rafmacalaba/datause-encoder-data huggingface dataset:
Table A.1: Robustness: Alternative ways of predicting GDP growth based on nighttime lights

||Drop<br>Northwest|Calibrated<br>lights|Altern<br>wh|ative slopes o<br>en predicting<br>growth|f light<br>true||Adding<br>when pred|more contr<br>icting true g|ols<br>rowth||Clark et al.<br>(2020)|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
||||One|Prov-<br>Spec|Region-<br>Period<br>-Spec|Squared<br>Lights|Electricity|Loans|Freight|All three||
|YearsInOffice|0.101<sup>∗∗∗</sup>|0.094<sup>∗∗∗</sup>|0.093<sup>∗∗∗</sup>|0.105<sup>∗∗∗</sup>|0.093<sup>∗∗∗</sup>|0.088<sup>∗∗∗</sup>|0.103<sup>∗∗∗</sup>|0.107<sup>∗∗∗</sup>|0.102<sup>∗∗∗</sup>|0.116<sup>∗∗∗</sup>|0.265<sup>∗∗∗</sup>|
||(0.030)|(0.031)|(0.028)|(0.032)|(0.029)|(0.033)|(0.032)|(0.033)|(0.030)|(0.035)|(0.081)|
|YearsInOffice<sup>2</sup>|−0.010<sup>∗∗</sup>|−0.008<sup>∗</sup>|−0.008<sup>∗</sup>|−0.010<sup>∗∗</sup>|−0.008<sup>∗</sup>|−0.008<sup>∗</sup>|−0.010<sup>∗</sup>|−0.009<sup>∗</sup>|−0.010<sup>∗∗</sup>|−0.010<sup>∗</sup>|−0.022<sup>∗</sup>|
||(0.005)|(0.005)|(0.004)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.013)|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|City FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Prov-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|# of Obs.|3399|3642|3642|3642|3642|3642|3639|3642|3640|3637|3636|
|# of Cities|262|281|281|281|281|281|281|281|281|281|281|
|Adj_R_<sup>2</sup>|0.61|0.50|0.58|0.57|0.60|0.53|0.53|0.55|0.56|0.49|0.40|

_Notes_ : This table presents several robustness checks for the political cycle of GDP manipulation. The dependent variable is the difference between reported real GDP growth and predicted real GDP growth imputed based on the nighttime lights data of prefecture-level cities. City controls include city-level time-varying economic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights) and leader controls are the same as the ones displayed in the table of baseline results. 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.

52
with NTM avoidance schemes. Robustness checks further reveal that NTM-induced trade fraud is more common for homogeneous products, as well as for trade between developed and developing countries, as predicted by the theoretical model.

The rationale of this paper to investigate trade fraud relates to the existing literature on trade discrepancies and tari¤ avoidance. In particular, this paper follows on the “missing trade” approach initially proposed by Fisman and Wei (2004) to explain discrepancies in the statistics between Hong Kong SAR, China, and China. This approach was also used by Javorcik and Narciso (2008) whose examination of the exports statistics of Germany ...nd that discrepancies with the statistics of the importing country tend to be greater when tari¤s are higher. Similar results are found for India by Mishra, Topalova and Subramanian (2008). In addition, focusing on Chinese exports to the United States, Ferrantino, Liu and Wang (2012) show that Chinese exporters may under-report export values in order to avoid paying China’s VAT, thereby helping to explain why reported export values are lower than the corresponding reported import values. Most recently, Demir and Javorcik (2020) also show import duty evasion as a margin through which ...rms adjust to changes in trade policy in Turkey, while Liu, Sheng and Wang (2021) show that trade data over-reporting is positively correlated with the exchange rate spread in China. This paper adds to the literature by looking at NTMs as another source of costs at the borders, which may result in similar schemes employing misdeclaration and misclassi...cation with the purpose to reduce costs. In addition, unlike previous papers that mostly focused on one country’s imports or exports, this paper uses detailed trade data from a wide range of importing and exporting countries, which allow us to detect customs origin fraud and product fraud.

