rafmacalaba/datause-encoder-data huggingface dataset:
As a further test we explore if, on average, changes in the efficiency of countries’ business regulations is correlated with a higher level of per-capita GDP growth. We look at changes instead of levels of DTF. We start from the assumption that GDP per capita is a function of the level of DTF and therefore GDP per capita growth is a function of the change of DTF. This brings in some additional complications to address and calls for a refinement in our empirical model. Let’s consider the following equation: In this specification DTF stands for the distance to the regulatory frontier and vector X represents the set of control variables. As most macroeconomic variables GDP growth tends to follow a cyclical dynamic that implies high levels of autocorrelation. Consequently, when regulatory changes are correlated with the business cycle coefficients estimated with (2) will be biased. For instance, if improvements are motivated by economic downturns subsequent improvements in the state of the economy will occur regardless of their actual impact because of the subsequent positive phase of the business cycle. In other words, coefficient β in (2) will be upward-biased. A better specification for the equation thus appears to be: Growthit = α(∆DTFit) + βXit + γ1Growthit−1 + ⋯+ γpGrowthit−p + δt+εit  ∆Growthit = α∆(∆DTFit) + β∆Xit + γ1∆Growthit−1 + ⋯+ γp∆Growthit−p + ∆δt+∆εit (3) Due to the presence of lagged dependent variables equation (3) cannot be solved using fixed effects. In fact the error term ∆εit is serially correlated with the lagged dependent variable ∆Growthit−1. Following Eiffert (2009) we solve this problem using the Arellano-Bond dynamic panel data estimator. The Arellano-Bond dynamic panel estimator uses lagged levels of Growthit as instruments for the lagged ∆Growthit terms. Again we control for a vector X, which includes a set of country-specific that can influence our dependent variable (growth) such as per-capita GDP, trade openness, rents from natural resources, government consumption, corruption, economic and political risk as well as civil freedoms.

In order to check if the entity of the regulatory change matters for the impact on growth, we estimate an alternative specification of (3) where we substitute the ∆DTFit explanatory variable with a set of dummy variables that divide economies in quartiles based on the magnitude of their yearly regulatory change (∆DTFit).

**4. Results**

7
each crisis started; unlike earlier databases, it does not provide end years, perhaps because the end of the crisis is usually much less clear-cut than its onset. (See Appendix Table 1 for a list of crisis onset years.) As a secondary source of crisis data, we use an earlier dataset compiled by Caprio, Klingebiel, Laeven, and Noguera (2005). Although less recent than LV, the Caprio et al. database (hereafter “CKLN”) is a useful complement to LV in two ways. First, it provides estimated end years for each crisis, allowing us to test whether effects on aid are larger during or after a crisis. Second, it includes both systemic and non-systemic banking crises, allowing tests of how the effects on aid vary with the extent of the crisis. Counting both types of crisis, CKLN identifies 23 crises in 17 of the DAC donor countries in our dataset. The systemic crises identified by CKLN generally match those of LV, with the main exception being the start date of the crisis in Japan. We have updated CKLN from its end date of 2002 through 2007, to match the LV year coverage. This entailed adding the only two crises that LV identifies as having begun since 2002 in the donor countries – the current banking crises in the US and UK, which LV codes as beginning in 2007. It also required specifying an end date for Japan’s crisis; we use 2006, but our CKLN results are qualitatively robust to assuming that it ended in 2005 or 2007 instead.

_Data on income, population, and other control variables_ For per-capita income, population, and the other control variables in Zit, we use data from a variety of sources, including the World Development Indicator database (World Bank 2009), World Development Reports (World Bank, various years), World Economic Outlook (IMF 2009), Luxembourg Income Study project (2009), International Country Risk Guide (ICRG 2009), Database of Political Institutions (Beck et al. (2001); updated 2008). Appendix Table 2 provides a detailed list of variables and sources for the data we use in this paper.

