rafmacalaba/datause-encoder-data huggingface dataset:
the age of politicians might play a role in the privatization process. If the age-cutoff effect only reflects local politicians’ incentives, it should appear only in the privatizations of SOEs under the control of the local politicians. In other words, the privatization of SOEs under the oversight of the _central_ or _provincial_ governments, although located in the same cities, was not determined by local politicians, and these SOEs’ privatization should not be affected by the age and promotion incentives of city-level politicians.

We thus conduct a placebo test by exploiting the central and provincial SOEs sample to rule out the effect of any unobservable city-level factors. Among the sample of 9,813 central and provincial SOEs, 8,695 of them had been privatized in the sample period. We re-conduct the RDD regressions with the new sample and expect that the relationship between the age of the _local_ politician and the privatization of these non-locally-governed SOEs does not change abruptly. The results are shown in Table 5. The coefficients of the local party secretary’s age being 58 and older are insignificant in both the parametric and the non-parametric RDD. The magnitude of all point estimates is much smaller than those reported in Table 4. The placebo test thus suggests that unobservable local factors do not explain our previous results on the age threshold effect of local politicians on privatization of local SOEs.

# **Robustness Test: Other Definitions of Privatization**

In our baseline results, we use a broad definition of SOE privatization: an SOE is considered to be privatized when the firm’s state shares dropping below 50 percent, or when they exit the database. Since the ASIF data include all SOEs and all private firms with sales above 5 million yuan, when an SOE exits the database, it could only be three cases: (i) merging with private firms, which is clearly privatization; (ii) the SOE is privatized and the post-privatization sales drop below 5 million yuan, which again is privatization; (iii) merging with an SOE, in which case classifying the exited firm as being privatized causes misclassification. Case (iii) is a likely a small-probability event, and it can still be regarded as one way of SOE restructuring. Nevertheless, it is a misclassification of privatization. To check the robustness of our conclusions, we re-conduct the main regressions using a narrow definition of privatization, i.e. the criterion of the state share dropping below 50 percent for firms remaining in the database. We reach similar results using this narrow definition of privatization in both the OLS regression ``` 16 ```
_Table 3: Effects of Time to Trade on Trade Values_

||**OLS**|**PPML**|
|---|---|---|
||(1)|(2)|
|GIS Time|‐0.00233***|‐0.00217***|
||(0.000187)|(0.000153)|
|Ln(tariff+1)|‐1.315***|‐1.871***|
||(0.376)|(0.285)|
|PTA Depth|0.0208***|0.0195***|
||(0.00290)|(0.00211)|
|Observations|1,205,865|8,287,250|
|R‐squared|0.619|0.499|

Note: Standard errors in parentheses are clustered by exporter‐importer pair. *** p<0.01, ** p<0.05, * p<0.1 The coefficient of our control variable, _PTA depth,_ suggests that BRI countries with deeper agreements trade more on average. Specifically, having one extra provision included in the agreement increases trade by 1.9 percent on average. This result is in line with the literature on the trade effects of deep trade agreements.<sup>9</sup> In terms of tariffs, a 1 percent increase in tariff rates will reduce exports by 1.8 percent on average. Other standard gravity variables (not reported) have the usual signs found in the literature.

Different products are likely to respond differently to changes in trade time because time sensitivity varies by product. To take a first look into the potential heterogeneity that the variable trading times can have on exports at the product level, we run equation (1) for each hs4 product<sup>10</sup> ‐‐we come back to this issue in Section 3.iv using a different specification based on Nunn (2007). Specifically, we estimate the following equation: X�� �exp �β� �β�GisTime�� �β�Tariff�� �β�PTA depth�� �Gravity�� � λ� �γ���ε��

> 9 Papers such as Orefice and Rocha (2014) find that deeper agreements increase trade across countries by 2 percent on average. More recent research by Mattoo, Mulabdic and Ruta (2017) finds that trade between country pairs that sign an agreement with the highest depth increases by around 12.5 percent.

> 10 Regressions cannot be run at the hs6 level as some of the products have very few observations and the PPML does not always converge.

