rafmacalaba/datause-encoder-data huggingface dataset:
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- [43] Maskin, Eric, Yingyi Qian, Chenggang Xu (2000). “Incentives, Information, and Organizational Form.” _Review of Economic Studies_ , 67(2), 359-378.

- [44] Megginson, William, Jefferey Netter (2000). “From State to Market: A Survey of Empirical Studies on Privatization.” _Journal of Economic Literature_ , 39, 321-389.

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- [46] Miquel, Gerard Padró I. (2007). “The Control of Politicians in Divided Societies: The Politics of Fear.” _Review of Economic Studies_ , 74(4), 1259-1274.

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56
welfare dynamics analysis is highly relevant and helps shed further light on the processes driving these changes. To our knowledge, this is the first time such analysis is being implemented in the context of the Arab countries.

One major obstacle that hinders the analysis of welfare dynamics in the Arab countries is the ubiquitous absence of panel household survey data. Even when such panel surveys exist, they are often plagued by data quality issues such as attrition bias due to the fact that some households drop out of the sample in follow-up survey rounds. We overcome the lack of actual panel data in the Arab countries by constructing synthetic panels from repeated cross sectional survey rounds using the methods developed in Dang et al. (2014) and Dang and Lanjouw (2013). These synthetic panels allow us to examine the movements among different categories of objective welfare, including the low-income, middle-income, and top-income groups, and subjective welfare, including the groups of the unhappy, struggling, and satisfied, with a particular focus on the Arab Spring countries. Furthermore, since these synthetic panels are constructed from fresher rounds of the repeated cross sections, they are (much) less affected by the issues discussed above.<sup>3</sup> Our findings highlight the value added of undertaking welfare dynamics analyses with synthetic panels using both objective expenditure data and subjective well-being data from micro survey data.<sup>4</sup> The results suggest strong upward mobility for objective welfare in Tunisia and the Syrian Arab Republic, and downward mobility in the Republic of Yemen and the Arab Republic of Egypt. However, subjective well-being has deteriorated in most countries prior to and after the Arab Spring (2009-2012). The share of dissatisfied people increased while those of the happier

> 3 In addition, the definition of “panel data” may itself vary for different contexts. For example, a panel may be defined based on whether the household head remains the same or whether the residence, where the data are collected, remains the same. Our analysis is not affected by this issue since we construct the synthetic panels for all countries using the same method.

> 4 On a macro level, the fact that happiness is observed not to increase as a country's income rises has long been known as the Easterlin paradox (see, e.g., Easterlin et al., 2010).

4
The payments mechanism also functioned well in Jaldhaka: 95 percent of the households received the payment transfer through the BPO mobile POS machines without any hassle. The processing time for the cash payments was affected by the large numbers of payments having to be made in one go – on average recipients had to wait 2.5 hours in queue. Even though households had the option of withdrawing their transfer amount at any time from the Upazila Post Office, to improve access by mostly poor mothers with limited access to public transport, the mobile withdrawal system was implemented. This significantly reduced the travel time to access the transfers: beneficiaries had to travel an average of 47 minutes to the BPO mobile set up whereas only about half of the households had access to the Post Office. Beneficiaries incurred a cost of about BDT 12 to receive their transfer.

# **Table** **<u>5:</u>** **_<u>Shombhob</u>_ Processes**

||**Narayanganj**|**Jaldhaka**|
|---|---|---|
|Households who knew about_Shombhob_(%)|90.2|88.8|
|Households knowledge of following eligibility criteria (%):|||
|- need to be poor|57.3|47.7|
|- need child aged 0-36 months|52.6|59.1|
|- need child going to primary school|42.7|51.4|
|Paid for picture (%)|46|61.4|
|Paid for school certificate (%)|12.6|29.4|
|Paid for birth certificate (%)|34.3|48.3|
|Paid for other documents (%)|3.6|6.5|
|No. of days needed to complete application|10|8.0|
|Amount paid for additional school certificate (BDT )|23.3|14.2|
|Travel time to collect the_Shombhob_cash transfer (minutes)|34.8|47.2|
|Waiting time to collect the_Shombhob_cash transfer (minutes)|131.9|154.4|
|Cost of collecting the_Shombhob_cash transfer (BDT )|38.2|12.3|
|Needed a new PIN over the last 12 months (%)|1.7|6.8|
|Knowledge ofpayment day (%)|95|90.9|

