rafmacalaba/datause-encoder-data huggingface dataset:
econometric challenges with a log specification. These zero-valued exports are predicted by the model as an exporter is rarely the lowest-cost producer for all countries in an industry. This study approaches this problem by separately testing the intensive and extensive margins of trade. Most of the focus is on the intensive margin of trade expansion, where the dependent variable is the log growth in the value of bilateral exports ∆ ln ( _x_  _ijtk_ ) . The intensive margin of exports captures both quantities effects and price effects (e.g., Acemoglu and Ventura 2002, Hummels and Klenow 2005). In tests of extensive margin of trade expansion—that is, commencing exports to new import destinations—the dependent variable becomes a dichotomous indicator variable for whether measurable exports exist. Differences in the sample construction for these two tests are discussed when describing the trade dataset.

Beyond the model’s background, the exporter-importer-year fixed effects perform several functions. They intuitively require that Germany’s technology expansion for auto manufacturing exceed its technology expansion for chemicals manufacturing if export growth is stronger in autos than chemicals. Thus, these fixed effects remove aggregate trade growth by exporter-importer pairs common across industries. These uniform expansions could descend from factors specific to one country of the pair (e.g., economic growth and business cycles, factor accumulations, terms of trade and price levels) or be specific to the bilateral trading pair (e.g., trade agreements, preferences<sup>6</sup> ). This framework is thus a powerful check against omitted variables biases, helping to isolate the Ricardian impetus for trade from relative factor scarcities and other determinants of trade. The fixed effects also control for the gravity covariates commonly used in empirical trade studies. National changes in factor endowments may still influence industries differentially due to the Rybczynski effect, which is explicitly tested for below. The importer-industry-year fixed effects control for tariffs imposed upon an industry in the importing country. More broadly, they also control for the aggregate growth in worldwide trade in each industry, relative price changes, and the potential for trade due to increasing returns to scale (e.g., Helpman and Krugman 1985, Antweiler and Trefler 2002).

> 6. Hunter and Markusen (1988) and Hunter (1991) find these stimulants account for up to 20% of world trade.
<!-- Start of picture text --> ooO Project Size by Net Commitment in IDA-only Portfolio<br>8<br>oO~<br>Ooo<br>Eo<br>el<br>oO<br>wv<br>8<br>Oo<br>2<br>o<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Closing FY<br>HE NeverFCS MERE Always+Partial FCS<br><!-- End of picture text --> <!-- Start of picture text --> o Project Size by Total Disbursed in IDA-only Portfolio<br>oO<br>oO<br>a<br>So<br>o<br>=<br>8<br>Eo<br>ae?<br>°owv<br>=<br>Oo<br>N<br>=<br>o<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Closing FY<br>ME NeverFCS MEN Always+Partial FCS<br><!-- End of picture text --> <!-- Start of picture text --> ooO Project Size by Net Commitment in IDA-only Portfolio<br>8<br>oO~<br>i=]o<br>°<br>wo<br>oO<br>wv<br>8<br>oO<br>2<br>oO<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Approval FY<br>HE NeverFCS [EE Aways+Partial FCS<br><!-- End of picture text --> <!-- Start of picture text --> Project Length in IDA-only Portfolio<br>o<br>wo<br>vw<br>g<br>2<br>>a<br>N<br>=<br>o<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Closing FY<br>ME NeverFCS MEN Always+Partial FCS<br><!-- End of picture text -->
TABLE 8 Regression results for labor force participation rates, robustness test: 10-year moving average (continued)

