricultural technology**

Technological innovation and adoption have undoubtedly been key drivers of economic development and growth. While technological change has impacted a wide range of economic activities, one of the main sectors where technology and its effects have been studied extensively, particularly in the developing world, is agriculture. The focus has been largely deliberate—with the majority of the developing world’s population, and particularly the poor, reliant on agricultural production and employment for their livelihoods, growth in agricultural productivity continues to be viewed as paramount for raising incomes and reducing poverty among agricultural households (see Thirtle et al. 2003 for a summary of the cross-country and microdata evidence). Much of the productivity and production growth that has occurred in this sector has been due to the introduction of new technologies such as high-yielding varieties, chemical fertilizers, herbicides and pesticides, mechanization, and improved soil and crop management techniques. Here, the Green Revolution stands out as the most prominent example of the success of these interventions.

# _Technology adoption and use_

Motivated principally by the importance of identifying and addressing constraints to technology adoption and use (hereafter referred to as just adoption), the individual-, household-, and community-level determinants of technology adoption and diffusion have been an area of longstanding research in agricultural economics. However, whether technology adoption behavior differs systematically between female and male farmers, what the key factors behind this difference are, and whether (and how) technology adoption affects women’s economic roles (both absolutely and relative to men’s) have been less studied, although these gaps are rapidly being filled as a result of increased empirical research in recent years. Nevertheless, much more research is needed as what is presently known remains about the nature of gender disparities in the use and impacts of technology remains far from conclusive.

# _Examining technology adoption and gender: The typical approach_

Most empirical studies of the determinants of technology adoption and diffusion in developing countries examine the adoption decision at the level of the household. Consequently, when studies do examine how gender affects adoption, they typically do so by including an indicator variable for female-headed household as an additional covariate in multiple regression analyses. The implicit assumption here is that the adoption decision is made by the household head and not by individual (male and female) farmers within the household, and that this decision applies broadly within the household.

23
**<mark>Appendix Table 5.6: Internal Consistency Check of Type of Insourced/Outsourced Worker and Business Practice Change</mark>**

||**S**|**urvey Round**<br>Marketing|**1**|**S**|**urvey Round**<br>Marketing|**2**|
|---|---|---|---|---|---|---|
||Finance &<br>Accounting|&<br>Sales|Digital<br>Marketing|Finance &<br>Accounting|&<br>Sales|Digital<br>Marketing|
|Hired a marketing worker or provider|0.068**|0.090***|0.109***|0.060*|0.116***|0.097***|
||(0.030)|(0.030)|(0.022)|(0.034)|(0.033)|(0.021)|
|Hired an accounting worker or provider|0.150***|0.028|0.017|0.124***|0.110**|0.054|
||(0.037)|(0.038)|(0.032)|(0.044)|(0.048)|(0.033)|
|Control group mean|0.631|0.588|0.221|0.635|0.551|0.219|
|Sample Size|394|394|394|393|393|393|
|P-value: marketing= accounting|0.010|0.071|0.003|0.093|0.891|0.171|
|Notes:|||||||
|Sample restricted to firms in insourcing, o<br>Regressions control for randomization str<br>Robust standard errors in parentheses, *,|utsourcing, and<br>atification batch<br>**, *** denote|control treat<br>, and baseline<br>significance a|ment groups.<br>level of pract<br>t the 10, 5, an|ices.<br>d 1 percent le|vels respectiv|ely.|

71
father’s schooling, and similarly for mothers, while the percentile rank for average parental education is calculated using the distribution of average schooling.