10
**Figure 8. Energy Subsidies As a Share (%) of Market Income Per Decile in Mali, 2014**

<!-- Start of picture text --> 0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Gas subsidy Electricity subsidy<br>Source: Authors.<br><!-- End of picture text -->

# **4. Effect of Taxes and Public Spending on Poverty and Inequality**

Fiscal policy has a favorable but marginal effect on reduction of inequality in Mali and Niger. The effect of fiscal policy on inequality is similar in the two countries (figure 9). In Mali, taxes and public spending reduce the Gini index by 4.5 percent, or 0.02 Gini points, from market income to final income (figure 9). Without in-kind benefits (health and education), Mali’s fiscal policy achieves a redistributive effect of 0.01 Gini points, corresponding to a 2 percent drop. In Niger, taxes and public spending reduce the Gini index by 5.6 percent from market income to final income, corresponding to a 0.02 Gini point decrease from market income to final income. Excluding in-kind benefits, Niger’s fiscal policy leads to a redistributive effect of 0.01 Gini points.

14
**Table 9: Climate predictions of AOGCM models for 2020, 2060 and 2100**

|**Model**||**Current**|**2020**|**2060**|**2100**|
|---|---|---|---|---|---|
|**CCC**||23.29|24.94|26.85|29.96|
|**CCSR**|**Temperature**|23.29|25.27|26.17|27.39|
|**PCM**||23.29|23.95|24.94|25.79|
|**CCC**||79.75|76.84|71.86|65.08|
|**CCSR**|**Precipitation**|79.75|73.99|76.67|62.44|
|**PCM**||79.75|89.58|80.72|83.18|

35
## **6.2 More robustness tests**

In the following, we test potential alternative mechanisms, other than the lack of competition due to policy privileges, that could explain connected firms’ higher capital intensity and comparable productivity despite their lack of innovation. First, politically connected firms could be led by lower quality managers that do not recognize the potential of adopting new product or process technologies in their line of business. Notably, this argument might explain their lower innovation activity but not their comparable productivity. In any case, our establishment data provide information on management quality, suggesting that it cannot explain connected firms’ lower investment in innovation since their managers have the same years of experience and are even more educated as managers of non-connected firms (see columns 1-4 of Table B.3 in the Appendix). The lack of innovation activity must thus stem from missing incentives rather than missing skills.

Second, connected firms might be less likely to innovate but could at the same time be more effective in converting their innovations into productivity-gains. Thus, while they are less likely to innovate, their higher innovation effectiveness could explain the comparable productivity performance. Our establishment data show that this is not the case. We find that the correlation between innovation activity and firm productivity is not higher among politically connected firms. We even find a stronger relation between the adoption of a new or significantly improved product and firm productivity among non-connected firms. The results thus suggest that, if anything, nonconnected firms innovate not only more frequently but also more effectively (see columns 5-8 of Table B.3 in the Appendix).

# **7. Conclusion**

We present novel evidence that cronyism reduces establishments’ innovation activities and thus countries’ long-term growth potential. The results are based on novel establishment survey data from Egypt which provide simultaneous information on establishments’ political connections as well as their innovation activities and access to country-specific policy privileges. We find that politically connected firms innovate less. Nevertheless, they are not necessarily less productive and are more capital intensive due to the generous policy privileges they receive, including their exclusive access to government procurement contracts, input subsidies, favorable official exchange rates, and bank financing. The latter is in part explained by their privileged access 17
|Meta-regression||||Numberofobs|=|37|
|---|---|---|---|---|---|---|
|REMLestimate of between-study variance||||tau2|=|51.89|
|% residual variation due to heterogeneity||||l-squared_res|=|100.00%|
|Proportion of between-study variance explained||||Adj R-squared|=|10.71%|
|With Knapp-Hartungmodification|||||||
|Single testfor Precision (against papercorrelation)|exp(b)|Std. Err.|t|P>|t||[95%Conf|. Interval]|
|Precision|1.12|0.06|2.2|0.034|1.009|1.242|
|Test forresidual between-study variance (of tau2=0)|Q_res (35|df)|||=|2.50E+06||
|||||Prob > Q_res|=|Q|
|Likelihood-ratiotest<br>oftau2=0:chibar2(01)||||Prob>chibar2|=|a|

