rafmacalaba/datause-encoder-data huggingface dataset:
countries that can affect, positively or negatively, the incentives to increase productivity. The following specification is used: ∆lnLPit = δ�∆lnLPitleader�+ σ(prodgapit−1) + γRegit + α(Reg∗prodgap)it−1 + βhumanKit−1 + τOpen_tradeit−1 + ∑γi _Country_ i _+_ ∑θt _time_ t +∈it Eq.(1) where i and t denote countries and years, respectively. LP is labor productivity, defined as value added per employee, where both value added and number of employees are based on Eurostat data.

∆lnLPitleader reflects the productivity shocks in the leader country; productivity growth in the frontier country widens the production possibility set leading to faster productivity growth in follower countries.<sup>15</sup> prodgapit−1 is the productivity gap and is defined as the log ratio of the level of productivity in each country relative to that of the productivity leader. Assuming that a country's distance from the technological leader measures the scope for technology transfer, the σ coefficient captures the effect of differences in productivity levels between each country and the productivity leader on productivity growth. As prodgapit varies in the interval [− _∞_ , 0], a negative coefficient would suggest that the higher the productivity gap the greater is the scope for productivity improvements that might arise from the technological catch-up process.

Regit is the indicator of economy-wide regulation and proxies the policy environment of each country. In this particular exercise, it is measured by the regulatory quality indicator from the Worldwide Governance Indicators (WGI) database and is available from 1995 to 2010. The indicator captures the ability of a government to formulate and implement sound policies and regulations that permit and ‐ promote private sector development. The indicator is standardized between 2.5 and 2.5 with high scores corresponding to better outcomes.<sup>16</sup> The γ coefficient measures the _direct_ influence of regulation on productivity growth.

In order to reflect the possible effect of regulation on the speed with which each country catches up to the productivity leader, the model includes an interaction between regulatory quality and the productivity gap. Thus, the α coefficient measures the indirect effect of regulation on productivity growth. As prodgapit is always negative, a favorable regulatory environment (captured by higher – and positive – values of Regit ) would imply a negative value for the interaction term. As such, a _negative_ and significant value of the α coefficient would suggest that a _better_ regulatory environment accelerates the catch up process. Figure 1 presents the evolution of the regulatory quality indicator for Romania and some selected EU countries (while Table II, Annex, presents the indicator values for all countries).

> 15 The productivity leader is allowed to change over time.

> 16 Details of how this indicator is constructed are presented in Kaufman, K., A. Kraay, and M. Mastruzzi. (2009). “Governance Matters VIII: Aggregate and Individual Governance Indicators, 1996-2008”, World Bank Policy Research Working Paper No. 4978.

4
**Table 1.  Sample Size of the Second STPS Study**

|_Group_|_Participated in 1st_|_Did not participate in_|_Total_|
|---|---|---|---|
||_study_|_1st study_||
|CIMO|139|456|595|
|Control|242|396|638|
|Total|381|852|1,233|

_Source_ : STPS, 1997 The survey questionnaires asked SMEs both quantitative and qualitative questions. Quantitative questions included information on staff remuneration, training, number of personnel, their professional profiles, sales, inventories, and fixed capital, among other areas. Qualitative questions explored the structure of the firms, market orientation, employment, organization, training practices, and production processes through a series of multiple-choice questions. These data were used in both studies for three types of analyses: (i) tabular comparisons of the treatment and control groups, (ii) simple regression analyses of the determinants of productivity outcomes, including program participation, and (iii) cost-benefit analysis of the program. The principal findings from both studies are summarized below.

# **A. Tabular Comparisons of Pilot and Control Groups from STPS Studies**

**In the 1995 STPS study** , which spanned the period between 1991 and 1993, enterprises that participated in the CIMO program:

- were more likely to provide employees with training, and more likely to invest more per worker in training as compared to the control group;

- were more likely to provide formal training courses (90 percent), as compared to 50 percent for the control group;

- had lower rates of capacity utilization in 1991 (72 percent versus 76 percent for the control group) but by 1993, there were no significant differences in utilization rates between the two groups;

- had lower absolute levels of production in both 1991 and 1993, but closed the gap over time because of a 22 percent increase for CIMO firms as compared to a slight decline for the control group; and

- were more likely to report introduction of organizational changes associated with productivity improvements as compared to the control group.

