rafmacalaba/datause-encoder-data huggingface dataset:
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# 6. Further research suggested

One first conclusion that clearly emerges from the review of the few empiric studies available about V.C. is the scarcity of aggregate quantitative information ("hard facts") about the results and impact of the venture capital industry, a scarcity that affects both developed and developing countries. Several explanations could be advanced to justify such lack of data. Among them: i) Until recently, venture capital has been an inward looking business where neither investors nor investees had any interest in spreading information about the results of their investments; ii) venture capital is, almost by definition, a business where investments have to be close-to-home where they can be easily capitalists, including the very largest, have expressed interest in investing or expanding their business overseas and in assessing the applicability of their procedures in new markets; iii) government participation, and its interest to gather information about the industry, has been either limited (e.g., in the US to the SB!Cs), or only very recent as is the case in many European countries; iv) very few institutions, among which IFC and the World Bank seem to be the frontrunners, have "discovered" the developmental potential of this mechanism and expressed interest in studying its applicability to developing countries. In addition, the few private data-gathering institutions which are specialized in venture capital have, in accordance with their objectives, concentrated their efforts in producing information and industry directories, concerning potential suppliers of capital (e.g., Guide to Venture Capital Sources in the US) and describing procedures for preparing applications and for contacting venture capitalists.

As a consequence of the above, one first and major topic of research would have to address a systematic review of the venture capital experience in selected countries. A multicountry empirical survey, including some developed and a few developing countries, could be carried out with the aim of: i) collecting data about the extent, results and impact of venture investments; and b) reviewing and identifying specific factors of success and failure of venture investments in different economic environments. Some of the factors to investigate could include: impact of government incentives and of government's direct participation with resources, degrees of involvement of the local banking communities, participation of institutional investors, divestment alternatives in practice, etc.

A second conclusion of the review is that most of the information available about the investees is reduced to the press coverages of success stories as described by their protagonists (i.e., investors, entrepreneurs or government officials), with very few outsider's reviews attempting a benefit/cost analysis or a critical assessment of the contribution to success of each one
Crump, Richard K., V. Joseph Hotz, Guido W. Imbens, and Oscar A. Mitnik, 2009, Dealing with Limited Overlap in Estimation of Average Treatment Effects, _Biometrika_ , 96: 187-199.

Cunha, F., J. J. Heckman, L. Lochner, and D. V. Masterov. 2006, Interpreting the Evidence on Life Cycle Skill Formation, In _Handbook of the Economics of Education_ , Volume 1, edited by E. A. Hanushek and F. Welch, 697-812, Elsevier, North-Holland, Amsterdam, Holland.

Glewwe, P. and M. Kremer, 2006, Schools, teachers, and education outcomes in developing countries, Ch.16, _Handbook of the Economics of Education_ , Vol.2, edited by E.A. Hanushek and F. Welch, Amsterdam: Elsevier B.V.

Jensen, R., 2010, The (perceived) returns to education and the demand for schooling, _Quarterly Journal of Economics_ , 125 : 515-548.

Hanushek, E.A., 2006, School Resources, Ch.14, _Handbook of the Economics of Education_ , Vol.2, edited by E.A. Hanushek and F. Welch, Amsterdam: Elsevier B.V.

Heckman, J., 1979, Sample Selection Bias as a Specification Error, _Econometrica_ 47: 153–61. Hirano, K. H., G. W. Imbens, and G. Ridder. 2003, Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score, _Econometrica_ 71: 1161-1189.

Hoxby, C., 2000, The Effects of Class Size on Student Achievement: New Evidence from Natural Population Variation, _Quarterly Journal of Economics_ , 116: 1239-1286.

Khattri, N., C. Ling, and S. Jha. 2010, The Effects of School-Based Management in the Philippines: An Initial Assessment Using Administrative Data, Policy Research Working Paper No. 5248, World Bank, Washington D.C.

Lanzona, Leonardo A., 1998, Migration, Self-selection and Earnings in Philippine Rural Communities, _Journal of Development Economics_ 56: 27-50.

