rafmacalaba/datause-encoder-data huggingface dataset:
- Gaduh, Arya. Tedeja Gračner, and Alexander Rothenberg. 2022. “Life in the slow lane: Unintended consequences of public transit in Jakarta,” _Journal of Urban Economics._ Vol. 128, Issue C.

- Gendron-Carrier, Nicolas, Marco Gonzalez-Navarro, Stefano Polloni, and Matthew A. Turner. 2022. “Subways and Urban Air Pollution,” _American Economic Journal: Applied Economics_ . Vol.14, No.1.

- Gonzalez-Navarro, M., J.D. Hall, H. Wheeler, and R. Williams. 2021. “Uber versus Trains? Worldwide Evidence from Transit Expansions”, _NET Institute Working Paper_ No.21-11. <u>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3959760</u>

- Graehler, M., R.A. Mucci, and G.D. Erhardt. 2019. “Understanding the Recent Transit Ridership Decline in Major US Cities: Service Cuts or Emerging Modes?” in Transportation Research Board<sup>9</sup> 8th Annual Meeting.

- Hall, J.D., C. Palson, and J. Price. 2018. “Is Uber a Substitute or Complement for Public Transit?” _Journal of Urban Economics_ , 108 (1), 36–50.

- Hanna, Rema, Gabriel Kreindler, and Benjamin Olken. 2017. “Citywide Effects of High-Occupancy Vehicle Restrictions: Evidence from the Elimination of ‘3-in-1’ in Jakarta,” _National Bureau of Economic Research_ (NBER). Working Paper 23295.

- Heger, Martin, David Wheeler, Gregor Zens, and Craig Meisner. 2019. _Motor Vehicle Density and Air Pollution in Greater Cairo._ <mark>Washington, D.C.: World Bank Group.</mark> <u><mark>https://documents1.worldbank.org/curated/en/987971570048516056/pdf/Motor-Vehicle-Densityand-Air-Pollution-in-Greater-Cairo-Fuel-Subsidy-Removal-and-Metro-Line-Extension-and-theirEffect-on-Congestion-and-Pollution.pdf</mark></u>

- <mark>Irawan, M.Z., Belgiawan, P.F., Tarigan, A.K.M., Wijanarko, F. 2020. “To compete or not compete: exploring the relationships between motorcycle-based ride-sourcing, motorcycle taxis, and public transport in the Jakarta metropolitan area.”</mark> _<mark>Transportation</mark>_ **<mark>47</mark>** <mark>, 2367–2389.</mark>

- Japan International Cooperation Agency (JICA). 2001. “The study on integrated transportation master plan for JABODETABEK (phase 1) : final report ; Vol.3. – review of MRT Project”.

- Japan International Cooperation Agency (JICA). 2019. “JABODETABEK Urban Transportation Policy Integration Project Phase 2 in the Republic of Indonesia. Annex 02: JABODETABEK Urban Transportation Master Plan”.

- Kreindler, Gabriel, Arya Gaduh, Tilman Graff, Rema Hanna and Benjamin A. Olken. 2023. “Optimal public transportation networks: evidence from the world’s largest bus rapid transit system in Jakarta. _Harvard Kennedy School Faculty Research Working Paper_ , RWP23-018.

- Nelson, E. and N. Sadowsky. 2018. “Estimating the Impact of Ride-Hailing App Company Entry on Public Transportation Use in Major US Urban Areas,” _The B.E. Journal of Economic Analysis & Policy_ , 19 (1).

- Pollmann, Michael. 2023. “Casual inference for spatial treatments”, _Working Paper_ , Duke University.

27
time, suggesting substantial within-PSU variation (see appendix A). Thus, the comparisons between box and household survey data are most accurately interpretable with summary statistics at a higher level of aggregation.

