rafmacalaba/datause-encoder-data huggingface dataset:
yet they may not burdened by no “access.” Firms without use of external credit may choose to remain so as their rates of return on capital are too low to justify formal finance or because they are not willing to provide the necessary information on their business to banks, and by implications to others, including tax authorities. Equally important, and even in the best financial systems financial services providers may not wish to provide access to all costumers as it is not profitable or sustainable to so. This does not reflect any market failures, but rather that finance, like other services, has its own demand and supply. This may mean that a country requires a certain overall level of development before more universal access is a viable proposition. Furthermore, there is plenty anecdotal evidence that some households may have difficulty managing access to credit (e.g., credit cards), suggesting that some restraint, say until financial literacy is more adequate, may well be overall welfare enhancing.

In order to answer whether there is a case for more universal provision of financial services, we need thus to know more on the benefits of access, the reasons why households and firms may (or may not) demand financial services and why financial services provides may (or may not) provide financial services, and, of course, the costs to society of providing greater access. Here, we face a number of questions, starting with the basic one that we have limited data across countries on the degree of usage of and access to financial services. While there is much data on financial sector development, there is very limited data on usage and access, both for households and for firms.<sup>3</sup> There is consequently also limited analysis on the dimensions in which access may be deficient. There can be deficient access geographically to branches and outlets. Or access can socio-economically be deficient, i.e., access is available only for some population segments. Or it can be deficient in an opportunity sense: the deserving do not have access. Data are insufficient in all respects, so far, making judgments on the relative benefits and costs of access difficult.

For some countries, there are data on households’ use of basic financial services, such as having a bank account, often obtained using commercial banks and central bank data, or

> 3 See Honohan 2004c and DFID 2005 on data availability and deficiencies

7
|(8)<br>ln(light)|-0.0866***<br>[0.0194]<br>4,879<br>tobit<br>yes<br>linear<br>263<br>ar, for a balanced<br> is the log of the<br>when applicable.<br> kilometers. _Poil_<br>barrel. The tobit<br>the ten, fve, and<br>cted for the frst|
|---|---|
|(7)<br>ln(light)|-0.344***<br>[0.108]<br>4,879<br>tobit<br>yes<br>linear<br>263<br>the city-ye<br>ent variable <br>hin a year <br>housands of <br>dollars per <br>ifcance at <br>t been corre|
|(6)<br>ln(light<br>+5.5)|-0.520***<br>[0.169]<br>4,879<br>OLS<br>yes<br>splines<br>of analysis is<br> The depend<br>lite-years wit<br>easured in t<br>hundreds of <br>** mean sign<br>rrors have no|
|(5)<br>ln(light<br>+5.5)|-0.616***<br>[0.215]<br>4,879<br>OLS<br>yes<br>linear<br>s. The unit<br> 1992–2008. <br> across satel<br> country, m<br>e FOB) in <br>ets. *, **, *<br> Standard e|
|(4)<br>ln(light)|-0.683***<br>[0.223]<br>4,879<br>tobit<br>yes<br>linear<br>263<br>fxed efect<br> the period <br>d averaged <br> city in the <br>t Spot Pric<br>re in brack<br>ot per city.|
|(3)<br>ln(light)|-0.684***<br>[0.240]<br>-0.539*<br>[0.324]<br>0.0227<br>[0.0175]<br>4,879<br>tobit<br>no<br>none<br>263<br>ountry*year<br>untries over <br> the city, an<br> the largest <br>urope Bren<br>ed by city, a<br>have one kn|
|(2)<br>ln(light)|-0.404***<br>[0.151]<br>0.168<br>[0.258]<br>-0.0398***<br>[0.0105]<br>4.227***<br>[0.238]<br>4,879<br>tobit<br>no<br>none<br>263<br>at includes c<br>l primate co<br> all pixels in <br>k distance to<br>ual average E<br>rrors, cluster<br>in column 6|
|(1)<br>ln(light)|-0.289*<br>[0.154]<br><br>-0.490<br>[0.303]<br>4,879<br>tobit<br>no<br>none<br>263<br>egression th<br>in 15 coasta<br>med across <br>oad networ<br>lly the ann<br> standard e<br>ely. Splines|
||distance(Primate)*_Poil_<br>dist(Primate)*_ln_(_Poil_)<br>ln(dist(Primate))*_ln_(_Poil_)<br>distance(Primate)<br>primate*year<br>distance(Primate)*year<br>primate<br>Observations<br>model<br>city FEs<br>city trends<br>left censored cases<br>Each column is a separate r<br>annual panel of 287 cities <br>lights digital number, sum<br>Distance(Primate) is the r<br>is the price of oil (specifca<br>cutof is light=5.5. Robust<br>one percent level, respectiv<br>stage estimation.|