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**Table 2 Evolution of key ewDomic indicatos for Sub.Saharan Afica, 1967-88 (average annual percentage change, unless otherwise indicated)**

|................................<br>....<br>..............<br>v*........|..........................<br>1967-7|3 1974-80|......<br>.......<br>1981-84|......................<br>1985-8|.....<br>.<br>7 1988|
|---|---|---|---|---|---|
|GDP|7.0|2.7|-1.1|2.6|2.5|
|Export volume|17.1|0.2|-7.5|2.1|1.2|
|Import volume|4.3|7.6|-6.8|-0.7|-2.8|
|GDI (percent of GNP) a/|16.7|22.3|16.7|15.0|16.6|
|GNS (percent of GNP) a/|13.0|17.2|9.0|8.8|8.0|
|Private consumption|4.1|1.8|1.1|1.3|2.2|
|Gross ODA (percent of GNP) b/|3.2|3.6|4.2|6.6|8.9|
|Terms of trade index (1980=100) a/<br>GNY|83.9|84.4|101.3|83.0|74.2|
|Total|3.9|_4.5_|-1.1|0.7|2.4|
|Per capita|1.2|1.7|4.1|-2.4|-0.9|

Notes: a/ Refers to 1966-73 b/ Refers to 1970-73 Source: World Bank data files The increase in the investment rate that took p!ace in Sub-Saharan Africa in 1974-80 did not translate into higher output growth in the following periods, as could have been expected. The drop in output that occurred in 1981-84 was caused in part by the very low efficiency of investment. (Once the reform programs began to help raise the efficiency of investment, GDP increased despite lower investment rates, see Table 2).

The crisis was pervasive, affecting all areas of the economies. During 1981-84 export and import volumes fell (by _7.5_ and 6.8 percent annually, respectively); investment as a percentage of GNP went back to the 1966-73 levels (16.7 percent); gross national savings as a percentage of GNP averaged only 9 percent, or about half their 1974-80 level; foreign direct investment expanded at the lowest rate of the iast thirty years; export market shares continued to erode. To make matters worse, net nonconcessional capital flows coUlapsed, and the negative real interest rates of the past gave way 8
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- Durand-Lasserve, A. and Selod, H. (2009). The formalization of urban land tenure in developing countries. In Lall, S. V. and Rajack, R., editors, _Urban land markets: Improving land management for successful urbanization_ , pages 101–132. Springer Netherlands, Dordrecht.

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- Field, E. (2005). Property rights and investment in urban slums. _Journal of the European Economic Association_ , 3(2-3):279–290.

- Field, E. and Kremer, M. (2005). Impact evaluation for slum upgrading interventions. _Working Paper 2008-0029, Weatherhead Center for International Affairs, Harvard University_ , June 2005.

- Friedman, J., Jimenez, E., and Mayo, S. (1988). The demand for tenure security in developing countries. _Journal of Development Economics_ , 29(2):185–198.

- Galiani, S. and Schargrodsky, E. (2010). Property rights for the poor: Effects of land titling. _Journal of Public Economics_ , 94(9-10):700–729.

- Geniaux, G. and Napoleone, C. (2008). Semi-parametric tools for spatial hedonic models: An introduction to mixed geographically weighted regression and geoadditive models. In Baranzini, A., Ramirez, J., Schaerer, C., and Thalmann, P., editors, _Hedonic methods in housing markets: Pricing environmental amenities and segregation_ , pages 101–127. Springer Science+Business Media, LLC, New York.

- Government of India (2013). Census of India 2011. Technical report.

- Government of Maharashtra (1971). Maharashtra Slum Areas (Improvement, Clearance and Redevelopment) Act.

