rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A13: Impacts of mental health on labor outcomes – Mental health at time** **_(t-1)_**

|Dependent variable:|Employment<br>status|Weekly labor income<br>($1,000 AUD)|
|---|---|---|
||(1)|(2)|
|Standardized mental health scores<br>at_(t-1)_|-0.049***|-0.144*|
||(0.005)|(0.079)|
|_First stage of 2SLS (dependent varia_|_ble is standardized m_|_ental health scores)_|
|Trauma experience*Year after<br>arrival|-0.041***<br>(0.012)|-0.085*<br>(0.045)|
|Kleibergen-Paap F stat.|16.972|18.444|
|AR 95-CIs|[-0.094, -0.025]|[-0.295, -0.084]|
|Dep. Mean|0.455|0.716|
|Other controls|Yes|Yes|
|Individual FE|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|2,805|2,805|
|Number of individuals|1,406|1,406|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Column (1) and IV-FE model in Column (2). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005) is 16.38.

45
Table 3. The political cycle of GDP manipulation: Robustness tests

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Mayor YearsInOffice|0.056<br>(0.036)|0.053<br>(0.035)|0.032<br>(0.036)|0.023<br>(0.034)||
|Mayor YearsInOffice<sup>2</sup>|-0.004<br>(0.005)|-0.005<br>(0.005)|-0.002<br>(0.004)|-0.003<br>(0.004)||
|PS YearsInOffice|||0.090<sup>***</sup><br>(0.031)|0.098<sup>***</sup><br>(0.030)||
|PS YearsInOffice<sup>2</sup>|||-0.008<sup>*</sup><br>(0.005)|-0.009<sup>**</sup><br>(0.004)||
|PS YearsInOffice=2|||||0.128<sup>***</sup><br>(0.038)|
|PS YearsInOffice=3|||||0.136<sup>***</sup><br>(0.049)|
|PS YearsInOffice=4|||||0.124<sup>**</sup><br>(0.053)|
|PS YearsInOffice=5|||||0.161<sup>**</sup><br>(0.074)|
|PS YearsInOffice=6|||||0.101<br>(0.085)|
|Mayor YearsInOffice =2|||||0.015<br>(0.040)|
|Mayor YearsInOffice =3|||||-0.039<br>(0.062)|
|Mayor YearsInOffice =4|||||-0.037<br>(0.071)|
|Mayor YearsInOffice =5|||||-0.017<br>(0.085)|
|Mayor YearsInOffice =6|||||-0.019<br>(0.108)|
|City Controls|Yes||Yes|||
|<br>Flexible City Controls||Yes||Yes|Yes|
|# of Observations|3642|3642|3642|3642|3642|
|Adj R<sup>2</sup>|0.59|0.65|0.60|0.66|0.66|

_Notes_ : This table presents the results of several robustness checks that take mayors’ characteristics into account. Column (5) uses year-in-office dummies instead of a continuous measure. All the specifications include leader controls, city FEs, and province-year FEs. Leader controls and city controls are the same as those in Table 2. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

41
release series of the government spending growth rate.<sup>78</sup> As discussed in detail in AG (2010, 2012), FE<sup>G</sup> provides a convenient way to identify unanticipated government purchases, which enables us to appropriately estimate ...scal multipliers. This surprise government spending shock captures unanticipated innovations in spending.

Given the absence of readily available government spending forecast error data in developing countries, we use –as has been typical in the SVAR literature –quarterly data on seasonally-adjusted real government spending as well as the identifying assumption that while contemporaneous changes in government spending can a¤ect output in the same quarter, the opposite is not true (i.e., it takes output changes at least one quarter to a¤ect government spending). We also use quarterly seasonally-adjusted real GDP.<sup>910</sup>

# 2.2 Methodology

We follow the single-equation approach advocated by Jorda (2005) and Stock and Watson (2007), which does not impose the dynamic restrictions implicitly embedded in the SVAR methodology and can conveniently accommodate nonlinearities in the response function. For this purpose, linear “local projections”(LP) of output growth on lags and current change of government expenditure and other controls are used for the construction of impulse response functions (IRF). As discussed in Jorda (2005), there are multiple advantages in the use of LP. In particular, LP (i) can be estimated by single-regression techniques (least-squares dummy

> 7 The list of OECD countries comprises Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Korea, Rep., Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Spain, Sweden, Turkey, United Kingdom, and United States. The sample periods covers 1986-2008.

> 8 The sources of data for government spending and GDP (for both OECD and developing countries) are IFS (IMF), Global Financial Data, Datastream, as well as local sources such as Central Banks.