31
receiving the grants are also considerably more likely to have used a consultant. Columns 5, 6 and 7 show no significant impacts on the likelihood the firm has a website, receives a new loan, or exports.

The heterogeneity in sales and high item non-response for a question on the volume of sales limits our ability to measure the return to the matching grant in terms of amount of sales. However, our questionnaire did ask firms how their sales in 2014 compared to their sales in 2013. 31.6 percent of the control group said their sales had grown over the year. The last column of Table 5 shows that recipients of the matching grant are 47.7 p.p. more likely to say their sales grew during 2014. This provides some evidence to suggest that the new products introduced and other innovative activities did translate into an improvement in firm outcomes in the first year.

# **5. Conclusions**

Matching grants have been widely used by governments to attempt to spur private sector innovation and growth. However, it has proved difficult to assess the impact of these programs, and a key concern has been whether they create additionality, resulting in benefits beyond those which the private sector would have otherwise produced even in the absence of the program (Phillips, 2001). This paper demonstrates the feasibility of a randomized evaluation of a matching grant program, and finds strong evidence that it has generated additional innovative activities. It provides the first such evidence from a fragile state environment, showing that even under these difficult circumstances, a well-designed program can succeed in getting firms to innovate.

There are several caveats that must be taken into account when interpreting the results of this paper. The first is that the eruption of civil conflict in Yemen led to higher than usual attrition in our follow-up survey. The data available suggest that this is not greatly biasing our results. Second, the conflict also means that we cannot say anything about the long-term impacts of these grants. Third, the fact that the second year of the grants program was cancelled halved our sample size from what was originally planned, which together with the heterogeneity in the sample of applicants, means we have no statistical power to examine impacts on employment or sales levels, nor to examine heterogeneity of impacts with firm characteristics. Finally, while our analysis addresses the issue of additionality, we are not able to measure whether these grants result in externalities for other firms in the economy. Nevertheless, given the popularity of these programs 12
on log Open does not change signi...cantly in the entire sample as well as the sample of non-oil countries. But the e¤ect of openness on returns to experience of highly skilled US immigrants - as shown in column (II) - is not signi...cant in any of the three samples. Apart from GADP, no explanatory variables is signi...cant in this speci...cation arising from the fact that the cross-country variance in returns to experience is substantially higher for more skilled workers. Since the variance of the returns to experience among highly skilled is relatively large, the mean square error of speci...cation (II) is about four times higher than in speci...cation (I), scaling up the standard errors of the coe¢ cients.

Using the average estimated return to home country experience evaluated at the skill distribution in the home country does also not alter the principal ...ndings. Column (III) of Table 12 uses average estimated returns to home country experience evaluated at the skill distribution in the home country as dependent variable. The results are very similar to the estimates presented in Table 7 for the entire sample and the sample of non-Oil countries. Increasing the share of trade in GDP from 0.2 to 0.3, raises the average return to home country experience in the non-Oil economies by about 0.35 percent. However, when restricting the sample to non-OECD economies the coe¢ cient on Open is about 50 percent lower than the respective coe¢ cient of Table 7. The coe¢ cient on log Open does also not change signi...cantly when evaluating returns to experience at the skill distribution of US immigrants as can be seen in the last column of Table 12.

Summarizing, these ...ndings reveal that the e¤ect of openness on returns to experience is not driven by cross-country di¤erences in the educational attainment di¤erential between home country residents and US immigrants. Controlling for heterogeneity in returns, however, decreases the coe¢ cient on openness in the non-OECD sample and renders it insigni...cant.