Importantly, this paper also contributes to the literature on assessing the e¤ect of trade policies, particularly non-tari¤ measures, on trade. In particular, this paper builds on the work of Kee, Nicita and Olarreaga (2009) estimating the AVE of non-tari¤ measures by 5
<!-- Start of picture text --> 1,200 4.50<br>4.00<br>1,000<br>3.50<br>800 3.00<br>in 2.50<br>= 600<br>a=)7]es 2.00<br>a3<br>o<br>400: 1.50<br>1.00<br>200<br>‘0.50<br>~~<br>0 ———— 0.00<br>1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br>Chile == DRC Zambia Copper Price<br><!-- End of picture text -->
turn, contribute to the rise in aggregate corporate debt during 2020. We then study how the risk that the credit program expansion entails is handled and shared between the banking system that grants the loans and the government that guarantees those loans. While Section 4 uses official aggregate data to display the patterns, here we compute the aggregate statistics from the micro data, allowing us to connect the micro and macro estimates.<sup>22</sup>

# **7.1 From Firm Indebtedness to Aggregate Indebtedness**

To determine how micro-level indebtedness reflects on the overall economy, we partition firms into four groups according to their predicted default risk, from high risk to low risk. The change in indebtedness in each risk group is obtained by multiplying the within-group change in the indebtedness of firms in each risk group by the weight of that group of firms in aggregate economic activity (measured by sales): We then obtain the aggregate change in indebtedness, relative to aggregate sales, by adding the contribution of leverage of the different risk groups: _G_ is a partition of firms according to risk, and _g_ indexes a specific group of firms. _Ygt−_ 1 = � _i∈g_<sup>_yit−_1is the sales of group</sup><sup>_g_of firms, where</sup><sup>_yit−_1denotes firm-level sales.</sup><sup>_Dgt_= �</sup> _i∈g_<sup>_dit_</sup> is the stock of debt of group _g_ of firms, where _dit_ denotes firm-level credit stock. _ωgt−_ 1 = _Ygt−_ 1 _/Yt−_ 1 is the weight of group _g_ in aggregate sales in year _t −_ 1. ∆ _Dt_ is the aggregate yearly change in debt between _t_ and _t −_ 1 (between 2020 and 2019). _Yt−_ 1 is aggregate sales in 2019.<sup>23</sup> Table 10 presents the results of this aggregation. We first consider the set of credit program users (Panel A). The change in indebtedness for these firms takes into account both program and non-program debt. Riskier firms experience larger within-group changes in indebtedness: the leverage of high-risk firms increases by 10.7 percentage points, while the leverage of low-risk firms increases by 9.18 percentage points (Column 1). That said, riskier

> 22The size of the aggregate indebtedness from the micro data is a bit smaller than that obtained from the aggregate data because we do not use information on indebtedness for firms that have missing information on sales or have not submitted their data to the tax authority.

> 23In alternative estimations, we use value added instead of sales, obtaining similar results. But the magnitude of change in debt relative to value added are larger than those relative to sales (as sales is a measure of gross output). We report the estimates relative to sales to link it better to the micro part and because, unlike value added, most firms in the economy report sales.

21
<!-- Start of picture text -->
|<br>MRP<br># Poor He |<br>|<br>MRP # Vulnerable HH<br><!-- End of picture text -->

<!-- Start of picture text -->
|<br>MRP # Lower-middle class Ho<br>MRP# Middle class -—*——_<br><!-- End of picture text -->

<!-- Start of picture text -->
|<br>MRP# Upper-middle class ) -——*——<br>|<br>00 O05 10 #15 20 25 30<br>Odds ratio<br><!-- End of picture text -->

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|<br>MRP# Poor —-—<br>|<br>|<br>MRP # Vulnerable -——<br>|<br>|<br>MRP # Lower-middle class po<br><!-- End of picture text -->

