rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: Effect of Fuel Subsidies on Log Hours of Fishing, Alternative Specifications

|||Depe|ndent var|iable: log|(fshing h|ours)||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Conventional|2.084|1.254|293.648|1.135|1.186|0.227|1.743|
||(0.259)|(0.202)|(0.271)|(0.240)|(0.223)|(0.207)|(0.280)|
|Bias-Corrected|3.139|1.732|54.018|1.402|1.631|0.356|1.740|
||(0.259)|(0.202)|(0.271)|(0.240)|(0.223)|(0.207)|(0.280)|
|Robust|3.139|1.732|54.018|1.402|1.631|0.356|1.740|
||(0.319)|(0.229)|(0.330)|(0.322)|(0.264)|(0.278)|(0.339)|
|Bandwidth|0.168|0.178|0.144|0.199|0.197|52.153|0.148|
|N|820|583|737|1,143|1,050|3,541|700|
|Impose bandwidth||||X||||

Notes: In each regression, the data are Chinese distant water fishing vessels for which the engine power ceiling is binding, the unit of observation is a vessel-month-year, the dependent variable is log fishing hours, we cluster standard errors at the vessel-level, we use default options from the rdrobust package, and we control for gear fixed effects, gross tonnage, and engine horsepower. All columns except for 7 also control for vessel length and year built. Column 4 imposes the bandwidth (BW) from Column 1 of Table 3. Column 1 only includes vessels built on or before 2014, Column 2 excludes 2020 data, Columns 3 and 4 include 2015 data, and Column 5 includes year fixed effects. The running variable in Column 6 is (non-normalized) gross tonnage distance.

the regressions estimate a variant of Equation 2 on the subset of binding vessels. Our results are similar to our preferred specification in Column 1 of Table 3 in that the coefficients are all large and positive.

In Column 1 of Table 6, we find that the effect of the policy remains large and positive when we require vessels to have been built on or before 2014, when the first vessel standardization regulation was published, and before the fuel subsidy policy we evaluate went into effect. In Column 2, we obtain a similar result when we subset the data to between 2016 and 2019, instead of 2016 to 2020, since 2020 was a transitional year between policy regimes (Section 2). In Columns 3 and 4, we subset the data to between 2015 and 2020, since the fuel subsidy policy we evaluate was applied ex post to 2015 fishing. The Column 3 coefficient is implausibly large, perhaps because the mean-squared error optimal bandwidth is small. When we re-estimate the regression with the optimal bandwidth from Column 1 of 32
21 Finally, the difference between (11) and the equation estimated for 1995: ln _wij_ 95 = _X ij_ 95 β 95 + _I ij_ 95 * _wp_ 95 _j_ + ( _I ij_ 95 * _S ij_ 95 ) _sp_ 95 _j_ + _F_ 95 − 1 (θ _i_ 95 ) (12) corresponds to differences in the joint distribution of observed characteristics between 1995 and 1988 (except for the changes in occupational structure due to trade, which had been predicted in equation 8). This step also accounts for changes in the correlation between the observed characteristics and the residual terms, including any changes in selection into the labor force.

Only equation (1), for 1988, and equation (12), for 1995 are estimated on observed data. 1 6 Equations (6)-(11) give rise to simulated wage distributions _G_ ( _w_ ) to _G_ ( _w_ ) . In Section 5, simulation results are presented in two ways. First, a number of inequality indicators are computed for each counterfactual distribution, so that we can decompose the observed changes between 1988 and 1995 into the components corresponding to each counterfactual. Second, we can see a fully disaggregated picture by plotting the observed wage growth incidence curve between 1988 and 1995, _g_ ( _p_ ) = _w_ 95 _p_ − _w_ 88 _p w_ 88 _p_ , and presenting each intermediate counterfactual growth incidence curve: _g s_ ( _p_ ) = _w sp_ − _w_ 88 _p w_ 88 _p_ , s = 1, ...,6.<sup>29</sup>

# **4. Estimation Results**

Before we turn to the simulation results in the next section, this section presents the estimation results on which they build. Table 3a presents the main first-stage results for the wage equation (1), while Tables 3b and 3c report the industry-specific wage and skill

