rafmacalaba/datause-encoder-data huggingface dataset:
argue that in developing countries, even if the legal environment creates complex and burdensome regulatory procedures, the state’s weak capability and desire to implement regulations may allow firms to circumvent those regulations, through bribery for example. They find that the numerical estimates provided by the _Doing Business_ survey of legally required time and costs to complete certain regulatory processes do not accurately square with the experience of firms as measured by the World Bank _Enterprise Survey_ , which asks firms about how business is actually done. We recognize that the _Doing Business_ indicators may be an imperfect measure of how conducive a country’s regulation is for business. Indeed, the indicators do not cover important elements of the business climate, like security, corruption, market size, financial stability, or infrastructure. Furthermore, they do not consider important labor market features, such as workers’ skills and the flexibility of working hours. Nonetheless, the indicators provide a timely, broad picture of institutional features that affect the business environment for all countries. Of course, countries can have good rules and bad practices: the difference between ”the rule” and ”the practice” is important, but this does not make the quality of the rules less relevant.

As mentioned above, analyzing the efficacy of World Bank financed policy interventions involves important methodological problems. This is because of the selection bias problems inherent in this kind of operation. The bias can go in two directions. On the one hand, countries receiving support are likely to be those that are most in need. Thus, a negative association between DPLs and outcomes does not necessarily imply that DPLs are counterproductive. On the other hand, the World Bank can choose to engage with countries more likely to achieve positive results, because, for example, they are more reform-prone. If this is the case, a positive association between the _Doing Business_ score and the World Bank’s operations may capture this reverse causality rather than the effectiveness of policy loans. These problems clearly call for robust identification strategies.

To address the first type of selection bias, following Easterly (2005) and Smets and Knack (2014), we focus on the cumulative number of policy loans, or alternatively their cumulative value, instead of considering policy loans as independent events. As Easterly (2005) points out, using cumulative policy loans changes the nature of the selection bias: if the ”treatment” is repeated, this suggests that either it has not been effective or, more favorably, that the adjustment is a 3
53 Table A1: Inverse elevation difference weighted rainfall index most important in predicting monthly rainfall in wettest months (April‐November), while inverse distance squared weighted rainfall index also important for predicting rain during drier months (December‐March)

|Depend<br>|ent variable =<br>|log (monthly rainfall)<br>|||
|---|---|---|---|---|
|month|constant|inverse distance squared<br>weighted rainfall index|inverse elevation difference<br>weighted rainfall index|mean rainfall<br>(mm)|
|1|‐0.19|0.64|0.34|24.7|
|2|‐0.27|0.49|0.52|27.5|
|3|‐0.56|0.34|0.75|41.3|
|4|‐0.95|0.06|1.11|78.1|
|5|‐0.72|0.02|1.10|220.8|
|6|‐1.05|0.07|1.10|232.1|
|7|‐1.16|0.08|1.11|277.1|
|8|‐0.95|0.07|1.09|314.7|
|9|‐0.20|0.05|0.97|242.2|
|10|‐0.51|0.16|0.91|194.8|
|11|‐0.30|0.32|0.69|117.2|
|12|‐0.13|0.58|0.38|65.6|

Notes: OLS estimated coefficients from regressing (log) monthly rainfall in 166 rainfall stations from across in Vietnam during 1980‐2006 on the logs of their respective inverse distance squared weighted and inverse elevation difference weighted indices, monthly dummies and their interaction terms; R‐ squared=0.768; Mean rainfall reflects averages of rainfall stations taken over 2001‐2006 R‐squared: 0.768
2

