rafmacalaba/datause-encoder-data huggingface dataset:
13 investment proportions are the solution to the following quadratic programming problem<sup>6</sup> : MINIMIZE subject to E(Rp) = E(Re) Nj= wi = 1 and w; 2 0 for i = 1 ...... N where: E(R,) = given level of return The constraints imply that the sum of the weights of the individual assets must equal **1** and the non-negativity restriction on w; precludes short-selling'.

The solution to this non-linear programming problem for the 14-asset international portfolio is reported in Table 4 and graphed in Figure 18. Notice in Table 4 that two of the developed markets, the United Kingdom and Germany, are never held in the optimal portfolio and the weight of the French market in the optimal portfolio is less than 1 %. The reason for the exclusion of the UK market is that it is highly correlated with the US market - a market which has almost the same mean return as the UK market but a lower standard deviation. The German market is probably excluded because of its high correlation with other European markets and because its return is lower and risk considerably higher than the US market.

6This problem was solved using a quadratic programming algorithm available in the GAMS (General Algebraic Modeling System) package developed by the World Bank.

7The portfolio model includes 14 assets. Thus, the standard deviation of the portfolio (ap) contains 14 standard deviation elements and (1/2).(14-1).(14) or 91 different covariance elements.

8Table 4 and Figure 1 show the results of minimizing standard deviation at given rates of return to derive the mean-standard deviation frontier set. Notice that the efficient set, however, corresponds only to rates of return equal to and greater than 1.4727.
**TABLE 7. Average wage**

||F<br>|E<br>|FE‐2<br>|SLS<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ICT|0.014*|0.011|0.080***|0.076***|
||(0.0072)|(0.0074)|(0.027)|(0.026)|
|ICT|0.013*|0.010|0.078***|0.076***|
||(0.0072)|(0.0074)|(0.026)|(0.026)|
|Skills|0.29***|0.16**|0.28***|0.12*|
||(0.071)|(0.065)|(0.072)|(0.066)|
|ICT|‐0.0013|‐0.0021|0.043|0.041|
||(0.0098)|(0.0098)|(0.030)|(0.030)|
|ICT * High Productivity|0.025**|0.022**|0.039**|0.040**|
||(0.011)|(0.011)|(0.019)|(0.019)|
|Skills|0.29***|0.16**|0.28***|0.13**|
||(0.071)|(0.065)|(0.072)|(0.066)|
|ICT|0.0044|0.0016|0.060**|0.057**|
||(0.0078)|(0.0079)|(0.026)|(0.026)|
|ICT * Skills|0.034***|0.035***|0.060***|0.059***|
||(0.011)|(0.011)|(0.016)|(0.016)|
|Skills|0.30***|0.16**|0.29***|0.13**|
||(0.071)|(0.065)|(0.072)|(0.066)|
|Observations|3,329|3,318|3,329|3,318|

Dependent variable: change in log average wage. Regressors: ICT investment dummy, ICT investment dummy interacted with firm‐level indicator variables defined as in Table 5, and change in share of skilled workers. Instruments defined as in Table 3. Robust standard errors in parenthesis.

31
Figure 14: _Officials in EAP prefer structured reading, in-service training and removing fees at the secondary level over supporting inclusion of children with disabilities._ <!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Building Providing Providing Hiring extra Removing In-service Giving<br>hospitals laptops to free lunches specialist fees for teacher teachers<br>schools in primary teachers for secondary training structured<br>school students school early grade<br>with<br>disabilities<br>project<br>Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP<br>Share of respondants selecting the<br><!-- End of picture text --> Source: Authors’ calculations from the Discrete Choice Experiment as part of the 2022 survey. See probit model results in the annex. The probabilities have been computed for a similar “average” respondent in all countries. Building hospitals are the least often chosen among the proposed possibilities. Officials were asked “Now I'd like you to imagine that the Department/Ministry of Finance is considering an additional allocation for education to one of two policy areas. Which one of the following would you choose?” The percentages in the figure don’t add to one hundred since individual officials were randomly offered two of the six options listed above and chose one. This means that for investing in inclusion of children with disabilities, when presented with a choice between this and another option, respondents chose to invest in inclusion 45 percent of the time.