# **3. Results: Donor-country financial, economic, and political characteristics and ODA**

Do donors give less aid after suffering banking crises? Figure 1 gives a straightforward first-cut answer to that question, by comparing aid flows for crisis and non-crisis countries before and after a 8
Figure 9: Mexico – Evolution of Domestic Figure 10: Cross-Country Comparisons of Credit to the Private Sector (1960-2004) Domestic Credit to the Private Sector (2004) <!-- Start of picture text --> 90<br>80<br>160<br>70<br>140<br>60<br>120<br>50<br>100<br>40<br>80<br>30<br>Chile<br>20 60<br>10 40 Brazil<br>Co lo mbia<br>MEXICO<br>0 20<br>Argentina<br>0<br>6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11<br>Mexico Chile Middle Income Ln of GDP per capita, PPP<br>% of GDP<br>Domestic Credit to Private Sector (% of GDP)<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004<br><!-- End of picture text --> Source: World Bank (World Development Indicators).

# **_Bank Lending_**

The evolution of total credit (excluding NPLs) by local commercial banks to households and firms can be conceptually divided into two periods. During 2000-2002, there was a contraction in lending in both absolute and relative (to GDP) terms, reflecting a relatively poor macroeconomic performance and on-going de-leveraging of bank balance sheets. The reactivation of bank credit – which has been uneven across different segments – broadly began in 2003 and has accelerated since then (see figures 11 and 12). In real terms, banks’ performing loan portfolio to the non-financial private sector grew by more than 27 percent since 2000, although it stood at around the same level of GDP (9 percent) in 2005Q3 as in 2000<sup>15</sup> .

> 15 See BBVA Bancomer (2005Q3) for an overview of the reactivation in commercial bank credit and its determinants.

12
|**Reason **|**Table A8**<br>BurkinaFaso|**: Reasons for va**<br>Kenya|**ccination**<br>Malawi|Nigeria|Tanzania|
|---|---|---|---|---|---|
|Protecting own health only|60.8|22.7|89.9|64.1|76.8|
||(55.1 to 66.5)|(18.4 to 26.9)|(86.5 to 93.3)|(59.5 to 68.7)|(70.8 to 82.9)|
|Protecting other's health|26.7|68.6|5.0|9.3|16.0|
||(21.4 to 31.9)|(64.1 to 73.1)|(2.7 to 7.2)|(6.4 to 12.2)|(10.8 to 21.2)|
|Government mandate|6.9||1.1|18.7|4.0|
||(4.0 to 9.8)||(0.0 to 2.4)|(14.8 to 22.5)|(1.5 to 6.5)|
|Attend school|0.3|0.4|0.0|0.2|0.0|
||(0.0 to 0.8)|(0.0 to 0.8)|(0.0 to 0.0)|(0.0 to 0.5)||
|Employer mandate|0.7|12.1|1.4|1.6|0.9|
||(0.0 to 1.4)|(9.5 to 14.6)|(0.4 to 2.3)|(0.2 to 2.9)|(0.0 to 2.0)|
|Avoid public health measures|4.4||0.9|3.2|0.5|
||(2.1 to 6.7)||(0.0 to 2.0)|(0.6 to 5.9)|(0.0 to 1.2)|
|Take part in social life|2.1|0.2|1.3|3.1|2.6|
||(0.6 to 3.6)|(0.1 to 0.3)|(0.0 to 2.7)|(1.3 to 5.0)|(0.3 to 4.9)|
|Be able to travel|10.7||1.3|10.4|3.1|
||(7.6 to 13.8)||(0.0 to 2.6)|(7.6 to 13.2)|(0.4 to 5.8)|
|People in community did it too|4.4||0.1|2.3|0.1|
||(2.0 to 6.8)||(0.0 to 0.2)|(1.2 to 3.3)|(0.0 to 0.2)|
|Received material incentive|0.0||0.0|0.0|0.0|
|||||(0.0 to 0.1)||
|Other reason|1.4|29.7|0.2|1.3|4.1|
||(0.0 to 2.9)|(25.8 to 33.7)|(0.0 to 0.4)|(0.1 to 2.5)|(0.7 to 7.5)|
|_N_<br>Note: Reasons for getting vac<br>in Kenya (29.2%). All values|690<br>cinated. 'Other' mai<br>in percent. 95% co|3,586<br>nly comprises of<br>nfidence interva|584<br>people citing ethi<br>ls in parentheses.|921<br>cal reasons as t|403<br>heir motivation|