**Table 2: Descriptive Statistics from Survey and SSBs**

||Al|l day||Bet|ween 1|8:00 an|d 22:00|
|---|---|---|---|---|---|---|---|
|Mean|Med.|SD|Obs.|Mean|Med.|SD|Obs.|
|**(A) Unconditional**<br>**(A1) SSB Obs level:** _psu-by-month, averaged across b_<br> <br>|_oxes with_<br>|_in each_<br>|_psu_<br>|||||
|One if outage<br>0.88|1.00|0.31|1776|0.77|1.00|0.41|1776|
|Total hours/day<br>2.19|0.52|3.78|1776|0.32|0.04|0.62|1776|
|<br>**(A2) SSB Obs level:** _psu-by-date, averaged across bo_|_xes within_|_each ps_|_u_|||||
|One if any outage<br>0.28|0.00|0.45|96644|0.14|0.00|0.35|96644|
|Total hours/day<br>2.29|0.00|6.20|96644|0.34|0.00|1.05|96644|
|**(A3) Household Data Obs level:** _household-by-m_|_onthly-ro_|_und_||||||
|One if outage||||||||
|over the past 10 days<br>0.28|0.00|0.45|12954|-|-|-|-|
|yesterday<br>0.18|0.00|0.38|12928|-|-|-|-|
|day before yesterday<br>0.19|0.00|0.39|12940|-|-|-|-|
|Length of outage||||||||
|over the past 10 days (days)<br>0.66|0.00|1.44|12954|-|-|-|-|
|over the past 10 days (hours/day)<br>2.00|0.00|5.03|12953|-|-|-|-|
|yesterday (hours/day)<br>1.85|0.00|4.74|12928|0.07|0.00|0.45|12941|
|daybeforeyesterday (hours/day)<br>1.94|0.00|4.89|12939|0.08|0.00|0.48|12937|
|<br>**(B) Conditional on having any outage (B1 an**<br>**(B1) SSB Obs level:** _psu-by-month, averaged across b_|**d B2) o**<br>_oxes with_|**r any s**<br>_in each_|**elf-reporte**<br>_psu_|**d outage (**|**B3)**|||
|Total hours/day<br>2.48|0.68|3.93|1572|0.42|0.09|0.68|1383|
|<br>**(B2) SSB Obs level:** _psu-by-date, averaged across bo_|_xes within_|_each ps_|_u_|||||
|Total hours/day<br>7.97|3.32|9.41|27848|2.34|2.31|1.69|14117|
|<br> <br>**(B3) Household Data Obs level:** _household-by-m_<br>|<br>_onthly-ro_|<br>_und_||||||
|Length of outage||||||||
|over the past 10 days (days)<br>2.31|2.00|1.86|3726|-|-|-|-|
|over the past 10 days (hours/day)<br>6.96|4.00|7.32|3724|-|-|-|-|
|yesterday (hours/day)<br>10.04|12.00|6.31|2382|2.09|2.00|1.25|462|
|daybeforeyesterday (hours/day)<br>10.12|12.00|6.48|2480|2.29|2.00|1.31|455|
|All observations are in or after 2016, Nov 1.||||||||

These comparisons are drawn out in alternative formulation in table 3, which reports t-tests comparing the average difference between the two data collection methods from each source for the same location and period. Although the data are structured differently (SSB data provide continuous coverage), SSB data can be harmonized to cover the given reference period (for instance, “yesterday” or “the day before yesterday” calculated using the interview date) and compared. Survey-based methods report about 10 percent fewer outages “yesterday” on average, and a similar rate with respect to the “day before yesterday.” When asked about the preceding ten-day period, respondents reported less, reporting outages at about half the rate that boxes recorded them.

8
to be observed as trade costs increase. The GDP ratio in the selection equations is in most cases positive. The reason is that since our model claims that larger economies export higher quality goods and in relatively small amounts, some trade relations may not appear in the data and hence the expected sign should be positive.

