rafmacalaba/datause-encoder-data huggingface dataset:
11

# **Figure 2.  Latin American Structural Reform Index Average (1985-2002)**

<!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>The Great Wave<br>0.3<br>1985 1990 1995 2000<br><!-- End of picture text --> _Note: The advance of the reforms is measured as the margin for reform existing in 1985 that has been utilized in subsequent years. The index combines measures of trade, financial, tax and privatization policies. Source: Lora (2001)._ Considering specific components of structural reform, action was concentrated in the area of liberalization, both in trade and finance. With respect to trade, the 1990s saw a confirmation of a liberalization trend that had started in the mid-1980s, involving mainly the removal of import quotas and the reduction of average import tariffs. The average tariff rate for the region, which had fallen from nearly 50 percent in the early 1980s to around 33 percent in 1990, declined further during the 1990s to around 10 percent by 1999 (Figure 5). The 1990s added to this trend a new feature—a significant reduction in the variance of import tariffs to only one fourth from 1990 to 1999.

But it was arguably in finance where—departing from Williamson’s cautious formulation—Latin America’s liberalization-oriented reforms were most aggressively implemented. While the region had lagged considerably behind the global wave of financial liberalization of the 1980s, it embraced it with vengeance during the 1990s. The financial liberalization index developed by Kaminsky and Schmukler (2003) shows that it took only the first half of the 1990s for the region to bring relatively closed and repressed financial systems to a level of liberalization comparable to that of developed countries (Figure 6). Financial liberalization was carried out on the domestic and external fronts. Direct credit controls were abandoned and interest rates deregulated. Restrictions on foreign investment were lifted, and other controls on foreign exchange and capital account transactions were dismantled. Foreign banks were allowed and encouraged to establish local presences.
7 each 2-minute case contained 4 questions, we implemented a piece rate for charity of 100 CFA – $0.21 – for each correct response (equating 200 CFA per round on average).

**Figure 1: Structure of the Experiment**

<!-- Start of picture text --> Motivation measure Ability measure 1 Ability measure 2 Effort task<br>Exit Survey<br>(Dictator game) (Medical w/ piece rate) (Slider w/ piece rate) (Blank/Slider/Medical)<br><!-- End of picture text -->

# **Assessing task motivation**

We employ a novel approach to identify motivating tasks. Our subjects are medical students who, by their costly decision to enter nursing or medical school, revealed a strong preference for health-related tasks. The task that we judge to be the most motivating, because it matches the realworld choices of the subjects, is the one that involves analyzing patient reports of illness. We contrast effort under this task with effort under two other (low motivation) tasks, one requiring subjects to sit idly in front of a blank computer screen, and another that asks them to manipulate sliders on a computer screen.

Pokorny (2008) also assesses effort differences between two tasks, taking an IQ test or counting the number of “ones” and “sevens” in blocks of random numbers. The IQ test, plausibly offering greater task motivation, elicits greater effort. With our medical task, we can further buttress our claim that it offers the greatest task motivation by pointing to the correspondence between the task and the real-world choices of our subject pool.

Most prior research relies on surveys to establish motivation. Subjects are first asked to perform different tasks, and then they are asked how attractive or enjoyable the task was (e.g., Bailey and Fessler 2011). We do not rely on subject assessments of task interest, which could give rise to consistency bias if individuals who indicate that they prefer a task subsequently work harder on it for precisely that reason.

# **Measuring mission motivation**

Mission motivation depends on the degree to which the task mission corresponds to the mission preferences of subjects. The greater is this match, the more effort subjects should exert. As in Banuri and Keefer (2016) and Ashraf, Bandiera and Jack (2014), we measure the mission motivation of subjects by asking them to play a modified version of the dictator “game”, with a poor
politicians that have been linked to mining activities in other countries such as Peru (Maldonado, 2017) and India (Asher and Novosad, 2018), such as clientelism, corruption and reelection of local politicians were not analyzed within the context of Ghana. We encourage future analysis along similar lines.

