rafmacalaba/datause-encoder-data huggingface dataset:
sample before their transition (i.e., 4.448). Moreover, the results of the models that include trade openness are in line with Acemoglu’s (2003) findings, which indicate that an increase in international trade leads to higher returns as a result of its positive association with labor demand and skilled-biased technological change.

|**Tabl**|(1)<br>**e 4: Impact o**|(2)<br>**f transition on**|(3)<br>**all sample cou**|(4)<br>**ntries**|
|---|---|---|---|---|
|DiD|3.172***|1.974***|1.953***|1.621***|
||(0.523)|(0.559)|(0.463)|(0.542)|
|Transition|-3.712***|-2.041***|-2.478***|-1.861**|
||(0.667)|(0.789)|(0.688)|(0.854)|
|Post|0.304|0.480|0.449|0.510|
||(0.502)|(0.502)|(0.524)|(0.520)|
|Schooling|0.131|0.017|-0.094|-0.152|
||(0.174)|(0.178)|(0.202)|(0.213)|
|Polity||0.136***||0.083|
|||(0.041)||(0.053)|
|Trade|||0.017***<br>(0.006)|0.018***<br>(0.006)|
|Constant|6.855***|6.487***|7.341***|7.015***|
||(1.426)|(1.322)|(1.407)|(1.368)|
|Prob > chi2|0.000|0.000|0.000|0.000|
|Observations|219|216|199|196|
|Groups<br>Robust standard<br>*** p<0.01, ** p|40<br>errors in parenth<br><0.05, * p<0.1|39<br>esis|40|39|

In conclusion, this analysis has unveiled that transition from a centrally planned economy to a market economy exhibits a positive and significant effect on the returns to education in postsocialist countries. Although post-socialist countries continued to increase their average years of schooling after 1992, schooling kept exerting a positive and significant effect on the returns of these countries. This might be explained by commercial openness, which also showed a positive and significant effect on the returns of post-socialist countries suggesting that trade might have driven additional demand for more educated human capital.

The transition induced by the fall of the Soviet Union was not the only major economic and political change undergone by post-socialist economies. Thirteen years later, a few of them joined the EU, for which they needed to follow a series of economic, political, and institutional reforms that might have affected their returns to education years after their transition out of a centralized economy. To unveil if the EU membership affected the returns of the post-socialist countries that joined it, we do a separate analysis also using the DiD method.

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_Second,_ Kenya provides an interesting ‘laboratory’ for financial access, given the emergence of Equity Bank, a pioneering institution that devised a banking service strategy targeting low income clients and traditionally under-served territories. Listed on the Nairobi Stock Exchange, it has no government ownership share and has played a key role in integrating microfinance with formal finance while being profitable in the process. Figure 1 shows that Equity Bank has experienced an explosive growth of its assets and a significant expansion of its banking services, becoming the fifth largest bank in Kenya in terms of share of gross assets and deposits of the whole system. The number of deposit and loan accounts of Equity Bank represents around 50% and 30% of the total number of deposit and loan accounts in Kenya, respectively. Its model is also expanding to other African countries, with Equity Bank ending the year 2010 with 5.4 million clients in Kenya, 474,000 in Uganda and 28,000 in South Sudan.

_Third_ , we overcome the challenge of obtaining micro-level data, merging a new dataset on bank branch penetration of all the commercial banks across all parts of Kenya with nationally representative household surveys of financial usage conducted by the Financial Sector Deepening Trust of Kenya (FSD Kenya) in 2006 and 2009. This is important given that a major challenge for empirical research on financial access is the paucity of micro-level data, which is useful to address potential endogeneity biases stemming from the nonrandom nature of bank expansion (Burgess and Pande, 2005).