> 29 The “wage growth incidence curve” is an application to the distribution of wages of Ravallion and Chen’s (2003) concept of growth incidence curve, which those authors originally defined on the distribution of household incomes or expenditures.
# **{Tables 4 and 5 here}**

Among the first six categories that are based on raw census data, three categories (raw scaled, R&R not scaled, and R&R scaled) are constructed through the summation of bilateral raw numbers and disaggregations of some aggregate categories in the original censuses. Since these categories together constitute around 45 percent of migrants in each census round, the original bilateral portion of each cell was compared with the final number assigned to them after the various calculations as a check on accuracy. For each decade, therefore, the overall percentage contribution of the raw bilateral data to the total is calculated (table 6).<sup>23</sup> In each census round, at least 92 percent of all those categories are derived from the raw data.

# **{Table 6 here}**

# _Simulating Missing Data_

Finally, to examine the reliability of the estimated missing census data and test the methodologies, several scenarios are assumed. All bilateral observations for a single year for four countries (Australia, United States, Switzerland, and Chile) in different parts of the world are deleted and the missing cells are filled using one of five methods.<sup>24</sup> The first simulation assumes that all bilateral data for 2000 are missing but that the total number of migrants is available. The missing bilateral numbers then have to be filled using the propensity measure (equation 1 in appendix 4) based on the data available in other years. The second and third simulations assume that the total is missing as well, and interpolation is used to fill in all missing data for 1960 and 2000. The fourth and fifth simulations remove all data for all years and then fill the missing years using data for the remaining portion of the subregion (table 7).

# **{Table 7 here}**

The simulations perform well. The four countries are examined one at a time, starting with Australia. The correlation coefficient between the predicted and actual data in each simulation is at least 0.945. Interpolating the data is the most accurate method of predicting the missing data, and simulation 2 for 1960 is more accurate than simulation 3 for 2000. Simulation 1 does not perform as well: the data from other years fail to adequately account for the fairly significant shift in the composition of the Australian immigrant stock after 1990. When simple subregional shares are used (simulations 4 and 5), the correlation coefficients remain high. The actual distribution of immigrants, however, is less accurate, especially in simulation 5. This is because New Zealand, the country in the subregion that has by far the greatest weight for apportioning migrants for 23 Although only aggregates for each decade are presented here, a full matrix detailing exactly how each cell was generated is available from the authors.

24 For all countries, data quality is highest for 2000 and lowest for 1960, except for Chile, for which 1980 has the worst quality data.

16
kind of policies explaining this result, one possible candidate are fiscal policies conducted in a procyclical manner.

They indeed remain highly procyclical, as found by Gavin, Hausmann, Perotti and Talvi (1996)12, among others. These authors show that not only fiscal outcomes are procyclical but that fiscal responses have deepen the cycle particularly during recessions when they tend to generate upright surpluses (see Table 1), usually after major financing crisis leave no other choice. For example, their statistical results show that during recessions Latin American countries tend to generate a small surplus of 2.2 cents for every dollar of real output fall. In contrast, OECD countries tend to generate significant counter cyclical deficits<sup>of</sup> 61 cents.

**Table 1**

|**ResponseofFis**|**calBalancet **<br>**HighGrowth**|**oGDPGrowth**<br>**Perlods**|In**GoodTime**<br>**Low Growth**|**sandBad**<br>**Periods**|
|---|---|---|---|---|
||**OECD**|**Latin**<br>**America**|**OECD**|**LatIn**<br>**America**|
|Totalsurplus|**0.174**<br>|0.154<br>|0.611<br>|-0.022<br>|
|Primarysurplus|0.136|0.123|0.580|-0.049|

**Note:** Number give the typical impact of a one percentage point increase in real GDP on the ratio of the surplus to GDP. A positive number for the surplus implies that it moves in a stabilizing direction.

