rafmacalaba/datause-encoder-data huggingface dataset:
the focus has been on narrow conceptions of industrial policy and its suspicious connotation of “picking winners” and generating private rents without social rewards.

Externalities in learning and discovery support an infant economy argument for government intervention that Greenwald and Stiglitz (2013) argue is far more robust than the conventional infant industry argument.

The consensus among economists and policymakers has grown wider on the need for governments to focus on promoting learning, infant industries and economies, exports, and the private sector, not only in manufacturing but also agriculture and services such as, health, information technology, or finance. Industrial policy is therefore not just about manufacturing. As President Obama argued, “[E]very dollar we invested to map the human genome returned $140 to our economy. Today, our scientists are mapping the human brain to unlock the answers to Alzheimer's; developing drugs to regenerate damaged organs; devising new material to make batteries ten times more powerful. Now is not the time to gut these job-creating investments in science and innovation. Now is the time to reach a level of research and development not seen since the height of the Space Race. And today, no area holds more promise than our investments in American energy.”<sup>6</sup> The production of knowledge is different from the production of ordinary goods. Arrow (1962b), for instance, highlighted the non-rival nature of knowledge and the associated disclosure problem, which makes the innovative projects that ignite and sustain technological developments quite different from traditional capital investments. The information problems surrounding projects that require research and development (R&D) make them difficult to finance: if one discloses enough information to a potential investor about an idea that one would like to develop to make him willing to finance it, he can often “steal” the idea.

True, inventors can try to limit these problems by requiring potential buyers to sign confidentiality agreements. However, these documents frequently prove to be difficult to enforce

> 6 _State of the Union Address_ , op. cit.

11
**_Energy demand models for policy_** **_<u>formulation</u>_**

over time, the determinant of the energy intensity of each mode reduces the fuel consumption efficiency.

Energy demand for freight transport depends on the volume of commodities, average distance of shipping, the modal structure of freight transport, and the economic and technical characteristics of each transport mode. The relationship among these variables is shown in Figure 5.

Many policy-oriented forecasting studies of the transport sector have relied on the enduse approach. Studies using the end-use related often rely on a standard model or computer package and contrary to the econometric studies end-use studies are hardly reported in the academic journals. However, examples of application of various popular models such as MEDEE, LEAP or other specially developed end-use applications can be found in, among others, Dhakal (2003) and Dakhal (2006), Dutton and Page (2007), and Zhou (2007).

Fig. 5: Determinants of energy demand for freight transport <!-- Start of picture text --> Production Amounts of<br>of goods goods to be<br>Freight traffic<br>transported<br>Production<br>structure<br>Average<br>distance<br>Traffic<br>by mode<br>Traffic structure<br>Geogra per merchandise<br>phic and distance Transport<br>charact energy<br>demand<br>Characteristics<br>of transport<br>modes<br><!-- End of picture text --> 68
environments sufficiently developed for at least some forms of SME financing to take off. Also, in these countries, competition for corporate and retail customers seems to have reached a sufficient intensity to induce banks to target SMEs as a strategic sector. Conditions might be different in other less-developed countries, where further research is needed. However, preliminary evidence gathered by Beck, Demirguc-Kunt, and Martinez Peria (2008) for 91 banks operating in 45 countries is largely consistent with our findings.<sup>47</sup> Second, considering the substantial investments and major changes in organizational arrangements being undertaken to serve SMEs, we have argued that this wave of banking SMEs is part of a structural change, and not just a reflection of a cyclical bonanza. However, more research is needed to ascertain if and when this new trend would hit binding constraints. The patterns we have observed cover a short time span, where the banks’ intensified engagement with SMEs was in part boosted by the recent benign environment of high liquidity and rapid growth, and the associated low default rates. As conditions change, the new patterns of engaging with SMEs will be tested.