# 6.3 English Speaking Origin

Returns to home country experience depend on whether the immigrant can transfer his knowledge and skills to the US economy. Transferability of human capital to the US is largely determined by English pro...ciency. Since countries where English is an o¢ cial language tend to be more open, the estimated coe¢ cient on openness may overestimate the e¤ect of openness on returns to home country experience.

The ...nding of a positive and signi...cant e¤ect of openness on returns to experience applies to immigrants from English speaking and non English speaking countries alike. The results presented in Table 13 provide 25
-)LICY RESEARCH WORKING PAPER i 984

# Summary findings

T-ypically the impact of thie North American Free Trade Agreement (NAFTA) is analyzed from a macroeconomic perspective, to examine the implications for capital market flows or for the aggregate degree of financial integration. This analysis often involves examining whether certain conditions of arbitrage or efficiency tend to hold, given greater integration of financial markets.

Alternatively, other work examines only the effects of greater financial integration for the efficiency with which financial services are provided microeconomically.

Glaessner and Oks combine the two approaches to examine how NAFTA will affect capital flows and the efficiency with which financial services are provided in Mexico.

They also call attention to domestic financial system and monetary and exchange rate policy issues that Mexico must address if greater financial integration is not to result in increased risk for the domestic financial system or greater macroeconornic instability.

The two approaches are rarely combined, nor are the effects of integration considered within such a combined framework.

- - This paper a product of the Latin America and the Caribbean Region is part of a larger effort in the region to examine the impact of cross-regional policy lessons. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Miguel Navarro, room 15-076, telephone 202-458-4722, fax 202-522-2106, Internet address mnavarromartin @worl dbank.org. Thomas Glaessner may be contacted at thomas glaessner@sfmny. com. September 1998. _(55_ pages)

_The 1'olicy Research Wlorking Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not nzecessarily represent the view of the W/orld Bank. its Executive Directors, or the countries they represent._ Produced by the Policy Research DisseminationI Center
across studies. Interestingly, using data from the Czech Republic, Andrew Weiss and Georgiy Nikitin (2002) find a positive effect of national (state) ownership on the rate of change of both operating profit per worker and operating profit per unit of capital, as well as a positive effect of municipal ownership on the rate of change of operating profit per worker. Using data of the publicly traded firms in the Czech Republic during 1993-1995, Hanousek and Kočenda (2003) in turn find a positive effect of foreign majority ownership on the rate of change in returns on assets. Finally, Hanousek, Kočenda, and Svejnar (2007) find positive effect of the subsequent ownership by banks on change in ROA but this effect is offset by negative effect of change in ownership. Foreign industrial owners exhibit positive effect of initial ownership on profit over sales, while effect of subsequent ownership by others foreign owners is negative. Overall, profitability is not significantly affected by the state keeping a golden share.

Three studies that control for endogeneity/selection examine the effect of ownership concentration. In the Czech Republic, Hanousek, Kočenda, and Svejnar (2007) find no effect of concentration that results from the initial large scale privatization, but they find a positive effect of majority ownership by domestic private owners as a result of ownership changes that took place after privatization. In terms of foreign ownership, the authors do not find any effect of high (majority) concentration among foreign owners, but do find that strong (blocking) minority (33-49%) foreign ownership has a positive effect on return on assets. Jeffrey Miller (2006) finds the effect of concentrated ownership on return on assets to be positive in Bulgaria, while Marko Simoneti and Alexandra Gregoric (2004) find concentrated management (but not employees) ownership to have a positive effect on profit/sales in Slovenia. Hence, concentrated domestic private ownership, managerial ownership, and to a lesser extent foreign ownership tend to have a positive effect on profitability, while state keeping a golden share or concentration of worker ownership appear to be unrelated to profitability.

