rafmacalaba/datause-encoder-data huggingface dataset:
of test scores, which is in line to the synthetic control matching step. Once the synthetic control is constructed by re-weighting the non-treated schools, DiD specifications from equations 4 and 5 are applied.<sup>12</sup> In doing so, the underlying identification assumption changes slightly. Once the observed covariates are taken into account, and schools close and far from the libraries follow similar time-trends or differ in a linear way, estimated impacts can be attributed to the mega-libraries. However, keep in mind that the identification will be invalid if there were events that were not considered and affected some of the schools (either close or far from libraries) and not the others.

Apart from the 2000-2002 test scores, the matching variables considered are the following: the proportion of teachers with graduate studies, pupil-teacher ratio, public school dummy, morning school day dummy, complete school day dummy, female-teacher ratio, 11th grade female-male students ratio, 11th grade students, total students, girls-students ratio, built area per student, classrooms area per student, sports area per student, and a dummy for the presence of a school library.

# **5.3 DiD-OB: Decomposition of the impact**

As discussed, the construction of the libraries implied a massive urban development. As a result, it is likely the mega-libraries triggered changes in other inputs. For instance, the construction of mega-libraries could lead to emigration from the area due to changes in real estate prices, also they could change the number of private schools or the teacher-student ratio. Thus part of the observed changes between schools close and far from libraries would be due to this channel. Hence, we would be interested on see if the program had an impact on the inputs and such variation explain part of the outcomes difference, let’s call that part ∆ _X_ , and if there is part of that impact that is not due to them, ∆0, instead this part of impact could be due to changes on the impact that teachers with high level of education could has with the presence of the libraries or could be due to changes in the efficiency of public schools who engage with the libraries’ services. In that case, ∆0 would be more likely to be related with the complementarity between schools and libraries. This is achieved by implementing a novel strategy, proposed in this study, that introduces the

> 12As the matching is based on discrete categories, the continuous approach cannot be implemented.

17
**Table 4: Relationship between the formal employment rate of older persons (55-64) and labor market outcomes of prime-aged persons (25-54)**

||(1)<br>Prime|(2)<br>Prime|(3)<br>Prime|(4)<br>Prime|(5)<br>Prime|
|---|---|---|---|---|---|
||Formal employment|Unemployment|Hours of work(log)|Hourlywage(log)|Monthlyincome(log)|
|Formal employment rate of old|0.684***|0.024|0.000|0.009***|0.010***|
|persons (55-64)|(0.077)|(0.015)|(0.001)|(0.003)|(0.004)|
|Natural log of hourly income of|8.028***|0.334|-0.059**|||
|prime-aged persons (25-54)|(2.044)|(0.387)|(0.023)|||
|Unemployment rate|||0.004*|0.012*|0.023***|
||||(0.002)|(0.006)|(0.007)|
|Poverty rate|0.029|0.034***|-0.001**|-0.001|0.001|
||(0.045)|(0.009)|(0.001)|(0.002)|(0.002)|
|Share of employment in the|-0.219***|-0.007|-0.004***|-0.002|-0.005***|
|agriculture sector|(0.035)|(0.008)|(0.000)|(0.002)|(0.002)|
|Share of employment in the|0.095|0.044***|-0.001|-0.005*|-0.003|
|industrial sector|(0.061)|(0.012)|(0.001)|(0.003)|(0.003)|
|Share of prime-aged persons|0.185|0.076*|0.001|0.027***|0.024***|
|(25-54)|(0.162)|(0.039)|(0.001)|(0.005)|(0.005)|
|Share of population with a primary|-0.109***|-0.045***|0.001|0.000|0.002|
|education or less|(0.036)|(0.008)|(0.001)|(0.002)|(0.001)|
|Natural log of GRP per capita|0.743|-0.028|0.039***|0.115***|0.162***|
|(constant 2010 IDR)|(0.604)|(0.111)|(0.007)|(0.021)|(0.023)|
|Growth in GRP per capita|0.015|-0.022**|0.001|0.003|0.004|
||(0.048)|(0.010)|(0.001)|(0.003)|(0.004)|
|Number of observations|238|238|238|238|238|
|R-squared|0.893|0.569|0.780|0.769|0.786|
|Year dummies|Yes|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

20
# Table 5: Performance of Hedged and Unhedged portfolios.