# **(5) Estimates of Truncation Bias in Rank-Based Measures of Intergenerational Mobility**

The estimates from three different samples are provided: all children sample (includes both sons and daughters), the father-sons sample, and the mother-daughters sample. In all of the rank-rank regressions, the dependent variable (the indicator of educational attainment) is children’s percentile rank in the distribution of years of schooling for all children. Define the bias in the estimate of parameter _ω ∈ δ_ ˆ0 _,_ ˆ _δ_ 1 _, β_ ˆ0 _, β_ ˆ1 _,_ ˆ _ρ_ 0 _,_ ˆ _ρ_ 1 as _PBω_ (short for [ ] percentage bias in the estimate of _ω_ ): _Estimate of ω f rom Coresident Sample − Estimate of ω f rom Full Sample PBω_ = [ _Estimate of ω from Coresident Sample_ ] _×_ 100 (7) Thus when truncation causes a downward biased estimate, then the estimated bias in definition (7) above is negative, and it is positive when the estimate of a parameter from the coresident sample is biased upward. The advantage of using the estimate from the coresident sample as the base in equation (7) above is that it is directly observable to most of the researchers facing data constraints with access to only coresident sample.

# **(5.1) Truncation Bias in Estimates of Relative Mobility**

# **(5.1.1) Estimates from the All Children Sample**

ˆ The estimates of rank correlation _δ_<sup>ˆ</sup> 1 are reported in Table 3; the estimates of IGC _ρ_ 1 and IGRC _β_<sup>ˆ</sup> 1 are also included for comparison. The top panel contains the estimates for Bangladesh and the lower panel for India.

The point estimate of rank correlation _δ_<sup>ˆ</sup> 1 from the coresident sample is _smaller_ than that from the full sample (including nonresident children) across all three different indicators of parental education for both Bangladesh and India. This is consistent with the _a priori_ expectation that truncation causes downward bias in the estimate of the slope parameter(s) 11
21

# **2.3. Using instrumental variables to deal with simultaneity bias**

To assess whether the finance-growth relationship is driven by simultaneity bias, recent research uses instrumental variables to extract the exogenous component of financial development. To do this, one needs instrumental variables that explain cross-country differences in financial development but are uncorrelated with economic growth beyond their link with financial development. Then, one can use standard instrumental variable procedures to examine the finance-growth relationship while formally controlling for endogeneity.

Levine (1998, 1999) and Levine, Loayza, and Beck (2000) use the La Porta, Lopez-deSilanes, Shleifer, and Vishny (henceforth LLSV, 1998) measures of legal origin as instrumental variables. In particular, LLSV (1998) show that legal origin – whether a country’s Commercial/Company law derives from British, French, German, or Scandinavian law – importantly shapes national approaches to laws concerning creditors and the efficiency with which those laws are enforced. Since finance is based on contracts, legal origins that produce laws that protect the rights of external investors and enforce those rights effectively will do a correspondingly better job at promoting financial development. Indeed, LLSV (1998) trace the effect of legal origin to laws and enforcement and then to the development of financial intermediaries. Since most countries obtained their legal systems through occupation and colonization, the legal origin variables may be plausibly treated as exogenous.

Formally, consider the generalized method of moments (GMM) regression: G(j) = a + bF(i) _+_ cX + u.

G(j) is real per capita GDP growth over the 1960-95 period. The legal origin indicators, Z, are used as instrumental variables for the measures of financial development, F(i). X is treated as an included exogenous variable.
29 Table 5: Estimates of impact of UC on employment, by sector

