rafmacalaba/datause-encoder-data huggingface dataset:
The estimation results from regression specification (3), which examines the potential heterogeneous effect of the invasion based on firm-level digitization, are presented in Table 6. The findings reconfirm that firms engaged in exports to Russia before the invasion experienced higher sales throughout the studied period. They also reconfirm that the invasion had a more pronounced negative effect on the sales of exporting firms to Russia compared to other firms. In addition, the estimated coefficients for the impact of digitalization on sales and employment are positive and statistically significant. This suggests that firms that had adopted digital solutions before the invasion exhibited higher average sales and employment during the studied period, with approximately 41 log points higher sales and 24 log points higher employment. The estimated coefficient for the mitigating effect of digitization concerning the invasion impact is insignificant. This could be due to the overall lack of significance in the invasion effect across different types of firms (the average effect in Table 3) or the insignificant or possibly slightly positive effect of the invasion on firms that did not export to Russia before the invasion.

**Table 6: Can Digitization Gains be Associated with Varying Effects of Russia’s Invasion of Ukraine on Firms?**

||(1)<br> **Sales**|(2)<br> **Employment**|
|---|---|---|
|Affected by the invasion|-0.115|0.083|
||(0.329)|(0.106)|
|Export to Russia|1.370***|0.001|
||(0.339)|(0.167)|
|Use of digital solutions pre-invasion|0.389*|0.251***|
||(0.201)|(0.074)|
|Invasion x Export to Russia|-1.800***|-1.241**|
||(0.547)|(0.540)|
|Invasion x Digital|-0.110|0.158|
||(0.415)|(0.222)|
|Manufacturing|0.621**|0.053|
||(0.283)|(0.082)|
|Retail|0.675**|-0.157*|
||(0.269)|(0.086)|
|Other Services|-0.143|-0.036|
||(0.248)|(0.085)|
|Small (5-19)|1.371***|1.075***|
||(0.145)|(0.037)|
|Medium (20-99)|1.970***|2.244***|
||(0.160)|(0.051)|
|Large (100+)|4.515***|4.004***|
||(0.659)|(0.291)|
|Constant|11.406***|1.157***|
||(0.319)|(0.085)|
|Observations|1145|1761|
|R-squared|0.791|0.734|
|_Robust standard errors are in parentheses._|||
|_*** p<.01, ** p<.05, * p<.1_|||

12
The Mexican Pension Annuity Market

**Table 4: Pension Annuity Providers and 2005 Market Shares (Percentage of Market**

**Premium)**

|**Company**|**Market share**|
|---|---|
|Pensiones BBVA Bancomer, S.A. de C.V., Grupo Financiero BBVA Bancomer 1/ 2/|19.7|
|Pensiones Inbursa, S.A., Grupo Financiero Inbursa 2/|17.0|
|Profuturo GNP Pensiones, S.A. de C.V.|15.6|
|Pensiones Banamex, S.A. de C.V., Grupo Financiero Banamex 1/ 2/|10.5|
|Pensiones Banorte Generali, S.A. de C.V., Grupo Financiero Banorte 2/|9.0|
|HSBC Pensiones, SA. 1/|6.8|
|Pensiones Comercial América, S.A. de C.V.|6.6|
|Metlife Pensiones México, S.A. 1/|6.1|
|Principal Pensiones, S.A. de C.V. 1/|4.4|
|Allianz Rentas Vitalicias, S.A. 1/|2.9|
|Royal & SunAlliance Pensiones(México),S.A. de C.V. 1/|1.4|
|Total|100.0|