The data from Narayanganj show more or less similar performance in terms of these processes. While the time to collect the transfer was less, the costs associated with withdrawal were thrice the amount compared to beneficiaries in Jaldhaka. The costs of paying for school certificates were also higher in Narayanganj but less people had to pay for this service. It appears that mothers in Narayanganj were better able to manage the electronic payment process – four 24
To assess empirically the predictions of the model and the mechanisms it proposes, the paper employs data on Italian firms’ innovation activity during the periods 1998-2000 and 2001-2003. The data offer a particularly good ground to test the theory, as they provide detailed information about the types of innovations that firms decide to carry out. The theoretical findings are found to be robustly consistent with observed behavior.

# **Literature review**

This paper relates to the literature on competition, imitation, and innovation. Standard theories such as the one provided by Schumpeter[45], Dasgupta and Stiglitz[19], Spence[49], Romer[43], Aghion and Howitt[5], Grossman and Helpman[25], and Vives[53] highlight a negative relationship between innovation and competition. Others such as Sutton[51], Shaked and Sutton[47], and Motta[39] suggest instead a positive association; while another group of papers, including Aghion, Harris, and Vickers[3], Aghion, Harris, Howitt and Vickers[4], Aghion, Bloom, Blundell, Griffith, and Howitt[6], claim a non-monotonic relationship.

The empirical evidence continues to be mixed. Some empirical works by Porter[41], Geroski[23], Baily and Gersbach[8], Nickell[40], Blundell, Griffith and Van Reenen[12], and Symeonidis[52] support the view that competitive pressures encourage innovation. Others such as Aghion, Bloom, Blundell, Griffith, and Howitt[6] show that the relation is inverted U-shaped.

Regarding the impact of imitation on innovation, the results are also quite diverse. Some contributions, including Helpman[28], Glass and Saggi[26], and Dinopoulos and Sergerstrom[19], remark the potential disadvantages of strong IPRs on innovation. Others, such as Lai[35] and Yang and Maskus[54], highlight, by contrast, that intellectual property rights protection (IPRs) foster innovation and technology diffusion. Interestingly, all the papers rely on the same assumption, that firms innovate exclusively in one dimension i.e., process-innovation, product-innovation, or quality-innovation. However, the literature has been silent about the role competition and imitation play to bias technical change. This paper comes to fill this gap.

The paper is structured as follows. Section 2 presents the model. Section 3 solves the equilibrium of the model. Section 4 presents the empirical results.

3
<!-- Start of picture text --> Doing Business Global Economic Prospects<br># of entries # of entries<br>250 10<br>200 8<br>150 6<br>100 4<br>° Z<br>o it)<br>FY16 Fya7 Pyle Fis FY16 FY17 FYB FYi9<br>BASA MDB Country/Regional Profiles OGL Other BASS HE GL MRE @ Other<br>World Development Report<br># of entries<br>25<br>20<br>15<br>10<br>5<br>=<br>FYI6 FY1? FY18 FY1S<br>ASA BRA BWD & Other<br><!-- End of picture text -->
|**s Ababa**|%|8.4|31.2|52.5|44.5|2.6|0.4|26.6|98.0|66.6|99.5|Mean|4.3|3.1<br>1871.9|4727.9|4126.1|1.7||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Addi**|Obs|751|751|751<br>386|105|15|1|751|751|751|751|Obs|751|751<br>156|708|751|751||
|**s Salaam**|%|19.6|17.7|0|100|0|0|26.4|34.9|88.6|91.9|Mean|3.2|2.6<br>1072.2|1142.1|1077.9|2.0||
|**Dar e**|Obs|466|466|466<br>0|466|0|0|466|466|466|466|Obs|466|370<br>465|466|466|466||
|**nanarivo**|%|9.1|51.1|14.7|85.0|0|0.3|6.0|17.1|65.3|99.2|Mean|4.1|1.7<br>895.8|4081.6|1372.8|2.0||
|**Anta**|Obs|919|919|919<br>135|781|0|3|919|919|919|768|Obs|919|919<br>785|134|919|916||
|**ccra**|%|44.5|40.2|20.3|44.2|29.4|6.2|15.6|45.3|73.9|96.7|Mean|3.4|1.5<br>463.6|106.8|258.3|1.5||
|**A**|Obs|**Exposure to foods**<br>Households directly affected by food<br>996|Households that perceive food risk as high<br>996|**Dwelling tenure**<br>996<br>Owner<br>202|Rented<br>440|Free<br>293|Other<br>61|**Housing conditions**<br>Access to improved toilet facility<br>996|Access to improved drinking water<br>996|Access to collected solid waste<br>996|Lighting with electricity<br>996|Obs|Household size<br>996|Number of rooms<br>996<br>**Annual rent**<br>Rent (2017 PPP US$)<br>482|Self-estimated rent (2017 PPP US$)<br>979|Combined rent and self-estimated rent (2017 PPP US$)<br>996|<br>**Job density**<br>817|_Notes:_ Figures are weighted.|

