rafmacalaba/datause-encoder-data huggingface dataset:
- 7 -

the soundness of its long-term investment policies. At the same time, 3i Ventures' pool of management expertiFe and its excellent recent record, have enabled the company to take the lead in managing many spectacular venture capital projects in recent years.

# 1.3 Venture Capital Results: Contribution to Entrepreneurship

There have been various attempts to measure the economic impact--in terms of jobs, sales, exports, taxes and other indicators-- of new venture-financed companies. While overall studies on the subject are rather scarce, the little available evidence is rather impressive. The most complete study, so far, was undertaken by the U.S. Government Accounting Office (GAO), in 1982, in a study ("Government-Industry Cooperation Can Enhance the Venture Capital Process") whose results were summarized as follows: "The experience of 1,332 companies that were started with venture, backing during the 1970s demonstrate benefits to the Nation's economy and productivity that are disproportionately large when compared with the amounts of capital invested. For example, with $209 million invested to create 72 of these firms, their combined sales in 1979 alone totaled $6 billion, growth in annual sales averaged 33 percent a year, and in the process, these firms created an estimated 130,000 jobs, over $100 million in corporate tax revenues, $350 million employee tax revenues, and $900 million in export sales. Moreover, most products were productivity enhancing, such as computer related equipment, fiber optics, industrial controls, lasers, robots, word processors, and numerous others." With respect to the performance of individual venture capital companies, the information is both scarce and also difficult to assess. Although some funds will disclose information on how well they have done, a lot depends on the risk profile of the portfolio which is not always available (were they all start-ups? were they leveraged buy-outs? what is the average age of the portfolio? does it represent a downturn or an upturn business cycle? etc. Only as a reference, by the mid-1980s an annual compound rate of return of 35 percent was considered a realistic goal by independent venture capital funds. The scarcity of more quantitative information has been, to a certain extent, complemented by numerous press coverages about successful venture capital-started enterprises which, by now, have become textbook cases: Apple, DEC, Lotus, Federal Express, Atari, etc.

The link of venture capital with the rest of the economy has to build up on the existing data about the overall impact of small businesses. A number of studies have shown that new companies are the major source of new jobs in the U.S. and in Europe. As an example, D. Birch and S. Mac Cracken, at MIT,
**Table 8. The Impact of Primary Education Spending on Education Outcomes**

_A. 7he impact on primary education "Failure" rate (from regressions without the Governance interaction term):_

||Reg|ression|s withoutadultill|iterac|y|Re|gression|swith adultilli|teracy||
|---|---|---|---|---|---|---|---|---|---|---|
|OLS (Table|||-. 35|||||-.36|||
|6)<br>2SLS(Table|||-1.13|||||-1.80|||
|7)|||||||||||
|_B.Theimpacto _<br>_term,i.e.,_**_22_**_+_|_nprim_<br>_24*Gd)_|_aryed_<br>|_ucation"Failure_|_" rate_|_(fromreg_|_ression_|_swithth_|_eGovernancei_|_nterac_|_tion_|
|_Evaluatedat_|_C_|_orrupt_<br>_index_|_ion_<br> <br> <br>_ofbu_|_Qualit_<br>_reau_|_y_<br>_cracy_|_C_|_orrupti_<br>_index_|_on_<br>_ofb_|_Qualit_<br>_ureau_|_y_<br>_cracy_|
||_2.1_|**_3_**<sup>**_,_**</sup>**_4 _**<sup>**_M_**</sup>|_4 _<sup>_._</sup>_7_<sup>_U_</sup><br>_19B_|**_3_**<sup>**_._**</sup>**_3_**<sup>**_M_**</sup>|_4 _<sup>_,_</sup>_7_<sup>_U_</sup>|_2.1_|_3 _<sup>_,_</sup>_4_<sup>_M_</sup>|_4_<sup>_._</sup>_7_<sup>_U_</sup><br>_I9B_|**_3 _**<sup>**_,_**</sup>**_3_**<sup>**_M_**</sup>|_4_<sup>_,_</sup>_7_<sup>_U_</sup>|
|OLS(Table<br>6)|.23|-.33|-.89**<br>.24|-.30|-.83**|.24|-.33|-.89**<br>-.28|-.28|-.81**|
|2SLS(Table<br>7)|.S2|-.64|-1.79**<br>.97|-.58|-2.13**|.62|-.60|-1.82**<br>.94|-.56|-2.07**|