**Note** : 1/ Foreign subsidiary; 2/ Belonging to a financial group. **Source** : CNSF

The structure of the market changed in the first nine years of operation in two main ways. Initially, the 1997 LGISMS reform stated that insurance companies licensed to practice life insurance were allowed to offer pension annuities for a five years transition period. After that period, those insurance companies would have had to separate annuities operations in specialized pension annuity companies. The 1997 LGISMS also established that, during this transitional period new insurance companies that would like to offer pension annuities would be licensed as specialized insurers. While in 1997 50 percent of the companies licensed where composite companies, after 2002 only specialized companies were left operating in the market. The rationale for the choice of specialized annuity providers was, on one hand, to avoid the contagion effect from composite life and non-life insurance companies operating annuities, and on the other hand, the implicit government guarantee associated with a benefit provision for which participation was mandated by the federal government. Notwithstanding the political economy argument of the choice, general life insurance companies would have been better placed to hedge the systemic improvements in longevity risk in the annuitant portfolio with the mortality risk in the pure life portfolio.

Another way in which market changed over the year is reflected by the increased importance of subsidiaries (especially foreign) and a decreased importance of financial groups. The following table summarizes the changes in market structured that took place since the SAR-97 reform.

**Table 5: Evolution of Market Structure (1997–2005)**

||**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|
|---|---|---|---|---|---|---|---|---|---|
|Foreign Subsidiaries|4|4|5|5|5|7|6|7|7|
|Local Financial Groups|6|5|5|5|5|3|2|2|2|
|Other|3|4|4|4|4|3|3|2|2|
|CR5(percent)|85.3|77.0|73.7|71.4|72.2|72.0|72.5|72.9|71.8|

**Source** : CNSF

- 5 -
service activities. Deficient electricity networks, plagued by frequent power outages and unstable voltage, may induce high costs and even deter some types of investments.

Indeed, people living in developing countries are well aware of how infrastructure shortcomings affect many aspects of their daily life and work.<sup>3</sup> Tap water that is available only a few hours a day, frequent power outages causing the breakdown of home appliances and machinery, communities that find themselves isolated each time it rains, frequently collapsed bridges, newly constructed roads already full of potholes, expensive mobile phone services as the only option when the hope of getting a fixed line installed is a distant and costly dream, are all common stories in these countries. At the entrepreneurial level, in surveys assessing the investment climate, businesses usually rank deficient infrastructure as an important barrier to their operation and growth. For example, the World Bank investment climate assessment (ICAs) indicate that a large proportion of respondents (between 20% in East Asia and the Pacific, and 55% in the Middle East and North Africa, as well as Latin America) view any of electricity, telecommunications or transport as a major or severe obstacle to doing business.<sup>4</sup> Similarly, 33% of Japanese firms operating in Vietnam consider poor infrastructure as the major obstacle to their business (ADB, World Bank, JICA, 2005). Recently, Escribano and Guasch (2005) have developed an econometric methodology to assess the impact of investment climate assessments (ICAs) variables on firm-level productivity. Applying it to samples from Guatemala, Honduras and Nicaragua (Escribano and Guasch, 2006), they find infrastructure to have a major impact on productivity. For example, they find a one percent increase in the average duration of power outages to decrease productivity by between 0.02 and 0.1%, while access to internet increases productivity by between 11 and 15%.

In this context, there is therefore an intuitive presumption that infrastructure levels and quality matter for firms’ productivity and growth, and that a large part of the output and

> 3 See for example the World Bank World Development Indicators and Briceño-Garmendia, Estache and Shafik (2004) for statistics on access around the world.

> 4 See <u>http://rru.worldbank.org/investmentclimate/, last visited on March 21, 2006.</u>

4
**are** sold. Thus, Chile should not be expected to have any major privatization spurts in the near future, but the next government in early 1994 might initiate a new privatization program.

## Honduras

In Central America, only Honduras appears to have a privatization program of substantial size. The program was started in August of 1986 as response to the disappointing performance of SOEs which were created primarily during the mid-1970s. During the period 1988-92, a total of 29 companies was privatized for $61.5 million in revenues.<sup>35</sup> A debt-equity swap program is in place for privatization purposes, and, based on official government sources, the external debt has been reduced by about $30 million since the inception of the program. While the Callejas administration is a strong proponent of privatization, no major transactions in infrastructural sectors have been undertaken as of yet, but some are scheduled for 1993. The program is generally open to foreign investors, but the interest has not been particularly strong; only five transactions involving foreigners have been recorded, having generated $13 million in foreign exchange.