The second step estimations results (flow equations) again corroborate the key predictions of our model. The coefficients of distance and GDP ratio are positive and statistically significant with only two exceptions. In the product group “footwear/headgear” the GDP ratio coefficient is negative, and in the product group “transportation” (86, 88, and 89) the distance coefficient is insignificant. Overall, the magnitude of the distance and GDP ratio coefficients varies, but it is similar to the one observed above. Future studies might try to investigate why the magnitude of both coefficients varies from one sector to another.

For our last robustness check we improve our Heckman set-up by over-identifying the selection equation. We therefore need to find a variable that is has an impact on whether or not trade relations are observed, but which is unrelated to the quality traded. We think that information about the cost of starting a business might fulfill this requirement. Costs to start a business can be thought of proportional to (business) cultural differences. Our model expresses this as trade costs and predicts that lower trade costs between two countries (i.e. similar business culture and market circumstances) allow more firms to export in both countries, assuming the market size between economies constant, and thus lower cost of starting business raises the probability of having observations. And hence we expect a negative sign in the selection equation.

The data base of Doing Business provides useful information about the cost of starting a business (World Bank, 2007). We have gathered the data on the number of procedures to register a firm and the official cost of each procedure (as percentage of GNI per capita) for all 15 EU countries. We are thus able to run a Heckman estimation with the following econometric specification: Selection equation: _zijk_ = υ +φ log( _distij_ ) +π log( _msi_ / _ms j_ ) + ο log( _dbi_ + _dbj_ ) +ξ _k prodk_ +ψ _iexi_ +ς _jim j_ + ε _ij_ (17) Flow equation: log( _pijk_ ) = α + β log( _distij_ ) +γ log( _msi_ / _ms j_ ) +ν _k prod k_ + ω _iexi_ +ι _jim j_ + ε _ij_ (18) 34
7 computed.<sup>13</sup> **This procedure** produced free-cn-board export prices for srmilar goods **shipped** from the four industrial countries to Madagascar and other destinations.

Table 2 provides summary statistics on Madagascar's relative pri:es **(expressed as a** percentage) for each year over 1979-88. Both sri;ple and trade- **weighted (by Madagascar's import values)** average prices are shown for shipments **from France, Germany, Italy** and Japan, along with similar statistics for these

> **1 0Several** modifications were made in the comparator country groups. Since some Sub-Saharan African countrie~s use pre-shipment inspection, and a study by seats (1990b) showed their imr-.. prices were not representative of those paid **by other countries,** they were excluded from the developing country group. Also, **Greece, Spain and** Portugal were included in the industrial country group even though **they are** categorized as "developing" ir. some World Bank classLfications. **These tabulations** permitted calculation of Madagascar's relative import price (Rm) for each five-digit product i

where **Vmi** and Vc* are the free-on-board value of imports by Madagascar and the **comparator** group **(i.e.,** industrial. or other developing countries), respectively, **and** Q is **the** corresponding quantity.
**Table 9: Disaggregation by stated reasons for hesitancy (among the hesitant, OLS estimation)**

