rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1. Episodes of Large Devaluations and the Timing and Magnitude of FDI and Export Surges** **<u>(quarterly data, 1990-2010)</u>**

||REER|Deva|luation|Timin<br>abov<br>(q|g of firs<br>e the 7<br>uarters|t increase<br>5 centile<br>after)|Growth rat<br>increase a<br>75 ce|e of first<br>bove the<br>ntile|
|---|---|---|---|---|---|---|---|---|
||||_Growth_||||||
|_Country_|_Year_|_Qtr_|_Rate_|_Exports_|_FDI_|_FDIfirst?*_|_Exports_|_FDI_|
|Argentina|2002|1|-0.54|1|6|C|0.19|0.69|
|Austria|2010|2|-0.02||0|A||0.61|
|Bangladesh|1996|3|-0.23|0|3|C|0.23|3.87|
|Belarus|1999|1|-0.37|1|0|A|0.16|3.04|
|Belgium|2010|2|-0.03|||D|||
|Bolivia|2009|2|-0.08|4|1|A|0.18|1.56|
|Brazil|1999|1|-0.33|1|2|C|0.23|0.58|
|Bulgaria|1996|2|-0.26|3|1|A|1.17|0.80|
|Canada|2008|4|-0.10|3|2|A|0.08|4.03|
|Chile|2008|2|-0.10|4|1|A|0.12|2.36|
|Hong Kong,|||||||||
|China|1998|4|-0.06|2|6|C|0.15|0.86|
|Colombia|1999|3|-0.13|0|2|C|0.10|0.43|
|Costa Rica|2009|2|-0.06|0||C|0.08||
|Denmark|1993|3|-0.04|1|3|C|0.11|0.32|
|Ecuador|1999|1|-0.29|1|3|C|0.12|0.96|
|Ethiopia|1992|4|-0.54|1||C|0.40||
|Fiji|2009|2|-0.19|1|1|B|0.39|0.76|
|Finland|1993|1|-0.11|3|1|A|0.15|1.19|
|France|2010|2|-0.04|||D|||
|Georgia|1998|4|-0.13|2|0|A|0.65|1.30|
|Germany|1991|1|-0.16|3|3|B|0.14|2.72|
|Guatemala|1990|3|-0.21|2|3|C|0.19|0.48|
|Hungary|2009|1|-0.11|3|1|A|0.18|11.51|
|Iceland|2008|4|-0.17|3||C|0.24||
|India|1991|3|-0.18|2|0|A|0.17|0.58|
|Indonesia|1998|1|-0.37|5|0|A|0.15|0.55|
|Ireland|1993|1|-0.09|1|0|A|0.06|1.90|
|Israel|2002|2|-0.07|2|3|C|0.08|5.73|
|Italy|1995|1|-0.09|1|0|A|0.09|1.59|
|Japan|1995|3|-0.15|7|3|A|0.06|12.44|
|Jordan|2009|2|-0.04||0|A||2.10|
|Korea,Rep.|1997|4|-0.34|4|2|A|0.14|1.31|
|Lithuania|1995|2|-0.08|0|4|C|0.18|0.67|
|Luxembourg|2010|2|-0.03|||D|||
|Mexico|1995|1|-0.36|0|1|C|0.11|0.47|
|Mongolia|2009|1|-0.17|1|2|C|0.37|1.73|

12
health considers all public spending on primary, secondary and tertiary health care of the three systems: the National Health Fund (FONASA),<sup>7</sup> and the health care for the armed forces (CAPREDENA) and the police (DIPRECA).