|||||Ye|ars since im|plementati|on||
|---|---|---|---|---|---|---|---|---|
||||-3|-2|0|1|2|3|
|By Formality|Fl|coef|-0.008|-0.003|-0.000|-0.008|-0.019|-0.018|
||orma|t|-0.83|-0.36|-0.07|-0.81|-1.43|-1.33|
||Ifl|coef|-0.014|-0.006|0.021*|0.051**|0.075**|0.097**|
||norma|t|-0.46|-0.30|1.76|2.13|2.23|2.17|
|Formal sector|Ailt|coef|-0.004|-0.003|0.001|0.000|-0.003|-0.001|
|By Industry|grcuure|t|-0.54|-0.57|0.28|0.00|-0.37|-0.14|
||Mfti|<sup>coef</sup>|0.001|-0.001|-0.002|-0.007|-0.011|-0.012*|
||anuacurn|<br>t|0.20|-0.76|-0.68|-1.30|-1.51|-1.92|
||Ci|<sup>coef</sup>|0.000|0.001|0.001|-0.001|-0.001|-0.000|
||onstructon|t|0.27|0.97|0.91|-0.82|-0.91|-0.32|
||C|coef|0.001|0.001|0.000|0.002|0.001|0.001|
||ommerce|t|0.27|0.46|0.08|1.06|0.27|0.42|
||Si|coef|-0.002|0.001|0.002|0.000|-0.004|-0.003|
||ervce|t|-0.74|0.60|1.32|0.14|-1.45|-1.07|
||All h|coef|-0.004*|-0.001*|-0.002**|-0.003*|-0.001|-0.002|
||oter|t|-1.78|-1.70|-2.24|-1.84|-0.77|-0.69|
|Informal sector|Ailt|coef|-0.044|-0.019|0.026|0.063**|0.092**|0.129**|
|<br>By Industry|grcuure|t|-1.15|-0.84|1.59|2.02|2.17|2.42|
|||<sup>coef</sup>|0.011**|0.006**|-0.001|-0.004|-0.009|-0.015**|
||Manufacturing|<br>t|1.99|2.32|-0.47|-0.73|-1.25|-2.10|
|||<sup>coef</sup>|0.009**|0.005**|-0.003|-0.006|-0.009|-0.014*|
||Construction|t|2.03|1.97|-1.18|-1.50|-1.60|-1.85|
|||coef|0.006*|0.002|0.000|0.001|0.000|-0.004|
||Commerce|t|1.70|0.60|0.14|0.17|0.03|-0.57|
|||coef|0.003|-0.001|-0.000|-0.002|0.001|0.002|
||Service|t|0.88|-0.37|-0.25|-0.77|0.25|0.45|
||All th|coef|0.002|0.000|-0.001|-0.001|-0.000|-0.001|
||oer|t|1.06|0.39|-1.33|-0.81|-0.01|-0.73|

Notes: *** p<0.01, ** p<0.05, * p<0.1. Results are from estimates of eqn (1) with the coefficients on UC and VF constrained as described in section IV. The regressions include – in addition to the UC dummies – the VF dummies, province- and quarter-specific fixed effects, as well as as the provincial minimum wage, educational attainment, age, the demographic mix of the respondent’s household, area of residence (rural versus urban). Standard errors are adjusted for clustering at the province level.
34

||1960-64|1965-69|1970-74|1975-79|1980-84|1985-89|1990-94|1995-99|2000-04|
|---|---|---|---|---|---|---|---|---|---|
|Olive|0|0|0|0|0|0|0|0|0|
|Onion|na|na|na|na|na|55|81|144|284|
|Orange|25|25|26|33|38|13|3|1|1|
|Pear|na|na|na|na|na|35|24|64|157|
|Peas|0|0|0|0|0|0|0|0|0|
|Pepper|na|na|na|na|na|175|245|146|197|
|Potato|24|19|16|48|27|9|8|4|0|
|Spinach|na|na|na|na|na|89|138|237|134|
|Strawberry|na|na|na|na|na|11|25|26|17|
|Tomato|-4|20|21|21|19|8|-5|3|2|
|Wine|10|-3|-3|-4|-9|-2|-10|-4|-2|

Source: Authors’ calculations based on data in Anderson and Valenzuela (2008).
prices, including increases in the incidence and depth of absolute poverty. Based on the analysis presented, three factors stand out as the most important contributors to the increase in poverty.

The first is the magnitude of the maize price increase. Maize, an important staple in Eastern and Northern Uganda as well as urban areas, more than doubled in price between October 2007 and October 2008. As described by Benson et al. (2008), much of the upward pressure on maize prices is a result of increased demand from Kenya, which experienced a production shortfall following the civil unrest in December 2007 and early 2008. Kenya is expected to remain a significant source of demand for Ugandan maize.