# **Acknowledgements**

I owe the writing of this paper to three lecture invitations that came in quick succession and allowed me to develop different aspects of a topic that had lived with me for some time. The first was an invitation from Dani Rodrik to speak at the Institute of Advanced Study, Princeton, on December 10, 2014. Soon thereafter, I delivered The Amartya Sen Lecture at the London School of Economics, on March 3, on “Law, Economics and the Republic of Beliefs,’ and The D. Gale Johnson Lecture at Chicago University’s Department of Economics, on 13 April, on ‘Why are Laws So Poorly Implemented? From Policymaking in India to Models of Law and Economics.’ I felt honored by the invitations to give lectures named after such iconic figures. But, more importantly, they turned out to be just the incentive I needed to formalize my inchoate thoughts. I have had a long-standing interest in this topic and have done some piecemeal writing over the years, but had left it at that. These invitations provided the impetus I needed for writing the paper. I also presented some related ideas at the LAMES-LACEA conference in Sao Paolo, and Indian Statistical Institute, Kolkata, in December 2014. As a consequence of the long gestation, I have accumulated many debts. For valuable comments and criticisms, discussion of the varied literature on the subject, and, in some instances, just getting the assurance from someone whose views I respect that he or she was persuaded, I am grateful to Craig Calhoun, Kalyan Chatterjee, Tito Cordella, Anandi Mani, Kalle Moene, Stephen Morris, Roger Myerson, Derek Neal, Martin Osborne, Debraj Ray, Phil Reny, Dani Rodrik, John Roemer, Valentin Seidler, Amartya Sen and Ram Singh. Finally, I owe a special mention of Gary Becker, who did so much of the early work that inspired this literature, for a long conversation I had with him, on 23 September, 2013, when he came to the World Bank to speak in my lecture series.
# <u>Table 7 (cont.)</u>

|||||<br>Women|||Unpaid dom|estic wor|<br>k||<br>Men||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Young|<br>Adult (20-<br>in the|44) alone<br>hhld|Adult (20<br>in the|-44) both<br>hhld|Adult an<br>adults|d senior<br>(45-64)|Young|<br>Adult (20-<br>in the|44) alone<br>hhld|Adult (20<br>in the|-44) both<br>hhld|Adult an<br>adults|d senior<br>(45-64)|
||(15-19)|With<br>children|Without<br>children|With<br>children|Without<br>children|With<br>children|Without<br>children|(15-19)|With<br>children|Without<br>children|With<br>children|Without<br>children|With<br>children|Without<br>children|
|Albania|3.34|4.62|4.66|6.64|6.69|5.39|5.60|0.23|0.08|0.46|0.45|0.72|0.65|0.85|
|Argentina|2.14|4.34|2.27|4.89|2.59|4.83|3.78|0.81|1.36|1.17|1.30|1.11|1.46|1.49|
|Bangladesh|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Colombia|2.47|4.30|2.58|5.14|3.36|5.11|4.64|0.96|1.15|1.17|1.19|0.93|1.25|1.39|
|Ecuador|2.60|6.15|3.01|7.73|3.41|6.31|5.09|0.90|1.49|1.22|1.55|0.94|1.57|1.61|
|Ghana|4.03|6.25|3.78|7.63|4.44|5.69|4.41|2.80|2.95|3.29|4.75|2.55|4.85|3.66|
|Guatemala|4.62|6.47|5.32|8.11|8.16|6.82|5.98|0.88|1.00|1.22|1.08|1.38|1.07|1.29|
|Iraq|3.49|4.93|4.21|4.85|4.50|4.61|5.02|1.11|1.17|1.33|1.36|1.37|1.87|1.94|
|Mexico|3.06|6.69|2.77|8.41|3.38|6.44|4.77|1.41|1.83|1.57|1.98|1.24|1.76|1.48|
|Moldova|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Mongolia|4.52|5.64|4.52|7.50|5.37|6.49|6.56|4.05|5.15|5.74|5.90|4.63|5.40|5.44|
|Pakistan|4.48|8.84|6.72|8.76|6.46|6.81|5.47|1.84|3.13|3.48|2.94|2.28|3.46|3.16|
|WB and Gaza|1.02|2.80|0.12|4.90|0.05|1.08|0.32|0.08|0.51|0.11|0.63|0.04|0.33|0.07|
|Peru|3.56|5.88|2.97|7.25|3.74|6.28|5.42|1.84|2.40|1.80|2.32|1.60|2.39|2.37|
|South Africa|3.30|5.46|3.49|5.78|4.43|4.84|3.86|1.95|2.35|2.25|2.01|1.89|2.27|1.95|
|Timor|1.86|2.32|1.77|2.85|1.78|1.73|1.43|1.28|1.16|1.24|1.43|1.03|1.07|0.82|
|Turkey|3.89|5.49|3.44|6.81|4.59|4.94|5.13|0.31|0.46|0.43|0.51|0.43|0.51|0.73|
|Uruguay|2.70|8.50|2.99|10.15|4.27|8.87|6.64|1.18|2.78|1.76|3.37|1.73|3.10|2.35|
|USA|1.11|4.03|1.99|5.04|2.24|4.65|3.23|1.08|2.61|1.22|2.81|1.77|2.85|2.36|

