rafmacalaba/datause-encoder-data huggingface dataset:
85 A residential consumer can purchase up to 18 kg of LPG during a three‐month period at the old subsidized price if monthly electricity consumption is less than 90 kWh. Food vendors with shops smaller than 20 square meters can purchase up to 150 kg of LPG per month at the subsidized price. They first need to register at the provincial Energy Ministry office or the Energy Service Center in Bangkok (Thai PBS 2015).

PTT, and not the government, has been carrying subsidies for automotive CNG. The government had fixed the price of CNG at B 10.50 per kg for a long time, and PTT provided an additional price discount of B 2 per kg for public transportation. In October 2014, the government began to reform automotive CNG prices, raising them in succession by February 2, 2015 to B 13 per kg for private transport operators and to B 10 per kg for public transport operators. According to PTT, the cost of automotive CNG was B 15.50–16 per kg, still above the prices charged to consumers. According to _Platts_ , PTT had incurred a loss of B 10 billion (US$0.31 billion) on CNG sales in the first half of 2014, up from 8.72 billion baht (US$0.29 billion) in the first half of 2013 ( _Platts International Gas Report_ 2015). In September 2015, the price of CNG was raised further to B 13.5 (US$0.37) per kg for personal vehicles but remained at B 10 per kg for public transport operators. B 13.5 per kg was still below the cost of supply, but the loss in the first half of 2015 fell to B 5 billion (US$0.15 billion) ( _Nation_ 2015b).

# **Tunisia**

Subsidies in Tunisia for petroleum products, natural gas, and electricity amounted to 3 percent or more of GDP in recent years until 2015 (Figure 25). The IMF estimates that, at US$86 per barrel of crude oil, domestic prices in 2014 were 96 percent of market‐based prices for gasoline, 84 percent for diesel, 52 percent for kerosene, 46 percent for heavy fuel oil, 18 percent for LPG, and 71 percent for electricity (IMF 2014m). At the end of August 2015, prices of kerosene, heavy fuel oil, and LPG were still 79, 71, and 27 percent of market‐based prices (IMF 2015k). The price of kerosene has not been raised since 2010 and that of LPG since 2011.

**Figure 25: Central government spending on energy subsidies in Tunisia**

<!-- Start of picture text --> 3,000 8%<br>2,500<br>Tunisian dinar<br>6%<br>2,000 US$<br>1,500 % of GDP 4%<br>1,000<br>2%<br>500<br>0 0%<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br>TD or US$ million % of GDP<br><!-- End of picture text --> _Source:_ IMF 2012a, 2015k.

_Note_ : The budgetary transfers in 2013 is a combination of energy subsidies for 2013 (TD 2,854 million) and payment of arrears for energy subsidies in 2012 (TD 880 million). The 2016 figures are projections.

The government has been gradually reducing generalized subsidies, replacing them with targeted compensation where appropriate. Energy subsidies for cement companies were reduced in January 2014 and eliminated by the end of 2014. Gas and electricity tariffs were raised in January and May 2014.
Table 7. Militarization and Fearon and Laitin’s measures of ethnic fractionalization adjusted for cultural/linguistic distance.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Military<br>Expend.|Military<br>Expend.|Military Pers.|Military<br>Pers.|Arms<br>Imports|Arms Imports|
|Cultfrac (Fearon)|-1.447|-1.488|-0.906|-0.939*|-1.703|-1.731|
||(1.54)|(1.55)|(1.62)|(1.68)|(0.95)|(1.00)|
|Relfrac (Fearon &|0.473|0.469|-0.620|-0.651*|0.012|0.046|
|Laitin)|||||||
||(0.61)|(0.60)|(1.62)|(1.70)|(0.01)|(0.02)|
|GNI p.c. (ln)|-0.169|-0.196|0.081|0.062|-0.432|-0.393|
||(0.67)|(0.77)|(0.70)|(0.54)|(1.10)|(1.00)|
|Economic growth|1.394**|1.369**|0.097|0.066|-0.311|-0.211|
||(2.24)|(2.22)|(0.15)|(0.10)|(0.12)|(0.08)|
|Polity|-0.037**|-0.036**|-0.012*|-0.011*|-0.098|-0.104*|
||(2.16)|(2.08)|(1.81)|(1.70)|(1.55)|(1.65)|
|Government|0.160***|0.158***|0.029**|0.029**|0.160**|0.162***|
|expenditures|||||||
||(4.52)|(4.56)|(2.24)|(2.22)|(2.44)|(2.61)|
|Population (ln)|-0.064|-0.086|-0.166***|-0.185**|0.323|0.338|
||(0.54)|(0.70)|(2.62)|(2.55)|(1.35)|(1.44)|
|Contiguous|0.131**|0.122**|0.579**|0.565**|0.117|0.126*|
|militarization|||||||
||(2.55)|(2.44)|(2.29)|(2.21)|(1.56)|(1.73)|
|Peace years (civil|-0.019***|-0.019***|-0.004|-0.004*|-0.021|-0.020|
|war)|||||||
||(3.51)|(3.48)|(1.55)|(1.77)|(1.55)|(1.58)|
|Peace years (intern.|0.006|0.006|-0.011*|-0.011*|-0.035**|-0.031**|
|confl.)|||||||
||(0.65)|(0.68)|(1.82)|(1.79)|(2.21)|(2.12)|
|Civil war|0.676***|0.675***|0.066|0.064|2.472**|2.487***|
||(3.35)|(3.37)|(0.90)|(0.89)|(2.55)|(2.59)|
|International<br>conflict|<br>0.778|<br>0.776|<br>0.623*|<br>0.630*|<br>3.299**|<br>3.234**|
||(1.24)|(1.25)|(1.80)|(1.81)|(2.17)|(2.16)|
|Observations|1561|1561|1362|1362|1374|1374|
|Countries|129|129|136|136|137|137|