||K|AZ|U|ZB||TJK|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Experiment Treatment|-0.068**|-0.065**|-0.008|-0.010|-0.010|-0.009|
||(0.029)|(0.028)|(0.020)|(0.020)|(0.089)|(0.087)|
|Negative past experiences|-0.050**|-0.033|-0.041***|-0.042***|-0.066|-0.065|
||(0.021)|(0.023)|(0.016)|(0.016)|(0.077)|(0.074)|
|Lack of trust in health system|0.020|0.025|0.018|0.017|-0.076|-0.075|
||(0.033)|(0.033)|(0.038)|(0.037)|(0.076)|(0.074)|
|Risk more than disease|0.041|0.037|-0.040***|-0.041***|-0.079|-0.078|
||(0.035)|(0.035)|(0.015)|(0.015)|(0.076)|(0.075)|
|Personal/religious beliefs|0.041|0.041|-0.005|-0.007|-0.073|-0.072|
||(0.048)|(0.046)|(0.019)|(0.019)|(0.076)|(0.073)|
|Counter-indication|0.037|0.037|0.023|0.021|-0.063|-0.057|
||(0.026)|(0.026)|(0.019)|(0.019)|(0.078)|(0.076)|
|Other|0.040|0.038|-0.013|-0.013|0.006|0.007|
||(0.026)|(0.025)|(0.015)|(0.015)|(0.005)|(0.005)|
|Inc. Per Capita||-0.000**||-0.000***||0.000|
|||(0.000)||(0.000)||(0.000)|
|Rural Location||-0.012||0.015||0.010*|
|||(0.019)||(0.012)||(0.005)|
|Household Size||-0.007||0.000||-0.001**|
|||(0.005)||(0.002)||(0.000)|
|Constant|0.090***|0.145***|0.051**|0.040|0.073|0.071|
||(0.028)|(0.042)|(0.021)|(0.028)|(0.075)|(0.071)|
|Observations|1,209|1,209|1,380|1,380|1,117|1,117|
|R-squared|0.020|0.030|0.007|0.009|0.021|0.024|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; Base category "Lack of trust in producers"; among the vaccine hesitant in each country.

# Discussion and conclusions

The experiment found that hypothetical incentive payments reduced vaccination intentions in two of the three countries studied. The findings suggest that the behavioral responses to incentives are not uniform across countries. While offering incentives markedly reduced overall vaccination intentions by 19 percent (p=0.000) in all three counties, significant reductions are observed in Uzbekistan and Kazakhstan but not in Tajikistan. These qualitatively large and statistically significant effects were directly opposite to those expected. The results contrast with the well-established efficacy of monetary incentives to influence vaccination behavior in other settings but are consistent with findings from the behavioral literature in which incentive payments signal inferiority or disutility.

Moreover, we find that the risk of backfire does not systematically depend on the monetary amount offered. In Kazakhstan, the size of negative coefficient is slightly lower for a higher incentive amount, while it does not change systematically according to the payment in Uzbekistan. Higher incentive payment values were not systematically associated with increased vaccination intentions.

14
Table 7: Passthrough between GDP growth and household welfare growth from surveys

|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|gdp_change|0.897***|1.103***|0.911***|0.998***|0.459|0.550**|
||(0.0999)|(0.149)|(0.107)|(0.119)|(0.549)|(0.258)|
|initial inequality|||0.0131|0.0127|-0.0156|0.0247|
||||(0.0454)|(0.0487)|(0.0594)|(0.0453)|
|intial survey mean|||-0.003**|-0.003**|-0.003**|-0.003**|
|interval|||(0.00125)<br>0.001|(0.00139)<br>0.002|(0.00126)<br>0.001|(0.00123)<br>0.002|
||||(0.002)|(0.001)|(0.002)|(0.002)|
|Baseline: other<br>regions|||||||
|SSA||||0.0155|||
|||||(0.0113)|||
|gdp_change x|||||||
|SSA||||-0.499**|||
|||||(0.217)|||
|Initial inequality|||||1.091||
||||||(1.293)||
|Baseline: <2000|||||||
|2000-2010||||||0.00769|
|||||||(0.0125)|
|2011 and above||||||0.0183|
|||||||(0.0119)|
|gdp_change x|||||||
|2000-2010||||||0.543*|
|||||||(0.303)|
|2011 and above||||||0.167|
|||||||(0.293)|
|Baseline:<br>Consumption|||||||
|Income|||0.0319**|0.0351**|0.0318**|0.0355***|
||||(0.0132)|(0.0139)|(0.0134)|(0.0130)|
|Constant|-0.00495|0.00117|-0.00374|-0.0112|0.00911|-0.0203|
||(0.00436)|(0.00614)|(0.0194)|(0.0189)|(0.0265)|(0.0236)|
|country fixed effect|No|Yes|No|No|No|No|
|Observations|575|575|574|574|574|574|
|R-squared|0.203|0.532|0.227|0.234|0.228|0.257|

Notes: Table shows results of a regression of the annualized log change in survey mean consumption or income per capita on the annualized log change in GDP per capita. Sample consists of all consecutive spells between 1981 and 2021, dropping spells with baseline and endline poverty rates lower than 2 percent. Robust standard errors in brackets. ***: Statistically significant at 1%; **: Statistically significant at 5%; *: Statistically significant at 10%.