Source: Authors’ elaboration based on survey data. Notes: Women and men aged 15 to 64. In Argentina, the starting age is 18.

36
Table 8: Program Exit and Entry

|De|pendent Vari|able: Indicato|r for Closing A|ny Program|||
|---|---|---|---|---|---|---|
|||Private HEIs|||Public HEI|s|
||(1)|(2)|(3)|(4)|(5)|(6)|
|L.GDP by Field|0.0015***|0.0018***|0.0016***|-0.0001|-0.0006|-0.0007|
||(0.0003)|(0.0003)|(0.0003)|(0.0008)|(0.0006)|(0.0006)|
|Open Program (year t+1)||0.1042***<br>(0.0213)|0.0983***<br>(0.0207)||0.1751***<br>(0.0377)|0.1565***<br>(0.0367)|
|Open Program (year t)|||0.1001***<br>(0.0182)|||0.1364***<br>(0.0291)|
|Constant|0.0193|-0.0008|-0.0041|0.0586|0.0417|0.0297|
||(0.0277)|(0.0211)|(0.0212)|(0.0348)|(0.0261)|(0.0258)|
|N. of Observations|6,703|6,087|6,087|2,339|2,170|2,170|
|Mean of Dep. Variable|0.175|0.122|0.122|0.139|0.098|0.098|

Note: In this table, each column presents the coefficients from a regression of an indicator for whether institution _j_ closes any (at least one) program in field _k_ and department _d_ in year _t_ , on indicators for whether institution _j_ opens any (at least one) program in field _k_ and department _d_ in year _t_ or _t_ +1, as well as the lagged GDP-by-field (measured in billions of COP), for 2004-2018. All regressions include department, year, and field-category fixed effects. *,**, and *** denotes significance at the 10, 5 and 1% level. Standard errors are clustered at the institution-year level.

infrastructure and faculty away from other programs onto the new one.<sup>16</sup> Another important finding emerges from Table 8. Consistent with our entry estimates, public HEIs do not respond to local demand shocks when deciding to close programs in a market (column 4). Meanwhile, private HEIs respond to them but in a counter-intuitive manner, since they close more programs in markets with higher labor demand (column 1). In other words, in response to positive demand shocks private HEIs not only open some programs (Section 5.1) but also close others, thereby contributing to program turnover or “churn.” This echoes well-know results from other industries (Dunne et al., 1988) where positive demand shocks leads not only to entry but also exit of firms and creates firm turnover.

To summarize, both public and private HEIs tend to open and close programs in a market simultaneously, likely due to capacity constraints. When choosing which program to close, private HEIs close those with lower enrollment growth and higher costs. Among private HEIs, positive local labor market shocks drive not only entry but also exit (and therefore turnover) of programs.

16For instance, in 2018 the LCI Technological Foundation in Bogota closed ”Technologist in Fashion Design” and opened ”Technologist in Photography and Digital Imaging,” and in 2006 the Latin American University Corporation in Atlantico closed ”Professional Technician in Executive Assistantship” and opened ”Professional Technician in Human Resources Administration.” 29
**Table B2. All scenarios: Decomposition of growth in GDP at factor cost by sector and simulation 2012-2020 (%)**