10
|||**Depende**<br>|**Ta**<br>**nt Variabl**<br>|**ble 6: Ta**<br>**e: Prefer**<br>|**x Regressi**<br>**Informal**<br>|**ons**<br>**to Form**<br>|**al Finance**<br>|<br>|||
|---|---|---|---|---|---|---|---|---|---|---|
||All<br>Enterprises|Small<br>Enterprises|All<br>Enterprises|Small<br>Enterprises|All<br>Enterprises|Small<br>Enterprises|All<br>Enterprises|Small<br>Enterprises|All<br>Enterprises|Small<br>Enterprises|
|**Corruption**<br>**Regressions**|#1|#2|#3|#4|#5|#6|#7|#8|#9|#10|
||-0.002|-0.031|-0.002|-0.031|-0.002|-0.031|-0.002|-0.031|-0.002|-0.031|
|Size|[3.54]**|[4.29]**|[3.54]**|[4.30]**|[3.54]**|[4.27]**|[3.53]**|[4.38]**|[3.54]**|[4.28]**|
||-0.362|-0.318|-0.354|-0.306|-0.353|-0.301|-0.326|-0.29|-0.325|-0.285|
|Legal Status|[2.87]**|[2.31]*|[2.81]**|[2.22]*|[2.77]**|[2.14]*|[2.74]**|[2.12]*|[2.56]*|[2.06]*|
||0.009|0.011|||||||||
|Taxpay|[1.74]+|[1.90]+|||||||||
||||0.011|0.024|||||||
|Taxtime|||[2.57]*|[4.81]**|||||||
||||||0|0.002|||||
|Taxrate|||||[0.10]|[0.46]|||||
||||||||-0.009|-0.004|||
|Tax Report Sales|||||||[1.11]|[0.31]|||
||||||||||-0.023|-0.022|
|Tax Report Labor|||||||||[2.11]*|[1.75]+|
|Country FE|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|IndustryFE|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Observations|3564|2246|3564|2246|3564|2246|3511|2201|3527|2222|

Robust z statistics in brackets + significant at 10%; * significant at 5%; ** significant at 1% 27
# **8. References**

<u><mark>Abraham, K. G., Spletzer, J. R., & Harper, M. J. (2010). Introduction to Labor in the New Economy.</mark></u> _<mark>Labor in the New Economy</mark>_ <mark>(pp. 1-13). Chicago: University of Chicago Press.</mark>

- <u>Acemoglu, D. (2002). “Technical change, inequality, and the labor market.”</u> _Journal of Economic Literature_ , XL:7-72.

- <u>Acemoglu, D. and D. Autor (2010). “Skills, Tasks and Technologies: Implications for Employment</u> and Earnings,” No. 16082, National Bureau of Economic Research (NBER), Cambridge, MA.

- <u>Altonji J. and N. Williams (2005). “Do Wages Rise with Job Seniority? A Reassessment,”</u> _International Labor Review_ , 58(3): 370-397.

- <u>Arozamena L. and M. Centeno (2006). “Tenure, business cycle and the wage-setting process.”</u> _European Economic Review_ , 50: 401-424.