21
<!-- Start of picture text --> ...mismatchin currentjob ...mismatchin firstjob<br>Marginal effect of each variable<br>on likelihood of... m1 m2 m3 m4<br>Female -0.071* -0.073* -0.071* -0.072*<br>(0.034) (0.034) (0.035) (0.036)<br><!-- End of picture text -->

||...mismatc<br>i|h<br>ncurrentjob|...mismat|ch<br>in firstjob|
|---|---|---|---|---|
|Marginal effectofeach variable<br>on likelihood of...|m1|m2|m3|m4|
|Female|-0.071*|-0.073*|-0.071*|-0.072*|
||(0.034)|(0.034)|(0.035)|(0.036)|
|Age||-0.0010||-0.00055|
|||(0.0016)||(0.0017)|
|Province||Yes||Yes|
|Observations|850|850|764|764|

* p<0.05, ** p<0.01, *** p<0.001
comfort zone, larger than 0.50 and close to 1.0. As is the case for the GDP data, the elasticities for the sample excluding high-income countries are larger than those of the full sample, the elasticities for the tenyear intervals are much larger than those for the annual data, and c2 > c1.

The visual relationship between _SoL_ and population is positive but loose when the data are expressed in log-levels. The correlation is barely apparent for 10-year growth rates and there is no obvious relationship in the annual growth rate data. (See Panel C.) Thus, it will not be surprising that the results reported below do not support the population hypothesis.

**Panel C: Population**

<!-- Start of picture text --> Figure 19: Levels Figure 20: 10 Year Growth Rates Figure 21: Annual Growth Rates<br>22 40 6<br>5<br>20 30<br>4<br>18 20 3<br>2<br>16 10 1<br>0<br>14 0<br>-1<br>12 -10 -2<br>6 8 10 12 14 16 18 -200 -100 0 100 200 -100 -50 0 50 100 150 200<br>ln(R*AoL) 10 Year Change ln(R*AoL) Dln(R*AoL)<br>ln(Population) Dln(Population)<br>10 Year Change ln(Population)<br><!-- End of picture text -->

||**Table 1**<br>|**3: Popul**<br>|**ation**<br>||||
|---|---|---|---|---|---|---|
||**Log-Level**|**s 1/**|**10 Year Growth**|**Rates 2/**|**Annual Growth**|**Rates 2/**|
||**Coeff.**|**Prob.**|**Coeff.**|**Prob.**|**Coeff.**|**Prob.**|
|**Full Sample**|||||||
|_Constant_|7.732|0.000|0.014|0.000|0.014|0.000|
|_AoL_|0.682|0.000|0.005|**0.120**|0.000|**0.641**|
|_R_|0.760|0.000|**-0.020**|0.003|**-0.002**|**0.258**|
|Adjusted R-squared|0.502||0.977||0.868||
|F-statistic|695||379.995||99.153||
|**Full Sample, Excluding Later Years**|||||||
|_Constant_|||0.015|0.000|0.015|0.000|
|_AoL_|||**-0.006**|**0.097**|**-0.002**|0.050|
|_R_|||**-0.026**|0.000|**-0.005**|0.027|
|Adjusted R-squared|||0.992||0.898||
|F-statistic|||611.600||78.190||
|**Excluding High Income**|||||||
|_Constant_|||0.016|0.000|0.017|0.000|
|_AoL_|||0.002|**0.648**|**-0.001**|**0.389**|
|_R_|||**-0.034**|0.000|**-0.003**|**0.183**|
|Adjusted R-squared|||0.978||0.854||
|F-statistic|||366.245||82.427||
|**Excluding High Income and Later Years**<br>|||||||
|_Constant_|||0.017|0.000|0.018|0.000|
|_AoL_|||**-0.016**|0.000|**-0.004**|0.026|
|_R_|||**-0.040**|0.000|**-0.007**|0.022|
|Adjusted R-squared|||0.991||0.868||
|F-statistic|||530.059||56.074||