**Table 1: Structure of Chile’s government spending, 2013**

|**Government spending**|**% of total**<br>**expenditure**|**% of GDP**|**Included**<br>**in analysis**|
|---|---|---|---|
|**Total expenditure**|**100.00%**|**21.65%**||
|**Social spending**|**63.14%**|**13.67%**||
|Social protection|21.10%|4.57%||
|Social assistance, of which:|7.59%|1.64%||
|Conditional/unconditional cash transfers|1.96%|0.42%|Yes|
|Non-contributory pensions|4.05%|0.88%|Yes|
|Near-cash transfers|1.47%|0.32%|Yes|
|Other|0.11%|0.02%|No|
|Social security, of which:|13.51%|2.93%||
|Old-age pensions|10.15%|2.20%|Yes|
|_Bonos de reconocimiento_|3.36%|0.73%|No|
|Education, of which:|19.80%|4.29%||
|Pre-school|2.38%|0.51%|Yes|
|Primary|7.05%|1.53%|Yes|
|Secondary|4.03%|0.87%|Yes|
|Adults|0.31%|0.07%|Yes|
|_Diferencial_|1.23%|0.27%|Yes|
|Tertiary|4.11%|0.89%|Yes|
|Non-separable by level|0.69%|0.15%|Yes|
|Health, of which:|17.59%|3.81%||
|Primary FONASA|3.36%|0.73%|Yes|
|Secondary/tertiary FONASA, MLE, FF.AA.|10.32%|2.23%|Yes|
|Sectoral investment|0.81%|0.18%|Yes|
|Supply of the national health system|0.04%|0.01%|Yes|
|Other|3.06%|0.66%|No|
|Housing and urban services of which:|4.65%|1.01%|No|
|**Subsidies, of which:**|**2.26%**|**0.49%**||
|Energy|0.00%|0.00%|No|
|Water|0.20%|0.04%|Yes|
|Gas in the Magallanes region|0.09%|0.02%|Yes|
|Public transportation|1.96%|0.42%|Yes|
|**Infrastructure, of which:**|**1.46%**|**0.32%**||
|Water and sanitation|0.55%|0.12%|No|
|Rural roads|0.92%|0.20%|No|
|**Defense spending**|**4.72%**|**1.02%**|**No**|
|**Other spending**|**28.41%**|**6.15%**|**No**|

_Source_ : Author’s elaboration based on the 2013 executed budget published by Chile’s Budget Office (Dirección de Presupuestos, DIPRES). _Notes_ : Other spending includes, for instance, legislative spending, or expenditures on culture and sports. MLE = _Modalidad Libre Elección_ (free-choice modality). FF.AA. = armed forces. The figures shown do not necessarily coincide with those published by multilateral organizations due to differences in concepts and definitions.

> 7 This considers the two modalities of FONASA: institutional and free-choice

4
assumed to be welfare reducing. However, this may not be the case because the income shocks, especially aggregate income shocks, may lead to new welfare maximizing equilibria, including those with lower human capital accumulation. Policymakers may then choose to intervene if the social welfare of the population can be increased by providing incentives to increase human capital investments.

These human capital investment decisions can pose long-term effects on households and as such, policy makers may find it advantageous to provide households with instruments to mitigate the impact of income shocks. For countries in Eastern Europe and Central Asia, following World Bank (2011), we suggest that governments can follow a three pronged strategy. First, strengthen automatic stabilizers, such as unemployment insurance and poverty targeted social assistance, to ensure that programs are able to respond to the increased demand for safety nets. Ensure unemployment insurance and social assistance program coverage is sufficiently broad, centralize social assistance financing so that local government budget constraints do not impede registering needy people for social assistance, and upgrade program administration to improve targeting and response times. Second, adjust safety net program parameters to reflect changing household conditions. For example, during natural disasters or economic recessions, safety net parameters can be adjusted to improve government responses; for example, by lengthening the duration of unemployment insurance benefit payouts or reducing the activation conditions associated with social assistance programs when jobs are scarce. Third, activate new safety net programs to fill coverage gaps. When existing safety nets cannot respond fully, new programs can be started to reach uncovered vulnerable people. Public works, for example, can be an effective labor market program during aggregate shocks, especially labor market shocks, because it creates jobs while also addressing small-scale infrastructure development goals.

19
# **Supplementary References**

- [1] GB Anderson et al. “Hurricaneexposuredata: Data Characterizing Exposure to Hurricanes in United States Counties”. In: _R Packag. version 0.1_ (2020).