||**15-19 y**|**ears old**|**20-24 y**|**ears old**|**25-49 ye**|**ars old**|**50-64 ye**|**ars old**|**65+ ye**|**ars old**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|
|Cycle * EMDE|-17.83***|-23.82***||-11.46***|-2.51*|||||-17.04|
||(0.000)|(0.000)||(0.000)|(0.033)|||||(0.526)|
|Cycle * life expectancy *<br>EMDE|<br>|<br>|<br>|<br>|<br>|<br>|<br>|<br>|<br>|0.057<br>(0.876)|
|Coefficient of fertility in<br>EMDEs|0.070***<br>(0.000)||-0.008<br>(0.251)||||||||
|Coefficient of secondary<br>enrollment in EMDEs|-0.133***<br>(0.000)|<br>|<br>|<br>|<br>|<br>|<br>|<br>|<br>|<br>|
|Coefficient of secondary<br>education in EMDEs|||-0.015<br>(0.470)|-0.046***<br>(0.000)|||||||
|Coefficient of tertiary<br>education in EMDEs|<br>|<br>|-0.035<br>(0.450)|0.047<br>(0.322)|<br>||||||
|Coefficient of cycle in<br>EMDE|-1.69*<br>(0.033)|-2.09*<br>(0.039)||0.220<br>(0.745)|-1.00**<br>(0.006)||||||
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Cohort fixed effects|No|No|No|No|No|No|Yes|Yes|Yes|Yes|
|County-cohort<br>fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age fixed effects|No|No|No|No|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Barro and Lee 2013; Key Indicators of the Labor Market (KILM), International Labour Organization; Labour Force Statistics, Organisation for Economic Co-operation and Development (OECD); UN Population Prospects; World Development Indicators, World Bank; and World Bank staff estimations.

Note: Sample of countries is balanced across gender and age specific regressions. Business cycles defined as deviation of real GDP from Hodrick-Prescott-filtered trend. Sample includes balanced panel of 34 advanced economies and 104 EMDEs for 1987-2020. p- statistics are shown in parentheses.

54
Table A14: Medium-term impacts on clicks on informative links

|_Dep. Var. (Y):_|_Click ge_|_nder-links_|_Click cli_|_mate-links_|_Duration _|_gender-link_|_Duration _|_climate-link_|
|---|---|---|---|---|---|---|---|---|
||(1)<br>ITT|(2)<br>ToT|(3)<br>ITT|(4)<br>ToT|(5)<br>ITT|(6)<br>ToT|(7)<br>ITT|(8)<br>ToT|
|Drama|-0.002||0.010||-1.059||-1.846<sup>_∗_</sup>||
||(0.017)||(0.014)||(1.206)||(1.028)||
|Documentary|-0.018||0.000||-0.967||-0.732||
||(0.015)||(0.013)||(1.174)||(1.373)||
|Play Drama||-0.003||0.013||-1.407||-2.411<sup>_∗_</sup>|
|||(0.022)||(0.018)||(1.563)||(1.316)|
|Play Documentary||-0.036||0.000||-2.005||-1.420|
|||(0.031)||(0.026)||(2.382)||(2.643)|
|Controls|✓|✓|✓|✓|✓|✓|✓|✓|
|R-squared|−0.001|−0.007|−0.001|−0.003|−0.022|−0.031|0.015|0.003|
|P-value equal coef.|0.298|0.197|0.544|0.637|0.889|0.660|0.199|0.588|
|Observations|619|619|619|619|589|589|584|584|
|Wald F-statistic||116.9||116.9||107.1||112.8|
|Mean Y (Control)|0.029|0.029|0.014|0.014|1.612|1.612|2.269|2.269|