A few caveats should be noted. As the gold mining industry in Ghana matures further, it will be important to determine the long sustainability of these economic effects. This paper does not tease out the effect of mine closure on local socio-economic conditions, an aspect that warrants future focus. Moreover, lack of clearly estimated effects both in the individual level and district level analysis could stem from limited sample sizes. We encourage future analysis to use more rounds of data to ensure consistent results. Lastly, for the health and employment effects that we observe, we cannot determine if they stem from changes in the market-based economy, from corporate social responsibility policies or public spending. Future studies should try to carefully disentangle the mechanisms at play.

# **References**

Aragón, F. M., P. Chuhan-Pole, and B. C. Land. 2015. “The Local Economic Impacts of Resource Abundance: What Have We Learned?” Mimeo. World Bank, Washington, DC.

- Aragón, F. M., and J. P. Rud. 2015. “Polluting Industries and Agricultural Productivity: Evidence from Mining in Ghana.” The Economic Journal.

- Aragón, F. M., and J. P. Rud. 2013. “Natural Resources and Local Communities: Evidence from a Peruvian Gold Mine.” _American Economic Journal, Economic Policy_ 5 (2): 1– 25.

- Aryee, B. N. 2001. “Ghana’s mining sector: its contribution to the national economy.” _Resources Policy_ 27 (2): 61–75.

<mark>Asher, S., & Novosad, P. (2018).</mark> _<mark>Rent-seeking and criminal politicians: Evidence from mining booms</mark>_ <mark>. Working Paper.</mark> Axbard, S., Poulsen, J., & Benshaul-Tolonen, A. (2016). Extractive industries, production shocks and criminality: Evidence from a middle-income country. CDEP-CGEG WP, (30).

- <mark>Benshaul-Tolonen, A. 2019. "Local industrial shocks and infant mortality."</mark> _<mark>The Economic Journal</mark>_ <mark>.</mark>

———. <mark>2018. Endogenous Gender Norms: Evidence From Africa's Gold Mining Industry</mark> . <mark>SSRN 3284519.</mark> 37
experts to formal questionnaires; (b) an exercise incorporating iterative, controlled feedback with respect to information provided at each round; and (c) statistical summary of the group’s responses. The approach was designed to minimize influence of dominant individuals, group pressure and irrelevant communication, and to reduce (statistical) noise. By the early 1970s, hundreds of studies had appeared all over the world. But by the mid-1970s, methodological development seemed to stall, perhaps due to heavy criticism of the method as unscientific and its results as speculative.<sup>5</sup> The critique seems to have been rebutted, for many, in a satisfactory way (see e.g. Ziglio, 1996). In any event, the basic method has more recently seen much use in a wide variety of contexts where it can produce information that is not readily obtainable in other ways.

The Delphi method has however seldom been used for environmental or similar valuation.<sup>6</sup> The only other similar such exercise of which we are aware is for the Fez Medina in Morocco, in 1998.<sup>7</sup> In both that study and the current one, experts predicted the outcome of a hypothetical CV survey questionnaire. The exercise is intended to provide expert predictions of population mean and median WTP in the event that such national CV surveys were to be actually conducted in the experts’ respective countries.

Other expert-based work on environmental valuation, related to our study, however exist. We will here mention three such studies. One is Weitzman’s (2001) study involving more than 2000 Ph.D.-level economists, who were each asked to provide their own assessment of the appropriate rate for discounting future climate-related damages. A second study, Roman et al. (2012), involved only three experts whose assessments of the (air-pollution related) value of statistical life in the United States were elicited. While the Weitzman study involved a broader range of “experts” (virtually all types of economists) and a less detailed valuation procedure than ours, the expert range in the Roman et al. study was narrower, and the procedure more detailed. The third study, León et al. (2003), considered whether environmental values elicited through expert opinion can be used as the basis for “benefit transfer”. It dealt with outdoor recreation at national parks in Spain, and compared experts’ predictions to the results of actual CV studies at

> 5 For a comprehensive summary of the critique see Sackman (1975).