|||_______________ O|ctober 1989 Hedge||
|---|---|---|---|
|Portfolio|Hedge Ratios|Retum<br>r<br>Variance|Risk Reduction|
|Unhedged|h = 0|.47<br>5.29||
|Naive|h = 1|-.32<br>2.96|44.0%|
|Risk-Minimizing|h* = .298|.23<br>2.10|60.3%|
|<br>|O<br>|ctober1990<br>Hedge<br> <br>||
|Portfolio|Hedge Ratios|Retum<br>Variance|Risk Reduction|
|Unhedged|h=0|-.34<br>3.80||
|Naive|h = 1|-1.36<br>8.41|-121.3%|
|Risk-Minimizing|h* = .309|-.66<br>1.54|59.5%|
|<br>|O<br>|ctober 1991 Hedge<br> <br>||
|Portfolio|Hedge Ratios|Retum<br>Variance|Risk Reduction|
|Unhedged|h =0|-.68<br>6.71||
|Naive|h=1|-.07<br>4.45|33.7%|
|Risk-Minimizing|h*=.314|-.49<br>4.54|32.3%|

Notes: A negative sign for risk-reduction indicates risk-increasing rather than risk-reducing. L = 0 means completely unhedged.

L = 1 means fully hedged.

h* = is the optimal hedge ratio.
# Higher Levels of No-Till Agriculture Associated with Lower PM2 _._ 5 in the Corn Belt

A. Patrick Behrer,<sup>1</sup><sup>_,_2</sup><sup>_∗_</sup> David Lobell<sup>1</sup> 1 Stanford University Center on Food Security and the Environment, Encina Hall, 616 Jane Stanford Way, Stanford University Stanford, CA 94305-6055, USA 2The World Bank, Washington, DC, USA

> _∗_ To whom correspondence should be addressed; e-mail: abehrer@worldbank.org.

Keywords: Air pollution, conservation agriculture, no-till, remote sensing
regulatory reforms have contributed to productivity growth in steam power-generation sector in Japan for the period 1978-2003. Deregulation of the Japanese power industry also contributed to productivity growth (Goto and Sueyoshi, 2009). In China, unbundling of the integrated electricity utility-the State Power Corporation (SPC) improved productivity and operational efficiency among the large coal-fired power plants controlling for substantial heterogeneity in the technical profile of the plants (Zhao and Ma, 2013).

**Table 6: Reforms and Efficiency and Productivity**

|**_Study_**|**_Approach_**|**_Region_**|**_Data_**|**_Method_**|**_Policy impacts_**<br>**_and relevance_**|
|---|---|---|---|---|---|
|Pollitt<br>(1995)|non-<br>parametric|global|768 thermal<br>plants from 14<br>countries<br>including South<br>Africa and<br>Thailand|DEA with 2<sup>nd</sup><br>stage<br>regressions<br>/Tobit|privatisation did<br>not lower costs in<br>the short run,<br>government<br>interference with<br>investment<br>increase costs|
|Yunos and<br>Hawdon<br>(1997)|non-<br>parametric|least<br>developing<br>countries|cross-section<br>generation data<br>for 27 countries,<br>panel of<br>electricity<br>generating<br>utilities from<br>Malaysia,<br>Thailand and the<br>UK for 1975-<br>1990|DEA|changes in<br>ownership do not<br>resolve efficiency<br>problems in the<br>absence of<br>competition,<br>efficiency gaps<br>between small<br>scale and large<br>scale providers|
|Rodriguez-<br>Pardina and<br>Rossi (2000)|parametric|South<br>America|30 electricity<br>distribution<br>companies from<br>10 countries<br>1994-1998|stochastic<br>production<br>function|partial evidence of<br>reformers<br>performing better<br>than non-<br>reformers|
|Estache,<br>Rossi and<br>Ruzzier<br>(2004)|parametric/<br>non-<br>parametric|Latin<br>America|84 electricity<br>distribution<br>companies 1994-<br>2001|stochastic cost<br>function,<br>DEA, labor<br>requirement<br>function|technical<br>efficiency<br>marginally<br>improved but<br>scope for<br>efficiency<br>improvement<br>exists|
|Estache and<br>Rossi (2005)|parametric|Latin<br>America|127 distribution<br>companies 1994-<br>2001|stochastic<br>production<br>function/labor|incentives in<br>embedded in<br>regulation crucial|

27
3 tourism and development (see e.g. Sinclair 1998). Thus, promoting tourism through lobbying appears to be a worthwhile enterprise, if such lobbying is indeed effective.

Engaged heavily in such an enterprise are Caribbean governments and lobbies in those countries connected with the tourism industry. Jobst’s (2002) data summaries of foreign lobbying (Jobst, 2002, Table 4) indicate that Latin American and Caribbean countries accounted for over 25 percent of total foreign lobbying in the U.S. over the 1990-2000 decade. Are foreign lobbies successful in achieving their aim of increasing tourism? What is the quantitative impact of lobbying on tourism? Via what economic mechanism might lobbying lead to increased tourism? These are the questions addressed in this paper.