<!-- Start of picture text --> MRP # Middle class He<br>|<br>|<br>MRP# Upper-middle class al<br>|<br>00 05 10 #15 20 25 30<br>Odds ratio<br><!-- End of picture text -->
Table 1 Comparison of key country metrics

|**Country**|**Côte**<br>**d’Ivoire**|**Mali**|**Senegal**|**Kenya**|**Tanzania**|**Uganda**|**Source**|
|---|---|---|---|---|---|---|---|
|**Population (m)**|25|19|16|51|56|43|(WorldPop, 2019)|
|**Rural Population**|49%|58%|53%|73%|66%|76%|World Bank?|
|**Area (km2)**|318,000|1,220,190|192,530|569,140|885,800|200,520|(GADM, 2019)|
|**Population Density**<br>**(per km2)**|79|16|83|90|63|214|Author<br>calculations|
|**GDP (US$ Bn)**|43|17|24|88|58|27|(World Bank,<br>2022)|
|**GDP Per Capita**|USD 1715|USD 901|USD 1,522|USD 1,711|USD 1,051|USD 643|(World Bank,<br>2022)|
|**Income group**|Lower-<br>middle|Low|Lower-<br>middle|Lower-<br>middle|Lower-<br>middle|Low|(World Bank,<br>2022)|
|**4G Population**<br>**Coverage**|60%|45%|75%|77%|13%|31%|(GSMA, 2020)|
|**3G Population**<br>**Coverage**|72%|72%|95%|96%|66%|87%|(GSMA, 2020)|
|**Smartphone**<br>**Penetration (Urban)**|36|36|36|55|36|36|(Research ICT<br>Africa, 2018)|
|**Smartphone**<br>**Penetration (Rural)**|24|8|24|17|24|8|(Research ICT<br>Africa, 2018)|

The sequential method used to obtain the results for each country is outlined in Figure 2 (reflecting the software module structure).

10
<!-- Start of picture text --> Percentage change of GDP with baseline<br><!-- End of picture text --> <!-- Start of picture text --> 2 1 _<br>1.8 - Exp. 2.1 Corporate tax<br>1.6 Exp. 2.2 VAT<br>--j Exp. 2.3 Personal Income Ta x<br>1.4 -_ *- Exp. 2.4 Final Demand Tax _ __<br>1.2<br>01<br>0.6<br>0.4<br>0.2<br>2089aRWZ4ZUU6 2U1U801U 212 5 Z14ZU1tDFZU1t ZUZ00 UZZ ZUZ4 eU;eZT2 2030 ZUJO ZUJ2J4 2USO ZUJS24 UZ0406Z425<br>-0.2<br><!-- End of picture text --> Figure 6. Change in GDP in experiment 2.1-2.4

# **Percentage change of private consumption**<sup>**with baseline**</sup>

<!-- Start of picture text --> 1T EXP. 2.1 Corp,orate tax . _<br>1 -- EXP. 2.2 VAT - -,-.<br>. Exp. 2.3 Personal Tax . + . x<br>0.8 - . ... Exp.2.4FinalDemandTax 3, ----------------- -f - -;- --<br>04<br>*~~~~~~~~~~~~~~ 2~1 91 -1 lb" -p -p -<br>- 0 .4 -- - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - -- - -- - - -- - - -- - -<br><!-- End of picture text --> Figure 7. Change in consumption in experiment 2.1-2.4 38
- Varian, H.R., Farrell, J., Shapiro, C. 2004., _The Economics of Information Technology: An Introduction_ , Cambridge University Press, Cambridge.

Varian, H.R. 2014. Big Data: New Tricks for Econometrics. _Journal of Economic Perspectives_ 28 (2): 3–28.

- Wang, N. 2003. Measuring transaction costs: An incomplete survey, Ronald Coase Institute Working Paper Number 2, Chicago.

- World Bank. 2011. ICT in Agriculture: Connecting Smallholders to Knowledge, Networks, and Institutions, Report Number 64605.

- World Bank 2012. “Agriculture Sector Report.” In Transformation-Ready: The Strategic Application of Information and Communication Technologies in Africa http://www.etransformafrica.org/sector

World Bank. 2016. _World Development Report 2016: Digital Dividends_ , Washington, D.C.