Notes: Absolute t-statistics in brackets. Constant and year-specific time-dummies included, but coefficients not reported. *, **, *** significant at .1, .05 and .01 level, respectively.

27
Policy Research Working Paper 9792

# **Abstract**

The prices of 27 internationally traded commodities are decomposed into transitory and permanent shocks by applying an ideal band-pass filter to monthly data from 1970–2020. The two types of shocks contributed roughly equally to price variations, but with wide heterogeneity. Permanent shocks ac-counted for two-thirds of the variability in agricultural prices but less than 30 percent in energy prices. The transitory shock component revealed three medium-term cycles. The first (from the early 1970s to the mid-1980s) and third (from the early 2000s to 2020 onward) exhibit similar duration and involve almost all commodities, while the second (spanning the 1990s) is mostly applicable to metals, with the notable absence of energy. The permanent shock components differ across commodities, with an up-ward trend for most industrial commodities and downward trend for agriculture. Moreover, the permanent component of commodity prices where investment is irreversible, including energy, metals, and tree crops, exhibits a high degree of nonlinearities, which also coincide with the two post–World War II supercycles. By contrast, the permanent component of annual agricultural prices is linear, reflecting greater flexibility in investment allocation and input use of these commodities. Prices of commodities subjected to widespread policy interventions, such as international commodity agreements, exhibit persistent deviations from linear trends.

This paper is a product of the Prospects Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jbaffes@worldbank.org.

_The Policy Research Working 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 necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> i<br>90<br>=<br>E<br>=<br>n<br>< 60<br>2<br>s<br>a<br>oO<br>aoOfa<br>30 4<br>.<br>.<br>0 = — 4<br>jan feb mar apr may jun jul aug sep oct nov dec<br><!-- End of picture text --> <!-- Start of picture text --> MilAnnual 16 -69precipitations (mm) [{'* wheeN<br>Ml 70 - 105 s<br>(7) 106 - 164<br>165 - 209<br>210 - 244<br>_ | 245 - 285<br>|) 286 - 330<br>MN 331 - 398<br>Ml 399 - 496<br>«<br>4<br>Noual@elionig<br>~~)<br>) czyGis’ ay venAbL"' po A |<br>0 200 400 km<br>| a i Gera suena cease wie<br><!-- End of picture text -->
**Table 5. Savings regressions, Listed Firms data**

|LABELS|1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|Log of net sales|0.001<br>(0.156)||||||
|Volatility of Sales||0.10***<br>(0.005)|||||
|Total Debt to Assets|||-0.01<br>(0.789)||||
|Interest Rate||||0.24***<br>(0.000)|||
|Gross Margin|||||-0.01<br>(0.748)||
|Net Income||||||0.08<br>(0.324)|
|year==2002|0.05|0.09*|0.07|0.07|0.05|0.05|
||(0.304)|(0.088)|(0.103)|(0.277)|(0.342)|(0.302)|
|year==2003|0.06|0.10*|0.09*|0.07|0.05|0.06|
||(0.210)|(0.055)|(0.051)|(0.209)|(0.268)|(0.212)|
|year==2004|0.06|0.11**|0.10**|0.10*|0.06|0.06|
||(0.174)|(0.030)|(0.025)|(0.070)|(0.176)|(0.151)|
|year==2005|0.11*|0.15**|0.13**|0.12*|0.10*|0.10*|
||(0.060)|(0.017)|(0.013)|(0.095)|(0.063)|(0.066)|
|year==2006|0.12*|0.17**|0.15**|0.15*|0.13*|0.12*|
||(0.062)|(0.018)|(0.012)|(0.081)|(0.058)|(0.067)|
|year==2007|0.06|0.11**|0.09*|0.10*|0.05|0.05|
||(0.171)|(0.042)|(0.059)|(0.064)|(0.259)|(0.214)|
|year==2008|0.04|0.08|0.08|0.07|0.05|0.04|
||(0.355)|(0.117)|(0.104)|(0.229)|(0.293)|(0.418)|
|Constant|-0.08|-0.13**|-0.07|-0.09|-0.04|-0.05|
||(0.107)|(0.028)|(0.108)|(0.124)|(0.440)|(0.278)|
||222|224|231|206|218|220|
||0.070|0.133|0.073|0.127|0.067|0.069|