_Note:_ 1. Evaluated at: MThe sample mean; B One standard deviation below the mean; and u One standard<sup>deviation</sup> above the mean.

2. Based on a 't' test for functions of parameters: Significantly lower than 0 at the 10-percent level; Significantly lower than 0 at the 5-percent level.

# **6. Conclusion**

In this paper we have studied the links between public spending, governance, and outcomes. Our primary investigation was to examine the role of governance-measured<sup>by the</sup> level of corruption and the quality of bureaucracy-and ask how it affects the relationship between public spending and outcomes. Using data from a cross-section of countries over two years, we found that public health spending has a negative impact on both child and infant mortalities in countries that have good governance. As the level of corruption goes down (or the quality of bureaucracy goes up), public spending on health becomes more effective in lowering child and infant mortalities. Our findings also indicate that in countries rated as very corrupt or rated to have a very ineffective bureaucracy, public health spending at the margin will be inefficacious. Linking public spending on primary education with failure to attain primary education, we found a similar result. Increasing public spending on primary education is likely to be more effective in increasing primary education attainment in a country with good governance.

_23_
3 Theoretical Model We now show that a heterogeneous firms trade model similar to Melitz (2003) can explain the stylized facts presented in the previous section.’ The major substantive difference between our approach and Melitz’s is that we allow for potentially asymmetric countries, trade costs, and market entry costs. We also derive some explicit results in relation to the extensive margin of trade, which we interpret in terms of export diversification. While the basic mechanics of the model remain unchanged, the introduction of these asymmetries prevents us from obtaining an analytical solution (Baldwin and Harrigan, 2007). We therefore proceed by numerical simulation using essentially the same parameter set as in Bernard et al. (2007). We consider unilateral changes in the fixed and variable costs of exporting and importing, as well as in the costs of domestic market entry. We then consider a reciprocal change in the variable costs of trade, which we assimilate with a variation in international transport costs. Simulation results not only accord with the preliminary empirical evidence presented in section 2, but also suggest an ordering of export diversification elasticities with respect to the different types of costs we have identified. We test both sets of predictions in section 4.

<!-- Start of picture text --> 3.1 Consumption Block<br><!-- End of picture text --> The world consists of two possibly asymmetric regions, Home (//) and the Rest of the World (R). Labor is the only factor of production, and the regions are endowed with L” and L” units respectively. Each region has S' + 1 productive sectors, of which one produces a freely traded homogeneous good under constant returns to scale with one unit of labor required for one unit of output. Wages are therefore equal to unity in equilibrium. The other S sectors each produce a continuum of differentiated goods under increasing returns to scale and costly trade. Absolute specialization is excluded from consideration.

Identical consumers in both markets maximize the two-tier utility function (3). The first tier is <u>~ 7Our presentation of the model draws heavily on Helpman</u> et al. (2004), and Baldwin and Harrigan (2007).

<!-- Start of picture text --> 11<br><!-- End of picture text -->
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market gender discrimination. The more important is differentially poor access. Evidence on this requires logistic techniques and sampling frames bas,.d upon households rather than employers. One such study, for rnral Tanzania, found extreme discrimination in access to non-farm wage employment, which was the highest return activity. Access was determined largely by education, age and gender: A 36 year old man with secondary education had a three-in-four chance of such employment whereas a women of the same age and education had half of that chance. With completed primary education she had only a quarter of the chance, and with partial primary or less she had only one-fifth of the chance, (Collier, Radwan, and Wangwe, (1986) Table 4.7). This declension suggests that discrimination may apply differentially at different levels of education. Thus general expansion of the education system may reduce the aggregate incidence of discrimination even if the educational expansion is not targetted to women.