The use of micro-level data within a country has additional benefits. Because institutional and macroeconomic context tends to be more homogeneous within a single country, we avoid obstacles confronted in cross-country studies of financial development and inclusion regarding omitted (or poorly measured) variables. In addition, as emphasized by Beck et al. (2007), country-level measures of financial outreach assume a uniform distribution of bank outlets within a country’s various areas and across its population, ignoring that bank branches are concentrated in urban areas and that bank expansions tend to target specific segments of the population.

We study two related questions. Do different types of banks pursue different branching strategies? And if so, do these different expansion strategies lead to different effects on households’ access to banking services? In our _first_ main set of results, we find that Equity Bank pursues different branching strategies from other banks. While banks of all ownership types (including Equity Bank) opened a greater number of branches in urban, highly populated and English speaking districts, 3
|Tender<br>Evaluation|Evaluating the winner of the<br>tendering process,<br>establishing responsiveness<br>ofthe bids|8 hours<br>30 mins|3 hours<br>15 mins|5 hours<br>15 mins|2 hours<br>30 min|45 mins|1 hour 45<br>mins|
|---|---|---|---|---|---|---|---|
|Tender<br>Approval|Preparing for sending the<br>decision of evaluation for<br>approval.|1 hour|15 mins|45 mins|1 hour|15 mins|45 mins|
|Total||13 hours<br>45mins|4 hours<br>30mins|9 hours<br>15mins|6 hours|1 hour 30<br>mins|4 hours<br>30mins|

The times mentioned in the table above correspond to the approximate times that procuring entity official inviting the tender takes to complete main aspect of that activity, that has been affected by the e- procurement process. However, this does not account for all the activities done at each stage of the tendering activity. For instance, verification of work completion letters of the winning bidder during the evaluation stage delays the process by multiple days, however, the PE official does not need to dedicate any significant portion of his time while the verification is being acquired. Similarly, the PE official only needs to prepare a document to send it for approval which takes less than an hour. The majority of the time is spent is receiving the approval from the Head of the Procuring Entity or other authority depending on the size of the contract. The average monthly remuneration of PE officials was assumed to be Tk. 70,000 (US$ 830) based on National Pay Scale, 2015. This corresponds to an hourly remuneration of Tk. 500 (US$ 6). The savings for each OTM tender was thus estimated to be Tk. 4,700 (US$ 55), while it was Tk. 2,300 (US$ 27). The total staff savings was estimated to be Tk. 30 crore (US$ 3.5 million) for about 69,000 tenders.

TABLE 43: SUMMARY OF ADMINISTRATIVE STAFF TIME SAVINGS

|Procedure|Savings per<br>tender (taka)|Savings per tender<br>(US dollar)|Number of<br>tenders|Total Savings<br>(taka)|Total Savings<br>(US dollar)|
|---|---|---|---|---|---|
|OTM|4,700|55|57,308|26.9 crore|3.2 million|
|LTM|2,300|27|11,932|2.7 crore|0.3 million|

3. Bidding Firm Transaction Cost savings

- a. Staff-time savings: The transition to e-procurement has enabled participating bidders to prepare and submit the bid in a shorter time. The staff-time savings for the bidders was estimated by multiplying the mean salary by the difference of the product of the number of bids if the tender had been paper based by the average tender preparation time for paper-based tenders and the product of the actual number of bids and the average tender preparation time for electronic tenders. The number of bids had the tender been manually administered is predicted using the same two models as used for the main results and have been given in the Table 3, Models 1a and 1b.

Based on the survey of bidders, we find that the average staff-time required to prepare a bid is found to be dependent on the contract value only for OTM tenders, as shown in the table. This is expected as 74
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- 27

-

used. Using a measure for return on investment, Tanzania's performanre, although improving during the reform period, lags far behind the comparator countries. Thus, while the data do not support the hypothesis that foreign aid has replaced domestic savings, it is likely that the return on foreignfinanced investment has been very low in Tanzania. The low productivity of investment can partly be attributed to the domination of the Tanzanian economy by a large and inefficient parastatal sector.