|Meta-regression||||Numberofobs||=|29)|
|---|---|---|---|---|---|---|---|
|REMLestimate of between-study vari|ance|||<br>tau2||=|0.001511|
|% residual variation due to heterogen|eity|||l-squared_res||=|8.25%|
|Proportion of between-study variance|explained|||Adj R-squared||=|59.16%|
|Joint test for all covariates||||Model F(10,18)||=|2.06||
|With Knapp-Hartung modification||||Prob > F||=|0.0879|
|pcorr|exp(b)|— Std. Err.|t|Pot|[95%Conf|.|Interval]|
|SurveyUsed|0.96|0.03|-1.31|0.21|0.89||1.03|
|Panel|1.02|0.04|0.41|0.69|0.94||1.10|
|Cross-section|1.01|0.10|0.07|0.94|0.82||1.24|
|Region ECA|1.08|0.07|1.14|0.27|0.94||1.24|
|Region LAC|1.12|0.14|0.89|0.39|0.86||1.46|
|Region EU|1.18|0.08|2.47|0.02|1.03||1.36|
|Region NorthAmerica/other|1.22|0.09|2.76|0.01|1.05||1.43|
|Late Stage|1.01|0.04|0.26|0.80|0.93||1.10|
|Quasiexperimentalmethod used|0.93|0.04|-1.91|0.07|0.86||1.01|
|Complementarity|1.06|0.04|1.68|0.11|0.99||1.14|
decreasing the urban cost of living. In the model, this implies a declining parameter yp, so that g(t) > 0. This encourages rural-urban migration and an urban labor force that grows faster urban population is not too large (ny, << R—6d—p

> <u>Ky“ (+</u> ~~<u>UGrbA</u>~~ <u>a-1 - ar")a (</u> ~~<u>(qq—wWw.</u>~~ <u>ia</u> °

Lu HbA
**Table 4: Robustness to Undercounting and Endogenous Education**

||Unad<br>as per|justed<br>Table 3|Adjust<br>Underc|ing for<br>ounting|Adjusti<br>2SLS ad|ng for Endo<br>justment|genous Ed<br>COP ad|ucation<br>justment|
|---|---|---|---|---|---|---|---|---|
||(1)<br>2SLS|(2)<br>2SLS|(3)<br>2SLS|(4)<br>2SLS|(5)<br>2SLS|(6)<br>2SLS|(7)<br>2SLS|(8)<br>2SLS|
|Years of Education|0.0800*<br>(0.0442)|0.1936***<br>(0.0564)|0.0793<br>(0.0520)|0.2076***<br>(0.0652)|0.5175***<br>(0.0775)|0.6735***<br>(0.0939)|0.6354***<br>(0.0929)|0.7936***<br>(0.1096)|
|Years of Education Squared|-0.0040**|-0.0106***|-0.0036*|-0.0108***|-0.0164***|-0.0232***|-0.0191***|-0.0251***|
||(0.0017)|(0.0029)|(0.0020)|(0.0034)|(0.0023)|(0.0034)|(0.0025)|(0.0036)|
|Community Migration Network|11.4788***|11.0387***|12.7339***|12.2867***|18.9076***|17.3689***|21.3500***|19.4326***|
||(2.2570)|(1.9387)|(2.5931)|(2.1293)|(2.9422)|(2.3138)|(3.3127)|(2.5343)|
|Education*Network|-0.4637**|-0.5521**|-0.5076**|-0.6203**|-1.3764***|-1.4589***|-1.6772***|-1.7545***|
||(0.2185)|(0.2248)|(0.2568)|(0.2487)|(0.2890)|(0.2945)|(0.3374)|(0.3337)|
|Age|0.1739***<br>(0.0328)|0.1944***<br>(0.0560)|0.1923***<br>(0.0365)|0.2133***<br>(0.0627)|0.1579***<br>(0.0321)|0.1654***<br>(0.0551)|0.1507***<br>(0.0319)|0.1532***<br>(0.0549)|
|Age Squared|-0.0034***|-0.0041***|-0.0038***|-0.0046***|-0.0030***|-0.0034***|-0.0028***|-0.0032***|
||(0.0005)|(0.0009)|(0.0006)|(0.0010)|(0.0005)|(0.0008)|(0.0005)|(0.0008)|
|Married dummy|0.8526***|0.9058***|0.9518***|0.9911***|0.8868***|0.9368***|0.9040***|0.9556***|
||(0.1650)|(0.2786)|(0.1862)|(0.3060)|(0.1657)|(0.2797)|(0.1664)|(0.2809)|
|Head of household dummy|0.3502**|1.0213***|0.3854**|1.1707***|0.3600**|1.0698***|0.3617**|1.0839***|
||(0.1669)|(0.2612)|(0.1859)|(0.2882)|(0.1678)|(0.2634)|(0.1682)|(0.2648)|
|Population range:|All|<100K|All|<100K|All|<100K|All|<100K|
|First-stage F-statistics:|||||||||
|Community Migration Network|34.69|54.14|34.72|54.19|34.69|54.14|34.92|55.07|
|Network * Years of Education|34.93|40.51|34.98|40.59|34.93|40.51|35.07|41.06|
|Observations|55848|25531|55848|25531|55848|25531|55848|25531|
|Number of Communities|288|254|288|254|288|254|288|254|