||**base**|**agrpweff in**|**dupweff**|**transeff**|**fuelpwm **|**remitdecr tr**|**nsfrpoor**|**allgrw**|**allgrw-tr**|
|---|---|---|---|---|---|---|---|---|---|
|**a-agr**|0.64|1.51|0.36|0.42|0.46|0.60|0.62|0.84|0.84|
|**a-mining**|0.02|0.01|0.00|0.02|0.02|0.02|0.02|0.01|0.01|
|**a-food**|0.17|0.31|0.05|0.14|0.14|0.15|0.16|0.15|0.15|
|**a-bevetob**|0.25|1.17|-0.18|0.05|0.19|0.27|0.23|0.01|0.01|
|**a-garment**|-0.33|-0.82|2.52|-0.58|-0.07|-0.27|-0.38|1.71|1.66|
|**a-woodpap**|0.04|0.02|0.02|0.03|0.04|0.04|0.04|0.03|0.03|
|**a-fuel**|0.02|-0.01|-0.02|0.01|0.01|0.02|0.02|-0.01|-0.01|
|**a-metplast**|0.15|0.03|0.13|0.15|0.12|0.13|0.15|0.15|0.15|
|**a-machequip**|0.09|-0.04|0.05|0.08|0.10|0.08|0.09|0.06|0.06|
|**a-enewat**|0.11|0.08|0.07|0.18|0.11|0.10|0.11|0.09|0.09|
|**a-constr**|0.26|0.40|0.39|0.36|0.22|0.19|0.26|0.46|0.45|
|**a-trade**|0.65|1.09|0.88|0.71|0.57|0.57|0.62|1.09|1.08|
|**a-hotrest**|0.09|0.02|0.01|0.08|0.09|0.08|0.09|0.02|0.02|
|**a-transp**|0.39|0.21|0.03|0.67|0.30|0.36|0.38|0.19|0.18|
|**a-posttele**|0.56|0.17|0.11|2.07|0.65|0.52|0.60|0.30|0.31|
|**a-finance**|0.39|0.46|0.42|0.48|0.37|0.35|0.39|0.49|0.49|
|**a-realest**|0.30|0.35|0.36|0.36|0.28|0.26|0.31|0.40|0.41|
|**a-itserv**|0.05|0.04|0.06|0.07|0.05|0.04|0.05|0.06|0.06|
|**a-othbusiserv**|0.13|0.16|0.10|0.16|0.12|0.11|0.13|0.13|0.14|
|**a-pubadm**|0.08|0.09|0.08|0.09|0.08|0.07|0.08|0.09|0.09|
|**a-educpriv**|0.02|0.02|0.02|0.02|0.02|0.02|0.02|0.02|0.02|
|**a-educpub**|0.04|0.05|0.04|0.05|0.04|0.04|0.04|0.04|0.04|
|**a-healthpriv**|0.08|0.09|0.08|0.09|0.08|0.08|0.09|0.08|0.08|
|**a-healthpub**|0.02|0.02|0.02|0.02|0.02|0.02|0.02|0.02|0.02|
|**a-sanipriv**|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|
|**a-sanipub**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**a-organiz**|0.02|0.00|-0.01|0.02|0.02|0.02|0.02|-0.01|-0.01|
|**a-recrea**|0.08|0.09|0.09|0.09|0.07|0.07|0.08|0.10|0.10|
|**a-othperserv**|0.02|0.03|0.03|0.03|0.02|0.02|0.02|0.03|0.03|
|**total**|4.35|5.55|5.74|5.86|4.11|3.97|4.27|6.57|6.51|

Note: For activity (sector) notation, see Table 3.