Maluccio, J. A., J. Hoddinott, J. R. Behrman, R. Martorell, A. R. Quisumbing, and A. D. Stein, 2009, The Impact of Improving Nutrition during Early Childhood on Education among Guatemalan Adults, _Economic Journal_ 119: 734-763.

Mani, Subha, 2008. Is there Complete, Partial, or No Recovery from Childhood Malnutrition? Empirical Evidence from Indonesia, Fordham Economics Discussion Paper Series dp2008-19, Fordham University.

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Table 15: Between-group differences in entry grade: YP and non-YP sample

|||YP|||Non-YP||
|---|---|---|---|---|---|---|
||Total<br>Difference|Characteristics|Structure|Total<br>Difference|Characteristics|Structure|
|M1-M2|n/a|n/a|n/a|0.58|0.05|0.54|
|MI-Fl|n/a|n/a|n/a|1.68|0.91|0.77|
|MI-F2|n/a|n/a|n/a|2.42|1.02|1.39|
|M2-FI|n/a|n/a|n/a|1.10|0.86|0.24|
|M2-F2|n/a|n/a|n/a|1.83|0.98|0.85|
|Fl-F2|n/a|n/a|n/a|0.74|0.12|0.62|

Note: Since all YPs enter at grade 21 there are no differences to decompose.

Table 16: Between-group differences in annual promotion rate: YP and non-YP sample

|||YP|||Non-YP||
|---|---|---|---|---|---|---|
||Total<br>Difference|Characteristics|Structure|Total<br>Difference|Characteristics|Structure|
|MI-M2|0.03|0.03<br>(0.016)|0.000<br>(0.018)|-0.02|-0.003<br>(0.008)|-0.01<br>(0.011)|
|MI-Fl|-0.01|0.002<br>(0.011)|-0.01<br>(0.016)|-0.06|-0.03<br>_(0.005)_|-0.02<br>(0.010)|
|M1-F2|0.002|0.005<br>(0.017)|-0.003<br>(0.023)|-0.08|-0.03<br>(0.010)|-0.05<br>(0.017)|
|M2-F1|-0.04|-0.03<br>(0.019)|-0.01<br>(0.011)|-0.04|-0.03<br>(0.011)|-0.01<br>(0.013)|
|M2-F2|-0.03|-0.03<br>(0.013)|-0.003<br>(0.016)|-0.06|-0.03<br>(0.006)|-0.03<br>(0.016)|
|F1-F2|0.01|0.003<br>(0.017)|0.01<br>(0.026)|-0.02|0.002<br>(0.009)|-0.02<br>(0.022)|

Notes: Standard errors are given in parentheses 44
3 point and also attempts to identify factors that often lead to differences between the NBER and Balassa RCA indices.

II. The Comparative Advantage Indices Heckscher-Ohlin measures of comparative advantage are generally similar to labor intensity indices derived by Lary (1968) for the NBER. Lary's analysis employed the criteria of value added per employee, both in the U. S. and other countries, for identification of products which were capital or labor intensive. The general rule followed was to classify labor-intensive products as those which met two conditions, the first being that value added per employee did not exceed the national average for all United States manufacturing by more than 10 per cent.<sup>3</sup> The specific factor .ntensity index calculated for industry j (L.) was defined as, where V and Vt represent value added in industry j and all United States manufacturing respectively, while Nj and Nt represent the number of workers in 3 The use of United States data is appropriate for identifying labcr intensive products if these items are genrally produced by labor intensive - processes in other countries. Lary (1968, Appendix D) analyzed U.S. U.K., U.S. - - Japan, and U.S. India production data and determined this was generally the case. However, based on these comparisons several additional products were added to the list derived from U.S. statistics.
Table 1: Descriptive statistics: Non-migrants and returnees who are wage earners