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changes in the returns to schooling across regions. These unobserved region-specific timevarying shocks could result in spurious correlation between rice prices and economic activities. In our regressions, we control for them by interacting each region indicator with a year indicator.<sup>16</sup> Second, Vietnamese communes differ in their accessibility. If more accessible communes are better integrated into international and national rice markets, these communes might undergo bigger rice price changes. Similarly, accessibility might be associated with a range of time-varying factors affecting labor supply. For example, more accessible communes are likely to have better transportation and thus better access to schools or workplace. The VLSS provides information on whether regular transportation is provided to a commune and whether the road to commune is paved. Based on this information, we create two indicator variables for a communes’ accessibility and interact them with the year indicator. Inclusion of these two interactions in our empirical framework allows for a different change in economic activities in accessible communes. Finally, Edmonds and Pavcnik (2003b) consider some additional commune-specific factors such as high yield seed use, droughts, pests, infrastructure changes and other shocks that could simultaneously affect changes in rice prices and changes in participation. We do not include them in our current analysis because previous research found that these factors are not substantively correlated with price changes. Moreover, we only have data on these factors for a subset of households, so their inclusion would limit the sample.

We estimate the relationship between the economic activities and rice prices using a linear regression framework. For a person _j_ in commune _i_ at time _t_ , we estimate: (1) _yijt_ = β 0 + β 1 _RPit_ + β 2 _RPit_ * _Male j_ +φ _X jt_ + α 1 _Cit_ + α 2 _Tt_ + α 3 _Ai_ * _Tt_ + λ _i_ + ε _ijt_ . _yijt_ is a measure of the economic activities of person _j_ in commune _i_ at time _t_ . In the empirical work below, this will be hours worked in a particular type of activity or a dummy variable

> 16 There are 4 to 35 sampled communes per region (the mean and the median are both 25 communes per region).

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**Table A6: Within-Week Profile of Days Worked and Declarations Handled**

|||**Inspec**|**tors**|||**Br**|**okers**||
|---|---|---|---|---|---|---|---|---|
||**Weekday**<br>Average|**s worked**<br>St. dev.|**Declar**<br>Average|**ations**<br>St. dev.|**Weekday**<br>Average|**s worked**<br>St. dev.|**Decl**<br>Average|**arations**<br>St. dev.|
|Monday|82.4%|4.9%|21.4%|2.1%|40.6%|22.7%|22.4%|13.2%|
|Tuesday|86.0%|7.9%|21.5%|2.2%|38.5%|22.4%|20.2%|9.8%|
|Wednesday|85.5%|7.8%|20.9%|2.1%|37.9%|23.0%|20.0%|10.9%|
|Thursday|84.0%|9.0%|19.4%|1.7%|39.0%|22.7%|20.8%|13.8%|
|Friday|79.7%|10.8%|16.6%|2.1%|32.5%|22.0%|16.5%|11.8%|

_Notes:_ An inspector (a broker) is defined to work on a given weekday if she assesses (registers) at least one declaration on that weekday. For each inspector and broker we identify the weeks they work as those when they work at least one day. Focusing on the statistic of 82.4 percent in the first column, its interpretation is that inspectors on average work 82 percent of the Mondays of all weeks they work. Focusing on the statistic of 21.4 percent in the third column, it indicates that inspectors clear 21% of their declarations on Mondays (the percentages in this column add to 100).

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# _Misperception of risk_

Misperception of a pesticides’ toxicity is also of interest as this may influence the dose decision by farmers. A priori, we expect farmers would be less likely to use a pesticide that is relatively higher in toxicity, if an equivalently effective and less toxic pesticide is available to the farmer. Studies have shown that farmers tend to rank pesticides more generally, with less distinction at the higher levels of toxicity (Warburton, Palis and Pingali, 1995). During the interview, farmers were asked to rank each chemical they applied in terms of its relative toxicity. Four categories were presented and scaled according to the corresponding WHO class description (1=slightly hazardous (WHO class III); 2=moderately hazardous (WHO class II); 3=highly hazardous (WHO class Ib); and 4=extremely hazardous (WHO class Ia). Misperception was then defined as any deviation from the true toxicity class.<sup>5</sup> This procedure was repeated for each chemical and summed by farmer and toxicity class (four of them). Misperception was then set =1 if the farmer was incorrect at least 50% of the time, 0 otherwise. By coding in this manner, on average, over 48% of farmers incorrectly classified at least one of the pesticides they are using, and over 34% were incorrect at least 50% of the time.