Third, since the new model to engage SMEs emphasizes cross-selling a relatively wide range of products and services, more data (which were not yet produced by most of the banks interviewed under the World Bank surveys) would be needed to obtain a more reliable measure of the revenues, costs, and risk-adjusted profits associated with banks’ SME business. For example, SMEs might generate revenues indirectly through the

> 47 Their study collects information through a questionnaire similar to the one used in our paper, but that focuses only on the 5 largest banks within each country. Their questionnaire is completed via e-mail so there is no face-to-face interaction with banks and, hence, there is no way to capture anecdotal evidence or to clarify information.

41
# Figure 2.2 – Log Real Wages (pesos)

<!-- Start of picture text --> Unskilled Border Unskilled Mexico<br> Skilled Border Skilled Mexico<br>8.52272<br>6.82648<br>1989 2000<br>year<br>log wages<br><!-- End of picture text --> Note: Wages are weighted averages of all education groups using cell sizes as weights. Border wages are the one observed in the 6 border states, Mexico wages are the wages observed in the remaining 26 states. Source: Author’s calculation based on tariff data from INEGI households surveys, 1989-2000.

48
eight households in each EA for a baseline survey implemented in December 2015 and January 2016, giving a total sample of 7,399.

In the follow-up survey, conducted in January and February 2018, we interviewed nine households in each EA, increasing the sample size to a total of 9,037 households.<sup>4</sup> For each EA, field supervisors were provided with a list of the eight households interviewed during the baseline as well as one additional household randomly drawn from the household listing. Households that could not be interviewed at follow-up were replaced with other randomly sampled households in the same EA. We re-interviewed 79 percent of baseline households in the second round (78 percent in the treatment group and 80 percent in the control group).

In both survey rounds, we conducted face-to-face interviews. The questionnaire focused on sociodemographic information, labor outcomes, usage of mobile phones and mobile money, and financial transactions. Survey questions on food security were added in the follow-up survey and are not available at baseline.

Table 1 shows household background characteristics from the baseline survey. Household heads in our sample were on average 42 years old at baseline. Only about 44 percent of households had at least one member with completed primary education. Most households (82 percent) experienced a negative shock, such as a flood, drought, theft, death, or illness, in the last six months. About 42 percent of respondents had access to a phone, although only about 28 percent reported owning a phone, suggesting households share phones.

Table 1 also describes mobile money usage at baseline. About 61 percent of households were aware of mobile money, but only 16 percent reported that they

> 4 The baseline survey interviewed households in EAs that were dropped prior to randomization for the reasons described in section 3. Instead of re-interviewing households in the dropped EAs, we increased the sample size in the 929 EAs included in the randomization. According to UBOS, the average number of households per EA in our sample 99, so that our survey covers about 9 percent of households in each EA.

10
References to international publications are cited in 30 percent of the papers, while references to Russian sources are included in 40 percent of the publications. Only five publications in the top 20 international economic journals (Kalaitzidakis, et al. 2003) were cited. In particular, Russian researchers cited one paper in: _American Economic Review_ (citation to a paper published in 1977) _, Econometrica_ (1980), _Quarterly Journal of Economics_ (1961), _Review of Economic Studies_ (1999), _Journal of Human Resources_ (1996). Out of a total of 280 references to international sources, only three economists were mentioned from the top-100 most-cited economists (Coupe 2003): J. Campbell (4 references), J. Heckman (1 reference), M. Ravallion (3 references). About a third of all citations to Russian sources are based on self-referencing.

# _Theoretical models, hypotheses, and predictions_

Generalized theoretical understanding of a natural or social phenomenon and the development of a theory or a hypothesis that yields a valid prediction about a phenomenon not yet observed is the ultimate goal of science (Friedman 1970, p 6). Scientific theories are representations of certain aspects of reality that can only be approximated by such models. Scientific theories enable researchers to organize and understand earlier observations and to make predictions about future observations and events. In order to be considered scientific a theory must be consistent, parsimonious, correctable, empirically testable or verifiable, useful, and progressive (Root-Bernstein 1984). Any scientific research should be guided by a model or theory that drives the research question, the use of methods, and the interpretation of the results. Theoretical models can be expressed in a form of mathematical equations or as a consistent description of the processes under investigation. A hypothesis originating from theoretical models could be tested against facts. As Popper (1965) noted, the scientific method works primarily through diligent and serious efforts to refute scientific hypotheses.