Since the rural non-agricultural labor market commonly contains a large public sector, discrimination is- the outcome of government recruitment policies. In turn, this may reflect the use of public sector employment as part of a patronage system rather than its being a competitive entry based upon job-related characteristics. Women may because of their low status in society be badly represented in the lobby for patronage.

In urban areas, the labor market is probably the main arena of discrimination. However, in rural areas, non-agricultural wage employment can usually only be available to a minority of women
# _Labor_

Labor is the major input for cocoa production. The peak labor demand is felt during the two harvest periods. The main cocoa harvest is from April to June, and the “fly,” or smaller crop, is harvested in October to December. Other major labor demands are for weeding and pruning (March, August) and activities related to fertilization and control of diseases and pests. Labor demand depends on yields and the extent to which “best practices” are used.

As is the case with coffee, no reliable data exist on total employment in cocoa production and marketing. The available labor pool for production has been influenced by out-migration to urban centers, especially by younger male adults, but experts in cocoa indicated that labor shortages in cocoa are not a serious problem. Most cocoa labor is Dominican, so, unlike coffee, problems associated with Haitian migrants do not have a major direct affect on the cocoa industry. Small-scale farmers rely on family labor to work the farm during the year with the exception of the harvest period. During harvest only about 50% of labor on medium-sized and semi-technified farms is family labor. Some small-scale farmers also tend to work on larger farms as wage laborers, and many seek work outside of agriculture.

# _Cocoa Producer Perceptions of Risks_

The CRMG survey asked producers to rate several risks in terms of their importance. Risks associated with cocoa prices, weather, diseases and pests, and access to credit all received similar scores (all were scored 2.6 and above on a 1 to 3 scale where 1 was not important, 2 important, and 3 very important). No significant variation in results was found by landholding size. Cocoa producers, like coffee producers, perceive that they are operating in a risky environment – despite the current upswing in international cocoa prices.

# _Weather Risks_

Hurricane Georges caused significant damage to the cocoa sector. Hurricanes in 1987 and 1979 were also damaging. Post-Georges rehabilitation provided an opportunity to upgrade the productive stock and massive replanting efforts followed.

About 95% of producers surveyed in the CRMG survey indicated their production suffered as a result of Hurricane Georges, and 20% of producers indicated that 81-100% of their production was affected (see tables IV.10a&b ). About 80% of all producers in the CRMG survey said that weather risk was “very important.” Producers who suffered severe damage from Georges were less likely to engage in _Hispaniola_ production and, while the results are not definitive, they suggest that hurricane risk is a constraint to improved quality.

|**Table IV.10a Cocoa Producers Affected by Hurricane Georges**|
|---|

|**Did your farm production suffer from Hurricane George?**|96%YES|
|---|---|
|**Did you replant cocoa since that time?**|59%YES|

|**Table IV.10b Production Losses**<br>|**for Coco**|**a** **Produc**<br>|**ers Affected b**<br>|**y Hurrican**<br>|**e Georges**<br>||
|---|---|---|---|---|---|---|
|**Percent production loss**|None|1-20%|21-40%|41-60%|61-80%|81-100%|
|**Percent producers in category**|6%|13|20%|19|22%|20%|

61
demonstrate this by employing three restricted simulations in which the 40 representative households in the analysis (differentiated by rural/urban orientation and by income level) have (i) identical consumption shares, (ii) identical factor income shares, and then (iii) identical consumption _and_ factor income shares. Since the results from simulation (i) are nearly identical to those generated when the heterogeneity of the 40 households is left intact, the authors conclude that “clearly, for the poor it is the source of income, not the pattern of expenditure that is driving the adverse impact relative to the average household” (p. 107).