Studies of the effects of ownership on profit of firms in China vary considerably in terms of their methodology, sample size and findings, and as yet only one uses sophisticated econometric methods. Thus Gary Jefferson and Jian Su (2006) estimate the effect of private ownership on profit/sales to be positive but significant only at the 10% test level. Other studies include Xiao-Yuan Dong, Louis Putterman, and Bulent Unel 23
_pop_HRSL_dens_km2_ to control for variations in population density across studied pixels, which could affect nightlights independently from disaster-induced changes in access to electricity. The second column of Table 6 includes the number of cloud-free observations that went into each pixel averaged over the adjacent pixels. This variable is insignificant, confirming that the nightlight radiance is not impacted by the quality of observations as represented by the number of observations that were used to calculate the monthly average values. The third column includes the urban dummy variable which is positive and significant reassuringly proving that nightlight is stronger in urban areas that in rural areas.<sup>12</sup> The last column includes the number of fatalities. This variable is not significant, indicating that the effect is not associated with the number of deaths related to the conflicts within the buffer zone.

Table 6. Regression results of equation (1) for conflicts within 60km buffer for 1 month post TCs. Dependent variable avg_radBuff05

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VARIABLES|Period -1 to 1<br>avg_radBuff05|Period -1 to 1<br>avg_radBuff05|Period -1 to 1<br>avg_radBuff05|Period -1 to 1<br>avg_radBuff05|
|tot_treated|0.0291**|0.0297**|0.0197**|0.0175*|
||(0.0113)|(0.0117)|(0.00990)|(0.0100)|
|temp_shock|-0.0366***|-0.0417***|-0.0366***|-0.0366***|
||(0.00538)|(0.00934)|(0.00538)|(0.00538)|
|**did_var**|**-0.0134***|**-0.0149***|**-0.0134***|**-0.0134***|
||**(0.00795)**|**(0.00889)**|**(0.00795)**|**(0.00795)**|
|pop_HRSL_dens_km|||||
|2|0.00635***|0.00635***|0.00511***|0.00511***|
||(0.000848)|(0.000848)|(0.000770)|(0.000770)|
|cf_cvgBuff05||0.00124<br>(0.00199)|||
|Urban|||0.384***|0.384***|
||||(0.0642)|(0.0641)|
|tot_fatalities||||0.00108<br>(0.00102)|
|Constant|0.199***|0.186***|0.176***|0.174***|
||(0.0131)|(0.0261)|(0.0114)|(0.0117)|
|Observations|16,880|16,880|16,880|16,880|
|R-squared|0.773|0.773|0.806|0.806|
|Treatment Obs|9684|9684|9684|9684|
|Control Obs|7196|7196|7196|7196|

Notes: Robust standard errors in parentheses;<sup>***</sup> p<0.01,<sup>**</sup> p<0.05,<sup>*</sup> p<0.1; avg_radBuff05 ranges from 1<sup>st</sup> to 99<sup>th</sup> percentile ( .02 to 21.8 nanoWatts/sr/cm<sup>2</sup> )

> 12 We also estimated this specification including interactions with the urban dummy variable to see if we would obtain different impacts for urban areas, but did not find a significant impact.