From an economic policy perspective, three main conclusions stand out. First, macroeconomic and structural adjustment has worked in Tanzania, and the increased foreign support has probably eased the adjustment path. Rather than financing continuing domestic imbalances, foreign-assisted reforms have contributed to reducing the disequilibria. Second, Tanzania has not been successful in using the increase in foreign assistance efficiently. It is beyond the scope of this paper to fully determine why productivity has been low, but future research could focus on the absorptive capacity of the Tanzanian aid administration and the composition of the assistance received by Tanzania. Finally, the emphasis of economic policy should shift towards increasing the productivity of investment. In the Tanzanian context, this would mean accelerating the reform of the parastatal sector, long a favored recipient of foreign assistance, and improving the environment for private sector development. The reform of the civil service, which administers the aid flows accounting for 50 percent of GDP, also stands out as an area where an improvement in productivity - 'd yield large benefits.
we use equations (2), (3) and (4), rather than (8) and (9). The simulations use the original population and migrant distributions from the data as the initial state variable. The population distribution includes agents’ birthplace, current location, and skill level, making it a 40 _×_ 40 matrix for each skill level.

The number of type _s_ individuals who were born in country _i_ living in country _j_ at time _t_ is denoted as _L_<sup>_s,i,j_</sup> _t_ . For the simulation, we change the parameter vector of the model to _θ_<sup>_′_</sup> . Then, we solve the optimization problem using the new parameters, _θ_<sup>_′_</sup> , and calculate the new migration probabilities, _p_<sup>_s,i,k,j_</sup> _t_ ( _θ_<sup>_′_</sup> ), for each agent type. Next, we find the new migrant stocks, _L_<sup>_s,i,j_</sup> _t_ +1<sup>usingthenewprobabilitiesandflowequationwhichisgivenbythefollowing:</sup> We repeat this exercise for each simulation scenario as described in the following sections.<sup>14</sup> Figure 4 helps to visualize the simulation scenarios where the United States is the final desti— nation. For simplicity, we present three countries in the figure Canada, the UK and other — countries even though the simulations are solved for all countries separately. People can migrate directly to the United States or via other countries. As we mentioned earlier, Canada and the United Kingdom are presented as the two possible transit countries in the figure and the discussion, but the model is solved for all potential transit routes.

# **5.1 Simulation 1: Moving cost from Canada to the US increases**

In our first set of simulations, we artificially increase the moving cost from Canada to the United States to a level such that migration levels decrease by more than 99%. Technically,

> 14We should note that the simulation exercises are of partial equilibirum nature, as we assume that countryspecific wages and other measures of attractiveness are fixed. However, it is possible that changes in migration flows due to policy restrictions will impact wages, further affecting migration flows across the world. Unfortunately, it would be impossible to fully endogenize these country parameters, without estimating production functions of all countries and regions.

24
3 impacts of choosing one system over another is a useful exercise. Such impacts are unlikely, however, to be accurately estimated simply by comparing countries that finance their health care through SHI with countries that do so through general revenues, the reason being that whether a country has a SHI system or a tax-financed system in place is likely to be endogenous— unobservable factors correlated with the outcome of interest are also likely to be correlated with the choice of system.<sup>3</sup> Two recent papers by Wagstaff and Moreno-Serra (2008a; 2008b) (hereafter WMS) attempt to get round this problem by focusing on _changes_ in the way countries finance their health care. If the problematic unobservable variables are time-invariant, or if the unobservables of the SHI and tax-financed countries follow a parallel trend, a simpledifferences-in-differences regression will reveal the effect of having one system in place (say, SHI) rather than the other. WMS test the parallel trend assumption (PTA) against two alternative specifications: one where the unobservables follow a country-specific linear trend, the other where they follow a system-specific trend that is not necessarily linear. They also test whether these three specifications properly account for the potential reverse causality, and estimate the impacts of SHI via instrumental variables (IV) where they do not.