# .'eru

Over the past two decades, government intervention in the economy steadily increased, and in the last few years, the state's 130 companies ran losses of an accumulated annual $2 billion.<sup>36</sup> In late 1991, the Fujimori Government established the legaVlorganizational framework for a broad privatization program. It is the Government's objective to privatize all state holdings by mid-1995. The program commenced in 1992 with the sale of 10 holdings for US$207 million, including Hierro Peru which was sold to the Chinese Shougang Corporation for $120 million with an additional $140 million in investment ccwimitments. In January of 1993, the government continued its privatization efforts by selling 70 percent of its national airline Aeroperu to Aeromexico for $54 million.

In 1993-94, the Government expects to sell its major holdings in mining (Minero Perui and Centromin), petroleum (Petroperui and its subsidiaries), telecommunications, electricity, banking and manufacturing. It also expects to grant a concession to a private investor for operation of the Lima water supply. Proceeds are expected to be US$1.2 billion annually.

## Mexico

Mexico began its privatization program in 1983 under President Miguel de la Madrid, who disincorporated about 600 small SOEs until mid-1988. Most of these companies were, however, liquidated and only about 150 were actually sold for about $500 million.<sup>37</sup> Under President Salinas, who came to office in December 1988, the process of privatizing

> **35** Four additional privatizations were only small-scale with marginal asset value and were therefore not included in the data set.

> 36 LatinFinance Supplement (March 1993), p.95.

> **37** Clutterbuck (1991), p.<sup>64</sup> .

35
**Table 3. Business entry and the overall level of regulation**

|VARIABLES|(1)<br>newbiz|(2)<br>newbiz|(3)<br>newbiz|(4)<br>newbiz|(5)<br>newbiz|(6)<br>newbiz|(7)<br>newbiz|(8)<br>newbiz|
|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|1.848**|1.859**|1.740**|1.836**|1.807**|1.465*|1.559*|0.775|
||(2.251)|(2.258)|(2.289)|(2.352)|(2.182)|(1.693)|(1.676)|(0.863)|
|FDI||-0.00261|-0.00365|-0.00372|-0.00410|-0.00409|-0.00329|0.00270|
|Political freedom and civil||(-0.779)|(-1.181)|(-1.191)|(-1.300)|(-1.298)|(-1.016)|(1.041)|
|<br>rights (FH)|||0.200|0.217|0.274|0.287|0.363|0.391*|
||||(1.098)|(1.178)|(1.331)|(1.391)|(1.510)|(1.920)|
|Trade (X and M)||||0.00589|0.00580|0.00549|0.00746|0.00109|
|||||(1.138)|(1.037)|(0.981)|(1.173)|(0.181)|
|Corruption (CPI)|||||0.0608|0.0559|-0.0607|-0.0283|
||||||(0.425)|(0.391)|(-0.339)|(-0.171)|
|Lagged GDP growth||||||0.0364|0.0410|0.0503*|
|||||||(1.358)|(1.268)|(1.747)|
|Country risk (ICRG)|||||||0.0132|0.0166|
||||||||(0.334)|(0.465)|
|Credit to private sector||||||||0.0184***<br>(5.327)|
|DTF|0.0449**|0.0447**|0.0454**|0.0448**|0.0441*|0.0465**|0.0534*|0.0624**|
||(1.988)|(1.976)|(2.153)|(2.072)|(1.930)|(2.029)|(1.765)|(2.247)|
|Constant|-16.08**|-16.16**|-15.77**|-17.16**|-17.29**|-14.50*|-16.40**|-10.94|
||(-2.269)|(-2.273)|(-2.406)|(-2.522)|(-2.406)|(-1.942)|(-1.991)|(-1.387)|
|Observations|832|829|821|808|782|781|646|548|
|R-squared|0.039|0.040|0.049|0.051|0.053|0.056|0.063|0.153|
|Numberofcod2|123|123|122|121|120|119|94|93|
|t-statistics in parentheses|||||||||
|*** p<0.01, ** p<0.05, * p<0.1|||||||||

