rafmacalaba/datause-encoder-data huggingface dataset:
to real-time anchoveta catch data from all vessels, and there is only anchoveta catch in an area if anchoveta are sufficiently abundant in the area. This information may be particularly valuable because reducing search costs is the main margin by which fishers can increase profits within a fishing season.

Fishers are largely unable to adjust output quantity, output prices, or input prices. First, individual vessel quotas limit the tons of anchoveta that vessels can land each season. This constraint on output quantity is typically binding. Second, output price is exogenous because firms pay fishers a fixed percentage of the international price of fishmeal for each ton of anchoveta they land.<sup>25</sup> Individual fishers or fishing vessels cannot affect the international price of fishmeal. Input prices are also not affected by fishers’ decisions. The price of fuel is exogenous and wages equal a fixed percentage of the international price of fishmeal.

Fishers can adjust input quantities, namely the time and fuel they spend searching for anchoveta. Fishers spend more than 20% of their time on fishing trips searching for anchoveta; fuel comprises about one-third of variable costs; and maintenance is about one-fifth of total costs (Kroetz et al., 2016; Joo et al., 2015).

Fishing near closures could also reduce costs by increasing tons caught per set of the fishing net. Tons caught per set is a measure of fishing productivity conditional on finding anchoveta because fishers only perform a set when they see anchoveta in the water.<sup>26</sup> An increase in tons caught per set would be valuable to fishers because each set requires about one and a half hours of physically demanding labor and increases the cost of maintaining the net (e.g., by causing wear and tear). Moreover, an increase in tons per set would be indirect

> 25Firms pay fishers per ton of anchoveta they land. The price per ton is a fixed percentage of the average monthly free-on-board (FOB) price of fishmeal in Hamburg (Fr ́eon et al., 2014). According to a collective bargaining agreement with companies that account for more than 33% of landings, fishers that land anchoveta that will be processed into fishmeal and fish oil receive 1.792% of the FOB price per ton of anchoveta (SUPNEP, 2017). Under this agreement, crews divide revenue among themselves in fixed proportions: the captain receives “two parts” (twice as much as a regular fisher), the second-in-command and first engineer receive one and a half parts, and regular fishers receive one part (SUPNEP, 2017). Interviews and analysis conducted by Hansman et al. (2020) indicate that fishers not covered by this agreement also receive a fixed percentage of the FOB price of fishmeal.

> 26For example, an executive at a large fishing company told me in an interview that tons per set is the primary performance metric his company uses to evaluate the captains of their fishing vessels.

35
Institutional quality and the level of corruption is instrumented with the fraction of people speaking English and another European language using Two-Stage-Least-Squares (2SLS) estimation. The corresponding first stage is where _Engi_ and _Euri_ denote the proportion of people in country _i_ , that speak English or another European language, respectively.

# **4 Data**

**Panel analysis** The sample used for the panel regressions contains 1,000 observations that represent 150 countries during 1970 (or later) and 2014<sup>7</sup> The time dimension is reduced to five-year averages (for log GDP per capita, the initial value is used), in order to net out business cycles and high short term volatility. One observation indexed ( _i, t_ ) thus corresponds to a country _i_ and the average during years _t, t_ + 4 or the initial value in _t_ . Table A.4 in the Appendix lists the countries and periods included in detail.

The sources and precise definitions of the variables used are summarized in Table A.2 in the Appendix. A measure of the real undervaluation of currencies is constructed by the price level of an economy adjusted by the Balassa-Samuelson effect<sup>8</sup> following Rodrik (2008). More specifically, the real effective exchange rate, calculated as the exchange rate over the PPP conversion factor, is regressed on (the logarithm of) per capita GDP and a set of time dummies. The difference of the real exchange rate and the predictions from this regression is used as a proxy for real undervaluation. The resulting index is centered around zero. A positive value indicates undervaluation, and a negative value indicates overvaluation. Figure 3 shows the natural logarithm of the undervaluation index for Nigeria, as compared to other Sub-Saharan African oil-exporting and non-oil-exporting countries. This comparison shows descriptively, that Nigeria’s exchange rates have been overvalued—considerably so compared to other Sub-Saharan African countries—during the 1980s, 1990s, and early 2000s. Only in the mid-2000s, the degree of undervaluation returns to the Sub-Saharan African average.