> 9 The list of developing countries comprises Argentina, Botswana, Bulgaria, Chile, China, Cyprus, Czech Republic, Ecuador, Egypt, Hungary, India, Korea, Rep., Latvia, Lithuania, Malta, Mauritius, Mexico, Paraguay, Philippines, Poland, Romania, Russia, Singapore, Slovak Republic, Slovenia, South Africa, Thailand, Turkey, Uruguay, and Venezuela. The sample period covers 1970-2011.

> 10 For consistency with our methodology for developing countries, we also computed all multipliers for OECD countries using the same frequency and methodology (described below) that we use for developing countries. The results (not reported for the sake of brevity) are qualitatively the same, though the estimated multipliers are smaller and with larger standard error bands.

7
# **Table S8a: Variations in IV sample composition**

||Base<br>estimation<br>(Column 3,<br>Table 3)|Excluding<br>nations with<br>fewer than<br>100 data<br>points|Excluding<br>nations with<br>fewer than<br>1000 data<br>points|<br>Excluding<br>nations with<br>fewer than<br>2000 data<br>points|<br>Examining<br>long-term<br>OECD<br>nations only|Excluding<br>exporters<br>with <100k<br>ethnic US<br>members|Excluding<br>largest<br>foreign<br>patenting<br>industry|Excluding 5<br>largest<br>foreign<br>patenting<br>industries|<br>Excluding<br>largest<br>foreign<br>productivity<br>outlier|<br>Excluding 5<br>largest<br>foreign<br>productivity<br>outliers|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|||The depend<br>Panel A|ent variable i<br>: Weighting b|s Δ log bilate<br>ilateral route|ral exports on<br>s by the intera|the intensive<br>ction of expo|margin by e<br>rter and imp|<br>xporter-impor<br>orter trade in|ter-industry<br>industry||
|ΔLog country-industry labor|1.592**|1.590**|1.470**|1.417**|1.322|1.669**|2.023*|Invalid|1.520**|2.563*|
|productivity|(0.637)|(0.636)|(0.568)|(0.614)|(0.816)|(0.797)|(1.098)|first stage|(0.586)|(1.554)|
|Observations|103,839|103,013|89,059|71,779|74,087|78,411|97,011|73,518|100,740|82,939|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|||||Pan|el B: Excludin|g sample wei|ghts||||
|ΔLog country-industry labor|2.429***|2.426***|2.315***|1.900***|2.205**|2.251***|3.072***|3.257*|2.853***|2.923***|
|productivity|(0.791)|(0.787)|(0.764)|(0.671)|(0.874)|(0.875)|(1.179)|(1.843)|(0.931)|(1.081)|
|Observations|103,839|103,013|89,059|71,779|74,087|78,411|97,011|73,647|100,740|82,939|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 3.
The risk premium for each maturity is estimated by using ex-post excess returns on emerging market bonds over comparable default-free (German and U.S.) bonds. To calculate the risk premium, we need to obtain fi rst bond yields, spreads, and prices. We begin by estimating time series of German and U.S. yields curves and emerging market spread curves. The maturity-τ spread, st,τ , is de fi ned as the difference between the yield, yt,τ , on an emerging market zero-coupon bond of maturity τ and the yield, yt,τ<sup>∗,onadefault-freezero-couponbondofmaturityτ,st,τ= yt,τ−y</sup> t,τ<sup>∗.</sup> We use this information to obtain bond returns at different maturities and over time for every country. This enables us to make cross-country, cross-maturity, and over-time comparisons. Note that it would be impossible to carry out the analysis using the raw data because each country has a different set of bonds at each point in time, with varying maturity and coupon structure. Appendix B describes the methodology used to estimate yields and spread curves.

Figure 2 displays the estimated spreads over time for each country. The fi gure shows spreads at two maturities to illustrate how short-term (3-year) and long-term (12-year) spreads move over time.<sup>23</sup> The fi gure shows some interesting facts. First, spread curves are, on average, upward sloping. Second, spreads increase during periods of fi nancial crises. For example, during the crises in Argentina, Russia, and Uruguay, spreads jump to more than 25 percent or 2,500 basis points. Third, short-term spreads are more volatile than long-term spreads. In fact, during periods of very high spreads there is an inversion of the spread curve, with short-term spreads becoming higher than long-term ones.