This paper employs the same methods as WMS but on a (mostly) different group of countries, namely those in the OECD, and over a longer time period, namely 1960-2006.<sup>4</sup> Between 1967 and 1986, no fewer than 10 OECD countries abandoned SHI in favor of the taxfinanced NHS-type model. By contrast, in the 1990s, all four of the OECD’s new central European countries abandoned the tax-financed Semashko model in favor of SHI. The OECD countries provide a potentially important learning experience for other countries: it was in

> 3 Van der Zee and Kroneman (2007), for example, compare mean outcomes between SHI and tax-financed OECD countries at different points in time. Implausibly, the authors’ conclusions are made on the basis of simple comparisons of means. No attempt is made to control for confounders, whether observed or unobserved.

> 4 WMS focused on the countries of central and east Europe, and central Asia, and covered the period 1990-2004.
In _The Banking Industry in the Emerging Market Economies: Competition, Consolidation, and Systemic Stability_ , BIS Papers, chapter 4, 1–44. Basel: Bank for International Settlements.

- Henry, P. 2000. “Do Stock Market Capitalizations Cause Investment Booms?” _Journal of Financial Economics_ 58: 301–34.

- ———. 2003. “Capital Account Liberalization, the Cost of Capital, and Economic Growth.” _American Economic Review_ 93: 91–96.

- Hoggarth, G., R. Reis, and V. Saporta. 2002. “Costs of Banking System Instability: Some Empirical Evidence.” _Journal of Banking and Finance_ 26 (5): 825–55.

- Holzmann, R. 1997. “Pension Reform, Financial Market Development, and Economic Growth: Preliminary Evidence from Chile.” _IMF Staff Papers_ 44 (2): 149–78.

- Hu, Y. 2005. “Pension Reform, Economic Growth, and Financial Development – An Empirical Study.” Economics and Finance Working Papers 05-05, Brunei University.

- ———. 2012. “Growth of Asian Pension Assets: Implications for Financial and Capital Markets. Macroeconomics Working Papers 23317, East Asian Bureau of Economic Research, Canberra.

- Huang, H.-C., S.-C. Lin, D.-H. Kim, and C.-C. Yeh. 2010. “Inflation and the FinanceGrowth Nexus. _Economic Modelling_ 27 (1): 229–36.

- Impavido, G., A. R. Musalem, and T. Tressel. 2003. “The Impact of Contractual Savings Institutions on Securities Markets.” Policy Research Working Paper Series 2948, World Bank, Washington, DC.

- Ito, H. 2006. “Financial Development and Financial Liberalization in Asia: Thresholds, Institutions, and the Sequence of Liberalization.” _North American Journal of Economics and Finance_ 17 (3): 303–27.

- Jappelli, T., and M. Pagano. 1994. “Saving, Growth, and Liquidity Constraints.” _Quarterly Journal of Economics_ 109 (1): 83–109.

- ———. 2002. “Information Sharing, Lending and Defaults: Cross-Country Evidence.” _Journal of Banking and Finance_ 26 (10): 2017–45.

- Johnston, R. B., and C. Pazarbasioglu. 1995. “Linkages Between Financial Variables, Financial Sector Reform, and Economic Growth and Efficiency. IMF Working Papers 95, International Monetary Fund, Washington, DC.

- Kendall, J. 2011. “Local Financial Development and Growth.” _Journal of Banking and Finance_ 36 (5): 1548–62.

- King, R. G., and R. Levine. 1993. “Finance and Growth: Schumpeter Might Be Right.” _Quarterly Journal of Economics_ 108 (3): 717–37.

- Klein, M. W., and G. Olivei. 1999. “Capital Account Liberalization, Financial Depth, and Economic Growth.” NBER Working Papers 7384, National Bureau of Economic Research, Cambridge, MA.