||Non-<br>|migrants<br>|Return <br>|Migrants<br>||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||Mean|St. Dev.|Mean|St. Dev.|Diference|
|_Individual characteristics_<br>||||||
|Age|34.055|10.679|42.458|9.939|-8.403***|
|Ever-married|0.740|0.439|0.949|0.221|-0.209***|
|No educational degree|0.189|0.392|0.195|0.396|-0.005|
|Primary or preparatory education|0.182|0.385|0.142|0.349|0.040***|
|Secondary education|0.382|0.486|0.484|0.500|-0.102***<br>|
|Above secondary education|0.247|0.431|0.179|0.384|0.068***|
|Rural|0.531|0.499|0.638|0.481|-0.108***|
|_Geographical regions_||||||
|Cairo|0.121|0.326|0.079|0.270|0.041***<br>|
|Alexandria and Canal cities|0.099|0.298|0.049|0.215|0.050***|
|Urban Lower Egypt|0.106|0.308|0.127|0.333|-0.020*|
|Urban Upper Egypt|0.145|0.352|0.110|0.313|0.035***|
|Rural Lower Egypt|0.278|0.448|0.385|0.487|-0.108***|
|Rural Upper Egypt|0.251|0.434|0.250|0.434|0.001|
|_Current job characteristics_||||||
|Job tenure|11.732|9.604|12.654|9.293|-0.922**|
|Incidence of work contract|0.420|0.494|0.488|0.500|-0.068***|
|_Sector of employment_||||||
|Public sector|0.319|0.466|0.435|0.496|-0.116***|
|Private sector|0.656|0.475|0.551|0.498|0.105***<br>|
|Other sector|0.025|0.156|0.014|0.117|0.011*|
|_Economic activities_||||||
|Agriculture, Forestry, Fishing|0.116|0.320|0.118|0.323|-0.003|
|Manufacturing, Mining, Quarrying|0.195|0.396|0.129|0.336|0.065***|
|Construction|0.164|0.370|0.185|0.389|-0.021|
|Wholesale, retail trade and transportation|0.237|0.425|0.160|0.367|0.077***|
|Professional and administrative activities|0.023|0.151|0.024|0.152|0.000|
|Other activities|0.265|0.442|0.384|0.487|-0.118***|
|Observations|7|,437|7|19||

*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Column 5 is t-test for whether the difference in means between the two groups is statistically significant.

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**Figure 15: Cargo Dwell Time by Frequency of Deliveries, 2011**

<!-- Start of picture text --> 12<br>10.32<br>10<br>7.74<br>8<br>6<br>4<br>2<br>0<br>< 10 deliveries ≥ 10 deliveries<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

# **Analysis by Market Structure**

Figures 16 and 17 present dwell time by the level of competition in the sector. In Figure 17, the categories 0, 1, and 2–5 competitors are aggregated into ―monopoly-oligopoly,‖ and the categories 6– 20 and more than 20 competitors are aggregated into ―competition.‖ Importers in monopoly-oligopoly situations experience a two days significantly shorter dwell time than importers in competitive situations (t(223.564) =2.694; p = 0.003).<sup>9</sup> . In fact, rational importers in monopoly-oligopoly situations are likely to have shorter dwell time because they seek to minimize their logistics costs (long dwell time generally translates into higher logistics costs) in order to optimize their profits.

**<u>Figure 16: Cargo Dwell Time, by Number of Competitors, 2011</u>**

<!-- Start of picture text --> 12<br>9.92<br>10 9.41<br>8.35<br>8<br>6.00 5.90<br>6<br>4<br>2<br>0<br>0 competitor 1 competitor 2-5 competitors 6-20 competitors more than 20<br>competitors<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

> 9 This also holds when comparing average dwell time of each category of competitors with the highest next category: shippers that do not have competitors have a three days shorter dwell time than those that have more than 1 competitor (t(14.426) = 4.346; p = 0.0003); shippers that face 1 competitor experience a three days shorter dwell time than those that have more than 2 competitors (t(10.568) = 2.735; p = 0.01); and shippers with 2-5 competitors have a one day shorter dwell time than shippers with more than 5 competitors (t(150.772) = 1.749; p = 0.041).

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establish de facto marital relationships. After its promulgation, some of them decided to formalize their unions. According to Sun and Zhao (2016), in societies where divorce is permitted, couples exhibit distinct behavior, with women having greater power in negotiations, leading to increased autonomy and empowerment, which could lead to greater labor participation for women.