24
<mark>Ongena, S. and Smith, D.C., 2001. The duration of bank relationships. Journal of financial economics, 61(3), pp.449-475.</mark> Ornelas, J.R.H., Silva, M. S. and van Doornik, B. F. N., 2020, Informational Switching Costs, Bank Competition and the Cost of Finance. Central Bank of Brazil Working Paper Series N. 512.

<mark>Pazarbasioglu, Ceyla; Byskov, Steen; Bonomo, Marco Antonio Cesar; Carneiro, Igor Andre; Martins, Bruno; Azevedo Hernandez Perez, Adriana. (2017). Brazil - Financial intermediation costs and credit allocation. Discussion paper for workshop. Washington, D.C. World Bank Group.</mark> <mark>Petersen, M.A. and Rajan, R.G., 1994. The benefits of lending relationships: Evidence from small business data.</mark> _<mark>The journal of finance</mark>_ <mark>, 49(1), pp.3-37.</mark> <mark>Petersen, M.A. and Rajan, R.G., 1995. The effect of credit market competition on lending relationships. The Quarterly Journal of Economics, 110(2), pp.407-443.</mark> <mark>Rajan, R.G., 1992. Insiders and outsiders: The choice between informed and arm's‐length debt.</mark> _<mark>The Journal of finance</mark>_ <mark>, 47(4), pp.1367-1400.</mark> <mark>Rajan, R. and Zingales, L., 1998. Financial Dependence and Growth, American Economic Review.</mark> 88 (1998): 559-586 Ru, H., 2018. Government Credit, a Double‐Edged Sword: Evidence from the China Development Bank. _The Journal of Finance_ , 73(1), pp.275-316.

<mark>Sapienza, P., 2004. The effects of government ownership on bank lending.</mark> _<mark>Journal of financial economics</mark>_ <mark>,</mark> _<mark>72</mark>_ <mark>(2), pp.357-384.</mark> <mark>Sharpe, S.A., 1990. Asymmetric information, bank lending, and implicit contracts: A stylized model of customer relationships.</mark> _<mark>The journal of finance</mark>_ <mark>, 45(4), pp.1069-1087.</mark> <mark>Stiglitz, J.E. and Weiss, A., 1981. Credit rationing in markets with imperfect information.</mark> _<mark>The American Economic Review</mark>_ <mark>, 71(3), pp.393-410.</mark> <mark>Von Thadden, E.L., 2004. Asymmetric information, bank lending and implicit contracts: the winner's curse.</mark> _<mark>Finance Research Letters</mark>_ <mark>, 1(1), pp.11-23.</mark> <mark>Yasuda, A., 2005. Do bank relationships affect the firm’s underwriter choice in the corporate-bond underwriting market?</mark> _<mark>Journal of Finance</mark>_ <mark>60, 1259-1292.</mark> 18
i

# **_Summary_** _Findings_

Empirical research into key exchange rate issues in Sub-Saharan Africa (SSA) is limited when compared with that in developing countries as a whole. Further, SSA countries possess significant differences from other developing countries in such structural features as the composition of imports and exports, the level of industrialization and the development of the financial sector. Consequently, the pros and cons of particular exchange rate arrangements for such issues as output supply, are not conclusively established from the experience of other developing areas.

Two issues are targeted in this paper. First, SSA countries are distinguished in terms of the exchange rate regimes they have adopted, and the achievement of objectives of exchange rate policy is contrasted across countries. Second, the output supply response of real devaluations in developing countries as a whole is reviewed; then the analysis focuses on SSA countries, with special attention given to the supply response in agriculture.