**Notes:** ***(**)(*) denotes significance at the 1(5)(10) percent level. Standard errors reported in parentheses. This table reports results from OLS panel regressions of new business creation on the distance to the overall regulatory frontier. Business creation is defined as number of new businesses per 1,000 people in working-age. Country fixed effects are included in order to capture unobserved between-country differences. The estimations reported include controls for lagged GDP per capita growth, per-capita GDP, trade openness, corruption, credit to the private sector, FDI inflow, economic and political risk as well as civil freedoms. All specifications include time dummies to control for any global changes in the macroeconomic environment.

19
**_<u>________________________________________________________</u>_**

# **Selected  Bibliography**

Amjadi and Yeats. 1995. _Have Transportation Costs Contributed to the Relative Decline of Sub-Saharan African Exports? Some Preliminary Evidence._ World Bank Working Paper 1559, The World Bank, Washington, D.C. APEC (Asia Pacific Economic Cooperation). September. 1999. _Assessing APEC Trade Liberalization and Facilitation_ . Update, Economic Committee. Bangkok APEC Conference.

APEC (Asia-Pacific Economic Co-operation), 2002. _Measuring the Impact of APEC Trade Facilitation on APEC Economies: A CGE Analysis_ . APEC Economic Committee, Bangkok.

Batra, G., D. Kaufmann, and A.H.W. Stone, 2003. _Investment Climate Around the World: Voices of the Firms from the World Business Environment Survey_ . The World Bank, Washington, D.C.: Carlos T. de Castro. 1993 _._ _<u>Trade and Transport Logistics: Facilitation Guidelines</u>_ <u>, SSATP Working</u> Paper No. 4, The World Bank, Washington, D.C.

Clark, X, D. Dollar and A. Micco. 2001. February. _Maritime Transport Costs and Port Efficiency_ . Working Paper No. 2731,: The World Bank, Washington, D.C.

DFAT (Australia’s Department of Foreign Affairs and Trade), 2001. _Paperless Trading Benefit to APEC, the Potential of the APEC Paperless Trading Initiative._ Working Paper, Australian Department of Foreign Affairs and Trade, Canberra.

Estache, Antonio. 1999. _Privatization and Regulation of Transport Infrastructure in the 1990s: Successes and Bugs to Fix for the Next Millennium._ Mimeo. The World Bank, Washington, D.C.(http://www.worldbank.org/wbi/regulation/pdfs/2248bugstofix.pdf) Fink, Carsten, Aaditya Mattoo, and Ileana Cristina Neagu. 2002a. _Trade in International Maritime Services: How Much Does Policy Matter?_ World Bank Economic Review 16(1): 81–108.

———. 2002b. _Assessing the Impact of Communication Cost on International Trade._ World Bank Working Paper 2929, The World Bank, Washington, D.C.

Fourgeaud, Patrick. 2000. November. _Measuring Port Performance_ . The World Bank, Washington, D.C. <u>(http://www.worldbank.org/html/fpd/transport/ports/con_docs/fourgeau.pdf)</u> Francois, J., H. van Meil, and F. van Tongeren, 2003. _Economic Benefits of the Doha Round for the Netherlands._ Project Report, Agricultural Economics Research Institute, The Hague.

Freund and Weinhold. 2000. _On the Effect of the Internet on International Trade_ . Board of Governors of the Federal Reserve System International Discussion Paper 693.