In order to measure institutional quality and the level of corruption in the countries, we rely on the recently established Varieties of Democracy Data Base (Coppedge, Gerring, Lindberg, Skaaning, Teorell, Tzelgov, Wang, Altman, Bernhard, Fish, Glynn, Hicken, Knutsen, McMann, Staton, and Zimmermann, 2015), that provides a wide array of indicators related to various aspects

> 7All figures presented here are lower bounds, and refer to the sample of 1,000 observations in columns (3) to (5) in Table 1 and columns (3) to (5) in Table 2.

> 8The adjustment accounts for the finding, that increases in income levels lead to a relative price increase of non-tradeable goods due to productivity improvements.

8
they consumed each food. Food was categorized into seven groups, following the dietary diversity guidelines from the Food and Agriculture Organization of the United Nations (FAO): (1) grains, (2) vitamin A-rich plant food, (3) other fruits and vegetables, (4) meats, fish, and seafood, (5) eggs, (6) dairy, and (7) oils and fats. This information was then combined into two measures of dietary diversity. The first measure is whether children consumed foods from a minimum number of dietary groups (four or more groups) and the second measure is whether children consumed foods from the maximum number of dietary groups (seven) in the past week.

Finally, we also asked parents to provide enrollment histories for each child from the 2015/2016 school year going back to the 2012/2013 school year. These were used to construct whether or not a child enrolled in a particular time of school service as well as a measure of their cumulative months of enrollment at the time of the survey. Descriptive statistics on all these variables are presented in Table A7.

We see that immunization rates and minimum dietary diversity rates are quite high in this population, yet the majority is not close to getting maximum dietary diversity. Mothers are well aware of pre-school opportunities around them but much fewer of them act on that knowledge on average. Differences between literate and illiterate mothers are sizeable, around 9 p.p. for immunization rates, 5 to 6 p.p. in dietary diversity and about 8.5 p.p. in attendance of pre-schooling institutions (or about 1.1 less months).

# **3 Empirical design and causal identification strategy**

Motivated by the correlation between child outcomes and maternal education, as well as by the opportunity to trace changes in maternal access to education with different parenting styles and parental investments, we focus on quasi-experimental variation generated by the INPRES schooling expansion to investigate impacts across generations. Following Duflo (2001), we combine geographic variation in planned school construction intensity (per 1,000 children) with a woman’s year of birth in order to formulate a difference-in-differences empirical model. We differ from that 15
Policy Research Working Paper 5043

# **Abstract**

Theories of international trade assume that all countries use similar and exogenous technologies in the production of any good. This paper relaxes this assumption. The marriage of literatures on biased technical change and trade yields a tractable theory, which predicts that differences in factor endowments and intellectual property rights bias technical change toward particular factor intensities, and thus unit factor input requirements can vary across economies. Using data on net exports of a single industry, computers, intellectual property rights and factor endowments for 73 countries during 1980– 2000, the paper shows that once technological choices are considered, countries with different factor endowments can become net exporters of the same product.

This paper—a product of the Trade and Integration Team, Development Research Group, and the Office of the Chief Economist for Latin America and the Caribbean—is part of a larger effort in both departments to study the how the structure of trade affects development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dlederman@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 2 (Continued)

