rafmacalaba/datause-encoder-data huggingface dataset:
commuting.

## **3.3 Auxiliary Data**

Since our study period overlaps with the COVID-19 pandemic period, we obtain COVID19 case data from Google Health. We also use the Indian Population Census 2011 to obtain district-level characteristics like population. We combine the two data sets and construct average daily new confirmed cases as a share of the population in a district. We include this variable in our regressions to account for the potential effects of COVID-19 on outcomes like labor market participation. To address further concerns that different states experienced different levels of mobility restrictions, we use data from the Oxford Covid Tracker on the share of days each month that state governments imposed a shutdown on public transportation.

# **4 Empirical Strategy**

States that implemented the _Pink Slip program_ may differ from other states. Apart from including only the neighboring states of the treated state as controls, we employ a synthetic difference-in-differences strategy (SDID) proposed by Arkhangelsky et al. (2021) to allay this concern further. The SDID re-weights the control units to make their time trend parallel to the treated units and then applies a DID analysis to the re-weighted panel. This method constructs a synthetic counterfactual for causal estimation. Since, in our setting, the treatment is applied at the state level, we construct a state-level panel using the CPHS data. With this panel, we can derive each control state’s SDID unit and A.1. time weights. A detailed explanation is provided in Appendix To estimate the impact of the _Pink Slip_ scheme on women (or households with women), we employ two regression specifications for individual (or household) _i_ in state _j_ and time _t_ weighted by the individual (or household) sampling weight, along with the SDID time and unit weights. Starting with a standard stacked event-study design where _Y_ are outcome variables such as household on _ijt_ monthly expenditure transport and individual time on travel. The variable _Treated_ takes the value 1 usage dummy _j_ if a state implemented the _Pink Slip program_ and 0 otherwise. **1** ( _Time_ = _t_ ) takes the 8
- Bhalotra, S., A. Chakravarty, and S. Gulesci (2016a): “The Price of Gold: Dowry and Death in India,” _IZA Discussion Paper 9679_ .

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- Bhalotra, S., M. Karlsson, and T. Nilsson (2016b): “Infant Health and Longevity: Evidence from a Historical Intervention in Sweden,” _Journal of European Economic Association_ , Forthcoming.

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- Bhalotra, S. and A. van Soest (2008): “Birth-Spacing, Fertility and Neonatal Mortality in India: Dynamics, Frailty, and Fecundity,” _Journal of Econometrics_ , 143, 274–290.

- Bhalotra, S. and A. Venkataramani (2013): “Cognitive Development and Infectious Disease: Gender Differences in Investments and Outcomes,” _IZA Discussion Paper 7833_ .

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30
**_<u>________________________________________________________</u>_**

railways from 1980-99. It compiles data from the annual reports of railway companies, available for free on the web. Railway Data Table contains selected size, scale and productivity data for all railways 1980-99. The Railways Concessions Database provides information on the performance of the railway concessions in Latin America and Africa, for the period beginning 1990 to the present.

# _<u>International Union of Railways (IUR)</u>_

The International Union of Railways offers various statistical publications. The annual volume “International Railway Statistics” provides data on lines, passenger and freight transport stock, staff, train movements, and freight traffic. “Railways time-series data” includes data on personnel, train performance, use of wagons, and industry revenues. “Supplementary Statistics” offers additional and more detailed data on the performance and structure of the railways industry. Extracts are available online (annual and quarterly statistics).

# _<u>OECD in Figures</u>_

This publication includes information on passenger transport, freight transport and road accidents for 30 countries.

Dutz, Mark (2005). Road freight logistics, competition, and innovation: downstream benefits and <u>policy implications, World Bank Policy Research Working Paper 3768.</u> This empirical paper sheds light on a significant element of the debate of whether infrastructure services have a strong impact on economic development by exploring the impact of innovative road freight services on downstream business users. The paper uses a new and purpose-specific survey of 165 logistics service providers and 493 user enterprises in food processing, food distribution, and the automotive industry in the Czech Republic, Hungary, and Poland. The main findings are that there are substantial downstream benefits from innovations in road freight services, both dampening cost increases and raising sales revenues of business users. The additional finding that increased intensity of competition in road freight services is significantly associated with the provision of innovative services suggests that easing any remaining barriers to competition in upstream business sectors should be a priority.