If the results show lobbying to be effective, they have a practical and straightforward policy implication for foreign countries. Countries endowed with natural amenities can, through legal lobbying channels, increase tourism by putting out information about those amenities as well as earning endorsements from the client country’s government. Further, if lobbying also makes tourism less price sensitive, then lobbying has the potential to enable tourism to become an engine of economic development.

The paper proceeds as follows. Section 2 describes foreign lobbying institutions in the U.S. Section 3 describes the econometric model and data used to examine an exploratory econometric model of the impact of lobbying on tourism. Section 4 discusses these results. In Section 5 we use Copeland’s theory to uncover another channel of influence, namely the potential for lobbying to make a destination less price-elastic. The exploratory model is extended to test this new hypothesis. Section 6 concludes.
24

# **Appendix Table A.2. Intermediate sets of fixed effects**

||Country-Prod|uct and Year|Fixed Effects|Countryand|Product-Year|Fixed Effects|
|---|---|---|---|---|---|---|
||Manufacturing|Agriculture|Natural Res.|Manufacturing|Agriculture|Natural Res.|
|Ln(Initial Quality)|-17.2***|-15.0***|-13.5***|-5.70***|-5.65***|-5.29***|
||(0.13)|(0.18)|(0.36)|(0.06)|(0.09)|(0.20)|
|Ln(Initial GDP p.c.)|0.5080***|0.4990***|0.0116|-0.0649*|-0.1170|-0.2210|
||(0.0399)|(0.1070)|(0.1810)|(0.0337)|(0.1000)|(0.1880)|
|Initial Institutional Quality|0.0061***<br>(0.0012)|0.0092***<br>(0.0028)|0.0142***<br>(0.0053)|0.0091<br>(0.0011)|0.0037<br>(0.0027)|0.0005<br>(0.0058)|
|Initial Human Capital|0.0042*|0.0056|-0.0123|0.0017|0.0031|-0.0037|
||(0.0025)|(0.0064)|(0.0113)|(0.0021)|(0.0060)|(0.0122)|
|Initial FDI inflows|0.0072**|0.0148*|-0.0129|0.0072***|0.0057|0.0409**|
||(0.0031)|(0.0077)|(0.0129)|(0.0026)|(0.0070)|(0.0164)|
|Initial Trade Lib.|0.5530***|0.9320***|0.2080|0.1730**|0.5930***|-0.0378|
||(0.0884)|(0.2320)|(0.4000)|(0.0741)|(0.2180)|(0.4200)|
|Initial Agric. Lib.||0.1970<br>(0.1700)|||0.3080**<br>(0.1570)||
|Observations|98,746|29,802|8,365|98,746|29,802|8,365|
|R-squared<br>Notes: All equations esti<br>variables is the annualize<br>percent, 5 percent and 1<br>presentation purposes.|0.577<br>mated using obs<br>d growth rate of<br>percent level, re|0.634<br>ervations ave<br>product qualit<br>spectively. Al|0.656<br>raged of 10-year<br>y. *, **, and ***<br>l coefficients an|0.540<br>non-overlapping<br>denote statistical<br>d standard errors|0.510<br>periods. The<br>significance<br>are multiplied|0.380<br>dependent<br>at the 10<br>by 100 for|
women's productivity. Finally,<sup>regardless of the outcome of these initial</sup> hypothesis tests, all the tests<sup>would have to be redone since the model</sup> specification and the data set might change as<sup>a resultofinformation obtained</sup> from the diagnostic analysis<sup>(see below). All hypotheses were tested using the</sup> sample likelihood ratio statistic<sup>compared with its 1 percent critical value.</sup> Table 4 gives the test results.'5 We could not reject the hypothesis of equality of parameters or structure<sup>in Lima and other urban areas. All pooling</sup> that included rural data, however,<sup>was rejected. That is, the structure of rural</sup> firms was found to be different<sup>from that of urban firms. Finally, we could not</sup> reject the hypothesis of equal<sup>parameters in the three classes of enterprises:</sup> male, female, and mixed."6 For these reasons our urban model is estimated on a sample pooled with respect to enterprise<sup>and location (Lima and other urban</sup> - areas). Tables Bl B3 in appendix<sup>B give the results for the rural model.</sup>