# HOUSEHOLD-LEVEL SCHOOLING REGRESSIONS: PERU, **1985-86**

|Independent<br>Equa|tion 1|Equation 2|Equat|ion 3|
|---|---|---|---|---|
|Variable.-<br>Female|<br>Male|Female<br>Male|Female|<br>Male|
|P|opulation A|ged 6 to 15 years|||
|Constant<br>0.765|<br>2.768|1.448<br>3.378|1.595|3.209|
|(1.40)|(1.41)|(1.38) (1.00)|(1.39)|(1.03)|
|Industry share<br>2.474|<br>1.201|2.047<br>1.152|2.518|<br>1.106|
|(1.58)|(1.31)|(1.52) (1.24)|(1.56)|(1.24)|
|Services share<br>1.680|<br>-0.297|3.101<br>0.179|3.827|<br>0.071|
|(2.00)|(1.65)|(1.92) (1.57)|(2.05)|(1.60)|
|Rural**dummy**<br>1.928|<br>0.168|1.383<br>0.142|1.424|<br>0.125|
|(0.49)|(0.40)|(0.49) (0.38)|(0.49)|(0.38)|
|Household income 0.001|<br>-0.000|0.000<br>0.000|0.000|<br>-0.000|
|(0.00)|(0.00)|(0.00)<br>(0.00)|(0.00)|(0.00)|
|Father's education||-0.042 -0.309|-0.010|-0.335|
|||(0.14) (0.12)|(0.14)|(0.13)|
|Mother's education||-0.465 -0.163|-0.433|-0.193|
|||(0.12) (0.10)|(0.13)|(0.11)|
|Father's occupation|||-0.129|<br>0.027|
||||(0.11)|(0.11)|
|Mother's occupation|||-0.056|0.109|
||||(0.10)|(0.09)|
|Adjusted R<sup>2</sup><br>0.092|<br>-0.009|0 t91<br>0.102|0.189|0.101|
|Sample size<br>156|170|156<br>170|156|170|

Note: Dependent variable is schooling shortfall - age - schooling - 5 Significant at 10 percent, at 5 percent level, at 1 percent level. Standard errors in parentheses.

30
Table B3.I Distribution of households according to labor market status of head and spouse

|Spouse<br>Head|Employed<br>(Atwork)|Employed<br>(Notatwork)|Unemployed|Notinlaborforce|
|---|---|---|---|---|
|Employed|49|13|9|11|
|(Atwork)|[3]|[5]|[201|112]|
|Employed|2|2|1|1|
|(Notatwork)|[1|**101**|**Il1**|**101**|
|Unemployed|3<br>[8]|1<br>[5]|3<br>[221|2<br>[15]|
|Notinlaborforce|1|**0**<br>**10L**|**1**<br>[3]|[31|

**Source:** Labor Force Survey, 1996.1-1996.4, 1999.1-1999.4, and 2000.1-2000.2.

Table B3.II Distribution of Social Assistance

||**%**receivesSA|%ofthose<br>receivingSA|%uneamong<br>thosew/SA|%NLFamong<br>thosew/SA|
|---|---|---|---|---|
|Head|6.44|40.05|88|9|
|Spouse|8.74|54.35|63**(6 7)A**|30(2 6)A|
|Other|0.42|5.60|98.5|_1.5_|

**Source:** Labor Force Survey, 1996.1-1996.4, 1999.1-1999.4, and 2000.1-2000.2.

**A** Not including maternity allowances.

59
subjective indicators have ensured that there are reliable data sets of perceptions of corruption among different groups (voters, policy-making elites, etc.) in a large number of countries. This is obviously critical when undertaking comparative work that requires the existence of consistent measures across countries and/or time, in order to ascertain how variation in institutions, across countries, affects variation in corruption.

**Reliability (Costs).** Of course, survey-based indicators also face challenges. Composite indicators, such as the CPI, CC, and GE, require the use of multiple sources in order to identify and mitigate the effect of non-representative survey results. Unfortunately, this means that, over time, the indicators are unlikely to use the same sources to arrive at a given country’s score. Thus, while using these indicators for cross sections is not a problem, their consistency over time is more questionable and such comparisons are generally discouraged (World Bank, 2012; Transparency International, 2011). However, the use of survey-based instruments for cross-sectional purposes has been shown to be robust, especially given how the large data sets allow for the inclusion of a large vector of control variables, and therefore have been extensively used in the empirical literature (see Persson and Tabellini, 2003).