When analyzed by _type of official_ across all countries, respondents from ministries of education favor structured lessons (63 percent) and training (55.8 percent) over removing fees for secondary school (43.3 percent), whereas respondents from ministries of finance have a stronger preference for removing secondary school fees (44.2 percent) relative to structured lessons (40.7 percent) and in-service teacher training (36.3 percent). The largest difference was between ministry of education officials and ministry of finance officials was for hiring extra teachers for students with special needs, where ministry of education’s officials were almost twice as likely to support hiring extra teachers (44.8 vs. 28.6 percent). Similarly, female respondents are more likely to support structured pedagogy and training relative to male respondents, who are more likely to favor removing secondary school fees. Female respondents also have higher preferences for hiring extra teachers to support students with special needs. To check for robustness, we ran an alternative model with country fixed effects, and found that they were not statistically significant. Therefore, cross country differences in per capita GDP or other development measures do not appear to play a role.

18
exports to the rest-of-the-world (i.e., excluding the United States). This methodology was chosen to avoid simultaneity issues between the exporters’ organization dummy and the tariff preferences.<sup>36</sup> This mapping process was repeated for each entry found on the FARA report for 33 countries in Latin America. We used the FARA reports for 1997, 1998, 1999 and 2000. Finally, we calculated the average lobbying expenditure over this four year period by 3-digit ISIC industry and by country. Trade related FARA contributions by Latin American exporters reached 120 million dollars during this period when both fi rms’ and governments’ contributions are included.

After selecting the contributions related to trade, a series containing exporters’ political contributions from private fi rms and Latin American governments was organized at the ISIC 3-digit level. Political organization of exporters is determined using quartile thresholds of the distribution of political contributions. After determining the exporters’ political organization at the ISIC 3-digit level we replicated the value of the variable Ik<sup>iattheHS8-digitlevel.</sup> Elasticities of Export Supply Data on elasticities of export supply was kindly provided by Marcelo Olarreaga. The elasticity data are described in Kee, Nicita and Olarreaga (2004). They estimate elasticities of export supply at the HS 6-digit and ISIC 3-digit levels for about 118 countries. They present elasticities of export supply for most Latin American countries. Dividing Latin America in regions<sup>37</sup> allow us to use the regional export weighted average of the export elasticities in a region to fi ll the gap for those Latin American industry codes missing in their study. In the cases a regional weighted export elasticity is not available to substitute the elasticity for a particular commodity, we use the export weighted average elasticity of Latin America instead. The elasticities of export supply at the HS 8-digit level used in this study are replications of the values obtained by Kee, Nicita and Olarreaga (2004) at the HS 6-digit level.

> 36More details about possible endogeneity of the organization dummies are provided in the estimation techniques section.

> 37Latin America is divided in South America, Central America and Caribbean.

25
Notice that in general there are many more moment conditions (5) than there are parameters to be estimated. More precisely, for each time period _t_ and each industry _j_ , there are two parameters ( _σεjt_<sup>2and</sup><sup>_σ_</sup> _ηjt_<sup>2),but</sup><sup>_n_momentconditions(5).Forexample,inourdataseton</sup> Mexico, for each industry _j_ we have _t_ = 48 quarters and _n_ = 4 quarters (individuals drop out of the sample after 5 quarters), and the number of parameters is therefore 2 _∗_ (48), whereas the number of moment conditions is approximately 4 _∗_ (48).<sup>17</sup> The system is thus Notice also that the objective function (6) is quadratic, which implies that the first-order conditions associated with the corresponding minimum-distance problem are linear in _σεjt_<sup>2</sup> and _σ_<sup>2afeaturethatfacilitatestheestimationsubstantially.Specifically,thefirst-order</sup> _ηjt_<sup>–</sup> conditions can be organized into a linear equation system _′_ where _σ_ = ( _σε,_<sup>2</sup> 2<sup>_....σ_</sup> _ε,t_<sup>2</sup><sup>_...σ_</sup> _ε,T_<sup>2</sup><sup>_,σ_</sup> _η,_<sup>2</sup> 2<sup>_..σ_</sup> _ε,t_<sup>2</sup><sup>_..σ_</sup> _η,T_<sup>2)</sup> is a 2(T-1)-dimensional vector of income parameters ( _T_ being the total number of time periods). Estimates of these income parameters can then easily be obtained through matrix inversion: _σ_ = **A**<sup>_−_</sup><sup>**1**</sup> **b** _._ Some intuition for the way in which our approach separates transitory from permanent income shocks can be obtained from the following simple example. Suppose that risk is time-invariant, _σεjt_<sup>2=</sup><sup>_σ_</sup> _εj_<sup>2and</sup><sup>_σ_</sup> _ηjt_<sup>2=</sup><sup>_σ_</sup> _ηj_<sup>2,anassumptionthathasbeenmadebymostof</sup> the previous empirical literature on income risk. In this case, the moment restrictions (5) become the following: Thus, the variance of observed _n_ -period income changes is a linear function of _n_ , where