1. Panel regression, no fixed effects.

2. Panel regressions with fixed effects across time and space.

3. Adjusted R-squared for constant and fixed effects only.

The population hypothesis was repeatedly rejected by the data. The coefficient for _AoL_ is not statistically different from zero, neither in the full sample or the sample excluding high-income countries. This is true for the ten-year growth rates and the annual growth rates. Moreover, in both samples, the coefficient for _R_ has the wrong sign and is insignificant in the annual growth rate test equation.
Table 4: Attrition by treatment.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||_Imm_|**Rapid R**<br>_ediate pos_|**esurvey**<br>_t-intervent_|_ion_|**Mid**<br>_12 m_|**line**<br>_onths_|**End**<br>_24 m_|**line**<br>_onths_|
|IPT-G (Any)|-0.018<br>(0.014)|-0.018<br>(0.014)|||||||
|IPT-G|||0.001|0.001|0.004|0.006|-0.007|-0.009|
||||(0.015)|(0.015)|(0.019)|(0.019)|(0.023)|(0.023)|
|IPT-G+|||-0.035|-0.035|-0.087|-0.084|-0.097|-0.101|
||||(0.014)|(0.014)|(0.014)|(0.014)|(0.021)|(0.021)|
|Control mean|0.139|0.139|0.139|0.139|0.205|0.205|0.261|0.261|
|Observations|1919|1919|1919|1919|1919|1919|1695|1695|
|Covariates|No|Yes|No|Yes|No|Yes|No|Yes|
|_t-tests p-value_|||||||||
|H0: IPT-G (any)=0|0.188|0.183|||||||
|H0: IPT-G=0|||0.965|0.974|0.838|0.748|0.754|0.687|
|H0: IPT-G+=0|||0.015|0.016|0.000|0.000|0.000|0.000|
|H0: IPT-G=IPT-G+|||0.012|0.011|0.000|0.000|0.000|0.000|
|_F-tests of joint orthogona_|_lity - p va_|_lue_|||||||
|H0: Controls=0||0.492||0.499||0.302||0.888|
|H0: IPT-G (any) int.=0||0.403|||||||
|<br>H0: IPT-G int.=0||||0.201||0.136||0.506|
|H0: IPT-G+ int.=0||||0.827||0.218||0.113|

Notes: This table shows the impact of treatment on attrition in each survey round; Columns 1-2 combine the IPT-G and IPT-G+ treatment arms because the cash was distributed after the rapid resurvey; Estimates are obtained by regressing an attrition indicator on treatment indicators and block fixed effects centered and fully interacted with treatment, adjusted models also include centered baseline covariates fully interacted with the treatment indicator(s), covariates include the poverty probability score, age in years, ever married, ever pregnant, and the PHQ-8 score; Standard errors clustered at the club level in parentheses; t-tests report p- values from the indicated null hypothesis on the treatment coefficients; F-tests report p-values from tests of the joint significance of the full set of indicated coefficients (the in rows marked _int._ refer to the full set of covariate interactions with the indicated treatment).

34
# Reference List

Amiti, Mary and Shang-Jin Wei. 2004. "Fear of Service Outsourcing: Is It Justified?" NBER Working Paper. Cambridge, MA: National Bureau of Economic Research. Anderson, Howard. 2004. "Why Big Companies Can't Invent." _Technology Review_ , May Baumol, William J. 2002. _The Free Market Innovation Machine_ . Princeton: Princeton University Press.