- <u>Arvanitis, S. and E. Loukis (2015). “Employee Education, Information and Communication</u> Technologies, Workplace Organization, and Trade: A Comparative Analysis of Greek and Swiss Firms.” _Industrial and Corporate Change_ 24(6): 1417–42.

- <u>Åslund, O. and D. Rooth (2007).</u> “Do when and where matter? Initial labor market conditions and immigrant earnings” _Economic Journal_ , 117(518): 422 – 448.

- <u>Auer, P., and Cazes, S. (2000). “Resilience of the Long-Term Employment Relationship: Evidence</u> from the Industrialized Countries.” _International Labor Review_ , 139: 379-389.

- <u>Autor, D., Dorn, D., and G. Hanson (2013). “The China Syndrome: Local Labor Market Effects of</u> Import Competition in the United States.” _American Economic Review_ , 103 (6): 2121–68.

- <u>Bachmann, R. and Felder, R. (2018). “Job stability in Europe over the cycle.”</u> _International Labour Review_ , 157(3): 481-518.

- <u>Bachmann, R., Felder R., Frings, H., Giesecke, M., and S. Rzepka, (2015)</u> “Job tenure in turbulent times,” Technical report, _Eurofound_ .

- <u>Bachmann, R., Bechara, P., Kramer, A., and S. Rzepka (2015). “Labour market dynamics and</u> worker heterogeneity during the great recession – Evidence from Europe,” _IZA Journal of European Labor Studies_ 4(19): 1–29.

- <u>Balsvik, R., Jensen, S., and K. Salvanes (2015). “Made in China, Sold in Norway: Local Labor</u> Market Effects of an Import Shock.” _Journal of Public Economics_ 127: 137-144.

- <u>Bardazzi, R. and M. Pazienza (2017). “Switch off the light, please! Energy use, aging population</u> and consumption habits,” _Energy Economics,_ 65: 161-171.

- <u>Bauer, T. and S. Bender (2004). “Technological Change, Organizational Change, and Job</u> Turnover.” _Labour Economics_ 11(3): 265–91.

- <u>Baum, Charles L. II (2022). “Seven jobs in a lifetime? An analysis of employee tenure.”</u> _Economic Inquiry_ 60 (2): 543-567.

- <u>Bell, D., and D. Blanchflower (2011a). “Young People and the Great Recession,” IZA Discussion</u> Paper 5674. _Institute for the Study of Labor (IZA)_ , Bonn

- <u>Bell, D. and D. Blanchflower (2011b).</u> “Youth unemployment in Europe and the United States.” _Nordic Economic Policy Review_ , _1_ (2011): 11-37.

25
the region. The relative difficulty of entry and exit, mandatory compliance with time consuming norms and regulations, excessive red tape and bureaucracy, unclear rules of the game, and a weak rule of the law, are among several governance-related factors that significantly increase the cost of doing business. The strong impact of an inefficient and burdensome regulatory environment is well documented (Guasch and Hahn, 1999, WDR 2005). The point of institutional quality and its effects on informality are also made by Loayza, Oviedo and Servén (2004).

That Latin America has burdensome regulation is illustrated a World Bank report on the investment climate (World Bank 2007). In that report, a majority of Latin American countries (about two-thirds) are below the institutional quality that income levels and levels of economic development predict for countries in the region. Latin America also performs poorly with regard to the Ease of Doing Business indicators produced by the _Doing Business_ data we discussed earlier. Latin America is the region with the largest number of both procedures (12) and days (66) required to start a new business. This compares with 9 procedures and 56 days in East Asia, or 7 procedures and 24 days in high income countries. Africa performs better than Latin America with respect to the number of days (11) and procedures (64) required to start a business. Latin America also scores poorly when we focus on the time required to enforce contracts – 470 days, compared to 423 in East Asia and 282 in high income countries. Similarly, according to the IMF Latin America is the region with the highest (i) total number of taxes paid by businesses (49); (ii) number of hours per year needed to prepare, fill an pay taxes (549); and (iii) taxes paid as a percentage of profit (54.5 percent). This compares to 40 taxes, 398 hours and 48.5 percent of profits in the average low and middle income country, and 18 taxes, 181 hours and 38.8 percent of profits in the average high income country.