# _Training and averting behavior_

As farmers mix and spray pesticides, they are naturally exposed to the toxicity of the chemicals. Exposure to pesticides can lead to an array of health effects, depending on the pesticide’s toxicity and the dose absorbed by the body.<sup>6</sup> However, the health effects of pesticide use can often be reduced significantly by averting behavior - wearing protective clothing (Cropper, 1994) and exercising precaution. The extent of averting behavior of a

> 5 For example, if a farmer reported a 2 (class II), when in fact the chemical was a 3 (class Ib), this represented a deviation of 1.  If a farmer was more precautious, and reported a class higher than actual, this yielded a negative coding.

> 6 For pesticides with high acute toxicity, exposure can produce intoxication symptoms within minutes or hours, and these acute effects run from mild headaches and flu like symptoms, to skin rashes, blurred vision, and other neurological disorders (World Resources, 1998-99).  In addition, prolonged exposure to pesticides can cause many chronic health effects: cardiopulmonary problems, neurological and hematological symptoms, and adverse dermal effects (Davies, Freed, and Whittemore, 1982; Spear, 1991).

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# **2. Experimental Design and Interventions**

# **2.1 Context: The Growth and Employment Project in Nigeria**

The Growth and Employment (GEM) project was a multi-year government program in Nigeria funded by the World Bank, with the objective of increasing firm growth and employment. It aimed to do this indirectly through improving the investment climate, and directly by offering programs to improve the performance of firms in five economic sectors: light manufacturing (including agribusiness); construction; hospitality and tourism; information and communication technologies (ICT); and entertainment. To facilitate promotion and recruitment activities, the Business Innovation and Growth (BIG) platform was launched on February 9, 2016 (see Appendix 1 for a detailed timeline). Firms interested in receiving business support under GEM had to apply online through this platform. After applying online, a first screening was done to ensure firms met three eligibility criteria: (i) operated in a targeted sector; (ii) had between 2 and 100 employees; and (iii) provided all necessary information. They were then invited on a rolling basis in batches to attend induction workshops. Here firms were administered a more detailed diagnostic questionnaire that determined which programs they were eligible for. This also doubles as our baseline survey.

# **2.2 Sample and Randomization**

Our experiment works with firms that attended induction workshops held in Abuja and Lagos from March to December of 2016. To qualify for the programs in our experiment, firms needed to pass a second screening step demonstrating they: (i) were not already insourcing or outsourcing both their marketing function and finance function; (ii) had between 2 and 15 workers; and (iii) received a score of 5.0 to 8.0 (out of 10) in terms of their baseline business practices<sup>3</sup> . This resulted in an experimental sample of 753 firms. Our initial plan was to continue enrolling induction batches until we reached a sample size of 2,000 firms, but concerns about other components of the overall GEM project led to the government’s program implementors not inducting new batches that we

> 3 This was done based on the induction workshop diagnostic questionnaire. 73% of firms that attended the induction workshops had scores in the 5.0 to 8.0 range. The 24% of firms that received lower scores were not eligible for support (if they scored below 4.0) or were eligible for basic online training only (if they scored in the 4.0 to 5.0 range). The remaining 3% of firms whose business practices were already above 8.0 (out of 10) were instead directed towards personalized consulting services and some grant windows.

6
“Migrants’ Transfers” include financial items that arise from the migration (change of residence) of individuals from one economy to another.

Following discussions with the IMF statistical department, the IMF country desks, and national authorities on the precise construction of the above measures in each specific country, “Compensation of Employees” was excluded from remittances for the following countries: Argentina, Australia, Azerbaijan, Barbados, Belize, Benin, Brazil, Cambodia, Cape Verde, China, Côte d’Ivoire, Dominican Republic, Ecuador, El Salvador, Guyana, Italy, Panama, Rwanda, Senegal, Seychelles, Turkey, and Venezuela.<sup>11</sup> In general, the “Other Current Transfers” item was not included in the definition of total remittances. However, the _Balance of Payments Statistics Yearbook_ (BOPSY) specifies explicitly that migrants’ remittances are in fact recorded under “Other Current Transfers” for the following countries: Kenya, Malaysia, and the Syrian Arab Republic.