The second major factor is the large increase in the prices of other staples. Prices of cassava, matooke, and rice have been trending upward in Uganda since 2004–05. These increases have accelerated in 2008, presumably from increased demand as consumers substitute away from maize. There are also indications that increased demand from southern Sudan for roots and tubers is an important contributor to higher prices, especially for cassava. Like maize prices, the higher prices for matooke have persisted through 2008 and into 2009.

The third key factor is that most of the poor are net buyers of staple foods, even in rural areas. As shown in the NBR analysis, the poor tend to be net buyers of maize, cassava, and sweet potatoes. Thus the incidence of the food price shock is highest on those households that are least equipped to deal with it. This is also observed at the sub-national level, as the poverty impacts are highest in the Northern region, which was already the poorest region before the food price shock. Contrast this with the impact in West Africa, where the adverse impact of higher food prices was much less in poorer areas than in richer areas (Wodon and Zaman 2008; Coulombe and Wodon 2008).

Although increased demand from regional trading partners is one of the proximate causes of food price increases in Uganda, it would be a mistake to inhibit this trade in an effort to bring food prices down. In this context, it is important not to blunt the price signals that can help spur agricultural investment and production. Uganda has invested heavily in its Plan for the Modernization of Agriculture, aiming for improvements in agricultural productivity and marketing efficiency. If it succeeds, Uganda will be well placed to supply regional markets as well as its own rapidly growing population. The rural poor can benefit if they are able to become net sellers of these foods. However, it should also be recognized 16
<!-- Start of picture text --> Figure 2: Ownership and Business Figure 3: Domestic vs. Foreign<br>Constraints Ownership and Business Constraints<br>60% 60.0<br>50% 50.0<br>40% 40.0<br>30% 30.0<br>20% 20.0<br>10% 10.0<br>0% 0.0<br>Cost of Financing Business licensing and S kills and educat ion of Tax admin. Cost of Electricity Transport Access t o<br>operat ing permits available workers Finance Land<br>DE NOVO P RIVATIZED S TATE/ S OCIAL Domestic Foreign<br><!-- End of picture text --> <!-- Start of picture text --> Figure 4: Exporters vs. Non-Exporters and<br>Business Constraints<br>40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Customs and Transport ation Access t o Land S kills and<br>trade regulations educat ion of<br>available workers<br>Exporter Non-Exporter<br><!-- End of picture text -->

# **Business community perceptions**

While the analysis described above provides guidelines about the relative importance of ownership and perceptions of the investment climate based on their effects on productivity and other performance measures, this section provides an alternative approach based on the ranking of the same perceptions by the survey respondents.

In the 2003 PICS survey, Serbian managers were invited to rate 18 dimensions of the investment climate in terms of whether they were important obstacles to doing business. These dimensions were rated from 0 (factor is no problem at all) to 4 (factor is a major obstacle). Economic policy uncertainty was considered the biggest obstacle (Figure 5), which is understandable in light of the assassination of Prime Minister Djindjic in March 2003, not long before the survey took place. The factor that managers believed presented the least problems was access to land, a finding that is not as surprising at it first appears, for reasons discussed below.

13
# **4.5 Singapore as an innovative city**

Singapore’s efficiency infrastructure is generally perceived to be better developed than its innovation infrastructure. 80% of respondents agreed that Singapore is an efficient city from a business point-of-view, whereas only 46.7% rated Singapore as having a high chance of becoming an innovative city within this decade ( **Table 4.5.1** ). Amongst those who disagree that Singapore is an efficient city, the most critical lack in the city’s efficiency infrastructure is a sound regulatory framework.

Firms in the KIBS sector are the least optimistic about Singapore’s efficiency and innovation infrastructure. Only 53.4% of KIBS firms agreed that Singapore is efficient, compared with over 90% of firms in the high-tech and creative content sectors. Moreover, 80% of the respondents who disagreed that Singapore is an efficient city were from the KIBS sector. Similarly, only one-third of KIBS firms give Singapore a high chance of becoming an innovative city within this decade, compared to 50%-60% of high-tech and media/creative firms.