# **_2.4.1 Air Transport Indicators_**

# _<u>International Civil Aviation Organization (ICAO) Statistics Digests</u>_

The “Digests of Statistics” contains financial data and summary traffic data for international airports, monthly and yearly statistics for airports open to international traffic, financial data for commercial air carriers, data on fleet and personnel for commercial air carriers, revenue traffic performed for city-pair, annual data of civil aircraft on register, traffic data for commercial airlines, and statistics on the flow of traffic carried from point to point. The digests are available on a commercial basis as well as electronically.

27 ____________________________________________________________________________________________________________
here due to data limitations or lack of variation. These possible determinants include pre-colonial institutions, and experiences during and after colonization. For evidence on these dimensions from elsewhere, see Michalopoulos and Papaioannou (2013), Nunn (2008), Dell (2010), Valencia Caicedo (2019), Dippel (2014), Akee et al. (2015), Akee et al. (2015), Feir (2016), and Feir et al. (2022).

Table 7: Precolonial Agriculture and Current Outcomes

|||Argen|tina|||No|rth||
|---|---|---|---|---|---|---|---|---|
||Average|Outcomes|Educ|ation|Average O|utcomes|Educ|ation|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Incipient Agriculture|0.084|0.022|0.726|0.174|0.241***|0.314***|1.917***|1.777***|
||(0.078)|(0.090)|(0.536)|(0.602)|(0.046)|(0.066)|(0.281)|(0.429)|
|Superior Agriculture|0.131*|-0.053|1.173**|-0.467|0.414***|0.689***|3.171***|2.647**|
||(0.070)|(0.153)|(0.479)|(1.029)|(0.037)|(0.188)|(0.223)|(1.212)|
|Centuries since Colonization||0.068||0.609*||-0.108||0.207|
|||(0.051)||(0.341)||(0.073)||(0.469)|
|Constant|-0.217***|-0.305***|8.424***|7.635***|-0.671***|-0.547***|4.890***|4.653***|
||(0.048)|(0.081)|(0.329)|(0.544)|(0.024)|(0.086)|(0.143)|(0.556)|
|Observations|31|31|31|31|19|19|19|19|
|R-squared|0.116|0.171|0.180|0.266|0.890|0.904|0.928|0.929|
|Pvalue Incipient=Superior|0.560|0.536|0.422|0.434|0.00260|0.0191|0.000633|0.362|

_Source_ : REDATAM INDEC Census Argentina 2010. _Notes_ : Weighted Least Squares. Standard errors in parentheses. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01

# **6 Conclusion**

We contribute to the study of differences in socioeconomic outcomes across Native and non-Native people by using new Argentine census data to describe the economic outcomes of Native Argentines. We find that, on average, Native Argentines obtain worse outcomes than non-Natives. These differences cannot be explained only by differences in location or educational attainment. We also find differences in the transmission of education that suggest that differences in educational attainment will not disappear with time. Interestingly, we find that differences among Native groups are much larger than the differences between Natives and non-Natives. We find that the economic outcomes of groups correlate with the practice of agriculture before the arrival of the Spanish.

These results suggest the importance of not considering the Native groups in Argentina as a homogeneous group, but stress the diversity of experiences among Natives. We hope that future research will help us understand the causes of these differences in outcomes and their implications for public policies aimed at improving the lives of Native people in Argentina.

24
<!-- Start of picture text --> 4<br>Cc<br>Re) arts<br>2 3 : ‘<br>oO teen! ON .<br>Ss *. ‘,<br>12)<br>><br>a.Oo S/N<br><2)<br>—<br>fe)<br>o -1<br>—<br>140)<br>aon<br>0)<br>0<br>% o> % % KS) 5<br>S S cS Ss > Ri<br>as v av v av v<br>» sv S we SS Si<br>2 v + v v v<br>Fiscal year<br>— All year Summer ---- Winter<br><!-- End of picture text -->
**Figure 20: Real GDP growth** <u>(percent)</u>

<!-- Start of picture text --> Real GDP Growth<br>Moldova Bulgaria Armenia Paraguay<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Source: Find my Friends Tool using IMF WEO Data

**Figure 21: Government Fiscal balances** <u>(percent)</u>

<!-- Start of picture text --> General Government Fiscal Balance (share of<br>Nominal GDP)<br>Moldova Bulgaria Armenia Paraguay<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Source: Find my Friends Tool using IMF WEO Data

**Figure 22: Revenue to GDP**

(Index)