**Tabl4.**

**Summarqestifool1iagtructure**

|HYPOTHESIS|TESTSTATISTIC<br>|<br>CRITICAQALUE<br>DECISION|
|---|---|---|
|l.Lima1thdfrbaArea;i&ur<br>%reas<br>calbeaggregated.|<br>83.040|56.123<br>REJECT|
|2.LimaandthdirbaAreas<br>calbeaggregated.|25.041|33.409<br>DO NOT REJECT|
|3.FoiLimandDthd1rbaAreas,<br>Femalftlean&ixegirms<br>caibeaggregated.|52.800|56.123<br>DO NOTREJECT|

# Note.TestwbaseAilikelihabeoatistics.

15 The results correspond to the final<sup>estimated model where education is</sup> represented by dummy variables.<sup>The pattern of results is similar to that</sup> obtained with the initial version<sup>of the model.</sup>

**16** This hypothesis was tested on<sup>the pooled Lima and other urban areas samples.</sup>

30
# **Appendix A1: Variable Definition and Sources**

|Variable|Definition|Source|
|---|---|---|
|**_Innovation Indicators_**|||
|New product line|Has your company developed a new product line in the last three years? Yes (1) No (0)|Investment Climate Survey|
|Upgraded existing product line|Has your company upgraded an existing product line in the last three years? Yes (1) No (0)<br>Has your company introduced new technology that has substantially changed the way that the main|Investment Climate Survey|
|New Technology|product is produced, in the last three years? Yes (1) No (0)|Investment Climate Survey|
||Has your company discontinued at least one product (not production) line in the last three years? Yes (1)||
|Discontinued at least 1 product|No (0)|Investment Climate Survey|
|Opened a new plant|Has your company opened a new plant in the last three years? Yes (1) No (0)|Investment Climate Survey|
|Closed at least one existing plant|Has your company closed at least one existing plant or outlet in the last three years? Yes (1) No (0)|Investment Climate Survey|
|New JV with foreign partner|Has your company agreed a new joint venture with a foreign partner in the last three years? Yes (1) No (0)|Investment Climate Survey|
|New Licensing Agreement|Has your company obtained a new licensing agreement in the last three years? Yes (1) No (0)|Investment Climate Survey|
|Outsourced a major activity|Has your company outsourced a major production activity that was previously conducted in-house in the<br>last three years? Yes (1) No (0)|Investment Climate Survey|
|Brought in-house a previously<br>out-sourced activity|Has your company brought in-house a major production activity that was previously outsourced in the last<br>threeyears? Yes(1)No(0)|Investment Climate Survey|
|**_Aggregate Innovation Indicators_**|||
||An aggregate measure of firm innovation that is formed by adding 1 for each of the following cases: if the<br>firm has developed a new product line, upgraded an existing product line, introduced new technology,<br>opened a new plant, signed a new joint venture with a foreign partner, obtained a new licensing agreement,<br>outsourced a major production activity or brought in-house a previously outsourced activity in the past||
|INDEX8<br>INDEX2|three years. The Index ranges from 0 to 8 with 8 indicating the firm is most innovative.<br>An aggregate measure of firm innovation that is formed by adding 1 if the firm has discontinued at least<br>one product, or closed at least one existing plant. The Index ranges from 0 to 2 and is meant to capture<br>activities that do not require external financing<br>An aggregate measure of innovation that is formed by adding 1 if the firm has developed a new product<br>line or introduced a new technology. The Index ranges from 0 to 2 and is meant to capture activities|Own Calculations<br>Own Calculations|
|CORE|constitute core innovation|Own Calculations|
||Firm Size Dummies consist of three dummies corresponding to small, medium, and large firms. Small||
|Firm Size Dummies|firms have 1-19 employees, Medium firms have20-99 employees, Large firms have over 100 employees.|Investment Climate Survey|

53
assumption that Vietnam will fulfill its stated ambition to raise minimum wages to the level of the minimum monthly living standard in 2018. By way of comparison, the growth of real labor productivity in the state, non-state domestic and FDI sectors are also depicted. Finally, Figure 3 shows vertical lines to highlight three key years for Vietnam’s labor market and labor market policies: 2007 as the year of WTO accession, 2012 as the year of the passage of the new Labor Code and Trade Union Law, and 2015 as the year when the ASEAN Economic Community is supposed to be established and the movement of skilled labor across the ASEAN region is deregulated.