# **(B) Objective Indicators**

**Validity (Benefits).** The single biggest advantage of objective indicators of corruption is that they measure observed actions/outcomes and can thus quantify the abuse of public office for private gain. For example, Olken (2009), and Gray-Molina et al (2004) were able to quantify the extent to which resources devoted to road projects were actually diverted by public officials, and Fishman and Miguel (2007) were able to record the extent to which officials abused their diplomatic immunity. In short, the objective measures are able to provide a ‘real number’ that captures the magnitude of abuse of public office for private gain, in a way that subjective indicators may not.

**Validity (Costs).** Despite being able to come up with numerical values, the validity of objective indicators is not unproblematic. A particularly pertinent issue is that of external validity (Transparency International, 2011). Many of the objective indicators measure only one narrow type of activity (e.g. road construction or abuse of parking

6
simply driven by a changing composition of characteristics of routine and non-routine workers, we estimate the following set of cross-sectional wage regressions separately for each year, _t_ : where _NR i,t_ is a dummy variable indicating that individual _i_ works a non-routine job in year _t_ and _Xi,t_ includes a variety of control variables. In particular, we include gender, race, and full time employment dummies, we control for the weeks worked, a full set of industry fixed effects (50 industries constructed from SIC industry codes by the NBER), as well as fourth order polynomials in age, education, and the interaction of education and age.

We estimate regressions (1) based on individual level data from the annual CPS march supplements and weight by the CPS sampling weights.<sup>13</sup> Panel B of Figure 3 plots the resulting time series of estimates _β_<sup>ˆ</sup> 1 _,q_ and the associated 95% confidence intervals based on standard errors that are clustered on industry. These estimates highlight that the rising relative wage for non-routine labor is not entirely driven by the rising skill premium or by specific industries. The latter observation is of particular importance, as it highlights that the wage premium is not simply due the steady decline in manufacturing as the estimates _β_<sup>ˆ</sup> 1 _,q_ are identified from within industry variation. These estimates therefore suggest that part of the increase in the non-routine income share is driven by a steadily increasing gap between routine and non-routine pay.

Our estimates suggest that the non-routine wage premium rose from around 14% in 1968 to more than 26% in 2013. Given the sheer size of this premium we find it unlikely to be driven by frictions (like barriers to entry). In our calibration exercise, we will therefore assume flexible labor markets and attribute the non-routine wage premium to some form of unobserved ability (e.g. managerial skills, “people” skills, etc.).

Similarly, to measure the trends in labor inputs we distinguish between raw labor (employment) and effective units of labor which take into account differences in worker attributes. In a

> 13Note that in an earlier version we estimated these regressions at the quarterly level based on the CPS MORG. The results are qualitatively equivalent but we prefer the longer time horizon provided by the annual march supplements. The CPS MORG results are available form the authors upon request.

10
INTRODUCTION XXiii Figure 6 Projected Population (billions) in Less Developed.Countries, 1985-2025, Compared with U.N. and Previous World Bank Projections <!-- Start of picture text --> 7.5 -<br>6.0 ~ - ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~Pev ious<br>5.5<br>4.5-Curn| 5 0<br>4.0<br>1985 1990 1995 2000 2005 2010 2C15 2020 2025<br><!-- End of picture text -->

Figure 7 Projected Population (billions) In More Developed Countries, 1985-2025, Compared with U.N. and Previous World Bank Projections <!-- Start of picture text -->
1.36<br>1  .35-<br>1.34  -<br>1  .33 -<br>1  .32-  reiu<br>1.31-<br>1  .3-  C3rent<br>1.29-<br>1 .286<br>1.2?<br>1  .26<br>1  .25<br>1 .24<br>1.23<br>1.22-<br>1.21<br>1 20-<br>1.19<br>1.17<br>1985  1990  1995  2000  2005  2010  2015  2020  2025<br><!-- End of picture text -->
**Table 6.3 Main Means of Hiring Workers (%)**