43
|PPR<br>Title|Working Paper Series<br>Author|Date|Contect|
|---|---|---|---|
|WPS172 The Effocts of Peru's Push to<br>Improve Education|Elizabeth<br>M.King<br>Rosemary T. Bellow|March 1989|C. Cristobal<br>33640|
|WPS173 Staffing and Training Aspects of<br>Hospital Managoemnt: Some Issues<br>for Research|Julio Frenk<br>Enriquo Ruelas|||
|WPS174 Trade Restrictions<br>with Imported<br>Intermediate<br>Inputs: When Does<br>the Trade Balance Improve?|Ramon E. Lopez<br>Dani Rodrik|March 1989|M. Aweal<br>61466|
|WPS175 An Intograted<br>Model of Perennial<br>and Annual Crop Production for<br>Sub-Saharan<br>Countries|Robert 0. Weaver|||
|WPS176 Credit Rationing,<br>Tenancy,<br>Productivity,<br>and the Dynamics<br>of Inequality|Avishay Braverman<br>Joseph E. Stiglitz|||
|WPS177 Cash-Flow or Income? The Choice of<br>Base for Company Taxation|Jack M. Mintz<br>Jesus Sea:,e|April 1989|A. Bhalla<br>60359|
|WPS178 Tax Holidays and Investment|Jack M. Mintz|April 1989|A. Bhalla<br>60359|
|WPS179 Public Sector Pricing In a Fiscal<br>Context|Christopher<br>Heady|April 1989|A. Bhalla<br>60359|
|WPS18O Structural<br>Changes In Metals<br>Consumptlon: Evidence from<br>U.S. Data|Boum-Jong Choe|April 1989|S. Lipscomb<br>33718|
|WPS181 Public Finance, Trade and Development:<br>What Have We Learned?|<br>Johannes F. Linn<br>Deborah L. Wetzel|April 1989|M. Colinet<br>33490|
|WPS182 The Experience<br>of Latin America<br>With Export Subsidies|Julio Nogues|April 1989|S. Torrivos<br>33709|
|WPS183 Private Investment<br>in Mexico.<br>An Empirical Analysis|Alberto R. Musalem|April 1989|L. Spear<br>30081|
Existing studies argue and demonstrate empirically that stricter environmental regulations cause the firms to invest in innovation and adoption of cleaner technologies, which would eventually lead to increased productivity. There is a strong argument that environmental regulations cause technological innovation that will increase productivity and that the productivity gain offsets the costs of environmental regulations (Porter, 1991; Porter and van de Linde, 1995). This hypothesis, popularly known as ‘Porter’s hypothesis’, has been tested empirically by many studies that are discussed in this paper. The empirical results are, however, ambivalent. While earlier studies do not necessarily prove Porter’s hypothesis, more recent studies do. For example, Jaffe and Palmer (1997) investigate whether the stricter environmental regulations in the United States have spurred R&D activities (i.e., increased patents of green/clean technologies) using panel data of manufacturing industries from 1975 to 1991. They find investments in pollution control technologies (i.e., clean technologies) have a significant positive effect on R&D expenditures. However, they do not find strong evidence of the deployment of clean technologies during that period. This is sometimes referred to as a “weak” version of Porter’s hypothesis in the literature because it does not confirm whether innovation or the use of clean/green technologies is good for firms. The result is not surprising, though, as the large-scale deployment of cleaner or greener technologies in local air pollution control occurred after the 1990s. Moreover, increased deployment of greener energy technologies, such as wind and solar, accelerated more recently, after 2005.

Some studies (e.g., Cao et al., 2021; Peng et al., 2021) also examine impacts on the productivity of green/clean investment resulted from environmental regulations in China. Cao et al. (2021) report that regulated firms are more motivated (or obligated) to invest in green/clean technologies to meet existing environmental regulations. Using sample data from environmentally dirty industrial firms (e.g., coal mining and washing, oil and gas mining, petroleum processing, coking and nuclear fuel processing, power, thermal production and supply, and gas production and supply) for the 2000-2007 period, they find that investments in green/clean technologies led to an increase firms’ productivity. Using firm-level panel data of Chinese industrial enterprises for the 1998-2007 period, Peng et al. (2021) provide the evidence of a positive relationship between the deployment of new green/clean technologies resulted from flexible environmental regulations and productivity of the firms.

10
substitution than between capital and unskilled labor ( ���� . They use national accounts data to build a time series of infrastructure and capital equipment from 1962 to 1993 and data on labor supply from CPS surveys. Their estimates are consistent with capital skill complementarity. Additionally, they find that capital grew more rapidly after 1975, when the skill premium started to rise more sharply. Their results indicate that after accounting for the capital-skill complementarity most of the changes in skill premium become accounted for.

Goldin and Katz (1998) study the complementarity of capital and skilled labor from a historical perspective using US Census of Manufacturers for 1909, 1919, 1929, and the NBER Manufacturing Productivity Database for later time periods. They argue that the complementarity between capital and skills is not a recent phenomenon and that it goes back to the beginning of their sample period.