28
privatization effect. These results suggest that foreigners’ advantages in accessing finance, new technologies, the latest managerial techniques, and world markets far outweigh any disadvantages due to unfamiliarity with local conditions and weak political connections.

Though having as high a percentage of foreign ownership as Hungary would make Russia and Ukraine’s overall privatization effects positive, this only partly explains the gap. Another difference across countries that could matter is the fact that the privatization methods used in Hungary and Romania resulted in concentrated ownership from the time of privatization, whereas Russia and Ukraine’s mass privatization led to dispersed ownership by employees and small outside investors.<sup>21</sup> Secondary trading has resulted in more concentrated ownership in Russia and Ukraine, but this has taken time. This raises the possibility that if ownership concentration is a necessary condition for privatization to have a positive effect, then the effect may have appeared sooner in Hungary and Romania than in Russia and Ukraine. As illustrated in Figure 6, the timing of the foreign privatization effect is quite similar across countries, with the main difference being that it was positive from the year of privatization in Hungary and Romania, while it doesn’t become positive until a year after privatization in Russia and Ukraine.

The timing of the domestic privatization effect varies considerably, however. Figure 7 shows that Romania and especially Hungary enjoy a strong positive effect already in the year of privatization, while Ukraine has a smaller positive effect a year later. Russia actually experiences a decline before the effect turns positive five years after privatization. This pattern is not inconsistent with the hypothesis that ownership concentration is an important precondition for productivity gains.<sup>22</sup> To investigate this hypothesis further, we use the fact that some privatization methods led to more concentrated ownership than others, and that privatization methods changed across time in each country. Brown, Earle, and Telegdy (2004a) estimate the privatization effect separately for each privatization year cohort. The differences across cohorts are insignificant in each country, so the in-country variation

> 21 Romania used management-employee buyouts early in the privatization process, but employees voted as a group, unlike in Russia and Ukraine.

> 22 Studies reviewed by Djankov and Murrell (2002) show that the privatization effect is stronger when ownership is concentrated.  Lizal and Svejnar (2002) find, however, that the domestic privatization effect in the Czech Republic is weak or insignificant, even though ownership is concentrated.  Many of the domestic firms were controlled by investment privatization funds run by majority state-owned banks.  So the identity of the owner appears to matter as well.

24
and/or birth cohort fixed effects _δa_ , and _γb_ since the marriage cohort matches were selected by their age relative to the reference woman. However, when many marriage cohorts are stacked within a given survey round, then age fixed effects can be included in the regression, since age of marriage varies across marriage cohorts. That is, within a given age there is still variation across marriage cohorts in the location in event-time.

However, age and birth cohort remain collinear for all marriage cohorts in a given survey round (country-year). However, when multiple survey rounds per country are stacked, birth cohort fixed effects _γb_<sup>_g_canbeincludedaswellastheageeffects.Alternatively,totakeinto</sup> account multiple surveys across countries, country and survey year fixed effects could also be included. Note, however, that in cross-country pooled sample, country-year, age, and birth cohort fixed effects are collinear, and so including any two of these three yields equivalent estimates.<sup>2</sup> The sample size in each country varies, so the analysis is weighted by marriage cohort size so that it is representative at the individual level, and provides identical coefficient estimates to estimation on the microdata in a “stacked” model. Standard errors are clustered at the marriage cohort level.

We also consider several heterogeneity analyses to investigate the mechanisms underlying the marriage penalty. To explore the role of urban residence, the same analysis as in equation 1 is conducted on the urban and rural samples separately. To explore the role of education, the analysis includes an interaction term between women’s higher education and the postmarriage indicator. A separate regression is run to analyze the role of husband’s education by including an interaction term between husband’s higher education and the post-marriage indicator. Higher education (or husband’s higher education) takes the value one if women (or their husbands) have more than secondary education. Finally, we explore the role of social norms similarly, interacting the post-marriage indicator with a marriage cohort-averaged gender attitudes index.

> 2In practice, we include birth-cohort and country-year fixed effects.