_Notes:_ Robust standard errors in parentheses with standard errors clustered at the municipality (community) level All coefficients are multiplied by 100 for display purposes All regressions also include a constant and the state-level controls included in Table 3 Instruments used are 1924 state migration rate, and its interaction with years of education.

Columns 5 though 8 adjust education using 2SLS and Censored Ordered Probit (COP) estimates of the impact of migration of parents on individual education levels, assuming all migrants have migrant parents and no migrants do (see text).

*, ** and *** indicate significance at the 10%, 5% and 1% levels respectively.
# **Figure 7. Contributors to changes in primary balances in small states and other EMDEs**

_Increases in average primary deficits in small states since the pandemic reflect higher government expenditures, and to a lesser extent, weaker revenues. The contributions to widening deficits from increased spending and weaker revenues in small states have both been greater than in other EMDEs._ <!-- Start of picture text --> A. Revenue, primary spending, and primary B. Contributors to changes in primary<br>balance in small states balances between 2011-19 and<br>2020-23<br>Percent of GDP Primary balance (RHS) Percent of GDP<br>45 Revenues 4 0.0<br>Primary expenditures<br>-0.5<br>2<br>40 -1.0<br>0<br>-1.5<br>Primary expenditures<br>-2<br>35 -2.0 Revenues<br>-4 -2.5 Primary balance<br>30 -6 -3.0<br>Small states EMDEs excl. small<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 states<br><!-- End of picture text --> _Sources_ : WEO (database); World Bank.

_Note_ : EMDEs = emerging market and developing economies.

A. Average primary balance, revenues, and primary expenditures as a percent of GDP for a sample of 32 small states.

B. Bars show the contributions of changes in average primary balances as a percent of GDP from changes in revenues and primary expenditures for a sample of 32 small states and up to 110 other EMDEs.

36
<!-- Start of picture text --> 2000000<br>1800000<br>1600000<br>z 1400000<br>® 1200000<br>—<br>> 1000000<br>3<br>E 800000<br>=<br>600000<br>400000<br>200000<br>0<br>B Total 2001 2004 2007<br>Dinternational<br>@lntercontinental excluding flights between NA and SSA<br>.<br>Domestic<br><!-- End of picture text --> <!-- Start of picture text --> 70<br>60<br>50<br>wn<br>= 40<br>a<br>=<br>© 30<br>20<br>19<br>0<br>2001 2004 2007<br>B Total © International<br>@\|ntercontinental @ Domestic<br><!-- End of picture text -->
Australian Bureau of Industry Economics (BIE).<sup>2 9</sup>