|**Man**|**ufacturing Othe**|**r industry**|**Services**|**Total**|
|---|---|---|---|---|
|Advertisement in mass media|12.3|9.1|16.4|14.2|
|Post announcement outside factory gate|9.7|9.1|.5|4.7|
|Through a training center|5.8|9.1|5.2|5.7|
|Through Labor Service Center|11.6|15.2|12.2|12.2|
|Through Management Board of EPZ/IPZ|0.6||0.5|0.5|
|Word of mouth through existing|24.5|24.2|15.0|19.5|
|employees|||||
|Word of mouth through friends and|34.8|33.3|49.3|42.4|
|family|||||
|Other|.6||1.0|.7|
|**_Total_**|**_100.0_**|**_100.0_**|**_100.0_**|**_100.0_**|

Source: World Bank Survey, 2001

# **Conditions for Expansion in the Next 12 Months**

71. The survey also explored whether companies had plans for expansion in the next 12 months. About 37 percent reported they had definite plans for expansion and another 24 percent said they might expand if certain conditions would be fulfilled. The most important condition for expansion mentioned by almost 50 percent of the respondents was gaining access to new markets, 24 percent mentioned access to land as the main condition and another 17 percent said their expansion would be dependent on getting access to commercial financing.

**Table 6.4 Main Condition for Expanding Labor Force (%)**

||**Industries**|||**Total**|
|---|---|---|---|---|
||**Manufacturing**|**Other**<br>**industry**|**Services**||
|Bilateral Trade Agreement with the US|2.7|0.0|0.0|1.0|
|implemented|||||
|Able to access new market|43.2|72.7|47.9|49.0|
|Able to access commercial financing|16.2|9.1|18.8|16.7|
|Able to again access to more land|27.0|18.2|22.9|24.0|
|Able to access benefits of specific|8.1|0.0|6.3|6.3|
|favorable government policy|||||
|Other|2.7|0.0|4.2|3.1|
|**_Total_**|**_100.0_**|**_100.0_**|**_100.0_**|**_100.0_**|

Source: Survey, 2001 35
“noisy,” filled with identity claims, emotional rhetoric, and demands for individual entitlements. Second, discourse often reflects the existing patterns of social standing and hierarchy, with elites dominating discussion in less fractionalized villages, and women remaining relatively silent even when they attend _en masse_ . While these inequalities are undoubtedly concerning, three sets of empirical findings reveal the ways in which deliberation in India still thrives. First, these inequalities have been challenged from _within_ the deliberative forums, with lower castes using the open discursive space to make claims of dignity that cannot be ignored by those in power. The use of such dignity claims, while not traditionally considered “rational deliberation,” still plays a vital role in enabling citizens to generate credibility, create empathy, and trigger a sense of injustice (Mansbridge 2015). Second, state policies and intervention have been shown to help lessen the extent of inequality within the deliberative sphere. Programs to train and empower citizens, like the People’s Campaign in Kerala or the women-centered Pudhu Vaazvhu Project in Tamil Nadu, suggest that facilitated deliberation can usher the _sabhas_ closer to the deliberative ideal – in which citizens of equal standing arrive at consensual decisions on matters of public interest. And third, literacy in India is on the rise; over the past decade alone, adult literacy has grown by roughly 10 percent to 74 percent, with the strongest gains in rural regions and among women (Census of India 2011).<sup>4</sup> Given these gains, one could speculate that the quality of deliberation should improve. The Kerala’s People’s Campaign has not translated well in other, less literate, parts of India (Ananthpur et al. 2014); thus, secular improvements in literacy levels should improve the efficacy of attempts to make deliberative democracy a more practical tool for collective action.

The deepening of democracy via deliberation, however, has also faced meaningful limitations. Perhaps most obviously, the 73<sup>rd</sup> Amendment only applies to rural India, leaving urban India devoid of any mandated deliberative institutions. This focus on rural rather than urban democracy is not surprising given the profound influence of Gandhi’s _Village Swaraj_ even on 16