Notes: Heteroscedasticity-robust standard errors in parentheses. In columns (1)-(2), the dependent variable takes value 1 if the respondent clicked on both gender links (PFI and UN women India). In columns (3)-(4), the dependent variable takes value 1 if the respondent clicked on both climate links (Delhi Green and UN environment program India). Visit duration is measured in seconds, and it refers to the PFI website in columns (5)-(6) and to the Delhi Green website in columns (7)-(8). Controls are described in the notes to Table 2. Independent variables Play Drama and Play Documentary take value 1 if the respondent has played half or more of the assigned video clips, as objectively recorded by the bot. These variables are instrumented using the random assignment indicators to the treatment groups. The first-stage Wald F-statistic is reported. * p < 0.10, ** p < 0.05, *** p < 0.01 33
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<!-- Start of picture text --> 10 10<br>9—Wind power 8= . 9<br>sees Fuel calls 5 ees ne<br>a — — Electric/Hybrid cars a 8<br>7 == LightingSolar PV EE : ‘4<br>reer Buildings EE = : r<br>=e All technology sectors .<br>6;[6<br>5,> é bh oa 5<br>4-4<br>3ES. .* 4<br>"4 7 “mle °<br>2 ee 2<br>aetnee ert le<br>Peeeeeeeee oon<br>0 . . 0<br>1980 1985 1990 1995 2000 2005<br><!-- End of picture text -->
realized a sales revenue greater than 100,000 dirhams (approximately US$11,000 at the average 19851989 official exchange rate). Descriptive statistics on the Moroccan manufacturing sector are provided in the Appendix.

The multi-factor productivity for each firm was estimated by assuming a Cobb-Douglas production technology. The reason behind choosing this functional form lies in the fact that census data are unlikely to support more intricate forms (Griliches and Ringstad, 1971), and that it provides maximum flexibility in dealing with data imperfections (Tybout, 1990).

Year dummies were included in the estimation to control for macroeconomic shocks. The panel data consisted of a total of 15,462 observations which incorporate 5 years and a varying number of firms each year (3933 firms appearing at least once each year). A joint regression on all industrial sectors would be meaningless since each sector uses a different technology, and therefore the production function parameters cannot be expected to be the same for all industries. For this reason, the production function was estimated for each industrial sector separately, allowing for the parameters to be different across sectors. Since the concept of productivity also relates to the technology used, and since technology is different across sectors, productivity in levels is therefore not comparable across sectors either. In order to be able to make such a comparison, the deviation of each firm's productivity level from the productivity of the most efficient firm (i.e. the firm with the highest productivity) within each sector was calculated and expressed in percentage term:

# (11) DTFP5 = [TFPU - max(TPP)J _I_ max(rFP)

where i refers to the firm and j to the two-digit industry. This variable is therefore going to be less than or equal to zero, and the smaller it is (or the larger in absolute value) the less efficient the firm compared to the most efficient one. The estimations were generalized to unbalanced panels since we do not observe 12
2 investment. In some of the literature, the view prevails that FDI can serve two purposes, namely, raise investment and relieve foreign exchange shortages.<sup>2</sup> Unless FDI affects national saving, however, it can raise domestic investment or provide additional financing for a preexisting current account deficit or achieve some combination of the two, but these two effects must always sum to one. In other words, if $100 FDI increases capital formation by only $75, it would provide $25 for additional current-account financing. If it simply substitutes for an alternative type of capital inflow, however, FDI neither raises capital formation nor does it provide additional current-account financing.

This study examines the question of whether FDI to a sample of developing countries substitutes for other capital flows, increases capital formation, or provides additional foreign exchange for balance-of-payments support. Using James Meade's (1951) distinction between autonomous and accommodating capital flows, Philip Turner (1991, 91-95) addresses the question of whether or not net **FDI** flows are accommodating and hence constitute additional balance-of-payments financing by regressing components of the capital account on the current account financing requirement (both in first differences) for some OECD countries.

Iurner's estimated equation takes the form: where _AKFi_ is the year-on-year change in the net capital flow item expressed as a percentage of GNP and _AKFR_ is the year-on-year change in the current account minus changes in official reserves also expressed as a percentage of **GNP** (both in current prices). Turner (1991, 92) finds that short-term bank flows are significantly correlated with the current account financing requirement with coefficients ranging from 0.27 (United Kingdom) to 0.88 (Canada) for seven of the 10 OECD countries. In the case of **FDI,** however, Turner concludes that this flow is much closer to being autonomous than accommodating; the coefficient is significant only in the case of France with a value of only 0.13.