**Box 6.3. Best practices in railways management indicators. Examples.**

|-.<br>.|Revenues and prices<br>Passengr revenut passenger-km<br>(in US$)<br>Freight revenue/ ton-ki (in US$)<br>Freightto passenger tarif_atio_(%f)<br>-<br>~~Commercial<br>services:>generaI<br>A<br>hveragetrain speed (inkm/h)<br>;-|E0.036<br>0. 19<br>;|0.04<br>0.03<br>60-90|
|---|---|---|---|
||.rrivals with small delays (among 10-S15<br>mm.) (in %)|96|90-95|
|Commercial<br>indicators:|<br> <br>Ratio of lost plus damaged freight (in %)<br>Commeial services: passengers||1|
||<br>Number of passegers per train.<br>-97.5||-|
||<br>Passengers-km.<br>per route-Ia (total)<br>-(ifthousds per kin)|_5237_<br>(136) -|.|
||Commercial services: freight<br>N-mber oftons pertrain.|604-13||
|.|<br>Tons-kin per route-km (total)<br>-2819.19.<br>**(in**tousandsperk3<br>-)|(352)|>2000<br>-|
||Labor productivitg|||
||<br>Passengers-km<br>per employee<br>Tons-km per emploee (in thousands)|11000|>750|
||Pass. tain-km per employee.|||
||<br>..reigtrain-km per employee..|||
|Operlational<br>indicators|<br>Total train-kan.<br>per employee<br>Cpia prdutivity<br>..|434.84||
||Availility of tocomotives<br>(in .)|-<br>.|>0%|
||Ton-km perwagon<br>Wagon-lm. per wagon|914.28||
|___________|<br>Freight and passeger wagons availability (in1')||>90%.|
||Cost coverage|||
|**Financial**<br>**indicators**|<br>costscovered with total revenue (in**0O/)**<br>Costs covered with typical revenue (in %)<br>.<br>Cost reduction required to reach break-even (in %)<br>,|-<br>|>100<br>>80<br><0|

**Source:** Gannon and Shalizi (1995) and BIE (1995). Desirable values are only approximate and should be taken as general references that might vary across countries and _regions._

One of the most useful insights that can be provided by examples like those in Box 6.3 is to make it clear that setting desirable values for indicators is a difficult task. Extreme care should be put into making exclusionary comparisons. For example, according to figures, the unit revenue ratio has a desirable value lower than 4 US$ cents (in passenger traffic) or 3US$ cent:s in freight. Lower values, as those achieved in several European countries (3.6 and 1.9, respectively) could be indicative of lower prices, or little fare collecting efficiency. In any case, the regulation in force in each country will notably affect the levels of prices charged by each company.

With regard to average train speed, this measurement should distinguish between passenger and freight transport, and among their different categories (urban, regional, long 29 The BIE publishes every year a benchmarking report (see BIE, 1995, for example) in which their main utilities (including rail transport) are compared worldwide.

- 54 -
10 unlikely that these will prevent affiliates saving 13-14% a month regularly without a bank account or with practically zero return over five years. Banks have some natural advantages -- existing banking relationships with employers/affiliates, participation in the payment system, a tradition of low or no fee accounts, and indirect marketing -- in handling high frequency, low balance accounts. (Actually, monthly deposits/payments would hardly constitute high frequency for banks.) Existing mutual fund and bank products would also spare the affiliates many of the set-up costs. The Table also shows the very high average real returns and volatility from investing in Chilean stocks, that may have been appropriate investment strategies for certain affiliates.