The review of the literature is supplemented by empiricai testing. We used pooled timeseries and cross-section data for 22 countries in Sub-Saharan Africa for the period 1971-1991 to address the following questions: the dynamic relationship between nominal and real devaluations, the impact of real devaluations on per capita income, and the causal relationships and the direction of causality between devaluations, inflation, income and exports.

## Exchange Rate Regimes

Our review indicates that there has Deen a wide diversity of exchange rate regimes in Sub-Saharan Africa during the 1980s, with the trend towards more flexible regimes and a reduction in the size of parallel markets. The lessons drawn from the SSA -experience concerning the choice of exchange rate arrangement are the following:

- When macro economic policies are inconsistent and there is failure to adjust to adverse macroeconomic shocks, fixed regimes lead to overvaluation and the development of widespread parallel markets for foreign exchange. Through their negative macro-effects this in turn leads to inflation, instability and decline in output growth.

- * The exchange rate unification has been attempted by SSA countries through several means: occasional devaluations, a crawling peg, official dual markets, foreign exchange auctions, and a market pricing rule. Most exchange rate unification experiences have been gradual and their outcomes have been mixed. Country experiences show that the success in exchange rate unification (the case of Ghana and Uganda) depends on three crucial elements: supportive monetary and fiscal policy, external budgetary and balance of payments support, and a credible reform process to which the authorities are committed.

- In the presence of significant adverse real shocks (internal and external), built-in monetary and fiscal rules in fixed exchange rate regimes with currency convertibility (e.g. CFA zone) may not be adequate to bring about the required adjustment in the shortrun.
The remainder of the paper is organized as follows. Section II documents the evidence of a middleincome trap for MENA. Section III explores the link between technology adoption and economic growth. Section IV presents evidence of the relatively slow pace of technology adoption of GPT in MENA. Section V concludes.

# **II. Empirical Evidence for the Middle East’s Middle-Income Trap**

MENA countries are less likely to escape the middle-income trap than other countries around the globe. Figure 2 illustrates that by comparing levels of income reached in 1975 to the ones in 2017. We follow Bulman et al. (2017) in grouping countries into three relative income groups, namely low-income, middle-income and high-income depending on their GDP per capita relative to that of the United States in the same year.<sup>6</sup> Countries in the middle-left quadrant escaped the low-income group in 1975 and shifted to the middle-income group in 2017. Countries in the top-middle quadrant escaped the middle-income group and shifted to the high-income group. Countries in the center quadrant have been trapped in the middle-income group for more than four decades. Among MENA countries, aside from the six countries that have remained high-income, five have been trapped in the middle-income group (Algeria, Egypt, Jordan, Morocco, and Tunisia), three fell from the high-income to middle-income group (the Islamic Republic of Iran, Lebanon, and Libya), while none have become “escapees”. The Republic of Korea; Hong Kong SAR, China; Cyprus and Portugal have become escapees.

**Figure 2: Illustrating the Middle-Income Trap**

<!-- Start of picture text --> QAT<br>HKG SAU KWT ARE<br>OMNBHR<br>KOR CYP<br>PRT<br>LBN IRN<br>CHN DZA<br>IDNLKA TUNEGY JOR<br>LBY<br>BTN MAR<br>VNM INDLAO<br>0 1 2 3<br>log of real PPP GDP per capital relative to the U.S. in 1975<br>2.5<br>2<br>1.5<br>1<br>.5<br>0<br>log of real PPP GDP per capital relative to the U.S. in 2017<br><!-- End of picture text --> Sources: International Monetary Fund, World Economic Outlook database, and authors’ calculations. Note: Data labels use International Organization for Standardization (ISO) country codes. Regions follows World Bank country groups.

> 6 A country is defined as low-income if its per capita GDP is lower than or equal to 10 percent of that of the United States; middle-income if between 10 percent and 50 percent of U.S. GDP, and high-income if above 50 percent.

4