**Figure 23: Expenditure to GDP**

(Index) <!-- Start of picture text --> General government revenue (Share of General government total expenditure (Share<br>Nominal GDP) (2005-2013) of Nominal GDP) (2005-2013)<br>Index 100=2009 Index 100=2009<br>120 Moldova Bulgaria 120 Moldova Bulgaria<br>Armenia Paraguay Armenia Paraguay<br>110 110<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Source: Find my Friends Tool using IMF WEO Data Source: Find my Friends Tool using IMF WEO Data Through the lens of force you can see the same story but from a different perspective. During the crisis in 2008 the force of fiscal expenditures that were rigid increased while discretionary items experienced a large amount of negative force. In the following year, both rebounded but rigid expenditures continued to gain force in 2010 and 2012 while discretionary items underwent another large loss of force in 2010 and 2012. In total, comparing the period of 2007-11 to 2008-12 there was a negative force of 43 percentage points of which rigid expenditures were 10 percentages points of the negative force while discretionary expenditures were 32 percentage points of the negative force. Armenia and Moldova’s momentum of rigid and discretionary items broadly follow a similar track with discretionary expenditures growing negatively in recent years while rigid items continue to gain momentum. Importantly for Armenia, it has greater fiscal space to absorb the increase in momentum in its rigid items as its fiscal deficit has markedly improved since the crisis and returned back to pre-crisis levels while Moldova’s has not (Figure 21). Armenia’s
|**Table 2: Ba**|**se Regressi**|**on Results fo**|**r Depth of Pe**|**tty Corrupti**|**on**||
|---|---|---|---|---|---|---|
|Dependent variable: Financially|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Constrained (dummy)|||||||
||**Panel A**|**: Estimated**|**log-odds ratio**||||
|Depth of Petty Corruption (Cell|0.759**|0.830**|0.850**|0.923**|0.993**|1.043***|
|average)|(0.377)|(0.381)|(0.381)|(0.385)|(0.395)|(0.366)|
|Annual Sales (2009 USD, logs)||-0.171***|-0.164***|-0.120***|-0.116***|-0.123***|
|||(0.037)|(0.036)|(0.035)|(0.036)|(0.037)|
|Age of Firm (logs)|||0.016|0.018|0.025|0.042|
||||(0.067)|(0.069)|(0.070)|(0.072)|
|Exports (proportion of sales)|||-0.589*|-0.506|-0.461|-0.447|
||||(0.309)|(0.324)|(0.350)|(0.364)|
|Firm Has Female Owners Y:1||||-0.179|-0.181|-0.194|
|N:0||||(0.116)|(0.118)|(0.119)|
|Foreign Ownership||||-0.026|-0.038|-0.050|
|(proportion)||||(0.279)|(0.273)|(0.283)|
|Multi-establishment Firm Y:1 N:0||||-0.287|-0.295|-0.283|
|||||(0.181)|(0.189)|(0.193)|
|Firm Purchased Assets Last Fiscal||||-0.565***|-0.532***|-0.532***|
|Year Y:1 N:0||||(0.105)|(0.106)|(0.109)|
|Firm is Audited Y:1 N:0||||-0.241**|-0.239**|-0.190|
|||||(0.118)|(0.120)|(0.125)|
|Hours of Power Outages in a|||||0.000|0.000|
|Month|||||(0.001)|(0.001)|
|Firm Experienced Losses from|||||-0.461**|-0.486**|
|Crime Y:1 N:0|||||(0.188)|(0.190)|
|Firm Inspected/Visited by Tax||||||-0.114|
|Officials Y:1 N:0||||||(0.118)|
|Informal Competition (average)||||||0.687**<br>(0.312)|
|How Much of an Obstacle?: Courts|(average)|||||0.312**<br>(0.135)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Number of observations|20,502|20,502|20,432|20,086|19,060|18,931|
|**Pane**|<br>**l B: Margi**|<br>**nal effect of t**|<br>**he corruption**|<br>**variable**|||
|Depth of Petty Corruption (Cell|0.082**|0.089**|0.091**|0.096**|0.107**|0.109***|
|average)|(0.041)|(0.041)|(0.041)|(0.040)|(0.043)|(0.039)|
|Other controls as above|Yes|Yes|Yes|Yes|Yes|Yes|

All coefficients in Panel A are log odds ratios obtained from logit estimation. Huber-White robust standard errors clustered on country time industry in brackets. *** (1%), ** (5%), * (10%). Sample size varies due to missing data.

37
reflected in) changes in government policies and institutions. Alm, Martinez-Vazquez and Torgler (2006) use therefore micro-level data for Russia from the World Values Survey and the European Values Survey for the years 1991, 1995, and 1999 to examine how these attitudes have changed during the tumultuous events of the 1990s.