- [2] Kirill Borusyak, Rafael Dix-Carneiro, and Brian Kovak. “Understanding migration responses to local shocks”. In: _Available at SSRN 4086847_ (2022).

- [3] Solomon M Hsiang and Daiju Narita. “Adaptation to cyclone risk: Evidence from the global cross-section”. In: _Climate Change Economics_ 3.02 (2012), p. 1250011.

41
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

regulatory oversight. The effectiveness of competition and incentive regulation will determine whether the benefits of reform will exceed the higher transaction costs in order to produce net performance improvements. The issue has been researched in the context of the UK power sector.

- _H.8.0: Transaction costs are significant for developing countries and are nonlinear with sector size. Below a certain size, some types of restructuring have prohibitive transaction costs._

- _H.8.1: There is a threshold size below which wholesale market competition in generation is not worthwhile, and power purchase agreements (PPAs) may be preferable._

Hypothesis H.8.0 examines the relationship between the system size and the increase in transaction costs resulting from restructuring of the sector. It suggests a cut-off point below which the increased transaction costs begin to exceed the benefits of unbundling of the industry. Hypothesis H.8.1 examines the effect of system size on choice of market design.

# **5.1.9 Sector endowment**

There is an ongoing debate on the extent to which successful liberalisation depends on resource endowment.

- _H.9.0: A diverse mix of generation mix facilitates introduction of competition in the sector._

- _H.9.1: The availability of natural gas facilitates introduction of competition in the sector._

- _H.9.2: Energy and fuel independence are positively correlated with introduction of market-oriented reform in developing countries._

Hypothesis H.9.0 reflects the issue of whether limitations on the available technology, or the dominance of hydroelectric capacity, limits the scope for free entry in power generation. Hypothesis H.9.1 reflects the potential of gas-fired generation which is efficient at small scale, has a low capital cost and fast build-time to enable new entrants to enter at lower risk than with other fuels. Hypothesis H.9.2 is based on the idea that countries (e.g. Chile, UK, Argentina, Norway) which have access to own primary energy resources such as hydroelectricity or natural gas seem to have been more likely to liberalise their energy sectors.

# **6 Conclusions and policy implications**

Although electricity sector reform has evolved over a number of years, it has largely remained a work in progress. There is a need to learn from the accumulated experience, as many developing countries are either contemplating to implement reform, or are at some intermediate stage. Our review of cross-country studies has revealed that there is 45
closely correlated, with pair-wise correlation coefficients around 0.95. See appendix C for details of the constructions of these and other TFP measures.<sup>20</sup> The various measures of TFP have the virtue of capturing productive efficiency, and using various popular ways to measure TFP allows us to ensure the robustness of the productivity results. We in addition consider two alternative outcomes: labor productivity ( _Labor Prod,_ measured as the logarithm of sales per employment) and ROA, respectively. Labor productivity is more transparent, but it does not capture differences in inputs. We also look at the ROA because improving profitability was an objective of the SOE restructuring program. The dummy variable, r r r i i , equals one for all the years after firm _i_ has undertaken privatization and 0 otherwise. We control for PPr rr i , the city-level macro variables (i.e., _GDP Per capita_ and _GDP Growth_ ). We further control for ZZ industry-year fixed effects, _ρjt_ , and the interaction terms between the firm-level pre-treatment characteristics, , and year dummies , in the regressions, thereby flexibly controlling for the time-varying effects of such characteristics on the outcome variables. In the OLS ii ii XX , λλ specification, we do not, but in the firm fixed-effects specification, we do control for the firmlevel fixed effects _γi_ . The FE specification has the advantage of controlling for all firm-specific time-invariant factors, but it cannot account for the endogeneity of privatization based on selection on time-varying firm factors.

According to the OLS results in Panel A of Table 6, privatizations are invariably associated with a large effect on firm productivity or profitability. Privatizations are associated with a higher TFP_OP or TFP_IN by 20-22 log points,<sup>21</sup> and a higher labor productivity by 26 log points. Privatizations are also associated with an increase in profitability by 1.1 percentage points.