Whether these regional conditions hold at the country level is the focus of the next section.

# **2.1 Is informal competition a serious obstacle in all countries in Latin America?**

Though informality is prevalent in Latin America, the countries in which firms cite informal competition as the most serious obstacle are a surprise. For Uruguay, Colombia, Paraguay, and Peru, competition from the informal firms is reported as the top obstacle. This is surprising since there is little correlation between the estimated size of the informal sector and whether formal firms identify informal competition as an obstacle to doing business. In a recent IMF study that estimates the size of the informal economy in most economies of Latin America, Vuletin (2006) finds that Paraguay, Nicaragua and Honduras have the largest informal
**Table A.4:   Including population density from 2011 and proportion of Muslims as additional** **<u>controls</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. var.|ln(Landgini, 2012)|ln(Landless, 2012)|ln(Tech use, 2012)|ln(Land Sale, 2012)|
|**ln(MA1996)**|**0.273*****|**0.691*****|**0.327****|**-0.162**|
||**(0.06)**|**(0.15)**|**(0.13)**|**(2.37)**|
|ln(Slope)|-0.012|-0.003|-0.048|-1.318|
||(0.03)|(0.09)|(0.05)|(1.24)|
|Elevation|-0.217|-0.009|0.267|4.322|
||(0.15)|(0.40)|(0.24)|(5.27)|
|Crop Suitability|-0.003**|-0.007**|-0.001|-0.003|
||(0.00)|(0.00)|(0.00)|(0.05)|
|ln(Rain)|0.042|0.160|0.036|-0.874|
||(0.06)|(0.16)|(0.09)|(2.21)|
|ln(Temperature)|-0.478|0.083|0.774|4.327|
||(0.44)|(1.04)|(0.69)|(15.09)|
|ln(Rain CV)|0.166|0.371|0.333|8.771|
||(0.14)|(0.41)|(0.25)|(5.80)|
|ln(Tem seasonalit)|-0.012|-0.015|-0.073|-5.132|
|p. y|(0.08)|(0.20)|(0.11)|(3.24)|
|Pop. density, 1961|0.335***|0.716*|0.145|-9.044*|
||(0.12)|(0.37)|(0.25)|(4.78)|
|Pop. density, 2011|-0.172***|-0.373*|-0.081|4.828*|
||(0.07)|(0.20)|(0.13)|(2.59)|
|Muslim prop.|0.251***|0.989***|0.529***|-4.683*|
||(0.10)|(0.25)|(0.14)|(2.82)|
|Constant|-2.566|-14.956**|-10.655***|-3.339|
||(2.53)|(6.11)|(3.76)|(89.29)|
|First Stage|||||
|ln(dist. to GQ) x ln(Slope)|-0.101***|-0.102***|-0.102***|-0.102***|
||(0.04)|(0.03)|(0.03)|(0.03)|
|ln(dist. to GQ) x Elevation|0.067|0.049|0.049|0.049|
||(0.17)|(0.16)|(0.16)|(0.16)|
|ln(dist. to GQ) x ln(Slope)|0.002***|0.001***|0.001***|0.001***|
||(0.00)|(0.00)|(0.00)|(0.00)|
|Weak identification|8.02|10.66|10.66|10.66|
||0.0000|0.0000|0.0000|0.0000|
|Observations|200|212|212|212|
|R-squared|0.2310|0.4024|0.5478|0.1567|

Note: All columns are estimated by 2SLS and include state fixed effects. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
# **<u>Table A3. Financial Integration and the Sensitivity of Consumption to Income:</u>** **_<u>De Facto</u>_** **<u>Measures</u>**