Source: Gavin, et al. (1996) _C. Procyclical fiscal policies add policy risk to the income risk of the poor_ Procyclical fiscal policies not only accentuate the cycle but are specially harmful for the poor. Indeed, we have found at the Bank<sup>13</sup> that social expenditures are kept at best constant as a percentage of GDP during downtums, and the more targeted social expenditures tend to fall as a percentage of GDP, when they should expand as the number of poor increases. As a consequence, in a typical downturn social expenditures per poor are reduced by 2% for each 1% of reduction in output. By contrast, social expenditures usually grow as a percentage of GDP<sup>in</sup> upturns, when they are less needed. That is, the procyclicality of safety nets -as<sup>a</sup> consequence of the procyclicality of fiscal policies- adds substantial policy risk<sup>to</sup>

> 12 Gavin et al. (1996) op. cit.

> 13 De Ferranti et al. (2000) op. cit.

4
average public sector wage premiums in developing countries are positive and larger than those in developed economies.

The rest of this paper is structured as follows: Section 2 provides background on the economic situation in Bhutan. Section 3 contains a description of the Bhutan Labour Force Survey (BLFS) 2014, the data set used henceforth. The same section also introduces the analytical strategy and describes the econometric model underlying the three-step procedure. The main empirical analysis follows in Section 4. The section consists of four parts: First, a compilation of descriptive statistics on wages, fringe benefits and other variables. Second, an analysis of self-selection into labor force participation and public or private sector as well as of the determinants of wages in either sector and a decomposition of the selection-adjusted difference in average wages between public and private sector into characteristics and coefficients effects. Third, an appraisal of whether results are robust to variations in the empirical setup. Fourth, similar decompositions for different quantiles of the wage distribution. Finally, Section 5 discusses possible implications for research and policy and concludes.

# **2. Background**

Bhutan is a landlocked country in South Asia bordering China to the North and India to the West, South and East. According to NSB (2014), Bhutan’s population in 2014 was around 745,000 and its land area covers 38,394 square kilometers. Thus, Bhutan has a population in the same order of magnitude as the Comoros or Guyana and covers a land area similar to that of the Netherlands or Switzerland. Bhutan’s official currency is the _ngultrum_ or _Nu_ . The _Nu_ is pegged to the Indian _rupee_ at par; in late 2014 one US dollar was worth about _Nu_ 63.5. Administratively, Bhutan’s territory is divided into 20 _dzongkhags_ (administrative districts). The 20 _dzongkhags_ are further divided into 205 _gewogs_ (groups of villages).

Bhutan is maybe most famous for introducing and popularizing the concept of Gross National Happiness (GNH), now enshrined as a principle of state policy in Article 9 of the country’s constitution. Exact characterizations of GNH differ but one widely cited publication defines it as a “multi-dimensional development approach that seeks to achieve a harmonious balance between material well-being and the spiritual, emotional and cultural needs of our society” (GNHC, 2012, p. 3). Over the last decades, Bhutan has seen rapid and sustained economic growth. In 2014, its GDP per capita reached US dollar 2,379 according to the World Development Indicators. Over the last

- 4 -
**Table 19: Basic earning functions based on years of schooling for the private sector workers, 1993-98**

|||1993|||1998||
|---|---|---|---|---|---|---|
||Mean|Coefficient|t|Mean|Coefficient|t|
|Years-school|6.01|0.031|5.96|6.8|0.039|8.64|
|Years-experience|16.92|0.032|6.30|17.4|0.019|2.95|
|Experience-sq|444.18|-0.0006|-6.37|440.2|-0.0004|-3.20|
|Sex((male=l)|0.65|0.380|11.38|0.7|0.292|10.51|
|North(North=1)|0.28|-0.268|-6.99|0.3|-0.389|-9.04|
|Urban(urban=1)|0.32|-0.057|-1.62|0.3|0.122|3.10|
|Seniority(Junior=1)|0.16|-0.004|-0.07|0.3|0.019|0.39|
|Constant||4.311|22.27||3.358|17.74|
|R-squared||0.200|||0.330||
|Numberofobservations||1285|||1897||

_Source:_ 1992-93 and 1997-98 VLSS Among factors other than schooling, experience, gender, region, and urban or rural location of the worker were statistically significant in determining earnings in both 1993 and 1998. An additional year of experience brings 3 % higher earnings in 1993, an amount equivalent to the impact of one more year of schooling. However, in 1998 experience was relatively less important, so that an additional year results in only a 2% increase in earnings.