Using the estimated spread curves and U.S. yield curves, we compute the price Pt,τ of a representative emerging market bond of given maturity τ and coupon c. In Figure 3, we plot the price of short and long-term bonds, with a semi-annual coupon of 7.5 percent.<sup>24</sup> To simplify the comparisons, the value at the beginning of the sample is normalized to 100 for each country. The fi gure shows that the prices of long-term emerging market bonds are more volatile than those of short-term bonds. In particular, at the onset of crises the prices of long-term bonds fall much more than those of short-term bonds, while during the recovery, the prices of long-term bonds increase much more than those of short-term bonds. Next, we show how these price changes are re fl ected

> 23 Our methodology allows us to compute spreads at every maturity and, therefore, construct the entire spread curve over time.

> 24 For prices and returns, we choose to use coupon-paying bonds because emerging markets almost never issue zero-coupon bonds. So the pricing errors for coupon-paying bonds are smaller than for zero-coupon bonds.

18
# **Table 4: Regression of local GDP on cropland**

||(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>FE|(5)<br>FE|(6)<br>FE|
|---|---|---|---|---|---|---|
|Ln cropland_t-_1|1.913***|0.956***|0.946***|0.024***|0.027***|0.024***|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Ln MA _t-_1|||0.075***|||0.160***|
||||(0.00)|||(0.00)|
|Avg. rainfall_t_||2.205***|2.191***||-0.734***|-0.707***|
|||(0.01)|(0.01)||(0.01)|(0.01)|
|(Avg. rainfall_t_)<sup>2</sup>||-0.226***|-0.225***||0.049***|0.048***|
|||(0.00)|(0.00)||(0.00)|(0.00)|
|Ln pop. density_t-_1||2.045***|1.917***||1.086***|0.853***|
|||(0.01)|(0.01)||(0.01)|(0.01)|
|Ln dist. to major port||-0.578***|-0.556***||||
|||(0.00)|(0.00)||||
|Constant|6.054***|11.093***|10.841***|8.115***|9.370***|9.238***|
||(0.00)|(0.06)|(0.06)|(0.00)|(0.02)|(0.02)|
|Country×yeardummies|No|Yes|Yes||||
|Observations|1,161,628|1,160,235|1,160,235|1,161,628|1,160,235|1,160,235|
|Number of groups||||290,407|290,130|290,130|
|R-squared|0.365|0.621|0.622|0.000|0.031|0.039|

Notes: This table presents estimates from OLS (columns 1, 2 and 3) and fixed effect (columns 4, 5 and 6) regressions of the logarithm of GDP at time _t_ on the natural logarithm of cropland area at time _t –_ 1 ( _Ln cropland t-_ 1). Controls included in the OLS regression (columns 2 and 3): natural logarithm of lagged cropland area ( _Ln cropland t-_ 1), natural logarithm of lagged market access ( _Ln MA t-_ 1), natural logarithm of average precipitation over the period ( _Ln avg. rainfall t_ ), natural logarithm of lagged population density ( _Ln pop. density t-_ 1), natural logarithm of distance to nearest major port ( _Ln dist. to major port_ ), country dummies, year dummies, and country × year dummies. Controls included in the FE regressions (columns 5 and 6): logarithm of lagged cropland area, natural logarithm of lagged market access, natural logarithm of average precipitation over the period, and natural logarithm of lagged population density. Robust standard errors are in parenthesis, where *** is significance at the 1% level, ** significance at the 5% level, and * significance at the 10% level.

17
# **Table 3. Estimations for the likelihood that the household has a deposit account**

This table shows marginal effects from the estimations of a probit model. The dependent variable is 1 if the household has a deposit account with a formal financial institution and zero otherwise. All independent variables are defined in Table 1. In columns (1)-(4), the variable _Remittances_ refers to a dummy for whether the household receives remittances from overseas, whereas in columns (5)-(8) _Remittances_ refers to per capita amounts received in remittances from overseas. Robust z-statistics are shown in brackets. *,**, and *** denote significance at 10, 5 and 1 percent, respectively.