According to the firm-FE results in Panel B of Table 6, the magnitudes of the privatization effects are in the same ballpark as the OLS estimates, though slightly smaller. Privatization is associated with an increase in TFP_IN and in TFP_OP by 14 and 16 log points, respectively. The TFP effects are all slightly smaller than the labor productivity effects of 17.7 log points.

> 20 In constructing the index function TFP ( _TFP_IN_ ), we rely on factor shares based on OECD countries (Bentolila and Saint-Paul 2003). We have also tried using the factor shares based on our own data and obtained qualitatively similar conclusions. Using factor shares based on our own data is inappropriate since the factor shares reflect choices associated with privatization itself and are thus contaminated. See appendices C and D.

> 21 We focus on TFP_OP and TFP_IN because they better deal with various biases associated with estimating TFP using the OLS method. But results using all three TFP measures are qualitatively very similar.

``` 18 ```
# _6.- REFERENCES_

# Anuario del Transporte, (1997). Transeditores, S.A. Spain.

Armstrong, M., Cowan, S. and Vickers, J., (1994). _Regulatory Reform. Economic Analysis and British Experience._ The MIT Press.

Ashford, N. And Wright, P.H. (1992). _Airport Engineering._ John Wiley & Sons, Inc. US.

Borenstein, S. y Zimmerman, M.B. (1988). "Market Incentives for Safe Commercial Airline Operation". _American Economic Review_ 78, 913-935.

Bureau of Industry Economics, (1995). _International Benchmarking an overview._ Report 95/20. Australian Government Publishing Service.

Chalk, A. (1983). _The Economics of Airline Safety._ Ph.D. dissertation. Washington University.

Christensen, P. et al. (1998). "Internalization of Externalities". _Deliverable 7 of Pricing European Transport Systems._ Project No. ST-96-SC.172. Research Project for the Commission of the European Communities.

Doganis, R. (1991). _Flying Off Course. The Economics of International Airlines._ Harper Collins. London.

Doganis, R. (1992). _The Airport Business._ Routledge. London.

Doganis, R. and Graham, A. (1995). "The Economic Performance of European Airports". Research Report 3, University of Cranfield.

European Commission, (1993). _European Economy. Annual Economic reportfor 1993._ Fleming, K. and Ghobrial, A. (1993). "A normative framework for assessing airport security". _Transportation Quarterly,_ vol. 47, No.3, pp. 413-419.

Forsyth, P. (1984). "Airlines and Airports: Privatization, Competition and Regulation". Fiscal Studies, Vol.5, pp. 61-75.

Forsyth, P. (1997). "Price Regulation of Airports in Australia". Department of Economics, Monash University, Clayton, Australia. Paper presented at The Air Transport Research Group Conference, Vancouver.

ICAO, (1991). Manual sobre los aspectos econ6micos de los aeropuertos.

_55_
# Table S7. Model With Interaction Between Inequality and Initial Income (Static Panel Model)

|Dependent Variable is:|l|n(yit)||
|---|---|---|---|
||(1)|(2)|(3)|
|Inequality Variable is:|Gini<br>Brueckner et al. (2015)|Net Gini<br>Solt (2015)|Market Gini<br>Solt (2015)|
|Inequalityit|64.55***<br>(19.35)|161.79***<br>(42.73)|90.23***<br>(17.43)|
|Inequalityit|-9.92***|-24.76***|-13.26***|
|*ln(yi1970)|(2.83)|(6.48)|(2.58)|
|Kleibergen Paap F-Statistic|34|26|38|
|Country FE|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|
|Observations|497|744|744|

Note: The method of estimation is two-stage least squares. Bootstrapped standard errors are shown in parentheses. The instrument for _Inequality_ is _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

51
33 the trigger point, leading to increasing foreign labor inflows that are mutually reinforcing and facilitated through the formation and accumulation of social capital.