Female workers are better educated than male workers. Although males and females have nearly the same level of education attainment in terms of number of years of schooling, the female workforce has a slightly higher proportion with university degree or competed upper secondary school than the male workforce. However, males' earnings are still 37 percent and 26 percent higher than females' earning in 1993 and 1998 respectively. In both years, a male worker earned much more than an identical female worker even after controlling for education, experience and the other variables in Table 19. In 1993 a male worker eamed 46 percent more than a female worker with the same (observable) characteristics. This differential decreased slightly by 1998, but it was still 40 percent. These result mean that there is still some kind of discrimination between males and females and something going on that keeps females' wage low. Further study is needed to fill gap in this area.

A structural test was used to see if there is any difference in returns to education between male and female workers for both 1992-93 and 1997-98 surveys. It is quite interesting that returns .to male workers' schooling in 1993 were higher for that of female workers, but it was reversed in 1998 when females had higher returns to schooling than males. However, the differences in 1998 were not statistically significant.

The wages of workers also varied by their location. Urban workers in 1998 can eam 12% higher than an identical worker in rural areas. Workers in the south earned more on average than those in the north even though average education level in the north is higher. Workforce in the north has on average one more years of schooling than that in the south, and it has higher proportions with upper secondary and university levels than the south. After taking into account other factors, in 1993 a worker in the south still eamed 24% higher than an identical worker in 23
In summary, although the model without distortions used for the analysis of the previous subsection suggests that some Latin American countries suffer true innovation problems, an alternative explanation is that rather than lack of innovation, these economies suffer from severe distortions that directly lower TFP. More research is necessary to understand how to disentangle static distortions from barriers to technology adoption. For now, we proceed (mostly) under the assumption that distortions play no role in explaining low productivity levels.

_Potential pitfalls of using international databases and common parameters: the case of Chile_ As mentioned above, one limitation of the analysis we have conducted so far is that it relies on international databases and assumed common parameters. Although this is fine for the purpose of establishing stylized facts, it is not satisfactory when analyzing a particular country. We now consider two specific issues. First, some countries may exhibit a high measured TFP as result of their large endowment of natural resources. Ideally, one would correct for this to make the results comparable across countries. Second, although the assumption of a constant Mincer coefficient may be a good approximation when studying broad regularities in the data, this is no longer the case when one is interested in a particular country. In that case, it is much better to use the particular Mincer coefficient for the country in question. In this section, we explore these two issues for the case of Chile, assuming first that there are no distortions (i.e., _z_ = 1).

First consider the impact of natural resources. In the case of Chile, it is clear that a significant part of its GDP is not so much the result of using human and physical capital according to the production function above but rather the result of “using” its large endowment of mineral resources. According to the Central Bank, mining contributed 6.7% of GDP in 1999, whereas according to the 1998 Household Survey employment in this sector accounted for 1.6% of total employment. Assuming that the physical and 22
# **2.3. Regulations on hours**

Overly restrictive regulations on working hours limit the choices of firms and employees in arranging a work schedule that works best for them and can therefore adversely affect their performance. Flexible regulations on working hours—regulations that do not limit the work schedule to the standard workweek and working hours—allow firms to adjust their labor supply to periodic changes in demand and thus better utilize their production capacity and increase their productivity. Such regulations also allow employees to better coordinate their work and life responsibilities and can boost their job performance (Golden, 2011; White et al., 2003).

An index of regulations on hours, based on data for five _Doing Business_ indicators, shows that Sweden has stricter regulations on hours than the majority of the countries in the sample—only France, Greece, Luxembourg, Norway and Spain have stricter regulations (Figure 6). Half the countries, including Sweden, have not changed statutory working hours between 2008 and 2011. Eight countries—Japan, Austria, Belgium, Finland, Germany, Italy, Portugal and Greece— undertook reforms making regulations on working hours more flexible between 2008 and 2011, while Norway and Spain made them more restrictive.