The 1990s were a transforming decade for Russia. This period begins with the dissolution of the Union of Soviet Socialist Republics in December 1991. It spans the presidency of Boris Yeltsin, who was elected President by popular vote in June 1991 and who served until December 1999, when he resigned and Vladimir Putin became acting President. Putin was elected president shortly after with considerable popular support and authority to carry out a backlog of reforms. The 1990s were also a period during which major legislation was enacted<sup>9</sup> . Abstracting these legal changes, many other events also shaped the perceptions of ordinary citizens toward the new state. Notable socio-political events include: the August 1991 coup attempt; the political struggle and final victory of Yeltsin over the communist-dominated parliament; the centrifugal separatist forces in ethnic regions from Chechnya to Tatarstan to Bashkiria; the massive privatization of state assets and the rise of the Russian oligarchs; the questionable alliance of the oligarchs with the Kremlin for Yeltsin‘s reelection; the financial crisis and debt default of August 1998; the beginning of the economic recovery and the consolidation of power in Putin‘s hands the next year. The Russian Federation is characterized by strong ethnic, economic, and fiscal disparities between the regions, including some sorts of secessionist forces (Polishchuck, 1996). In general, the relationship between the Krelim and the regions during the 1990s went from laissez faire in the early Yeltsin years, to open conflict and full defiance in the middle of decade when many of the regions maintained secret bilateral treaties with Moscow, to recentralization and the imposition of more uniform discipline toward the end of the decade, especially after Putin took power. However, the relationship between Moscow and the 89 regions varied considerably across regions. Since then, richer, better-off regions have pushed for higher retention rates of tax

> 9 This legislation transformed the Russian Federation: the Law on Basic Principles of the Tax System in the Russian Federation (December 1991); the new Constitution of 1994; the Law on the General Principles of Organization of Local Self-government in the Russian Federation (August 1995); the introduction in 1997 and 1998 of important reforms based on the draft Tax Code, the Budget Code, the Law on Financial Foundations of Local Self-governments, and the Concept of Reform of Intergovernmental Fiscal Relations in the Russian Federation.

38
Figure 3: Level of data policy restrictiveness over time.

<!-- Start of picture text --> Level of data policy restrictiveness (weighted)<br>2008 2010 2012 2014 2016 2018<br>1<br>.8<br>.6<br>.4<br>Restrictiveness index (0-1)<br>.2<br>0<br><!-- End of picture text --> Source: ECIPE. Note: Countries include the ones covered under Figure 2. A weighted average is constructed using GDP as weights in order to reflect size of market (some markets such as China are huge, whereas others are small such as Hong‐Kong). Checks with population have been done with similar increasing trend (albeit smaller).

20
Figure 6: _Private sector participation in consultations_ <!-- Start of picture text --> Share of economies (%)<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Before After During Not consulted<br><!-- End of picture text --> _Source: Reform Committee data Note: Figure based on 107 economies with a functioning business reform committee._ Private sector engagement in the process of policy formulation can broadly be simplified into two stages: pre- and post-legislation enactment. Most economies engage the private sector in the legislative process, but not throughout the entire cycle of policy formulation, implementation, and evaluation. In fact, economies across all income groups tend to consult before the formulation of the legislation, rather than after its enactment. In close to three-fourths of all economies with reform committees, the private sector is involved before the formulation of the business regulatory and legislative proposals, through in-person or online discussions or surveys. Similarly, in approximately 40 percent of economies with reform committees the private sector is less frequently engaged during reform implementation and provides feedback on practical challenges in the implementation stage (Figure 6). Importantly, in most of these cases the consultations are usually focused on implementation issues rather than impact evaluation. An exception is Slovenia, which in its ex-post evaluation efforts focusses on reducing administrative burdens for businesses.

For example, in Colombia the private sector is involved in the formulation of new legislation and in presenting the main challenges of the sectors. In Hong Kong SAR, China, the Business Facilitation Advisory Committee (BFAC) and its three Task Forces provide a platform for the trade actors and the government to communicate and exchange views on new regulatory proposals at all levels and take forward regulatory reviews or other initiatives to facilitate business compliances. Through the BFAC and Task Force meetings, the government consults the business sector on new regulatory proposals and discusses the implementation issues of new or proposed regulations. In tandem, relevant government departments follow up on trade groups’ concerns and work out improvement measures. They also report on major business facilitation initiatives devised by, and new regulatory proposals implemented at, the BFAC and Task Force meetings.

Feedback from the private sector can be solicited through formal or informal consultations. For formal consultations, regulators can choose either to consult selected private sector representatives or allow any private entity or citizen to provide feedback. According to the data, formal consultations through selective outreach to the private sector is the most common practice during the early-stage stakeholder consultation 14