**Table 5: Comparison of Performance of Financial** Instruments **in** Chile

||90-365 CD<br>Real Rate<br>(a)|Fixed Income<br>Real IRR<br>(b)|IFCG Total<br>US$ Index<br>(c)|Pension Fund<br>Returns<br>(d)|Annual IRR<br>to date<br>(e)|
|---|---|---|---|---|---|
|1982|12.4%||-54.6%|28.8%|-3.2%|
|1983|7.9%|9.6%|-31.5%|21.3%|-1.3%|
|1984|8.4%|8.0%|-23.7%|3.5%|-5.9%|
|1985|8.2%|8.7%|49.3%|13.4%|-2.3%|
|1986|4.1%|_5.1%_|154.8%|12.3%|0.3%|
|1987|4.3%|6.0%|30.2%|5.4%|_0.5%_|
|1988|4.6%|5.8%|37.1%|6.4%|1.4%|
|1989|6.8%|7.7%|51.2%|6.9%|2.1%|
|1990|9.4%|7.3%|40.4%|_15.5%_|4.2%|
|1991|5.4%|5.8%|98.1%|29.7%|7.9%|
|1992|5.3%|7.6%|16.2%|3.1%|6.9%|
|1993|6.4%|6.6%|34.6%|16.2%|8.0%|
|1994|6.4%|6.1%|45.0%|18.2%|9.1%|
|1995|5.9%|6.9%|0.6%|-2.5%|7.4%|
|AvgafterSyears(|f)<br>8.2%|7.9%|18.1%|15.9%|0.3%|
|Avg after 10 yea|rs (f)<br>7.1%|7.1%|35.1%|14.3%|7.9%|
|Avg after 14 yea|rs(f)<br>6.8%|7.0%|30.4%|12.7%|7.4%|

- (a) Interest rates for 90-365 day deposits; deflated by the UF (Unidad de Fomento-the economic index that reflects inflation variations according to the CPI (IPC)).

- (b) Real annual average intemal rate of return for fixed income instruments traded on the Chilean stock exchange.

(c) IFC Global Index; From 1994 forward, Index constitutes 60% to 75% of the total capitalization of the local stock exchange. This index takes into account the effect of dividend reinvestment.

- (d) Real pension fund index without adjustments for fees and commissions.

- (e) Annual Intemal Rate of Retum reflects the IRR up to the corresponding year.

- (f) Simple average is taken for the CD rate (a); Fixed Income Real IRR (b); IFCG Total (c); and Pension Fund Retums (d). IRR returns reflect average returns for an affiliate over the period 1982-1995.

Sources: _Bolsa Comercio de Santiago;_ IFC Emerging Markets Database; "Informe Econ6mico y Financiero," Banco Central de Chile.
Figure 3: Quality Growth vs. Financial Depth <!-- Start of picture text --> Risk vs. Financial Depth<br>Countries with more than 50 products. Slope = .015 (t−stat=3.56)<br>.08<br>United Kingdom<br>Japan Swi<br>Australia<br>Denmark Nethlds<br>.06 Argent SwedenFrance Ireland Portugal<br>Italy Austria Germany<br>Hongkon<br>Bel_luxNorway<br>MexicoPeruBrazilPhilippinesFinland Israel Canada Singapore<br>.04 VenezGuatmalaPolandIndiaSri_lkaColombiaEl SalvadorEgypt Spain ThailandKorea<br>IndonesBngldsh<br>Turkey South AfricaMalaysia<br>Pakistan<br>Dom_rep<br>Macau<br>.02<br>Cos_rica<br>Hondura<br>0 .5 1 1.5<br>Private Credit by Deposit Money Banks and Other Financial Institutions / GDP<br>Quality growth<br><!-- End of picture text -->

**Notes:** This figure plots the median quality growth against financial development, for countries that export at least 50 HS10 products to the US. Slope = 0.15 ( _t_ -stat=3.56).

Table 1 reports the results. Columns 1, 4, 7, and 10 show that in a bivariate regression, there is a significant and positive relationship between all four measures of financial development and average quality growth. Since financial development increases with income levels, it may be that our proxies are capturing other correlates of overall development. Hence, columns 2, 5, 8, and 11 control for GDP per capita. The magnitude and significance of the coefficient on the financial development proxy falls, losing significance in panels B and D. This result turns out to be partly due to a few countries showing conditionally very high rates of financial development without corresponding quality growth. The overwhelming majority of these countries turn out to be financial off-shoring destinations. Columns 3, 6, 9, and 12 control for this by including an indicator variable for offshoring locales.<sup>11</sup> Our financial depth proxies again emerge as strongly significant.

# **4 Conclusion**

This paper studies the dynamics of product quality changes in US imports to explore the tradeoff between risk and return in quality improvements in the exporting countries. Across

> 11Offshoring locales are Macao, Singapore, Costa Rica, Dominican Republic, Malaysia, Hong Kong, Luxembourg, Switzerland, and South Africa. See International Monetary Fund (2006) for a complete discussion.

15