Poverty studies focusing on behavior of households, individuals, or farms, most often employ theoretical models related to concepts of utility or profit maximization, or both. Such theoretical models lead to corresponding empirical specifications. For 10
16 after the universal health care scheme was introduced in 2001, but not zero. They attribute the failure of the scheme to eliminate impoverishment from out-of-pocket expenses to people bypassing their designated provider and hence making themselves unnecessarily liable for out-of-pocket payments and non-coverage of certain interventions including renal dialysis and chemotherapy. Knaul et al.<sup>9</sup> report that the difference between the pre-payment and post-payment poverty gap narrowed following the introduction of the _Popular Health Insurance_ scheme in Mexico.

Van Doorslaer et al.<sup>23</sup> use data from 11 Asian countries to compare pre-payment and post-payment poverty headcounts and poverty gaps using the World Bank’s dollar-aday poverty line (as well as its $2-a-day poverty line). They find that the dollar-a-day poverty headcount is, on average, almost three percentage points higher after deducting out-of-pocket spending from household consumption. In Bangladesh and India, the difference is almost four percentage points. In Malaysia and Sri Lanka, by contrast, the difference is just 0.1 and 0.3 percentage points respectively.

# **Is health spending involuntary?**

Both the catastrophe and impoverishment approaches outlined above make two key assumptions. The first is that health care payments ought to be thought of as involuntary and non-discretionary—the result of a “shock”, unforeseen and unwanted, rarely the result of a deliberate choice by the individual concerned. Health care payments, in this view, stand apart from other items of household consumption that contribute to household welfare or utility.
Table 5: Pre-Shipment Inspections (PSI)

||(1)<br>TFPQ|(2)<br>Markup|(3)<br>TFPR|
|---|---|---|---|
|Pre-shipment inspections (output)|0.056|0.330**|0.386**|
||(0.060)|(0.155)|(0.184)|
|Pre-shipment inspections (input)|-0.052|-0.906**|-0.958*|
||(0.206)|(0.398)|(0.534)|
|Observations|59,791|59,791|59,791|
|R-squared|0.981|0.694|0.955|
|Plant FE|yes|yes|yes|
|2-dig ind-year FE|yes|yes|yes|
|Region-year FE|yes|yes|yes|
|5-dig HH-year FE|yes|yes|yes|
|Other NTMs|yes|yes|yes|

**Note:** This table shows the effect of pre-shipment inspections (PSI), applied either to output or inputs, on plant’s TFPQ (col. 1), markup (col. 2) and TFPR (col. 3) in the years between 2008 and 2015. _Output PSI_ is the share of HS-10 digits products subject to at least one PSI within the corresponding five digit industry. For each five digit industry, _Inputs PSI_ is the weighted sum of output PSI in all five digit supplying industries. The coefficients are obtained by the estimation of Equation 1. All specifications include controls for other NTMs and import tariffs applied on plant’s outputs and inputs, plant fixed effects, 2-digit industry-year fixed effects and macro region-year fixed effect. The Herfindahl-Hirschman Index is computed for each 5-digit industry in the base year and interacted with year fixed effects. Standard errors are robust to heteroskedasticity and clustered at the industry level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

23
-8-

without Bank support. But as the data are derived from loan documents, they are assumed to have been taken as part of the Bank-supported adjustment program. Second, preliminary performance outcomes are available at this time. They are presented, nevertheless, as the paper is intended to oe a review of i.mplementation experience rather than a rigorous assessment of the benefits and costs of divestiture.14 Finally, the discussion pertains to a wide range of countries with sufficient implementation experience, including the Congo, Ghana, Guinea, Niger, Senegal, Togo, Jamaica, Mexico, Panama, and the Philippines. Thus, there is a possibility that a global review of t±is sort may mask the specific characteristics of the divestiture program or transactions in a given country.