Bhagwati, Jagdish 2004. _In Defense of Globalization_ . New York: Oxford University Press.

Bloom, David E., David Canning, and Jaypee Sevilla. 2001. "Economic Growth and the Demographic Transition." NBER Working Paper. Cambridge, MA: National Bureau of Economic Research.

Branstetter, Lee and Hyeog Ug Kwon. 2004. "The Restructuring of Japanese Research and Development: The Increasing Impact of Science on Japanese R&D." RIETI Discussion Paper Series. Tokyo: Research Institute of Economy, Trade, and Industry.

"Budget Plans Hint at Lean Times Ahead for Japanese Research" 2005. _Nature._ September 8.

Campbell, David W. 2004. "Explaining Japan's Saving Rate." _Journal of Asian Economics_ 15 797-815.

"Canon and Toshiba Go Their Own Way in Flat Panels" 2004. _IEEE Spectrum._ November.

"Canon Targets Software Development" 2004. _Financial Times._ October 27.

Chesbrough, Henry William 2003. _Open Innovation: The New Imperative for Creating_ _and Profiting From Technology_ . Boston, MA: Harvard Business Review Press.

"The China Price" 2004. _BusinessWeek._ December 6.

Christensen, Clayton M. and Michael E. Raynor 2003. _The Innovators' Solution_ . Boston: Harvard Business School Press.

Citron, Paul and Robert M. Nerem. 2004. "Bioengineering: 25 Years of Progress-but Still Only a Beginning." _Technology in Society_ 26(1): 415-431.

38
The sector-by-sector survey in the preceding section shows a close relationship betwem export marketing choices and sector and firm characteristics that is genc rally consistent with the hypotheses made at the outset. As with other types of SME support, collective marketing support received relatively low use and usefulness ratings compared to private channels of support. Despite these generally lower ratings, Table IV. 10 shows that more than half of all SMEs in our survey used some form of collective marketing services. While KOTRA's services were used most widely, KTC ranked highest in terms of usefulness.

We close by summarizing the major conclusions that can be drawn from the survey data in this chapter: 1) Korean SMEs have a wide choice of marketing support systems, both private and collective, to use in mitigating their export marketing problems. The channels they chose varied in predicrable ways according to the characteristics of the firm and the sector they are in.

2) Of these characteristics, the two basic determinants of export marketing channels are the presence of preexisting networks at the initial stage of expansion into export markets, and the importance of the SME's prior endowments at the time exporting begins.

3) Another important predictor of choice of marketing channels and strategies is timing; i.e. whether a firm begins exporting early or late in the industry's export cycle, and whether the firm has been exporting a relatively long or short time. In the case of both the sector and firm exporting cycle, it generally holds true that the earlier the firm is within the cycle, the more important it finds collective forms of marketing support.

4) Other important characteristics that help determine export marketing choices are the size of the firm and the availability of private support in the form of subcontracting networks and readily available foreign buyers. Where available, private sources are preferred over collective sources of marketing support.

_5)_ Collective marketing support, on the other hand, is relatively useful to firms that have fewer pnvate options and those that have relatively large export volumes. In either case, it generally seems to hold true that the more sector-specific services are generally viewed as the most useful.

V. FinEancl Support Systems 29
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results are in Table 3. There is indeed a bias when calculating the welfare effects with aggregated data. The size and direction of the aggregation bias calculated by comparing measures derived from aggregated data (presented in Table 3) instead of disaggregated data (presented in Table 2), are summarized in Table 4. At the developing world level, for instance, the welfare gains would be 2.39%, instead of the 2.50% obtained when we use disaggregated data; using aggregate data thus leads to an underestimation of the average gains across countries by approximately 4 percentage points.