**Table 4.5.1: Singapore as an efficient and innovative city (Percentage of respondents)**

|High-tech|KIBS|Media/<br>ti|Overall|
|---|---|---|---|
|Singapore is an efficient city from a business point of view<br>||creave||
|Strongly disagree|6.7||2.2|
|Disagree|20.0|7.1|8.9|
|Neutral<br>6.3|20.0||8.9|
|Agree<br>50.0|46.7|50.0|48.9|
|Strongly agree<br>43.8|6.7|42.9|31.1|
|Total<br>100|100|100|100|
|Singapore’s chances of becoming an innovative city within this decade<br>||||
|Very low|6.7|7.1|4.4|
|Low<br>25.0|40.0|21.4|28.9|
|Neutral<br>18.8|20.0|21.4|20.0|
|High<br>37.5|20.0|50.0|35.6|
|Very high<br>18.8|13.3||11.1|
|Total<br>100|100|100|100|

One factor that may have contributed to the lower optimism of the KIBS sub-sector overall is the high presence of venture capital firms in the KIBS sample (6 out of 13 firms), which collectively gave lower rating to Singapore. As these firms use Singapore as a base to source for investment deal flows regionally and even globally, they found the supply of investible, innovative high-tech start-ups to be lower than in larger economies like Taiwan and China.

44
<!-- Start of picture text --> 14 men<br>faa<br>84 1 j / “0<br>Vi /<br>Bo port fngm Consumption<br>325@64 /// =<br>s r / Income<br>2gE:44 J c ___. Consumption and<br>oO qf / income-converted<br>27 Whee / ___. consumption-convertedIncome and TN<br>o|T,,£_€_,Hd.20-- 30 40 50HH]60 —-70<br>Gini coefficient<br><!-- End of picture text -->
# **7. SPECIAL SAFEGUARDS (SSM) AS POLICY DECISION RULES**

From the perspective of developing countries’ interests, an SSM or any other trade policy mechanism should not be judged by the frequency with which it would allow import restrictions. It should be judged by how effectively it distinguished instances in which restriction was the better choice from instances in which to not restrict was the choice that better advanced the developing country’s interests.

The first paragraphs of this paper reminded the reader that imposing an import restriction sometimes can be the correct choice. To not restrict might force an unnecessary burden on resources whose long-run comparative advantage does lie in agriculture, or force other resources out more rapidly than alternative income-earning possibilities develop.

On the side of not acting is the need to maintain constructive pressure on the sector to modernize. In developing countries as well as in developed countries the cost of agricultural support is considerable. For example, the 2008 WTO Trade Policy Review for Korea reports that ‘Korea's net agricultural support exceeds the sector's GDP contribution (3.3% versus 3.2% in 2006).<sup>48</sup> Also on this side of the issue are the interests of the food consumers and an increase of food prices is a significant reduction of real income. Mistakes on the side of restricting can be costly. For example, the WTO’s 2007 Trade Policy Review for Indonesia reports that ‘[t]he two thirds of farming households in Indonesia who are net consumers of rice were adversely affected when the Government announced a seasonal import restriction on rice from January 2004, which has been extended repeatedly and effectively become permanent.<sup>49</sup> In the following subsections I present the results of two simulations that look at the SSM from the perspective of a guideline for when an import restriction would or not be the correct policy choice.

## **Identifying incidents of price undercutting**

Raul Q. Montemayor (2008) has conducted a sophisticated simulation exercise to better understand how the proposed SSM would perform. Though his primary objective is different from determining when the mechanism would make the correct choice between imposing versus not imposing an import restriction, his results do provide considerable insight on that issue.

Montemayor applied the proposed SSM rules to historical data; his data base included monthly imports mostly from 2000 to 2005 of six countries: China, Ecuador, Fiji, Indonesia, the Philippines and Senegal.

> 48 WTO Document WT/TPR/S/204, page 90.) 49 WTO document WT/TPR/S/184, page 67.

31