More specifically related to the use of digital technology, Krueger (1993) finds that workers who use computers at work earn a wage premium of 10% to 15% and estimates that about one-third of the rise in skill premium is due to increased computer use. DiNardo and Pischke (1997), however, cast doubt on these findings by estimating substantial wage returns to the use of pencils, telephones, and working in a sitting position as well. Autor, Katz and Krueger (1998) use CPS data sets to assess how on-the-job computer usage varies across industries and argue that the rise in the demand for skills is actually increased demand for computing skills.

# _The tasks approach_

Autor, Levy and Murnane (2003) introduce a tasks approach to labor demand. The tasks approach emphasizes the role of automatization of tasks in relation to technological progress and labor markets. Autor et al (2003) state that computers can substitute workers who perform cognitive and manual tasks that can be accomplished by following an explicit set of rules (routine tasks), while computers complement workers performing non-routine and problem-solving and complex tasks. They model these ideas with a production function that takes the form where �� and �� are routine and non-routine workers and � are computers. Computers are perfect substitutes for routine labor and have a declining price. They use data on information on the task composition of jobs from 1960 to 1998 and find that computer usage is linked to a reduction of employment in routine tasks and with an increase in demand for college graduates.

A following strand of literature argues that the patterns of wage inequality and the demand for skills have been shifting since the 1990s towards an increase in polarization (i.e.

5
Urban S - _A0+_ j IPor-Capita-GDP + paShare-of-Industry **+** #3 Share-of-Services + f (178b) and for rural areas Rural St- **0** +0Per-Capita-GDP + **i** Share-of-Industry I #0 ~~~~~~~~~a **+** *3Share-of-Servicos + **e r(iS)** Rural S - p0 + " Per-Capita-GDP + **p** Share-of-Industry + p 3Share-of-ServLces + c (18b) The results for urban areas are rows 3 and 4; for rural areas, rows 5 and 6 in Table 5.

Regressions for groups aged 15-19, 20-24, and 25-29 years confirm the theory. The coefficients for the share of services in GDP are significant and larger in magnitude for females than for males, and larger in rural than in urban areas. The coefficients for the share of industry in GDP are generally insignificant for female schooling, and generally significant for urban male schooling. This seems to confirm the hypothesis that industry rewards schooling more than the main omitted class, agriculture, and that men have a comparative advantage in industry. Women, on the other hand, have a comparative advantage in services. The sign and magnitude of the coefficient for GDP indicate that female schooling increases by more than male schooling when income increases, and that the increases are larger for both females and males in rural areas.

Regressions that include variables proxylng Z1, roads per square kilometer, telephone lines per capita, and post offices per square 44
22 ist varies between $66, in the case of 1 group per extensionist, and $30, in the case of 10 groups per extensionist.

# Measuring the Performance of **ATP2**

In the case of ATP2, there are several private companies that provide extension services to producers that are contracted individually, but are also arranged in groups, such as in the case of the ATPI program. In November 1997, there were seven private companies providing extension services, but data on financial reports of the companies are not available for 1997. In May of 1996, there were five private extension companies operating in Nicaragua. Figure 8 provides their market share in the producer population.

Figure 8: Market share of the various private companies participating in ATP2 in Nicaragua (1996) <!-- Start of picture text --> Coanpany 5 Conpany 1 Conpany 1<br>17% 21% ~ oTay<br>St > * Conpany 2<br>Cornpany4 y 2 Conpany 3<br>20% 20%Conpany 4<br>Conpany 3 # Conpany 5<br>22%<br><!-- End of picture text --> Source: Based on financial statements submitted to the coordinating unit in ATP2.

In 1997, with the addition of two more companies, the market shares for the seven extension-providing companies were 13.7, 17.2, 13.4, 13.5, 12.7, 16.7, and 12.8 percent for companies 1, 2,7, 6, 3, 4, 5, respectively (INTA 1998). In 1997 as in 1996, the market share of the private extension-providing companies remains more or less equal.