Gannon, Colin A. and Z. Shalizi. 1994. _Use of Sectoral and Project Performance Indicators in Bank financed Transport Operations_ World Bank Discussion Paper, The World Bank, Washington, D.C. (http://www.worldbank.org/transport/publicat/twu_21.pdf) Gausch and Kogan. 2001. _Inventories in Developing Countries: Levels and Determinants, a Red Flag on Competitiveness and Growth_ . World Bank Working Paper 2552, The World Bank, Washington, D.C.

66 __________________________________________________________________________________________________________
ensure that baseline and endline exam scores could be compared on a common scale. Placing the baseline and endline exam scores on a common scale allows us to measure absolute gains (or losses) in learning from the start of grade ten until the end of grade ten.

We administered and closely proctored the standardized computer and math exams during the baseline (October 2011) and endline surveys (May 2012). The exam scores were then normalized into z-scores (for computers and math separately and for the baseline and endline exams separately) by subtracting the mean and dividing by the standard deviation (SD) of the exam score distribution.<sup>7</sup> In addition to gathering data on our outcome variables, our survey included three blocks pertaining to student background characteristics. The first block asked students to report their gender, age, whether their household registration ( _urban_ ) status was rural or urban, and whether they had migrated before. As a part of this block, we also asked students to report their HSEE scores, the year they took the examination, and the prefecture where they took the examination.<sup>8</sup> The second block gathered information on students’ families. This block included parental education level (a dummy indicator equal to 1 if neither parent finished junior high and

> 7 Although it is standard in education studies, we did not implement a reading test because principals and local education administrators were concerned that the time of the survey would be too long. Likewise, our Institutional Review Board was concerned that we would take too much instructional time away from our respondents by adding a third test.

> 8 Students are unlikely to suffer from recall bias when reporting on their HSEE scores because they received their scores only two months before the time of the survey. Granted, it is possible that vocational students remembered their scores as being lower than they actually were precisely because they ended up in vocational school. However, this would bias the results toward finding a positive effect of vocational school. As such, it would not challenge the findings of the paper.
To measure infrastructure, we have taken data from the World Development Indicators database. This includes data on the percentage of paved roads out of total roads, on the number of fixed and mobile telephone subscribers (per 1,000 people), on the number of telephone mainlines (per 1,000 people), on telephone mainlines in largest city (per 1,000 people), telephone mainlines per employee, mobile phones (per 1,000 people), and freight of air transport (million tons per km). Interpolation is used for years where no data are available.

Since both sets of indexes are highly correlated, we have used principal component analysis to produce a set of summary indexes. The results are reported in Table 1. Ideally, principal component analysis identifies patterns in data and based on these patterns it reduces the number of dimensions of the data without a lot of loss of information. From the results in Table 1, we take the first two components to produce four indexes; two institutional indexes, and two infrastructure indexes. These reflect between 70 percent and 77 percent of variation in the sample. From the weighting factors in the table, we interpret the first infrastructure index as measuring communications, and the second the physical transport system. We interpret the first institutional index as measuring general correspondence with the market-oriented legal and institutional orientation flagged by the Fraser indexes (in a sense the correspondence to the Anglo-U.S. socioeconomic model). The second institutional index then measures less interventionist systems with lower taxes and more market friendly regulations (deviations toward the Anglo-US social model).

9
Duranton, Gilles, and Diego Puga. 2020. "The Economics of Urban Density." Journal of Economic Perspectives, 34 (3), 3-26.

- Duranton, Gilles, Diego Puga. 2004. Micro-Foundations of Urban Agglomeration Economies, ch 48 in J. Vernon Henderson, Jacques-François Thisse eds, Handbook of Regional and Urban Economics, 4, 2063-2117

- Eslava, Marcela and John Haltiwanger. 2021. “The Size and Life-Cycle Growth of Plants: The Role of Productivity, Demand and Wedges,” NBER Working Paper, 27184.

- Faberman, R.J. and Freedman, M., 2016. The urban density premium across establishments. Journal of Urban Economics, 93, pp.71-84.