9
|(8)|(-3.95∗∗∗)|-(0.41)∗∗∗|(-1.15∗∗∗)<br>-(0.07)∗∗∗|(-Yes∗∗∗)|(-No∗∗∗)|(-Yes∗∗∗)|-13156∗∗∗|-0.20∗∗∗<br>sehold/Household-head<br>ance levels: ∗∗∗p<0.01,|
|---|---|---|---|---|---|---|---|---|
|Livestock<br>(6)<br>(7)|(-3.95∗∗∗)<br>(-2.91∗∗∗)|-(0.41)∗∗∗<br>-(0.43)∗∗∗|(-1.15∗∗∗)<br>(-0.16∗∗∗)<br>-(0.07)∗∗∗<br>-(0.08)∗∗∗|(-Yes∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|-13156∗∗∗<br>-13156∗∗∗|-0.20∗∗∗<br>-0.31∗∗∗<br>ors are in parentheses. Hou<br>d type of dwelling. Signifc|
|(5)|(-3.65∗∗∗)|-(0.32)∗∗∗|(-1.12∗∗∗)<br>-(0.07)∗∗∗|(-No∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-13156∗∗∗|-0.03∗∗∗<br> standard err<br>ousehold, an|
|(4)|(-0.50∗∗∗)|-(0.48)∗∗∗|(-0.79∗∗∗)<br>-(0.06)∗∗∗|(-Yes∗∗∗)|(-No∗∗∗)|(-Yes∗∗∗)|-13156∗∗∗|-0.10∗∗∗<br> = 0. Robust <br>of males per h|
|+ Silver<br>(3)|(-0.39∗∗∗)|-(0.48)∗∗∗|(-0.90∗∗∗)<br>-(0.07)∗∗∗|(-Yes∗∗∗)|(-Yes∗∗∗)|(-No∗∗∗)|-13156∗∗∗|-0.18∗∗∗<br>etting β2 = β3<br>size, number|
|Gold<br>(2)|(-0.51∗∗∗)|-(0.48)∗∗∗|(-0.79∗∗∗)<br>-(0.06)∗∗∗|(-Yes∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-13156∗∗∗|-0.10∗∗∗<br>tion (2) after s<br>on, household|
|(1)|(-0.65∗∗∗)|-(0.53)∗∗∗|(-1.11∗∗∗)<br>-(0.06)∗∗∗|(-No∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-13156∗∗∗|-0.05∗∗∗<br>ents of equa<br>tatus, educati|
|Variables|Variance of Income (log)||Income (log)|Household Controls|Province Fixed Efects|Confict|Observations|R-squared<br>The table shows the estimated coefci<br>controls include gender, age, marital s<br>∗∗p<0.05, ∗p<0.1.|

25
19 Since industry duimies are already included, the level of turnover only controls for country-specific differences in turnover rates. These can be due to cross-country differences in product mixes within given industries, or to differences in credit markets and other determinants of sunk costs.<sup>19</sup> The pattern that emerges is expected: high turnover is associated with a relatively large number of small plants.

To check the robustness of the findings concerning trade exposure and plant size, we next replace the trade exposure measures ln(X/Q) and ln(M/Q) with the effective protection measure ERP.<sup>20</sup> The coefficient on ERP in the regressions can be interpreted as the difference in size distributions that is correlated with differences in effective protection rates, controlling for country-wide plant-size differences, and for indus try specific effects.

Results are reported in the bottom half of Table 2. Note first that there is a positive correlation of the employment size distributions with effective protection. Just as with the X/Q and M/Q measures of trade exposure, higher rates of effective protection are associated with larger plant sizes. Moreover, the ize effect is less extreme in high turnover industries. In both these senses the results conform to the findings in the top half of Table 2: demand contraction and other effects associated with high trade exposure appear to dominate elasticity effects.