Additional adjustments or additions to the series for remittances were made on the basis of information received from IMF country desks and national authorities, as follows:

1. Bosnia and Herzegovina: Desk provided data for 1998–2003. 2. Bulgaria: Other current transfers are included in remittances. 3. Caribbean countries: Desk provided data for 1991–2002. 4. Iran, I.R. of: Other current transfers are used as figure for Remittances. 5. Lebanon: Desk provided data for 1997–2003.

> 11For most of these countries, the _Balance of Payments Statistics Yearbook_ states explicitly that no information on border and seasonal workers is included in this category.

30
level, the model also contains a variable measuring distance to the _"cabecera municipal”_ which is an indicator of distance to the governing center of the municipality (and likely the largest locality of the municipality).

Given that dependent variable is binary, equation (1) is estimated with a probit model. Table 1 presents the 2DIF estimates (summarized by the parameter β _TR_ in equation 1) of the impact of PROGRESA on the probability of working of male and female adults for the sample of individuals form eligible households (E=1).<sup>12</sup> The clustering of the households within villages implies that the household-specific error terms η ( _i_ , _t_ ) are likely to be correlated within each village (as well as across time). Thus standard errors were estimated taking into account of the clustered nature of the sample. The results are presented showing the initial level of participation in work activities (that is prior to program implementation) and the impact estimates for each round of the survey carried out after program implementation. The impact from each round should be interpreted as the percentage point difference from the pre-program level (not from the previous round). In other words, the estimates reported represent the marginal effects of being in a household eligible for PROGRESA benefits on the probability of being in the labor force.<sup>13</sup> Beginning with men, the results of the impact of PROGRESA on overall participation levels show little impact. The only exception is for men in the age groups 35 to 54, for which in the November 1999 round there is a positive and significant increases in the probability of working. The magnitudes of these effects, however, are relatively small, reflecting the already high pre-program participation of men in the labor market.

> 12 The complete set of parameter estimates is available directly from the authors upon request.

> 13 The estimates reported were obtained using the “dprobit” command in STATA v7.0. They can be easily converted into percentage changes or elasticities by dividing the marginal effect by the pre-program level, both reported in table 1.

16
services sectors are excluded from the sample, as we focus mostly on manufacturing goods for which we have available international trade data. Thus, we end up with a balanced panel of 701 firms.

As our main measure of IT use, we selected computers-per-worker, a measure which has been used in previous studies such as Bloom et al. (2015). As these authors argue, the main two advantages of this indicator is that it is a physical quantity measure that is recorded consistently across firms and sectors and it avoids the use of price deflators.

Additionally, to evaluate the robustness of our results we also use two additional proxies of IT use. The first one is the share of labor that uses Internet in the firm, and the second one is the share of labor with computer, which should be almost equal to computers-per-worker and is used mainly in our robustness tests. In fact, Bloom et al. (2012b) use the share of labor with computer as a robustness test as there could be measurement in their IT stock capital variable that could be biasing their results.

# **4.2 Trade data**

Trade data were obtained from the World Integrated Trade Statistics (WITS) using the HS 1996 classification at the 6-digits level. Considering that some HS6 codes appear in more than one NAICS 4-digits code, we reclassified the ones that had more than one match using the Mexican Tariff Classification (8 digits), by including them into the NAICS sector that had the highest share of the 6-digits HS code for total Mexican imports in 2013.<sup>7</sup> Competition is measured for the period of 2000-2008. This period was selected considering that the first year of our panel is 2008 (ENTIC 2009 gets information for the previous year). We use imports data for Mexico and the U.S. in order to analyze Chinese competition. Specifically, we calculate the share of China on imports for each NAICS-4-digits code included in the base.

## Where

_Mj, China_ =Imports of country _j_ from China _Mj,k_ =Imports of country _j_ from country k _j_ =Mexico and the U.S.

> 7The correspondence tables fore HS 1996 and NAICS generated through this exercise are available upon request.

7