For individual countries the biases are much larger and can be positive (in 25 countries) or negative (29 countries). The average absolute difference between average gains estimated using aggregated vs disaggregated data is 0.75 percentage points, or approximately 30 percent of the average gains from trade across countries. In the Central African Republic, Pakistan, Indonesia, or the Republic of Yemen, the welfare gains using aggregate data are roughly half of the estimates using disaggregated data. More extreme differences appear when we inspect countries with positive biases. In Cambodia and Ethiopia, the welfare gains are overestimated by a factor of 9 or 4, respectively. There are many instances where the estimates are biased by a factor of close to or above 2 (Burkina Faso, Cambodia, Ethiopia, Guinea-Bissau, Madagascar, Niger).

Using more aggregated data also gives different estimates of the distributional impacts of the gains from trade. Using the aggregated data trade policy is estimated to be pro-rich in 29 of the 54 countries, whereas it is estimated to be pro-rich in 23 of the 54 countries when using disaggregated data. More aggregated data tend to underestimate the gains accruing to rich households slightly more than they underestimate the gains accruing to poor households.

We now turn to examining product heterogeneity in the welfare effects. This is the heterogeneity that arises when we decompose the gains from trade into the consumption and income effects and, fundamentally, into disaggregated effects across goods. Since our templates build up from granular 4-digit categories of goods, we can exploit this feature of the data to showcase this heterogeneity. To do this, we select one country, Vietnam, and report the aggregate effects, the consumption effects and the income effects of each one of the 53 4-digit goods in the template (see Figures 1 and 2). Results are in Table 5.

11
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE government of Senegal is fully aware of this issue and, as part of its strategy to modernize transport infrastructure and diversify financing sources for the road sector, has started to promote private participation in the road sector. In March 2009 a 30-year concession was granted for the DakarDiamniado toll highway, to build a 20.4 km (Pikine-Diamniadio) road and to manage and maintain the highway toll road from Patte d’Oie to Diamniadio (including a 4.2-km road between Patte d’Oie and Pikine).

There has also been a consistent effort to improve the institutional capacity to deal with road maintenance, a major issue in the sector. In 2008 Senegal established a sound system for funding road maintenance by adopting a second-generation road fund, FERA,<sup>2</sup> and creating the AATT<sup>3</sup> (road maintenance executing agency). Given the evidence from other African countries, putting such institutions in place usually increases the likelihood that maintenance funding will be better protected and mobilized (table 4).

**Table 4. Senegal is on the right track to secure road maintenance resources**

Percentage of main road network in good or fair condition, by country <u>grouping</u>

|**Country catego**|**ry**|**Institutions**||**Geography**|**Financing**||
|---|---|---|---|---|---|---|
|Middle-income|81|Road fund and agency|82|Flat and arid|77<br>High fuel levy|79|
|Low-income,aid|75|Road fund only|70|Rollingand humid|70<br>Low fuel levy|70|
|Low-income, oil|||||||
|producer|70|Road agencyonly|62||No fuel levy|75|
|_Source:_Gwilliam and|others 20|08.|||||

Finally, the country has made important progress in road safety. The number of accidents has declined from 4,074 in 2003 to 3,446 in 2005 (World Bank 2008), thanks to the stricter enforcement of licenses and registration and the inclusion of standards for signaling, marking, and lightening of roads in the road safety code.

# **Challenges**

Once in relatively good shape, Senegal’s road infrastructure has been deteriorating since the early 1990s. In 2007 the government, supported by donors, set in place the Program Sectoriel des Transports II (PTST2) to revamp the road network. After a couple of years of visible improvements, 40 percent of the paved network and 60–63 percent of the unpaved network still remains in poor condition (World Bank 2010c).

Part of the problem of deteriorating road conditions is that Senegal has not been able to fully procure and protect resources for road maintenance, despite the great strides toward institutional reform made by creating the FERA and AATT. Over the period 2005–09, Senegal’s spending on network segments that could be maintained was short of estimated maintenance requirements by 78 percent, while spending on the whole network was about 11 percent short of estimated maintenance and rehabilitation requirements.

> 2 Fonds d’Entretien Routier Autonome.

> 3 Agence Autonome des Travaux Routiers.

11