The general equilibrium analysis of rice export taxation in Thailand by Warr (2001) also suggests that factor earnings effects are the driving force behind welfare and distributional effects. Although an export tax generates government revenue and lowers the price of rice for consumers, it also lowers the return to unskilled labor, which is used intensively in the Thai rice industry. Because both the rural and urban poor derive more than 40% of their income from unskilled labor (according to the Thai survey upon which the stylized households are based), the negative income effect ends up _outweighing_ the consumption benefit, such that both the rural _and_ urban poor are harmed by the export tax in Warr’s analysis.

Despite the apparent importance of factor earnings effects, until recently they have generally not been accounted for in studies that quantify the effects of external shocks on the poor in developing countries. As will be seen in the next section, this is particularly the case historically for analyses based on detailed household surveys.

# **III.  Partial Equilibrium Analysis**

The simplest, and most logical, place to begin our survey of previous work is with studies that focus on the impact of the commodity price changes flowing from trade liberalization (point (a) in Winters’ list). This takes us into the realm of partial-equilibrium/cost-of-living analysis. While this characterization is quite broad and somewhat awkward, these studies are all “partial equilibrium” in nature, since they focus on one or a limited number of markets in an economy. Additionally, most can be considered “cost-of-living” studies since they tend to focus on the impact of commodity price changes on household expenditure and hence on poverty. The majority of studies in this category can also be regarded as micro-simulation models. Micro-simulation is distinguished by a focus on 8
22 The efficiency of private training is borne out In this<sup>comparison. The public sector fared less</sup> well, but a more favorable comparison emerges from a<sup>subsequent estimate of social rates of return</sup> for public secondary vocational schools and private technical<sup>junior colleges (Lee, J.-K., 1986). In this</sup> case, the social rates of return are roughly the same,<sup>exceeding 14 percent. While exceptions might</sup> be found, the evidence suggests that public training in<sup>open, competitive economies can be efficient</sup> alongside private training. Further evidence of this is offered<sup>by Hopkins (1990) in Malaysia, although</sup> here the data are more problematic. In terms of<sup>internal efficiency, Hopkins indicates that dropout</sup> and repeater rates seem to be low for both private and<sup>public vocational schools. Public schools</sup> exhibit better capacity utilization. However, placement<sup>rates, a measure of external efficiency, are said</sup> to be higher for private institutions.

The evidence in Malaysia is not sufficiently robust<sup>to build a case for parity between public</sup> and private sectors in VET. The evidence would probably<sup>favor the private over the public sector. To</sup> reiterate, the more important question is whether the competitive<sup>forces generated by an open</sup> economy create incentives for the public sector to emulate<sup>the private sector in terms of VET being</sup> responsive to market forces. Rather than simply looking<sup>at static comparisons of VET efficiency, this</sup> requires comparisons of change. The issue of change<sup>in the structure of VET was framed above in</sup> terms of whether the private sector share of VET increases<sup>as an economy shifts to an export-led</sup> development strategy. Here, the issue of change i; whether<sup>an open economy encouragas an</sup> Inefficient public sector VET system to become more efficient,<sup>internally and externally.</sup> There is evidence of this happening in Malaysia<sup>with the government currently engaging in a</sup> major reform of the public VET system to make<sup>it more demand-driven. By the same token, South</sup> Korea has regularly engaged in reforms of its public<sup>training system. As the economy's needs for</sup> skills have changed through various stages of development,<sup>Stevenson (1988) reports that South</sup> Korea has been quick to adapt its public training system<sup>to meet these needs. Middleton, et al.</sup> (1991) offers further insight into the complexities governing<sup>public VET and its responsiveness to</sup> competition in Thailand. The authors argue that public<sup>VET is of good quality, but less flexible to</sup>
# **IV.  Mexico**

# **A. Primary Markets Characteristics**

Primary market activity in Mexico has mirrored that of Chile in several ways. After the crisis in 1994, there was a boom in equity market issuance. As in Chile’s case, the 1998 Russian crisis dried up equity issuance, and subdued bond issuance in the short run. As interest rates started to fall, companies started to look at domestic financing options on a greater scale. However, while the total size of issuance has grown, there have been only a limited number of names which have accessed the market, as discussed below.