> 17We say “approximate” because towards the very the end of the sample period, clearly fewer than _n_ = 4 income changes are observed. In the penultimate quarter, for instance, only one income change is observed. However, this does not pose a problem for the estimation of any but the parameters of the very last quarter.

12
| POLICY RESEARCH WORKING PAPER 1941

# Summary findings

There is a long tradition of viewing as disadvantaged the roughly 40 percent of workers in developing countries who are unprotected by labor legislation and work in small "informal" firms.

Maloney offers an alternative to traditional views of the relationship between formal and informal labor markets: For many workers, inefficiencies in present labor codes and relatively low levels of human capital (labor productivity) may make employment in the informal sector more desirable.

He offers the first study of worker transitions among sectors, using detailed panel data from Mexico, and finds little evidence to support the traditional dualistic view.

He shows that traditional earning differentials cannot prove or disprove segmentation in developing countries, and patterns of worker mobility do not suggest a rigid labor market - or one segmented into formal and informal divisions.

It is possible that the market is dualistic in the sense used in the industrial world, but the division between good jobs and bad jobs seems to cut across issues of formality.

- This paper a product of the Poverty and Economic Management Unit, Latin Arnerica and the Caribbean Region- is part of a larger effort in the region to reexamine the role of the informal sector. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Marta Cervantes, room 18-095, telephone 202473-7794, fax 202-522-0054, Internet address mcervantes@worldbank.org. The author may be contacted at wmaloney@worldbank.org. June 1998. (34 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
<!-- Start of picture text --> OOOO |<br>SAAN | AAA |<br>SA | ROO<br>Ps Se |<br>SSS AA<br>RA Ls)<br>AAS sa<br>a Singapore Singapore mBIOECS Bf Caribbean Hubs Caribbean Hubs Hubs a Singapore Singapore mOECS B@ Caribbean Hubs Hubs<br>g Continental CGF @ Pacific islands g Continental CGF @ Pacific islands<br><!-- End of picture text -->

<!-- Start of picture text --> a Singapore Singapore mOECS B@ Caribbean Hubs Hubs<br>g Continental CGF @ Pacific islands<br><!-- End of picture text --> <!-- Start of picture text --> a Singapore Singapore mBIOECS Bf Caribbean Hubs Caribbean Hubs Hubs<br>g Continental CGF @ Pacific islands<br><!-- End of picture text -->
And as a later discussion in this chapter shows, adults across all regions are more likely to save to finance investments in business than they are to borrow for this purpose.

Globally, about 15 percent of adults reported having saved in the past year for some other reason. This might include saving to buy a home, for another large purchase, or for a wedding or funeral.

# Credit and its purposes

Most people borrow from time to time. They may want to buy land or a home, seek to invest in an education or a business, or need to cover the costs of a health emergency. When they lack the money to do so, they turn to someone who will lend it to them—a bank, a friend or family member, an informal lender. And in some parts of the world many people may sometimes rely on credit cards in the place of loans.