# A. DIVESTITURE OUTCOMES

14. The Ouantitative Record. Despite the technical and political complexity of such reform (see below), the record in terms of numbers of divestments is quite large in several countries.15 (Quantitative results for a selected group of countries is presented in Annex 1.) Jamaica, Guinea, Togo, Mexico, and the Philippines offer cases in point. Guinea, for example, closed some 70 publicly owned unLts (although a large majority of these are small retail outlets; see below). Togc, Mali, Madagascar and Benin each liquidated 12 to 15 firms. And in the Philippines, five government corporations have been closed. Several examples of ownarship change are also available. Jamaica divested over J$500 million of PE assets, exceeding the loan tranche target of J$150 million. Both Guinea and Togo sold a significant number of industrial PEs. In Mexico, as of March 1989, 260 fEs had been liquidated, 180 sold, 70 merged, and 25 cransferred to local governments. And in the Philippines, 26 government corporations have been sold (10 partially) at the time of writing. At the same time, countries such as Ghana and Senegal have achieved few results.

15. Performance Outcomes. Preliminary findings15 offer some early insights. First, while the absolute number of enterprises divested may be quite large, the magnitude or impact of these divestments in terms of total PE 14 Research on these issues is currently under way in CECPS for a sample of four countries. See Ahmed Calal, op. cit.

15 For a survey of world-wide divestments, see Charles Vuylsteke, Helen Nankani, and Rebecca Candoy-Sekse, The Technigues of Privatization of StateOwned EnterRrises, Volumes 1, II and III, World Bank Technical Paper No. 89, *1988.

16 Data are gathered from supervision reports, President's Reports, and other Bank reports.
This version: 15 Feb 2024 32 Malaysians feel financially secure only through dual incomes and multiple jobs. Multiple jobs ensure greater financial security but also feel like a trap.

_Most of the people in my salary range actually work two jobs. Having two jobs makes it more secure. I cannot go up. I cannot go down. Yeah, I am stuck here. I am trapped._

[Chinese, Selangor] Women are now earning outside the home so that the family can be better supported financially.

_I work for the government. I have a second job. My wife has her own business. She is into the food business. We cannot depend on one job only with the current situation. The wives have to work too._ [Urban Terengganu]

# **No savings and growing indebtedness**

An outside observer may conclude that young Malaysians are spending their disposable incomes in frivolous ways, but one respondent noted that this is because prices are too high to invest in real assets or build any savings.

_<mark>The prices are so high, young people cannot afford any real assets. But they buy something else to give themselves. You go to any bubble tea shop. It's ridiculous</mark>_ <mark>[to see so many people there]</mark> _<mark>. It's all they can afford!</mark>_ <mark>[Chinese, Selangor]</mark> Respondents depend on loans because survival is difficult on their current incomes, given their commitments on housing, car, student and other loans. This is consistent with the Malaysian central bank’s data which showed high debt service ratios among 32
We run three different models, depending on the nature of the dependent variables: Ordinary Least Squares (OLS) for continuous dependent variables, Logit for binary dependent variables and Ordered Logit for ordered ordinal variables, where higher numbers correspond with a better outcome.

We estimate the key correlates for seven behavioral outcomes in Mexico and six outcomes in Chile. We run separate analyses for each country since the data are sufficiently different to not allow pooling. However, to the extent possible, similar variables are used in our analysis of Chile and Mexico. We estimate separate regressions for men and women to ensure that differences between genders would not confound results.<sup>4</sup> We assume that factors are constant during a person’s youth. For example, we assume that family risk factors do not vary extensively over a youth’s age range. This assumption is necessary since we do not have longitudinal data that would allow variance in factors over time.

# **IV.  Data and Summary Statistics**

# **Data**

We use data from two surveys: the 2003 National Youth Survey from Chile and the 2000 National Youth Survey from Mexico. These cross-sectional data contain rarely available information on perceptions, family background, attitudes, and behaviors, thus providing a rare insight into the youth populations in these countries.