**Figure 2:  Domestic Debt Issuance in Mexico, 1999-June 2003**

**Source:  Bancomer**

In 1999, the new social security law created new pension fund companies, called Afores, which have grown to be the largest institutional investor in Mexico. The rise of these institutions, along with a growing mutual fund industry, has accounted for a large growth in domestically invested funds. At the end of 2003, the size of the invested portfolio of the Afores was $401.5 billion pesos (just under US$40 billion). Every few months, the Afores receive about US$500 million in mandatory contributions, suggesting that even without compounded returns, the relative size of these institutions in the market will continue to drive market dynamics.

Mirroring the Chilean environment, one of the largest ways these investors sway the market is due to the impact of their investment restrictions. Currently, the Afores can only invest in debt securities, and have fairly strict rating restrictions as well. While allowed to invest in AA and above, in practice the pension companies conservatively restrict their purchases to securities with a AAA rating (to allow for credit deterioration. Consequently, the market for securitization / structuring is growing, as companies seek to 11
|to increase (annual growth(“) = 1%), the excess capital costforinformal firms to decrease<br>(annual growht(@) = —0.25%),the ratiobetween TFP productivity inthemoderninformal<br>and formal sector to increase (annual growth(a) = 0.25%), and the ratio between TFP<br>productivityintherudimentaryandformaleconomytoincrease(annualgrowth(b)=|
|---|
|Percent female|525|52.923|18.200|0.000|100.000|
|---|---|---|---|---|---|
|Low income clients|451|0.681|0.467|0.000|1.000|
|Years of schooling|539|9.890|3.463|3.200|13.600|
|FL in population|523|44.170|14.668|24.000|66.000|
|Mandatory|480|0.240|0.427|0.000|1.000|
|Incentivized|445|0.247|0.432|0.000|1.000|
|Teachablemoment|539|0.479|0.500|0.000|1.000|

Notes: “RCT” is a dummy variable with “1” if selection into treatment was conducted through randomization and “0” otherwise (such as matched designs). “TOT” is a dummy variable with “1” if the effect size estimate is derived from the treatment effect on the treated and “0” if it is derived from the ITT estimate. “Delay” is a continuous variable indicating the delay between treatment and measurement of outcomes in weeks. “1/SE” is the inverse standard error for each effect size estimate. “Intensity” is the total number of hours of financial education exposure to the treated. “Duration” indicated the time-frame of financial education in weeks. “Age” is the mean age of the sample in years. “Percent Female” is the relative frequency of female participants in the sample in percent. “Low income” is a dummy variable with “1” if the mean annual income per capita of the sample is below the country average income per capita. “Mandatory” is a dummy variable with “1” indicating mandatory participation in financial education and “0” voluntary participation. “Incentivized” is a dummy variable with “1” when incentives to participate where provided and “0” if participation was unconditional on incentives. “Teachable moment” is a dummy variable indicating whether the financial education intervention was offered at a teachable moment.

Source: Authors’ analysis based on data sources discussed in the text.

# _Meta-regression Models Explaining Intervention Impacts_

This section examines determinants of financial education effectiveness using a multivariate metaregression framework including the above discussed potential correlates as right-hand side variables. Our procedure is motivated by economic and econometric considerations. From an economic point of view, we aim for including all variables that have a substantial theoretical foundation. From an econometric viewpoint, the specification should be parsimonious, especially in the presence of a relatively small sample size of studies.

Thus, we start with a specification where we include all reasonable and available variables (table 3, column 1). In order to keep the number of studies considered high, we impute average or default values for missing observations (we show in supplemental appendix S3 that our main results are insensitive to imputation). The discussion considers groups of variables in four blocks, following their introduction in section 5.1.

16