|||Depend<br>|ent variable: du<br>|mmyfor whethe<br>|r household has<br>|a deposit acc<br>|ount<br>||
|---|---|---|---|---|---|---|---|---|
|Variables|Remittance:<br>remittances<br>|dummy for wh<br>from overseas<br>|ether househol<br>|receives<br>|Remittance<br>capita recei<br>|s: the amoun<br>ved from ove<br>|t of remittan<br>rseas<br>|es per<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Remittances|0.109|0.126|0.143|0.291|0.047|0.049|0.060|0.136|
||[5.33]***|[5.82]***|[5.30]***|[4.64]***|[6.82]***|[6.96]***|[6.66]***|[4.93]***|
|Real per capita income|0.014|0.014|0.015|0.023|0.014|0.014|0.015|0.025|
||[5.28]***|[5.15]***|[4.96]***|[2.78]***|[5.27]***|[5.11]***|[4.70]***|[2.86]***|
|House and land ownership|0.043|0.045|0.047|0.091|0.042|0.045|0.047|0.075|
||[2.62]***|[2.74]***|[2.42]**|[1.12]|[2.54]**|[2.71]***|[2.37]**|[0.90]|
|Adults average education|0.025|0.024|0.029|0.033|0.024|0.023|0.028|0.032|
||[8.44]***|[8.09]***|[7.94]***|[2.40]**|[8.11]***|[7.66]***|[7.64]***|[2.30]**|
|Agriculture|-0.023|-0.016|-0.010|0.046|-0.023|-0.017|-0.012|0.044|
||[-1.02]|[-0.72]|[-0.37]|[0.78]|[-1.02]|[-0.75]|[-0.48]|[0.73]|
|Electricity|0.106|0.108|0.127|0.306|0.103|0.105|0.123|0.295|
||[6.13]***|[6.27]***|[6.06]***|[3.92]***|[5.97]***|[6.07]***|[5.82]***|[3.61]***|
|Returned migrant|0.090|0.090|0.114|0.168|0.099|0.106|0.125|0.112|
||[2.37]**|[2.37]**|[2.40]**|[1.17]|[2.57]**|[2.71]***|[2.57]**|[0.76]|
|Observations|2374|2374|2043|795|2348|2348|2004|783|
|Pseudo R<sup>2</sup>|0.15|0.16|0.21|0.14|0.16|0.17|0.22|0.15|
|Log likelihood|-965.2|-951.6|-829.7|-460.3|-939.9|-927.6|-807.6|-448.7|
|Department fixed effects|No|Yes|No|No|No|Yes|No|No|
|Municipalities fixed effects|No|No|Yes|No|No|No|Yes|No|
|Household fixed effects|No|No|No|Yes|No|No|No|Yes|

22
# Figure VII: Withholding Rate Reform

<!-- Start of picture text --> A: Withholding Rate (Full Sample) B: Withholding Rate (Prediction Sample)<br><!-- End of picture text --> <!-- Start of picture text --> 2010m8 2011m8 2012m8 2013m8 2014m8<br>Month<br>2008m8 2009m8 2010m8 2011m8 2012m8 2013m8 2014m8<br>Month Realized Rate Predicted Rate<br>C: Number of Declarations D: Number of Declarations Matched<br>2008m8 2009m8 2010m8 2011m8 2012m8<br>2008m8 2009m8 2010m8 2011m8 2012m8 Month<br>Month<br>Share Sales Tax Declarations Matched With Withholding<br>N Sales Tax Declarations N Withholding Declarations Share Withholding Declarations Matched With Tax Declaration<br>E: Total Share of Card Sales F: Average Share of Card Sales<br>Slope= .004 (.002) ∆ Slope= 0.000 (0.000)<br>Slope= .004 (.005) ∆ Slope= 0.000 (0.000)<br>∆ Constant= -.01 (.013) ∆ Constant= -.01 (.005)<br>2008m8 2009m8 2010m8 2011m8 2012m8 2013m8 2014m8 2008m8 2009m8 2010m8 2011m8 2012m8 2013m8 2014m8<br>Month Month<br>Share Linear Fit Share Linear Fit<br>5 5<br>4<br>4<br>Average Rate Average Rate (%) 3<br>3<br>2<br>2<br>90000 40000 .27 .52<br>.26<br>85000 .51<br>35000 .25<br>80000 .24 .5<br>N Sales Tax N Withholding Share Sales Tax Share Withholding<br>30000 .49<br>75000 .23<br>.22 .48<br>70000 25000<br>.85 .6<br>.8<br>.75 .55<br>.7<br>.65<br>.5<br>.6<br>Share of Card Sales .55 Share of Card Sales<br>.5 .45<br>.45<br>.4 .4<br><!-- End of picture text --> Notes: The gure displays the rst stage of the withholding rate reform and analyzes the reform's eect on credit/credit card use. Panel A shows the average realized withholding rate among all rms subject to withholding. Panel B shows the average realized and predicted withholding rate among rms for whom we can predict the withholding rate based on previous semester's tax returns and the withholding rate schedule in Table II. Panel C shows the number of sales tax declarations and credit/debit card withholding declarations presented to the tax authorities, correcting for revisions and duplicates. Panel D shows the share of sales tax declarations matched with at least one withholding declaration and vice-versa. Panel E shows the share of card sales in total sales among rms with a credit/debit card machine, and panel E shows the average over the rm-specic shares of card sales among rms with a credit/debit card machine. The black solid line in all panels marks 08/2011, when the increase in the withholding rate for the sales tax entered into eect. Panel E and F show a linear t that allows for a dierent trend and constant after the reform. The text displays the pre-reform slope of the linear t, and the change in slope and constant after the reform, along with standard errors in parantheses.