**Figure 3 – Rising faster than productivity: Time series of statutory minimum wages and labor productivity**

<!-- Start of picture text --> 600<br>WTO Establishment of ASEAN<br>accession Economic Community<br>500<br>Minimum wage<br>region 1<br>Minimum wage<br>400 region 2<br>Passage of new Labor Minimum wage<br>Code and Trade region 3<br>Union Law Minimum wage<br>300 region 4<br>Labor<br>producitivity state<br>Labor productivity<br>200 non-state domestic<br>Labor productivity<br>FDI<br>100<br>0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Year<br>labor productivity (GNI deflator); 2006=100<br>Real minimum wage (CPI 2005; private domestic sector) and real<br><!-- End of picture text -->

**Source: World Bank staff calculations based on MOLISA, VGCT, GSO, World Bank WDI and IMF WEO data. Notes: “**  **” denotes projections. Depicted are non-government sector minimum wages.**

As is evident from Figure 3, Vietnam’s non-government domestic sector minimum wages have been rising rapidly. Their real rate of increase has been in or near the double digits for each of the last ten years. Growth was particularly rapid in 2012 when minimum wages in the domestic sector and the FDI sector were aligned. The prolonged period of high growth rates has led to today’s situation where minimum wage levels are high by international standards (and therefore a threat for job creation and

- 9 -
within a household. Consequently, this lack of data can pose a serious challenge for researchers and stakeholders in assessing and monitoring poverty and health for individuals across the world.

Most household consumption and dietary data can be classified as incomplete in one of three ways: (1) incomplete household survey data as either completely missing individual consumption data; (2) partially missing such data, as in a case in which the data is only available for certain administrative areas; or (3) missing such data over time (Dang et al., 2019). The NNS does not exhibit incompleteness in this way and provides an ideal opportunity to investigate questions of individual food consumption and expenditure within a household.

In our analysis, we use data from 2013 (hereafter NNS 2013), which includes the most recent and publicly available household and individual food consumption data. The NNS 2013 includes 10 modules: (1) Anthropometry, (2) Biochemical, (3) Clinical, (4) Dietary – Household, (5) Dietary – Individual, (6) Food Security, (7) Infant and Young Children Feeding, (8) Maternal Health and Nutrition, (9) Socio-Economic – Household, and (10) Socio-Economic – Individual. Note that the Dietary – Household module applied standard food weighting procedures and collected data on all meals (breakfast, lunch, dinner, and snacks) prepared and cooked in the house for all the household members, whereas the Dietary – Individual module conducted face-to-face interviews using a 24hour Food Recall Form to collect food consumption at the individual level. Although more rounds of the NNS (such as the NNS 2015 and NNS 2018) were conducted after 2013, these more recent NNS are not ideal for this study. First, the NNS 2015 did not implement food consumption modules and second, the NNS 2018 was not publicly available at the time at which this work was undertaken.

The data includes 160,888 individuals, from 33,400 households, which were surveyed in NNS 2013. However, some modules were only asked of some subsets of the full sample. For example, the Infant and Young Children Feeding (IYCF) module included 4,628 individuals. This, though, approximately matched the number of infants and young children (0-23 months) in the full Individual Socio-Economic module (4,938). Further, the Maternal Health and Nutrition module included 6,232 women, which was also roughly in line with the number of pregnant women and mothers with the youngest child being 0-36 months in the Individual Socio-Economic module (6,419 individuals). Finally, and of most importance, the Dietary (Household) module only 8
40 _Table_ 12. Poplation of Countries **and** Economies in the Other" Categories

|Countries or<br>economies|Populatfon<br>1985<br>(thousands)|<br>Countries or<br>economies|Population<br>1985<br>(thousands)|
|---|---|---|---|
|other West Africa||Other Europe|250|
|St. Helena|7|Andorra|35|
|||FaeroeIslands|46|
|Other North Africa||Gibraltar|29|
|Western Sahara|155|Holy See|1|
|||Isle of Man|62|
|Other Latin America||Liechtenstein|28|
|and the Caribbean|201|Uonaco|27|
|Anguilla|7|San Narino|22|
|Aruba|67|||
|British Virgin Islands|12|Other licronesfa|23|
|Caymen Islands|22|Nauru|8|
|Falkland Islands|2|Tuvalu|8|
|French Guinana|83|Johnston Island|1|
|Turks and Caicos Island|s<br>8|Midsay Islands|2|
|||Pitcai<br>m|0.1|
|other Northern America|115|Tokelau Islands|2|
|Bernuda|56|wake Island|2|
|Greenland|53|||
|St. PierreandMiquelon|<br>6|Other PoLynesia|70|
|||Amwrica Samoa|36|
|||Cook Islands|18|
|||Niue|3|
|||Wallis and Futuna|Islands<br>13|