#### FIGURE 3.6

<!-- Start of picture text --> Origination of new formal or informal loans<br>Adults borrowing from any source in the past year (%), 2014<br>54<br>46 47 World<br>41 40 40<br>33<br>East Asia Europe & High-income Latin Middle South Sub-Saharan<br>& Pacific Central Asia OECD America & East Asia Africa<br>economies Caribbean<br>Source: Global Findex database.<br><!-- End of picture text -->

## What are the sources of new loans?

Globally in 2014, 42 percent of adults reported having borrowed money in the past 12 months (excluding through the use of credit cards). The overall share of adults with a new loan—formal or informal—was fairly consistent across regions and economies, with Latin America and the Caribbean at the low end with 33 percent and Sub-Saharan Africa at the high end with 54 percent (figure 3.6). But the sources of new loans varied widely across regions.

### FIGURE 3.7

<!-- Start of picture text --> Sources of new formal and informal loans<br>Adults borrowing from source in the past year (%), 2014<br>Financial institution<br>East Asia & Pacific Family or friendsPrivate informal lender<br>Retail store (store credit)<br>Europe & Central Asia<br>High-income OECD economies<br>Latin America & Caribbean<br>Middle East<br>South Asia<br>Sub-Saharan Africa<br>0 10 20 30 40<br>Note: Respondents could report borrowing from more than one source.<br>Source: Global Findex database.<br><!-- End of picture text --> In high-income OECD economies a financial institution was the most frequently reported source of new loans, with 18 percent of adults reporting that they had borrowed from one in the past 12 months (figure 3.7; map 3.2). In all other regions family and friends were the most common source of new loans. Overall in developing economies, 29 percent of adults reported borrowing from family or friends, while only 9 percent reported borrowing from a financial institution.

Borrowing from family or friends is especially common in Sub-Saharan Africa, where 42 percent of adults reported doing so. Some

> THE GLOBAL FINDEX DATABASE 48
**_More important, among countries that are off track, the top half are, on average, only about 11 percent away from being on target._** The mean distance of this subgroup is only 4–9 percent for gender parity in primary and secondary education, child mortality, primary education completion, and hunger. Indeed, countries close to the target need to increase primary education completion only by 9.2 percent (or 1.5 percent a year), on average, to be on track to reach the 2015 target.

**Table 3. Many countries are within 10–20 percent of being on target**

|||**Distribution of**<br>|**lagging countries**<br>||
|---|---|---|---|---|
||**Gap ≤ 10 p**|<br>**ercent**|**Gap ≤ 20 p**|<br>**ercent**|
||**Number of countries**|**Proportion of**<br>**countries**|**Number of countries**|**Proportion of**<br>**countries**|
|**MDG 1.a Extreme poverty**|9|24%|13|34%|
|**MDG 1.c Hunger**|10|33%|18|60%|
|**MDG 2.a Primary education completion**|23|40%|39|68%|
|**MDG 3.a Gender parity in primary education**|28|74%|36|95%|
|**MDG 3.a Gender parity in secondary education**|16|42%|23|61%|
|**MDG 4.a Child mortality under five**|33|31%|48|46%|
|**MDG 5.a Maternal mortality**|20|21%|37|39%|
|**MDG 7.c Access to safe drinking water**|10|15%|32|48%|
|**MDG 7.c Access to sanitation**|6|6%|25|26%|
|Source: Authors’ calculations based on data fr<br>**Simple average**|om the World Develop<br>17|ment Indicators d<br>32%|atabase.<br>30|53%|

Source: Authors’ calculations based on data from the World Development Indicators database.

Indeed, many lagging countries are already within striking distance. From another perspective, table 3 provides the proportion of countries within 10 percent or 20 percent of getting on target. A third of off-target countries have, on average, a gap of 10 percent or less from being on target across the MDGs. Countries like Bangladesh (extreme poverty, hunger, and maternal mortality), Indonesia (hunger, child and maternal mortality, access to safe drinking water), and Mali (gender parity in primary education and access to safe drinking water) are in this category. More than half have a gap of 20 percent or less. Of the countries within 20 percent of target, the best results are for gender parity in primary education, primary education completion, gender parity in secondary education, and hunger. The worst results are for access to sanitation, extreme poverty, and maternal mortality, with access to safe drinking water and under-five mortality in the middle. Table 4 lists countries that are within 10 percent of being on target by MDG.