# **_All Countries_**

||**Total**|**Assets**|**Total Li**|**abilities**|**FDI**|**Assets**|**FDI Li**|**abilities**|
|---|---|---|---|---|---|---|---|---|
||Chinn-Ito|Quinn|Chinn-Ito|Quinn|Chinn-Ito|Quinn|Chinn-Ito|Quinn|
|Output|0.926<br>[0.011]***|1.086<br>[0.025]***|0.934<br>[0.016]***|1.123<br>[0.014]***|0.961<br>[0.018]***|1.103<br>[0.028]***|0.948<br>[0.009]***|1.086<br>[0.017]***|
|Output x<br>_De Jure_|**-0.305**<br>[0.012]***|**-0.568**<br>[0.044]***|**-0.238**<br>[0.025]***|**-0.577**<br>[0.023]***|**-0.300**<br>[0.033]***|**-0.498**<br>[0.047]***|**-0.261**<br>[0.025]***|**-0.576**<br>[0.022]***|
|Output x<br>_De Facto_|0.071<br>[0.002]***|0.061<br>[0.004]***|-0.011<br>[0.002]***|-0.006<br>[0.002]***|-0.041<br>[0.006]***|0.085<br>[0.036]**|0.041<br>[0.012]***|0.196<br>[0.022]***|
|Observations<br>Countries|3,517<br>88|3,294<br>85|3,519<br>88|3,300<br>85|3,507<br>88|3,289<br>85|3,526<br>88|3,308<br>85|
||**Equity**|**Assets**|**Equity L**|**iabilities**|**Debt**|**Assets**|**Debt Li**|**abilities**|
||Chinn-Ito|Quinn|Chinn-Ito|Quinn|Chinn-Ito|Quinn|Chinn-Ito|Quinn|
|Output|0.954<br>[0.012]***|1.117<br>[0.019]***|0.960<br>[0.018]***|1.137<br>[0.030]***|0.918<br>[0.007]***|1.107<br>[0.026]***|0.911<br>[0.009]***|1.133<br>[0.018]***|
|Output x<br>_De Jure_|**-0.267**<br>[0.020]***|**-0.510**<br>[0.023]***|**-0.310**<br>[0.024]***|**-0.584**<br>[0.046]***|**-0.259**<br>[0.021]***|**-0.628**<br>[0.041]***|**-0.165**<br>[0.016]***|**-0.576**<br>[0.031]***|
|Output x<br>_De Facto_|-0.020<br>[0.003]***|-0.017<br>[0.005]***|0.049<br>[0.005]***|0.134<br>[0.006]***|0.097<br>[0.004]***|0.104<br>[0.004]***|-0.025<br>[0.005]***|-0.045<br>[0.005]***|
|Observations|3,473|3,255|3,490|3,271|3,523|3,300|3,525|3,306|
|Countries|88|85|88|85|88|85|88|85|
|||**_Advanced_**|**_Economies_**|||**_Developing_**|**_Economie_**|**_s_**|
||**Total**|**Assets**|**Total L**|**iabilities**|**Total**|**Assets**|**Total L**|**iabilities**|
||Chinn-Ito|Quinn|Chinn-Ito|Quinn|Chinn-Ito|Quinn|Chinn-Ito|Quinn|
|Output|0.926<br>[0.415]**|1.388<br>[0.546]**|1.041<br>[0.098]***|0.966<br>[0.079]***|0.870<br>[0.016]***|1.134<br>[0.056]***|0.910<br>[0.016]***|1.226<br>[0.036]***|
|Output x|-0.256|-0.706|**-0.431**|**-0.288**|**-0.318**|**-0.801**|**-0.185**|**-0.674**|
|_De Jure_|[0.428]|[0.572]|[0.093]***|[0.106]***|[0.017]***|[0.123]***|[0.050]***|[0.058]***|
|Output x|0.022|0.008|0.032|0.009|0.119|0.179|-0.030|-0.088|
|_De Facto_|[0.007]***|[0.006]|[0.006]***|[0.007]|[0.009]***|[0.026]***|[0.013]**|[0.013]***|
|Observations|890|918|890|918|2,627|2,376|2,629|2,382|
|Countries|22|22|22|22|66|63|66|63|

Note: Dependent variable is consumption growth. GMM Dynamic Panel Estimates. Lags 2-4 of consumption growth, all right hand side variables and lagged saving are used as instruments. De Jure refers to financial integration measures of Chinn-Ito and Quinn. _De Facto_ measures refer to financial integration measures such as stocks of assets and liabilities from Lane and Milesi-Ferretti dataset.

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