44
coefficients for these variables would be biased particularly due to possible endogeneity associated with high correlations between the trade facilitation and income variables. WMO (2003a, 2003b) however find weak evidence of endogeneity.

Other variables not explicitly associated with trade facilitation are also defined in the database. These include for example the bilateral trade flows in manufactured goods. The trade flow data aggregate the trade flows over the manufactured goods for a given importerexporter pair. We use import data as it is likely to be more reliable than export data since imports constitute a tax base and governments have an incentive to track import data. We deflate the trade flow with the world import index taken from IFS. We use weighted average of applied tariff rates obtained from UNCTAD TRAINS for the manufactured goods under the above definition where bilateral trade values corresponding to each tariff line are used as the weighted gross national income (GNI) and GNI per capita data. Consumer price index and GDP deflator are taken from the World Development Indicators database. Geographic data and the dummies for same language and colonial links are extracted from the Centre d'Études Prospectives et d'Informations Internationales (CEPII) database<sup>9</sup> . The distance data are calculated following the great circle formula, which uses latitudes and longitudes of the relevant capital cities.

# **3.3. The empirical model**

The gravity model, is a short-hand representation of supply and demand forces in which the amount of trade between countries is assumed to be increasing in their sizes (as measured by their national incomes), and decreasing in the cost of transportation between them (as measured by the distance between their economic centers). By assuming first Cobb-Douglas preferences and then CES preferences, Anderson (1979) was the first to derive the gravity equations from models that assumed product differentiation. In both cases, he made the Armington assumption that products were differentiated by country of origin. Anderson and Wincoop (2003) developed a method that consistently and efficiently estimated a theoretical gravity equation, and correctly calculates the comparative statistics of trade frictions.

Anderson and Wincoop (2004) introduced the border costs as premium on the export prices. In the so called augmented gravity models, most authors (e.g. Carrere, 2004; Musila, 2005;

> 9 http://cepii.fr/anglaisgraph/bdd/distance.htm
second household in the survey, all men who were aged 15-49 and who were either usual residents of the household or who spent the night before in the household were eligible to complete the men’s questionnaire. In total, 16,021 households were surveyed with a common questionnaire; 15,198 women were administered the women’s questionnaire across 11,193 households;<sup>6</sup> and 7,333 men were administered the men’s questionnaire across 5,374 households.<sup>7</sup> Using these data sets, we update estimates of monetary poverty and the World Bank’s MPM, and then use these indicators to estimate the change in well-being following the end of the last resource boom in PNG. There are three reasons these particular indicators are the primary focus. First, the official well-being measure previously adopted by the government in 2009 is monetary poverty (e.g., World Bank 2013), and thus is a critical well-being measure by which we estimate how the population has been affected by growth in the resource sector. Second, poor delivery of essential services is a national crisis, and improvement in access is something that would be captured by the World Bank’s MPM and which would significantly improve well-being in the country (e.g., World Bank 2018b; Pandey and Howes 2022). And lastly, much of the rest of the world reports identical statistics to the World Bank, and thus well-being in PNG can be compared in each of the indicators analyzed here.<sup>8</sup> This last advantage of the measures on which we focus helps us to better construct a counterfactual of how well-being changed in comparator countries that had either less economic growth or a different composition of growth.

# **Section 3b. Updating Key Well-Being Measures**

We first update estimates of monetary poverty using the 2016-2018 DHS. As discussed above, there is no direct way to estimate the poverty status of individuals in the survey. In order to estimate monetary poverty using the 2016-2018 DHS, we impute poverty following the Survey of Well-Being via Instant and Frequent Tracking (SWIFT) framework (e.g., Yoshida et al. 2022). In this approach, we first find all the variables that are

> 6The number of women interviewed in each household varied between one and 10.

> 7The number of men interviewed in each household varied between one and eight.

> 8These statistics are publicly available to be downloaded on the World Bank’s Poverty and Inequality Platform (”http:www.pip.worldbank.org.”).

8