Chile initiated its National Youth Survey, _Encuesta Nacional de Juventud_ (ENJ), in 1994 and has repeated it every three years. We use the 2003 data, which was the most recent that could be accessed at the time of the analysis. The survey contains information about risk factors – household poverty, ethnicity, family cohesion, neighborhood violence, and social exclusion – and protective factors - trust in institutions,

> 4 Bagby and Cunningham (2007) find large differences between male and female youth typologies, including different incidence of certain behaviors and different exposure to/levels of risk and protective factors.

7
coup assets. Rather, we merely point out that, in many cases, the upper bound on asset collection may be somewhat lower than what is reflected in residual balance sheets.

**Table 6: Total Fiscal Costs Associated With Privatization Under Various Residual Asset Recovery Scenarios**

|Province|Amount<br>Paid for<br>Privat.<br>Entity<br>(millions<br>pesos)<br>(1)|Value of<br>Privat.<br>Shares<br>Retained<br>by Prov.<br>(millions<br>pesos)**28**<br>(2)|Total<br>Liabil. of<br>Residual<br>Entity<br>(millions<br>pesos)<br>(3)|Total<br>Assets of<br>Residual<br>Entity<br>(millions<br>pesos)<br>(4)|Total<br>Fiscal<br>Costs of<br>Privati-<br>zation: 0%<br>Asset<br>Recovery<br>[3+2-1]<br>_(5)_|Total Fiscal<br>Costs of<br>Privati-<br>zation:<br>20% Asset<br>Recovery<br>(6)|Total Fiscal<br>Costs of<br>Privati-<br>zation:<br>50% Asset<br>Recovery<br>(7)|
|---|---|---|---|---|---|---|---|
|Chaco|6.3|2.9|233.1|245.3|229.7|180.6|107.0|
|Ent Rios|15.1||N/A.|N/A.|N/A.|N/A.|N/A.|
|Formosa|9.3|4.0|244.9|135.7|239.6|212.4|171.7|
|Mendoza|20.1|0.6|666.6|666.6|647.1|513.8|313.8|
|PS Mend|8.2|0.2|292.1|292.1|284.1|225.7|138.0|
|Misiones|9.1|0.0|331.8|144.2|322.7|293.9|250.6|
|R Negro|10.2|1.8|47.4|49.2|39.0|29.2|14.4|
|Salta|4.4|1.1|73.4|91.6|70.1|51.8|24.3|
|San Juan|11.3|3.8|175.3|78.6|167.8|152.1|128.5|
|San Luis|5.4|0.0|81.8|29.7|76.5|70.5|61.6|
|Sant Est|6.7|0.0|227.3|199.6|220.7|180.7|120.9|
|Tucuman|10.3|2.6|262.9|261.7|255.2|202.9|124.4|

Source: Fondo Fiduciario.

Notes: N/A.- not applicable. In the Entre Rios privatization, no residual entity was created. As computed here, the total cost estimates are not meaningful for that case. PS Mend is the former Prevision Social de Mendoza; R Negro is Rio Negro; and Sant Est is Santiago del Estero. The value of shares left in public hands is an indication of the capitalization costs borne by the province. Most of the amounts paid for privatized entities went back to the new private banks either in the form of capital integrated or as deposits made by the provincial government. These small amounts should not, therefore, be seen as necessarily having improved the fiscal situation of the provinces.

Given so little data, analyzing the effect of the liquidation contracts on asset recovery is beyond the scope of this paper. In the future, however, such an analysis may be possible, although it would require very detailed data on the quality of the assets recovered, the terms of each liquidation contract, and the terms on which credits were re-paid. Table 6 summarizes the total fiscal costs associated with privatization for individual provinces under various asset

> 2S Because the province could presumably sell them, the eventual fiscal implications of any shares it holds in the privatized entity are likely to be negligible. However, there is an opportunity cost associated with not selling them that should probably be considered among the costs of privatization.

14