9
Table 2: The decision to become an exporter

||OLS|OLS|OLS|OLS|OLS|IV|IV|
|---|---|---|---|---|---|---|---|
|Subj. fnancial||-0.005||-0.004||||
|constraint||(0.008)||(0.008)||||
|Unable to obtain a|||0.021||0.022|0.450|0.205|
|loan? (Yes=1)|||(0.015)||(0.015)|(0.299)|(0.423)|
|Foreign shock|0.007|||0.007|0.007||0.004|
|(%∆GDP)|(0.005)|||(0.005)|(0.005)||(0.009)|
|Domestic shock|-0.005**|||-0.005**|-0.005**||-0.006*|
|(%∆GDP)|(0.002)|||(0.002)|(0.002)||(0.003)|
|Log(Labor|0.039***|0.040***|0.040***|0.039***|0.039***|0.048***|0.042***|
|Productivity)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.008)|(0.008)|
|Firm Age|0.001*|0.001|0.001|0.001*|0.001*|0.001|0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Small Firm? (Yes=1)|-0.120***|-0.120***|-0.118***|-0.120***|-0.118***|-0.079**|-0.102***|
||(0.017)|(0.017)|(0.017)|(0.017)|(0.017)|(0.033)|(0.039)|
|Foreign Ownership?|0.218***|0.216***|0.217***|0.218***|0.219***|0.227***|0.224***|
|(1=Yes)|(0.027)|(0.027)|(0.027)|(0.027)|(0.027)|(0.031)|(0.030)|
|Industry F.E.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year F.E.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|0.269|0.269|0.269|0.269|0.269|0.269|0.269|
|F stat||||||57|64|
|Kleibergen-Paap LM test||||||0.008|0.107|
|Hansen J stat||||||0.099|0.000|
|Endog. test||||||0.033|0.093|
|N|23342|23342|23342|23342|23342|23342|23342|

The dependent variable is the _Prob_ ( _Exportit >_ 0). Robust standard errors are reported in brackets. The symbols ***,**,* represent significance at the 1%, 5%, and 10% level. Under the null hypothesis of the Kleibergen-Paap LM test, the structural equation is underidentified, that is instruments are not relevant. Under the null hypothesis of the Hansen J test, the instruments are valid. The null of the endogeneity test is that the endogenous regressor(s) can be treated as exogenous.

16
**Table 2. Government expropriations of Chinese firms**

||Tax/Sale|Fee/Sale|Special Assessment/Sale|ETC/Sale|
|---|---|---|---|---|
|**Panel A: Government**<br>|**expropriations ov**<br>|**er time**<br>|||
|1993|0.056|0.015|.|.|
|1995|0.067|0.030|0.040|0.089|
|1997|0.063|0.025|0.008|0.021|
|2000|0.058|0.023|0.005|0.014|
|2002|0.056|0.021|0.007|0.017|
|2004|0.063|0.029|0.012|0.021|
|2006|0.060|0.025|0.009|0.015|
|2008|0.065|0.027|0.009|0.018|
|2010|0.074|0.022|0.008|0.020|
|2012|0.074|0.023|0.015|0.022|
|**Panel B: Government**<br>|**expropriations ov**<br>|**er firm size (A**<br>|**sset quantiles)**<br>||
|0-25%|0.070|0.033|0.021|0.040|
|26%-50%|0.062|0.022|0.012|0.025|
|51%-75%|0.060|0.017|0.007|0.015|
|76%-100%|0.060|0.016|0.004|0.008|
|**Panel C: Government**|**expropriation: In**|**land regions v**|**. coastal regions**||
|Inland|0.067|0.028|0.013|0.026|
|Coast|0.064|0.021|0.010|0.020|
|**Panel D: Political conn**|**ection and gover**|**nment exprop**|**riation**||
|Non PC or CPPCC|0.066|0.026|0.015|0.030|
|PC or CPPCC|0.063|0.020|0.007|0.013|

_Data sources: National Surveys of Privately Owned Enterprises in China_ 38