- Feenstra, Robert C., “New Product Varieties and the Measurement of International Prices," American Economic Review, 84 (1994), 157-177

- Foster, Lucia, John Haltiwanger, and Chad Syverson. 2008. "Reallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability?" American Economic Review, 98 (1): 394-425.

- Gandhi, A., S. Navarro, and D. A. Rivers. 2020. “On the Identification of Gross Output Production

Functions.” Journal of Political Economy 128 (8): 2973–3016.

Glaeser, Edward L. and Matthew G. Resseger, 2010. The Complementarity Between Cities and Skills, Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 221-244, February.

Glaeser, E. L., and D. C. Maré. 2001: Cities and Skills, Journal of Labor Economics, 19 (2), pp. 316--342.

Gollin, Douglas, Remi Jedwab, and Dietrich Vollrath. 2016. "Urbanization with and without industrialization." Journal of Economic Growth 21, 1, 35-70.

Grover, A., S. V. Lall, and J. D. Timmis. 2021. “Agglomeration Economies in Developing Countries: A MetaAnalysis.” Policy Research Working Paper 9730, World Bank, Washington, DC.

- Handbury, J., and D. Weinstein. 2015. “Goods Prices and Availability in Cities.” Review of Economic Studies 82 (1): 258–96.

Henderson, J. V. 2003. Marshall's scale economies. Journal of Urban Economics, 53, pp. 1–28.

- Henderson, J. Vernon, Nigmatulina, Dzhamilya and Kriticos, Sebastian.2019. “Measuring urban economic density”. Journal of Urban Economics. In press.

- Henderson, J. V., and M. A. Turner. 2020. “Urbanization in the Developing World: Too Early or Too Slow?” Journal of Economic Perspectives 34 (3): 150–73.

- Holl, A., 2016. Highways and productivity in manufacturing firms. Journal of Urban Economics, 93, pp.131151.

Hommann, K., and S. V. Lall. 2019. Which Way to Livable and Productive Cities? A Road Map for Sub-Saharan Africa. International Development in Focus. Washington, DC: World Bank Group.

Klette, Tor Jacob and Zvi Griliches. 1996. “The Inconsistency of Common Scale Estimators When Output Prices are Unobserved and Endogenous.” Journal of Applied Econometrics 11 (4):343– 361.

15
Table 4: Vietnam: Earnings Functions

|(dependent variable i|s the natural log of m|onthly earnings)||
|---|---|---|---|
|Variable|All|Males|Females|
|Constant|0.775|1.045|0.583|
|Years of schooling|0.048|0.034|0.068|
||(8.4)|(4.5)|(7.6)|
|Years of experience|0.064|.0.059|0.065|
||(10.1)|(6.9)|(6.8)|
|Experience-squared|-0.001|-0.001|-0.001|
||(8.3)|(5.6)|(5.5)|
|Log hours-worked|0.726|0.722|0.697|
||(11.7)|(9. 1)|(7.1)|
|R-squared|0.112|0.095|0.137|
|N|2,259|1,355|904|

Source: VLSS 1992-93 Note: t-statistics in parentheses When earnings functions are estimated separately, males experience lower returns to schooling than do females. This is similar to the pattern found in China in the mid 1980s (Jamison and van der Gaag 1987). In Vietnam, males receive a 3 percent increase in earnings for every year of schooling; females experience a 7 percent increase.

The estimates for Vietnam are low compared with the returns to education estimated for other developing countries. Worldwide, another year of schooling increases earnings by about 10 percent (Psacharopoulos 1994). But estimated returns to schooling are low in general in centrally planned and transition economies. For example, in China the estimates of the returns to schooling ranged from 1 to 5 percent in the mid 1980s (Jamison and van der Gaag 1987; Byron and Manolato 1990; Meng 1995), and 3 percent in 1989 (Xie and Hannum 1996). Gregory and Meng (1995), in a very useful analysis of rural industrial data, disaggregated their sample into two groups: those who were allocated their jobs and those who found them on their own. Returns to 14