However, comparing the different size percentiles, one finds that the statistically significant effects of increased protection appear in the lower percentiles, which suggests that small plants expand at a relatively rapid rate when protection is increased. Contrary to our earlier findings, these results are consistent with the hypothesis that trade exposure increases
**Table 4 – Fixed Effects Estimations, Panel Sample**

||**Pooled sample**|**Hungary**|**UK**|**Germany-West**|**Germany-East**|
|---|---|---|---|---|---|
|Deprivation|-0.896***|-0.794***|-0.399***|-1.581***|-1.380***|
||-0.0295|-0.0568|-0.0523|-0.0719|-0.0617|
|United Kingdom|1.458***|||||
||-0.0258|||||
|Germany-West|1.773***|||||
||-0.0232|||||
|Germany-East|0.922***|||||
||-0.0241|||||
|year_1992|-0.233***||0.0889|-0.143***||
||-0.0367||-0.0737|-0.0482||
|year_1993|-0.200***|0.0213|0.00811|-0.263***|0.375***|
||-0.0367|-0.0691|-0.0728|-0.0484|-0.0608|
|year_1994|-0.194***|0.237***|-0.00915|-0.385***|0.451***|
||-0.0367|-0.0691|-0.0729|-0.0483|-0.061|
|year_1995|-0.218***|-0.0716|0.0531|-0.363***|0.482***|
||-0.0366|-0.0683|-0.073|-0.0482|-0.061|
|year_1996|-0.163***|-0.0948|0.0884|-0.286***|0.585***|
||-0.0366|-0.0684|-0.0727|-0.0483|-0.061|
|year_1997|-0.257***|-0.224***|0.188**|-0.444***|0.436***|
||-0.0366|-0.0683|-0.0729|-0.0482|-0.0606|
|year_1998|-0.139***||0.488***|-0.407***|0.438***|
||-0.038||-0.0733|-0.0483|-0.0609|
|year_1999|-0.115***||0.388***|-0.401***|0.597***|
||-0.038||-0.0735|-0.0483|-0.0611|
|year_2000|-0.0427||0.351***|-0.273***|0.659***|
||-0.038||-0.0731|-0.0482|-0.0612|
|Observations|70719|8640|14130|31740|16209|

Source: Elaborated from CHER database. Dep. Var. = Income satisfaction. Panel sample. Ordered Logit estimations. Standard errors under coefficients. (*) significant at 10% level; (**) significant at 5% level; (***) Significant at the 1% level.

28
Two alternative measures of market remoteness are tested. The first measure is the distance in kilometers<sup>18</sup> between a selected market and the nearest main consumption center, the second one is the quality of the road<sup>19</sup> connecting the market with its main consumption center. **Table 3** reports the results obtained. Columns (1) and (2) present the results obtained with the kilometric distance and road quality, respectively.

In column (1), the finding that remote markets exhibit greater maize price volatility than markets located close to main consumption centers is confirmed again through the strongly positive coefficient on distance in kilometers. The same result holds in column (2) of **table 3** . Market remoteness proxied by unpaved road connected to the markets is positively and significantly associated with maize price volatility. The coefficient on the road quality is positive and statistically significant at the 1% level. The positive effect of market remoteness on maize price volatility in Burkina Faso holds for each of the three empirical specifications used.

# **Estimation with nominal price**

We analyze our initial results with nominal price **.** We test whether our results are sensitive to price specification. **Table 4** reports the results obtained. It indicates that even with nominal price series, the positive and significant impact of time distance on maize price volatility still holds and appears identical with the results obtained in **table 2** . Therefore, we show that the results are not sensitive to the functional form retained.

> 18 To compute information about kilometric distance, we relied on data from the Ministry of Agriculture, specifically from the DGESS (Direction Générale des Etudes et des Statistiques Sectorielles) and data from google maps. We used DGESS data and resort to google maps to fill in for missing data due to the fact that information about some markets are not communicated by DGESS. However, it is reassuring to note that the DGESS data are comparable to data from google maps.

> 19 Road quality is equal to 1 if the national road connected to the selected market is unpaved, 0 otherwise. Road conditions are taken into consideration by using the 2009 map of the **“Institut Géographique du Burkina Fas”** .

20