- Koivu, T. 2002. “Do Efficient Banking Sectors Accelerate Economic Growth in Transition Countries.” BOFIT Discussion Papers 14/2002, Bank of Finland, Institute for Economies in Transition, Helsinki.

- Kroszner, R., L. Laeven, and D. Klingebiel. 2007. “Banking Crises, Financial Dependence, and Growth.” _Journal of Financial Economics_ 84 (1): 187–228.

- Laeven, L., and F. Valencia. 2010. “Resolution of Banking Crises: The Good, the Bad, and the Ugly.” IMF Working Papers 10/146, International Monetary Fund,

34
provinces and the related variation in which birth cohorts of children were exposed to the fighting. We estimate a standard health production function with cohort of birth fixed effects, province of residence fixed effects, and province specific time trends and find that the civil war had a significant adverse effect on height for age z-scores. Children who were exposed to the violence during their lives have a height for age z-score that is between 0.515 and 0.618 standard deviations lower, depending on the specification, than that of non-exposed children. This causal effect of war on children’s health is robust to the inclusion of a variety of household level control variables, different time trends, and alternative sample specifications that exploit the variation in conflict/non-conflict regions in addition to the variation in the timing of the crisis.

The poor health status of Burundian children could lead to adverse welfare effects in the long-run. Recent research based on panel data analysis has shown that chronic malnutrition during childhood (measured by height for age) is associated with substantially less schooling during adolescence, worse adult health, and lower adult productivity. Hence, it is likely that the civil war, by negatively affecting child health, will reduce the future welfare levels of these children.

15
e-invoicing was to combat tax fraud and reduce the informal economy, to which the Mexico Tax Authority (SAT) was said to have lost USD3.4 billion between 2007 and 2009 (The Economist, 2014). In order to issue an e-invoice, businesses must first register with the SAT and obtain an electronic signature and a certificate of digital stamp from SAT. In addition, only an XML format is valid for an e-invoice (KPMG, 2013).

# _Singapore_

Singapore has allowed e-invoice since 2003. Businesses can issue an electronic invoice without prior approval from the Inland Revenue Authority of Singapore (IRAS) but must comply with the Record Keeping Guide for GST-Registered Businesses” (IRAS, 2014). Businesses may also outsource e-invoice and e-credit notes to a third party. According to the recordkeeping guide, invoice records may be kept electronically using an accounting software such as the Microsoft office programs, an off-the-shelf accounting software, and customized accounting software (IRAS, 2013). In addition, the computer output and records must operate with the necessary reliability and authenticity, although the IRAS does not require digital signatures (IRAS, 2013b). Beginning in November 2008, businesses that supply goods and services to the government are required to issue electronic invoices.

# _Taiwan, China_

Taiwan, China’s e-invoice initiative was largely based on its traditional paper invoice practices. Taiwan, China, had adopted a “uniform invoice” system for more than 60 years with government-prescribed formats to ensure the validity of invoices for tax purposes. In 2000, Taiwan, China, introduced e-invoice that permitted businesses to transmit uniform invoice via the Internet (Chen et al, 2014). Since then, the government has been promoting e-invoice to businesses as an effective way to lessen operational cost and help protect the environment by reducing paper consumption. Taiwan, China seems to emphasize e-invoice use in B2C, where a significant portion of invoices are issued. In 2005, the Taiwanese Ministry of Finance (TMOF) promulgated regulations on issuing online shopping invoice in order to facilitate the blooming B2C e-commerce (Chen et al, 2014). Many additional measures have also been taken to encourage consumers to receive e- invoice in B2C transactions with incentives such as an e-invoice lottery giving e- invoice receivers a chance to win prizes every other month. A barcode system enabling consumers to get easily identified and receive e-invoices was also instituted (Taiwan Today, 2012). In 2011, the government made it mandatory for all businesses issuing e-invoice to use the e-invoice platform established by the TMOF (Chen et al, 2014), thus automatically directing all e-invoice transaction information to the TMOF data center.

15