15
# **4.1 Timing and initial conditions**

The superscript _j ∈{m, f }_ denotes gender, and the superscript 2 denotes a couple.<sup>22</sup> Periods in the singles’ problem are indexed by the individual’s age ( _t_ = _a_<sup>_j_</sup> _t_<sup>),whilethecouples’</sup> problem is indexed by the age of the female ( _t_ = _a_<sup>_f_</sup> _t_<sup>).Forsingles,thedecisionproblem</sup> begins at age _t_ 0 = 35.<sup>23</sup> For couples, the decision problem begins when the wife turns _t_ 0. Thus, the age of the husband in the first period, _a_<sup>_m_</sup> _t_ 0<sup>ispartoftheinitialconditionsand</sup> becomes a state variable thereafter. Any household assets ( _At_ 0) or work experience ( _Xt_<sup>_m_</sup> 0<sup>_, X_</sup> _t_<sup>_f_</sup> 0<sup>)</sup> accumulated prior to the first model period, as well as any children born prior to female age _t_ 0 ( _Nt_ 0) are also taken as initial conditions. The initial conditions also include pension savings ( _Bt_<sup>_m_</sup> 0<sup>_, B_</sup> _t_<sup>_f_</sup> 0<sup>),whichincludeanypensionrightsaccumulatedbythetwospousesunder</sup> the earlier INP retirement system prior to age _a_<sup>_j_</sup> _t_ 0<sup>(“Bonosdereconocimiento”)orunder</sup> the new AFP system. Finally, the initial conditions include two permanent characteristics: completed schooling levels of men and women ( _e_<sup>_j_</sup> ), and their birth cohorts ( _bc_<sup>_j_</sup> ). We denote the set of initial conditions for a single household by: The set of initial conditions for a couple is then: At ages _t_<sup>_f_</sup> _C_<sup>=60and</sup><sup>_tm_</sup> _C_<sup>=65yearsoldrespectively(orsooneriftheyqualifyforearly</sup> retirement), males and females begin to withdraw money from their pension savings accounts. For tractability, we did not incorporate the choice about whether to take retirement savings as an annuity or as a phased withdrawal. Rather, we assume phased withdrawal, because the formula is a simple function of age.<sup>24</sup> The level of pension benefits is calculated according

> 22 We use the terms husband and wife, but the model applies to cohabiting, non-married couples 23 Singles are assumed to remain single after age _t_ 0. Married couples are able to transition to being divorced or widowed, as further described below. We estimate the model for singles on people who remain single after age _t_ 0.

> 24In computing the programmed withdrawals, we used the life tables RV-2004 published by the _Superintendencia de Pensiones_ . The 2009, 2010 and 2011 rates of return were used for the corresponding years. To discount years more than 20 years in the future, the 20th discount rate was repeated. For years after 2011, the 2011 vector was used. For years before 2009, a single discount rate of 5 percent was used. If a couple qualifies for the PASIS pension, our simulation assigns the PASIS pension to the woman (only one member of the household can get the PASIS).

11
Eurasian countries are confronting a shared problem of future water shortages. Declines in river runoff due to climate change will compound an already unsustainable management of water (World Bank 2009). In Kazakhstan, where the decline in runoff is expected to be milder, there is a potential problem of water resource management in the Lli River basin, which is shared with China. The Kyrgyz Republic and Tajikistan will have enough water for their own needs but may not be able to meet demand in their role as critical suppliers of water to the region. The water situation in Turkmenistan and Uzbekistan is dramatic, even without climate change: with an economy largely based on irrigated farming, Uzbekistan is the main water consumer of the Central Asia region. Almost all of its water resources come from the mountains located in other countries. Unsustainable water management has caused the Aral Sea to shrink, and measures to manage the stresses in the Amu-Darya River delta—a key source of water for Uzbekistan, Tajikistan and Turkmenistan—will be even more important to refine as the climate changes.

# **_Impact of Infrastructure on Competitiveness and Growth_**

Cross-country comparisons of infrastructure quality are bedeviled by measurement problems, statistical gaps, and the inherently subjective nature of such assessments. Using the World Economic Forum (WEF)’ survey of global business leaders’ perceptions, we find that the quality of overall infrastructure remains uneven across the region, with an index ranging from 3.4 (Moldova) to 4.6 (Georgia) on a scale from 1 to 7. We also find that countries with poor infrastructure quality have a lower ranking on the global competitiveness index (Figure 4). Compared globally, Eurasia belongs to the middle-tier of both infrastructure quality and global competitiveness. It lags behind the EU12 and EAP12 on both indicators.

**Figure 4:** _Global competitiveness and infrastructure quality_

<!-- Start of picture text --> 6<br>5<br>CHN<br>KAZ RUS<br>AZE<br>4 UKR<br>MOL GEO<br>ARM<br>TAJ<br>3<br>KYG<br>World EU12 EAP12 Eurasia<br>2<br>2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7<br>Quality of Overal Infrastructure (1-7 )<br>Global Competitive Index (1-7)<br><!-- End of picture text --> Source: World Economic Forum (2012) Note: annual Executive Opinion Survey, ranking out of 144 economies and scores measured on a 1-7 scale.

12