> 6 The first mention of the method as suitable for environmental valuation that we have found (albeit with no reference to particular applications) was in Hufschmidt et al (1983).

> 7 Carson, et al. (2013). That study involved two researchers in this project, Richard Carson and Stale Navrud.

4
On the other hancd, if real income gains from "full" trade facilitation (i.e., beyond what is committed to date) are compared to gains from "total" tariff elimination (i.e., 100 percent tariff reductions extended by APEC members to both members and nonmembers), the gains from facilitation would be less than from tariff elimination. However, the study also emphasizes that "trade facilitation has a large undeveloped potential for the improvement of economic efficiency and productivity."<sup>2 03</sup> Non-tariff measures (NTMs) pose a significant problem for trade liberalization. NTMs for both industrialized and developing countries are in many cases of more than 20 percent frequency on several sectors and are not considered to be WTO consistent but rather are for the purpose of protecting domestic producers. Furthermore, while a few countries (e.g., the Philippines and Thailand) have future plans to remove their NTMs, many countries give no indication that they plan to reduce them.<sup>204</sup>

# **ECOTECH**

By most accounts, Economic and Technical Cooperation (Ecotech) has been the hardest element of APEC to define and agree upon. In 1994, Indonesia insisted that development cooperation should be the "third pillar" of APEC, in addition to trade liberalization and facilitation measures.<sup>205</sup> However, the proposal was very negatively received as a possible distraction from the liberalization program. Members could not agree upon a name, signifying resistance to the idea or signifying a misunderstanding of the real intent of the proposal. The developed countries seemed to see it as a request for development funds. The developing countries viewed it as a way of building capacity. The 1996 Manila Declaration attempted to clarify that Ecotech was not "foreign aid" and that the effort was intended to take advantage of the expertise, technology, experience, and information available in the APEC countries.

In the beginning, E]cotech had other problems as well. "A typical APEC Ecotech activity is a pet project proposed and coordinated by a member, financed mainly by the sponsor, and partly supported by APEC."<sup>206</sup> In an attempt to extricate Ecotech from this "pet project" syndrome, the Manila Declaration attempted to build in some accountability by stating that the effort should be goal-oriented with measurable criteria. The following priority areas were identified.<sup>207</sup>

1. develop human capital

2. develop stable, safe and efficient capital markets 3. strengthen economic infrastructure 4. harness technologies for the future _5._ safeguard the quality of life through environmentally sound growth 6. develop and strengthen the dynamism of small and medium enterprises

# **APEC's Significance**

APEC has made a substantial contribution to the Asia-Pacific region's economy. One estimate shows that the impact of APEC trade liberalization is "equivalent to one fourth of the full impact of implementation of the Uruguay Round (UR) trade liberalization."<sup>20 8</sup> Given the magnitude of the coverage of issues in the Uruguay Round, not to mention that it took nine years to negotiate, the impact is large.

63
- 40 -

coincided with a shift in donor priorities away from long-term investments in institution building. This period was marked by optimism about the direct transfer of existing technologies to developing countries. Much of the limited funding for research was used in a piecemeal fashion attached to individual area development projects. In the absence of adaptive and applied research, such efforts not only failed to produce profitable technological packages, but had adverse effects on the development of national research systems.

81. More recently, interest has growu in the development and improvement of national extension systems for communicating information to farmers. It is all the more important, therefore, that urgent attention now be given to financing the development of a sound research capacity at the national level in the countries of the region. The approach should be an integrated one, with experimental research activities, applied farm systems studies, and extension services closely interrelated.

82. International research centers will continue to play an important role in furthering our knowledge on basic and appplied research. Adaptive research, however, remains the crux of the problem and requires a strong management and organizational capability at the national level. Adaptive research will be successful to the extent farm level problems are identified and given priority in setting the research agenda. There is a
SOEs.