Figure 6: Sweden has stricter regulations on hours than the majority of countries in the EU15, G7 and Nordic region <!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>2008 2011<br>Regulations on hours index Japan Italy Spain<br>Canada Ireland United States G7 average Austria Belgium Denmark Finland Germany Iceland United Kingdom OECD average Nordic average EU15 average Netherlands Portugal Sweden France Greece Luxembourg Norway<br><!-- End of picture text --> Source: Gwartney et al. (2013).

Note: The Economic Freedom of the World regulations index on hours is calculated as the average of _Doing Business_ data on the five indicators shown in table 3. The index ranges from 1 to 10; higher values indicate more flexible regulations. Countries and country groups are in descending order by the index value in 2011. The Nordic average excludes Sweden.

14
well as indicators for general child health. For adults, health included both physical and mental health. _Motor development_ encompasses indicators of fine and gross motor development.

# **Appendix 2. Process for standardizing outcomes across studies**

## **Continuous variables**

To standardize continuous variables that were not already standardized, we needed to scale the estimated coefficient (or “raw” effect size, ESraw) by the standard deviation (sd) of the outcome variable for the control group at follow-up to get the standardized effect _d_ .

Very few studies, however, report this standard deviation. When the standard deviation was not available, we use an approximation for standard deviation using the sample sizes of treatment (NT) and control (NC) groups: where _SE_ corresponds to the standard error of ESraw.

When studies did not include even a standard error of the estimated coefficient, we used the p- value (or an approximation of it) to estimate the standardized effect size for outcomes using the following formula: where _qnorm_ gives us the z-score of the _p_<sup>th</sup> quantile of the normal distribution.

Some studies reported baseline or follow-up sample sizes only for the entire sample, not separately for the treatment and control groups. In these cases, we assume that the total sample size was equally distributed across experimental groups, unless information in the text or elsewhere indicated otherwise. For example, in the Baker et al., (2008) differences-in-differences study, we assigned 23 percent of the sample to the treatment group since 23 percent of the Canadian population lives in Quebec. When the follow-up sample size for an estimation is not specified, we assume the sample size reported at baseline and adjust for attrition, if reported.
# **Utility from shopping**

Perhaps people make frequent, small-quantity purchases because there is a utility value from shopping—e.g., from the socializing and community engagement that one enjoys in the market. An obvious problem with this hypothesis is that people can visit shops and markets without making purchases, and do so frequently. That said, if one believes in a very specific social contract that relies on frequent purchasing in small quantities, that could generate the observed patterns in the SHWALITA data.

It is straightforward to show that the data contradict this hypothesis. Many households make purchases both above and below _qmin_<sup>_∗_.</sup> By rearranging purchases to always purchase exactly _qmin_<sup>_∗_, households could increase the total number of purchases while always</sup> buying in bulk.

To illustrate, define _Khi_<sup>_∗_=</sup><sup>_e_ˆ</sup><sup>_hi/e∗_</sup> _min_<sup>.Thisisthemaximumnumberoftransactions</sup> that household _h_ could make on item _i_ at the lowest available unit price, without changing total expenditure on the item. The actual number of transactions that household _h_ makes on item _i_ , _Khi_ , could be smaller or larger than _Khi_<sup>_∗_.Thevalue</sup><sup>_K_</sup> _h_<sup>_∗_=�</sup> _i_<sup>_K_</sup> _hi_<sup>_∗_tellsushow</sup> many purchases the household could make if its goal were to maximize total purchases—to shop as much as possible—while only buying at the lowest available unit prices.

Table 8 shows the mean values of _Khi_ , _Khi_<sup>_∗_, and the difference</sup><sup>_K_</sup> _hi_<sup>_∗−Khi_, as well as the</sup> same measures aggregated across items. The results in column 3 indicate that the average household in all four groups could shop at least as much, or more, than it does at present, while only buying in bulk. The average household could make 20.8 more purchases than at present. Households that are loss-prone in levels (only) could make 30.9 more transactions on average. The high percentage-loss group, who are relatively poor and tend to shop less, could make 0.1 more purchases. Finally, households that are in the 25% worst group in both levels and percentages could make 5.2 more purchases on average.

These counterfactual shopping patterns are unlikely to be optimal for a variety of reasons. However, this analysis demonstrates that the desire to shop frequently cannot explain the failure to take advantage of bulk discounts, because households could already do more of both.

24