**Figure 10: Effect of the marriage rate on the female labor force participation rate**

<!-- Start of picture text --> 6.0<br>4.0<br>2.0<br>0.0<br>-2.0<br>1960 1970 1980 1990 2000 2010 2020<br>Range of variation Marriage rate<br>Percentage points<br><!-- End of picture text --> Source: Own calculations.

In contrast with the previous decades, the period from 2010-2018 saw a dramatic shift, with the coefficient surging to 2.84pp. This marked change suggests a significant transformation in the dynamics between marriage rates and women's labor participation. Amador, Bernal, and Peña (2013) noted that this surge in female labor participation was largely driven by the increasing participation rates of married or cohabiting women.

# v) **Male homicide rate**

During the late 1960s, Colombia experienced a period of social unrest marked by various factors. According to Luna and Villarreal (1994), the political structure of the National Front<sup>9</sup> started showing signs of exhaustion during this time, leading to the emergence of peasant self-defense groups and radicalized urban sectors that eventually transformed into guerrilla organizations. In addition, drugrelated violence, starting with the marijuana wars and later evolving into the cocaine wars, became more prominent in the mid-1970s. Furthermore, the 1980s witnessed the emergence of paramilitary groups. Throughout this period, the homicide rate per 100,000 inhabitants fluctuated around 61.

> 9 The National Front was a political coalition between the conservative and liberal parties between 1958 and 1974 to face a period of violence and partisan retaliation that affected the country.

18
being in the lowest bin, we define our earnings variable to be the share of households in the top two bins (with the highest earner earning 5,000 rupees or more).

For an alternative way of aggregating information across assets, we create an index at the village level by taking the primary component of the indicator variables described above in the SECC microdata, normalized to have a mean of 0 and standard deviation of 1 within our sample.

We generate another consumption proxy using lights at night, as measured by satellites. Night lights are a proxy for consumption that have the advantage of high resolution and objective measurement over a 20+ year period (Henderson et al., 2011). We match gridded data to village polygons, sum over all pixels in the village and then take the log of the value plus 1 in order to not drop observations that take the value 0. To increase precision, we define our dependent variable as the log of the mean value from 2011, 2012 and 2013 (plus 1), and include a control for log mean baseline light (plus 1) in 2000-2002.

# **A6 Spillovers**

Spillover effects of PMGSY road construction on nearby villages are assessed using 2001 Population Census GIS data purchased from ML InfoMap. Catchment areas with radii of 5 km were constructed by measuring distances from the centroids of villages in the sample to the centroids of all other villages. Outcomes were then aggregated across all villages within these catchment areas, constructed in the same manner as for the non-spillover regressions. On average, there are 15 villages per 5 km catchment area. 55 percent of non-sample villages within a catchment appear in more than one catchment at 5km. These villages are double counted, but should not bias the estimates due to the exogeneity of road construction in our regression discontinuity sample.

# **A7 Family-wise indices**

In order to address concerns of multiple hypothesis testing, we follow Anderson (2008) in generating five indices for our main families of outcomes: transportation, labor market, firms, agriculture and consumption. Each of these is generated by demeaning its component outcomes and converting to effect sizes through dividing by control group standard deviation; demeaned values are then combined by weighting according to the inverse of the covariance matrix. The transportation index is comprised of five indicator variables for availability of motorized transit: public buses, private buses, vans, taxis and auto-rickshaws. The labor market index is comprised of the share of workers in agriculture and the opposite of the share of workers in manual labor (so that their covariance is positive). The firms index is comprised of log of employment plus 1 in all nonfarm firms; it does not include the other firm outcomes as they are simply disaggregations of total employment by sector. The agriculture index is comprised of our favored measure of agricultural yields (differenced NDVI, described above) and each of the measures of agricultural inputs: share of households owning mechanized farm equipment, share of households owning irrigation equipment, share 63
_* *_ Substituting for _PLL_ = _PiQi_<sup>_- C_</sup> _i_<sup>_(Q_</sup> _i , W, E_ ) from (5) The present value of the welfare change is thus: The Ricardian model takes either (8) or (9) depending on whether data are available on annual net revenues or capitalized net revenues (land values VL). The model in (8) was employed for this research, as data on land prices for the selected samples were not available. This is the same approach followed by Sanghi et al. (1998) and Kumar & Parikh (1998) for India.