Decentralization, our key variable, is defined as those firm-years that experience a change in oversight government to a lower level. In 1998, the initial year of our sample, firms under the oversight of the central, provincial, and municipal governments account for 15.2%, 27.4%, and 57.3%, respectively. In total, 1,116 firms, or 7.7% of the sample, experience decentralization. Of these decentralized firms, there are 318 (14.5%), 384 (9.7%), and 414 (5.0%) firms whose original oversight government were the central, provincial and municipal governments, respectively. The incidence of decentralization was spread out throughout our sample period: the numbers of SOEs being decentralized for each year from 1999 to 2007 was 168, 161, 162, 97, 151, 222, 82, 40, and 33, respectively.

# **3.2. Specification and Identification**

To examine what determines the likelihood of decentralization, we estimate the following equation: Here, _Decen_<sup>_*_</sup> _ijkt_ is the index function for the tendency to be decentralized, and _Decenijkt_ is an indicator variable that equals one when the firm is decentralized. The subscripts _i, j, k,_ and _t_ represent firm _i_ in industry _j_ under the initial oversight government with level _k_ at year _t_ . _Distanceik_ measures the logarithm of (one plus) the physical distance (in km) between firm _i_ and the city in which the initial oversight government is located. The constant is added to accommodate the fact that the distance could be zero. We obtain the distance based on GIS data.<sup>12</sup> In some robustness checks, we also discretize the variable into an indicator variable

> 12 We do not have the specific location of the firm within a county (or district), and the distance is from the city center of the oversight government to the county/district center in which the firm was located.

14
<!-- Start of picture text --> Panel A: Public Panel B: Private<br>65 | = LowGrowth -* High Growth 65 -+ LowGrowth -* High Growth<br>|<br>| |<br>| |<br>c.6 | 6 | f<br>2| 2 |<br>8 55 a 55 |<br>‘S ‘S<br>© €<br>oO | oO<br><!-- End of picture text -->

<!-- Start of picture text --> xx<br>5<br>S 8 | S5 |<br>||<br>45 | a 45 |<br>||<br>4 A |<br>1990 1993 1996 2000 20032005 2008 2011 2014 1990 1993 1996 2000 20032005 2008 2011 2014<br>Year Year<br><!-- End of picture text -->
dependency ratio, the male/female ratio and number of children under 5). On average, we find that an additional year of education has a return of 2.9 percent (Appendix Table A.11, model 1).

To separately estimate the returns to years of schooling for primary and middle school, high school, and post-high school education, we introduce a linear spline in model 2. We find a higher return to primary, middle school and post-secondary years of schooling than to high school. Specifically, we estimate an average return of 4.0 percent to primary and middle school, but only a statistically insignificant 0.3 percent return to a year of high school (Appendix Table A.11, model 2). The return to post-secondary education, consistent with findings Heckman and Li (2004), is higher at 4.7 percent, but also statistically insignificant.

These estimated returns to schooling are much below recent estimates of returns among urban workers, may be biased downward as family member estimates of daily wages of migrants are likely to be imprecisely estimated.<sup>7</sup> Moreover, the selection coefficient on years of high school education is negatively related to whether an individual is a migrant or not, implying that individuals who stay in the village are more likely to go to high school. Returns to high school for rural migrants in urban areas are low, while individuals with a high school education in rural areas are able to qualify for more lucrative positions in village or township government or as managers (or owners) of local enterprises.

# **References**

Antman, Francisca. 2011. “The Intergenerational Effects of Paternal Migration on Schooling and Work: What Can We Learn from Children’s Time Allocations?” _Journal of Development Economics_ , 96(2): 200-208.

- de Brauw, Alan, Jikun Huang, Scott Rozelle, Linxiu Zhang, and Yigang Zhang. 2002. “The Evolution of China’s Rural Labor Markets during the Reforms,” _Journal of Comparative Economics_ 30(2): 329-353.

Heckman, James J. and Xuesong Li. 2004. “Selection Bias, Comparative Advantage and Heterogeneous Returns to Education: Evidence from China in 2000,” Pacific Economic Review 99(3): 155-71.