Using Turner's. methodology with annual data on net capital flows from _International Financial Statistics_ **CD-ROM** for a sample of 16 developing countries<sup>3</sup> for the period 1975-

**2For example, Laurence Cockcroft and Roger Riddell (1991, 3) note: "Two of the principal factors inhibiting higher levels of economic growth in Sub-Saharan Africa in the 1990s are low levels of investment and foreign exchange shortages. The first attraction of foreign investment lies in its potential to address both these constraints."**

> **3Argentina, Brazil,** Chile, **Egypt, India, Indonesia, Korea, Malaysia, Mexico, Nigeria, Pakistan, the** Philip-
whether smoking affects earnings of employees 25 to 55 years old. Depending on the model specification, the estimated losses in earnings for smokers range from 2 to 8 percent compared to non-smokers.

Several studies examine the simultaneous effect of alcohol and smoking on earnings. Van Ours (2002) focuses on the simultaneous wage effects of alcohol and tobacco using the sample of respondents 16 years and older in Netherlands. Controlling for unobserved heterogeneity and endogeneity of drinking and smoking, the study estimates that the wages of smokers are 10 percent lower than the wages of otherwise identical non-smokers. Auld (2005) estimates the model of drinking, smoking and wages using the sample of employed Canadian men. After accounting for the endogenous nature of the decision to use alcohol and tobacco and their simultaneous effects on wages, he finds that daily smokers have wages about 30 percent lower than non-smokers.

The limited set of studies of the wage or earnings consequences of smoking consistently show significant negative effects. However, these results are restricted to high-income settings; estimates of the economic costs of smoking in developing countries are non-existent to our knowledge. This paper addresses this gap in the literature by estimating the economic losses related to the negative effect of smoking on wages in a developing country, namely Albania, where 60 percent of adult males and 18 percent of females smoked in 2002 and the number of smokers has been increasing rapidly over the last decade (WHO 2002). Anti-smoking policies are high on the country’s agenda. In January 2006 the Government of Albania submitted a new draft law on tobacco and smoking that outlaws sales of cigarettes to minors younger than 18, and presents stricter rules for the tobacco industry regarding sale and advertising of tobacco products (ENSP 2006).

Using recent household survey data, we estimate a system of three equations: the smoking decision (never having smoked and current smokers) and two separate wage equations, one for smokers and one for non-smokers. By jointly estimating this system by Full Information Maximum Likelihood we control for the effects of observed and unobserved characteristics that may be correlated with wages and smoking status. Moreover, we introduce new and arguably improved instrumental variables to identify the causal effect of the smoking decision on wages. The results show that, after 3
the DVAR of aggregate exports as follows: Similar to an industry’s DVAR, the aggregate DVAR constructed based on (7) is a weighted average of the DVAR of all ...rms, with weights re‡ecting the export shares of the ...rms.<sup>21</sup> While our ground-up approach is appropriate for inferring the aggregate DVAR, there are two caveats. The ...rst caveat is about multi-industry exporters, for whom the allocation of imported materials (IMPij) to the production of output in di¤erent industries (EXPij) is generally unobservable in the data, making the inference of an industry’s DVAR based on (6) impossible. Thus, we only use the subset of single-industry exporters to infer industry DVARs.<sup>22</sup> The second caveat relates to processing exporters importing indirectly through other ...rms in China. Under the current customs regulations in China, processing ...rms can legally sell imported materials to other ...rms and bene...ted from tari¤ exemption, as long as the buyers are also registered processing ...rms. Complicating this problem is that such transactions are not con...ned within the same industry or geographic location.<sup>23</sup> The transactions of imported materials between two processing ...rms in the domestic economy appear to be widespread according to our data.

This practice of indirect importing certainly impacts the way we construct the ...rm-level

> 21In reporting the aggregate DVAR, we ...rst aggregate ...rm DVARs to the industry level. To make sure that the industry-level analysis, particularly the between-and-within analysis, is not driven by potential noises due to merging the customs data with the ...rm data, we use industry weights based on the export value of single-industry exporters in the customs data set.