# **_5.2 Data description_**

The household data for this study was based on a sample of 1000 farmers randomly selected from different agro-ecological settings of the country, who were believed to be representatives of the whole nation (Table 1). A total of 50 districts (20 farmers from every district) were purposely selected, starting from the extreme highlands of the southeastern regions of the Oromia Regional State to the lowlands of the Afar Regional States. Yale University and the University of Pretoria provided the questionnaire for this study, which asks about a variety of household attributes. The interviews with the farmers took place during the 2003/2004 production seasons. Almost all were small-scale farmers with rainfed farms, as more than 95% of Ethiopian farmers are of this type.

The temperature data for this study was derived from the satellite data provided by the US Department of Defense and the precipitation data from the African Rainfall and Temperature Evaluation System (ARTES). The soil data for this study was obtained from the Food and Agricultural Organization. The FAO provides information about the major and minor soils in each location, including the slope and texture. The hydrological data (flow and runoff) was obtained from the University of Colorado (IWMI/ University of Colorado 2003). The hydrology team calculated flow and runoff for each district using the hydrological model for Africa.

16
drawback of all firm survey data arises from the exit of firms from the sample. Exit of a firm does not necessarily mean that the firm has truly disappeared from the economy. To mitigate this issue, the article corrects for selection bias in the estimation of the production function. Notwithstanding this limitation, the dataset is well suited to the present study and has been extensively used to investigate issues affecting African manufacturing firms, such as size distribution and productivity (S ̈oderbom and Teal (2004), Van Biesebroeck (2005b)) and trade policy (Van Biesebroeck (2005a), Bigsten et al. (2004)), among others.

The tariffs data were compiled and provided by the _Centre d’Estudes_<sup>_ ́_</sup> _Prospectives et d’Informations Internationales_ . The tariffs data consist of the applied bilateral three-digit sector-level tariff rates between Ghana and its trading partners. Ghana’s export of manufacturing goods is concentrated in a few countries; hence, tariffs weighted by sector-level bilateral trade share is very similar to simple average tariffs. Because of the similarities between the simple average tariffs and trade-weighted average tariffs, using either of the two produces similar results in the estimation of the main econometric specification.<sup>9</sup>

# **3 Theoretical framework**

The key point of the analysis in this article is to evaluate the effect of trade openness on competition. In an institutional environment as described in 2.1, market imperfections and distortions are prevalent and expected. On the other hand, trade liberalization has the potential to increase competition and improve the allocative efficiency of the economy. Indeed, the theoretical model of Melitz (2003) predicts that trade induces competition by raising the minimum productivity survival threshold; consequently, the resources of exiting firms will be reallocated towards more productive firms.

Whether trade liberalization does induce competition becomes an empirical question that needs to be investigated. Previous empirical studies of developing economies have focused on Latin American and Asian countries (Pavcnik, 2002; Amiti and Konings, 2007; Topalova and Khandelwal, 2011) with the exception of Harrison (1994) which studies Cote d’Ivoire. While trade and productivity linkages have dominated the existing literature on evaluation of the effect of trade openness, this article takes a different approach by analysing firms’ price-cost margins. Other articles preceding the present work include; Brandt et al. (2017) on China, De Loecker et al. (2014) on Belgium and De Loecker et al. (2016) on India.

In view of the above, this section provides a detailed description of the computation of markups and market imperfections parameters using firm-level production data. The theoretical framework follows the work of De Loecker and Warzynski (2012), which is an extension of the seminal work of Hall (1988) on price-cost margins.

## **3.1 Markups**

Firm _i_ produces output _Q_ at time _t_ according to the following production function: 7