# **VII. Policy Failure**

Foreign labor management has been largely successful in Singapore and Malaysia for a variety of reasons, not least of which is effective immigration policy. The news is not all good, however. The shortcomings of immigration policy are evidenced by the existence of a large informal sector of illegal foreign workers. Anecdotal evidence indicates that employment of foreign workers in the informal sector has grown rapidly in Singapore in recent years, concurrent with the liberalization of restrictions on legally imported labor. Malaysia's legal foreign labor force more than doubled between 1990 and 1996, and currently exceeds a million workers, equivalent to 13 percent of the total labor force according to official statistics. Unofficial estimates that include illegal workers put the figure closer to 2 million, however. Illegal workers are found primarily in the construction sector, but also work in services, manufacturing and hotels and restaurants. In 1991, nearly 30 percent of all foreign labor in Malaysia was employed in the construction sector, while almost half of non-Malaysian workers were employed in agriculture and forestry where they constituted 30 percent of sectoral employment (World Bank, 1995).

Restrictions and permit fees have given rise to evasion through illegal recruitment and forged documents (a thriving industry) as well as informal trading of permits. Firms employing workers on unofficial contracts can pay lower wages and fewer benefits than stipulated by law, and can impose harsh working conditions on illegal workers who are powerless to complain for fear of deportation. Most evidence suggests that employers retain the premium saved by avoiding levy payments and compensate illegal workers below their legal counterparts; there is some countervailing evidence, however, that savings are passed on to the illegal workers, whose wages are effectively higher than their legal counterparts (Wong, 1997).

It is important to recognize the disadvantages associated with informal employment. Workers on informal contracts do not benefit from protective legislation and social benefits. Stories of abuse are not uncommon, especially involving domestic workers (e.g. the Filipina maid incident in Singapore cited above). Whereas the informal sector effectively acts as a buffer to the formal sector, insulating total economic output from negative exogenous shocks, losses are not altogether avoided. On the contrary, they are incumbent on the informal sector. Without labor market institutions that protect workers' rights,
# **Table 2: Sample summary statistics**

|Variable name|Definition|Mean|Standard<br>error|
|---|---|---|---|
|Dependant<br>variables:||||
|_EMPLOYED_|0-1 dummy variable, =1 if individual was employed (2000)|0.314|0.464|
|_COLLEGE_|0-1 dummy variable, =1 if individual was in college (2000)|0.287|0.453|
|_EARNINGS_|Individual’s annual earnings in IDR/Indonesian Rupiah (2000)|2,073,100|2,281,100|
|_EBTANAS2_|Senior secondary school EBTANAS score|0.390|0.488|
|Explanatory||||
|variables:||||
|_RURAL_|0-1 dummy variable, =1 if household was in rural area (1997)|0.306|0.461|
|_FEMALE_|0-1 dummy variable, =1 if individual is female|0.503|0.500|
|_AGE_|Individual’s age (1997 )|19.545|1.336|
|_MARRIED_|0-1 dummy variable, =1 if individual was married (2000)|0.065|0.246|
|_SMK_|0-1 dummy variable, =1 if individual was attending SMK (1997)|0.390|0.488|
|_EDUHH_|Highest education level in years of schooling of household|||
|___|parents or household head (in absence of parents) (1997)|8.587|4.013|
|_HH_INCOME_|Household annual income in IDR/Indonesian Rupiah (1997)|5,491,500|6,365,000|
|_EBTANAS1_|Junior secondary school EBTANAS score|33.836|6.322|
|_AGE 0 T0 5_|Number of household member age 0-5 (1997)|0.337|0.622|
|_AGE 6 TO 14_|Number of household members age 6-14 (1997)|1.077|1.054|
|_AGE ≥ 15_|Number of household members age >=15 (1997)|5.089|2.042|
||Proportion of SMK schools among frequently mentioned high|||
|_P_SMK_|schools by households in village (2000)|0.353|0.347|
|_PPUBLIC_|Proportion of public high schools among frequently mentioned|||
|___|high schools by households in village (2000)|0.469|0.372|

In order to determine the probability of achieving employment after secondary school graduation, initially a methodology similar to that in a variety of existing literature is used, with a sub-sample with SMK or SMA as terminal education. Female and male samples are also distinguished for purposes of the estimation. The sample generates results that conform to those found in the majority of the existing literature, demonstrating that SMK graduates 11