> 22Nevertheless, since the construction of the ...rm-level DVAR is not restricted by the multi-industry concerns, we will also include multi-industry exporters in the ...rm-level regressions below.

> 23See Regulations Concerning Customs Supervision and Control over the Inward Processing and Assembling Operation by China’s Ministry of Commerce. For example, a shoe processing exporter may import leather and sell it to a handbag processing exporter.

10
# **Table 5C: T and Rank Tests for Listed vs. Non-Listed Banks in Non-GCC Region**

Tests are conducted on annual bank-level data for the period 2001-08. Column 5 shows the p-value of a t-test which tests for the equality of means of the groups (allowing for inequality in variance). Column 6 shows the p-value of the Wilcoxon ranksum test which tests whether the groups are samples from populations with the same distribution. Non-GCC countries are Egypt, Jordan, Lebanon, Morocco, Tunisia, and Yemen. State-led countries are Algeria, Libya, and Syria.

|**Variable**|**Listed**|**Non-listed**|**Pvalue for t**|**PValue for Rank**|
|---|---|---|---|---|
|Total Assets|2.9E+06|3.3E+06|0.20|0.01|
|ROA|1.03|0.64|0.00|0.00|
|ROE|11.55|9.32|0.02|0.00|
|Net Interest Margin(Total Assets)|2.32|2.21|0.14|0.03|
|Total Interest Income to Assets|5.57|5.93|0.01|0.00|
|Total Interest Expenses to Assets|3.23|3.77|0.00|0.00|
|Non-Interest Income to Assets|1.57|1.03|0.00|0.00|
|Securities to Assets(All Countries)|40.01|31.70|0.00|0.00|
|Securities to Assets(ExcludingLebanon)|15.86|23.94|0.00|0.00|
|OHCosts to Assets|2.09|1.76|0.00|0.00|
|Personnel Expenses to Assets|1.16|0.93|0.00|0.00|
|Employees to Assets|0.06|0.05|0.09|0.07|
|Wage(Personal Expense)|24.76|24.55|0.87|1.00|
|Wage(Overheads)|44.14|42.10|0.37|0.52|
|Loan Loss Provisions to Gross Loans|1.37|2.28|0.00|0.00|

39
Criteria for exclusion were

- papers that did not present information for the variance, standard error or confidence intervals of the association estimate;

- reports based on small populations or excessively large confidence intervals or standard errors;

- papers that did not control for temperature and seasonal variation over the study time period; and

- papers that did not correct for ozone effects when addressing PM1O and vice versa.

According to these criteria, 126 publications were selected for the statistical analysis of ozone and PM1o health effects (the list appears in Cesar and others 2000).

## _Exposure-Response Functions_

Most studies express the health effect (y) as a function of the degree of change in health and the measured change in air pollutant levels (AC). The calculation of the corresponding change in health impact (Ay) depends on the exposure-response (ER) functions from epidemiological studies. The ER-function estimations may differ from each other in several ways, for example, in the use of standard definitions of health endpoints, baseline populations and the functional form of the estimated relationship. Some studies assume linear relationships, while others use log-linear functions.

The linear relationship is of the form The log linear relationship is of the form: Despite some statistical limitations, results from different studies were transformed to percent changes in the health effect for each 10 units of variation in the pollutant concentration.

# _Pooled Estimates_

We obtained a single pooled estimate of the health effects reported from the selected studies by using a weighted average. Deciding on a method to obtain an average estimate is not an easy task. Estimates from different studies could be different because of random variation and also because of a true difference coming from differences in exposure and susceptibility factors. To take into account heterogeneity of effects of reported studies we applied a random-effects model to pool the studies (DerSimionian and Laird 1986.) Random-effect models assume that the true effect is decomposed into the mean population effect and between-study variability. With a random-effects model the estimate of the average value is the _22_