- Yang, Dean and Claudia Martinez. 2006. “Remittances and Poverty in Migrants' Home Areas: Evidence from the Philippines,” pgs 81-121 in _International Migration, Remittances, and the_

> 7Parents may not know how much of the time children living away from home were working, and may not have precise information on earnings.

55
_Socio-Emotional Instruments and Scores_ The collection of socio-emotional indicators was based on the Age and Stages Questionnaire (ASQ)<sup>8</sup> which covers a comprehensive list of growing indicators for children between 4 and 24 months of age. The subscales applied through the impact evaluation surveys included communication, gross motor and social-personal skills. The questionnaires were divided into two- to three-month age intervals for use with children 4-24 months of age. Scores are normed to indicate whether children are developing appropriately according to their age based on statistically standardized transformation to compare how far children are from the normal developmental thresholds. The ASQ-3 results in a score (out of 60) for each area (communication, gross motor, and personalsocial skills) and these are compared to cut-off points on the scoring sheet. Scores beneath the cut-off points indicate a child may not be developing fully according to his or her age; scores near the cut-off points call for monitoring development stages; and scores above the cut-off suggest the child is on track developmentally.

The structure of the questionnaire first identifies eligible children for the ASQ questions, including age, presence of any type of developmental disorder or disease, and caregiving responsibilities. Once the subgroup of eligible children was identified, the modules were split according to the age of children. For the youngest children (ages 4-6 months), communication skills collected basic individual communications behaviors or expressions prevalent at children’s age, including phonetic means of communication; for gross-motor skills of this age cohort caregivers are asked to provide basic movements done by children in particular situations and then corroborated with actual situations to replicate such behaviors; finally social-personal skills collected data based on child’s reactions to personal stimuli or social environments.

As the age cohort of children progresses the types of questions becomes more elaborated to capture developmental stages according to current reported age. For the highest age cohort (22-24 months of age) the communications questions focused on

> 8 ASQ is a standardized cognitive and socioemotional tests applied in most countries in the World to assess children’s development. For more information see ASQ3‐Guidelines, Materials and Methods. by Jane Squires, Jane Farrell, Jantina Clifford, Suzanne Yockelson, and Elizabeth Twombly Paul H. Brookes Publishing Co.

12
Table 12 Regressions of Employment on Selected Firm Characteristics

|||Regressio|nmodels||
|---|---|---|---|---|
|Explanatory variables|||||
||(1)|(2)|(3)|(4)|
|empl99|0.9022***|0.8946***|0.8895***|0.8939***|
||_9763_|_90.63_|_90.12_|_8362_|
|foreign|0.0007*|0.0007|0.0003|0.0003|
||_1.7_|_1.54_|_0.58_|_0 54_|
|export|0.0011**|0.0009**|0.0008**|0.0010***|
||_3.53_|_2.32_|_2.23_|_251_|
|credit|0.0399|0.0423|0.0149|0.0166|
||_1.51_|_1.58_|_055_|_057_|
|capform|0.0233**|0.0230***|0.0220***|0.0193***|
||_362_|_3.55_|_3.42_|_262_|
|ktol|||0.0319**|0.0320***|
||||_305_|_2 86_|
|Iwage||||0.0874***|
|||||_3.62_|
|productiv|||0.0357**||
||||_218_||
|profitab||||0.0095|
|||||_0.82_|
|industry|no|yes|yes|yes|
|constant|0.3215|0.2780|-0.3689|-0.7550|
|No.ofobs.|885|885|885|650|
|Adj.R<sup>2</sup>|0.939|0.941|0.942|0.947|

Dependent variable: log of average employment in 2000 Definitions of explanatory variables: empI99 = log of average employment in 1999 foreign = share of foreign ownership, % export = share of exports in total sales, % credit = I if a firm has a debt towards financial institutions, 0 otherwise capform = log of mcrease in fixed tangible assets over a year, % ktol = log of capital-to-labor ratio Iwage= log of average wage productiv=log productivity (sales per employee).

profitab = log of the ratio of operating profit to sales 35