Table 3: Effects of LEAP on production and income

||(1)<br>Meat and fish<br>Production<br>|(2)<br>Meat and fish<br>sales<br>|(3)<br>Income crop<br>production<br>in 2012 in<br>|(4)<br>Off-farm<br>income<br>|(5)<br>Average<br>effect|
|---|---|---|---|---|---|
||in 2012 in log|in 2012 in log|log|in 2012 in log||
|_Full sample_||||||
|LEAP|1.47**|2.90**|-0.87|-0.44|0.13**|
||(0.56)|-0.69|(0.93)|(0.41)|(0.06)|
|constant|10.68**|6.74**|7.14**|14.19**||
||(0.51)|(0.53)|(0.68)|(0.27)||
|N|548|548|548|548||
|_ID Poor 1 and 2_||||||
|LEAP|1.26**|2.81**|-1.39|-0.07|0.13*|
||(0.55)|(0.71)|(1.00)|(0.39)|(0.07)|
|constant|10.79**|6.59**|6.73**|14.09**||
||(0.46)|(0.53)|(0.76)|(0.26)||
|N|367|367|367|367||
|_Non-poor_||||||
|LEAP|1.91**|3.07**|0.14|-1.20|0.13*|
||(0.79)|(1.15)|(1.26)|(0.76)|(0.07)|
|constant|10.46**|7.06**|7.98**|14.40**||
||(0.72)|(0.92)|(0.84)|(0.47)||
|N|181|181|181|181||

Note: Columns 1-4 report OLS estimates with village-clustered standard errors. Column 5 reports the standardized average effect, as described in Kling et. al. (2004) and Clingingsmith et. al. (2009), across the measures in columns 1-4

* = significant at the five percent level, **=significant at the one-percent level

33
- <mark>Solimano, A., M. Pollack, U. Wainer, e</mark> t al <mark>. 2007. “Micro Empresas, Pyme y Desarrollo Económico. Chile y la Experiencia Internacional.” Documento de Trabajo Working Paper 3. Santiago, Chile: Centro Internacional de Globalizacion y Desarrollo.</mark>

- Zhao, H., and S. Seibert. 2006. <mark>“The Big Five personality dimensions and entrepreneurial status: A meta-analytical review.”</mark> _<mark>Journal of Applied Psychology</mark>_ <mark>, 91(2): 259-71.</mark>

29
the African mean based on the explanatory variables in our base models. Still, the results are instructive.

Table 7 reports the determinants of financial development and financial inclusion in SubSaharan African countries. Several coefficients are significant at standard confidence levels and all of these have the expected sign. Perhaps the most important difference between Africa and the rest of the developing world is that population density is much more strongly linked to both financial development and financial inclusion than it was elsewhere in the world. Moreover, our proxy for natural resources is strongly negatively linked to financial development and financial inclusion. GDP per capita and the KKM index are positively linked to financial development and financial inclusion for the African sample. In all, the determinants are more tightly linked to financial development and inclusion for African countries than for non-African countries. In part, this could be because the global financial crisis disrupted relationships between variables that held from 1990 to 2006 for non-African countries.

# **6. The Impact of Mobile Banking on Financial Development and Inclusion in Africa**

Having established population density as a key factor for financial development and financial inclusion in Africa we explore whether innovations and financial services, such as mobile banking, have helped to overcome the financial gaps in Africa. As noted, the development of mobile banking in Africa started in Kenya with M-Pesa, which is a mobile phone–based service that greatly facilitates money transfers and remittances by the poor. It has been used primarily to transfer money between individuals rather than as a vehicle for saving.

According to the World Bank Global Financial Inclusion (Global Findex) Dataset, by 2011 67% and 60% of the adult population in Kenya used mobile telephones to receive and send money, respectively. Mobile banking spread quickly in Kenya thanks, in part, to the fact that the operator of M-PESA, Safaricom, controls two-thirds of the telecoms market in Kenya. However, as shown in Table 8, mobile banking has also taken off in other African countries such as Angola, Gabon, Republic of Congo, Nigeria, Somalia, Sudan and Uganda.

15