37
# **1 Introduction**

Over the last 25 years low-income countries have reduced their share of state ownership by more than half.<sup>1</sup> In most cases, governments have privatized public assets because of critical budgetary conditions.<sup>2</sup> While international donors and creditors, like the World Bank or the IMF, made privatization programs a condition for economic assistance during the 1980s debt crisis, governments have been keen on using privatization proceeds to relax their budget constraints.<sup>3</sup> Privatization yielded about $50 billion per year in non-OECD countries, no less than one-third of the worldwide proceeds of privatization (Mahboobi, 2000; Gibbon, 1998, 2000). The fiscal benefits of privatization are not limited to the divesture proceeds of state owned enterprises (S0E). They also encompass the possible termination of recurrent, inefficient subsidies to the latter. In particular, when governments hardly discriminate between good and bad projects and/or management teams, they are likely to transfer too many resources to public firms. Privatization eliminates governments’ legal obligations to subsidize unprofitable firms. In practice, privatization reforms have resulted in substantial decreases of government subsidies to former SOEs.<sup>4</sup> The present paper aims to study the impact of poor fiscal conditions on the privatization decision in regard to infrastructure and public utilities in low-income countries.

Privatization brings well-known economic costs when industries are characterized by strong economies of scale. By giving up the direct control of firms’ operations, governments lose control over prices to the disadvantage of consumers. Private firms benefit from large market power and have large incentives to enter. In practice price increases

> 1According to a study by Megginson and Netter (2001) between 1980 and 1996 alone it went from 16% to 8% of GDP.

> 2This has also been the case in rich countries. For instance, the first Japanese privatizations were initiated in 1982 when the Japanese public deficit reached 41.2% of GDP. Similarly in the U.S. Lopezde-Silanes _et al_ . (1997) show that privatizations have been more likely in states where fiscal constraints were more binding.

3 Using a panel of 18 developing countries, Davis _et al._ (2000) show that budgetary privatization proceeds have been used to reduce domestic financing on a roughly one-for-one basis.

> 4For instance, the privatisation commission of Burkina Faso reports that government subsidies to SOEs went from 1.42 percent of GDP in 1991 to 0.08 percent of GDP in 1999 as a result of privatisation (AfDB-OECD 2003).

2
children in developing countries and one that may be particularly responsive to an event that induces a substantial income reduction. We consider the permanent departure of the father through either death or divorce, which are the two events in our data that consistently result in substantial income reduction for households. Although the channels through which these events might affect outcomes may differ,<sup>2</sup> our empirical work suggests that income reduction is the main driver of the observed effects.

More generally, our work is related to the literature that considers the causal effects of family disruption on child outcomes, which has concentrated on the United States and other developed countries. Although this literature has mainly focused on the permanent absence of a parent resulting from death or divorce, recent work has also highlighted the negative effects of parental migration on children’s schooling— particularly the migration of the father because it is males who migrate in most contexts (see Antman 2013 for a survey; Giannelli and Mangiavacchi 2010; Lahaie et al. 2009). A key finding throughout this literature is that the absence of a parent is likely to be correlated with unobserved factors, which may also explain the poorer outcomes of the children. Several methods have been used to account for the influence of such factors. For example, some studies have used sibling-difference (household fixed effects) models (Case, Lin, and McLanahan 2001; Ermisch and Francesconi 2001; Ginther and Pollak 2004; Gennetian 2005) that account for the fixed and unobservable endowments that are shared by siblings from the same family or the same mother, whereas others have compared children’s outcomes before and after the divorce of their parents (Cherlin et al. 1991; Painter and Levine, 2000), which assumes that preexisting disadvantages of the family or the child are captured by child fixed effects. Finally, quasi-experimental studies 6
Life Insurance Consumption across Countries”, _World Bank Economic Review_ 17, 51-88.

- Bekaert, Geert and Campbell R. Harvey (2000): “Foreign Speculators and Emerging Equity Markets,” _Journal of Finance_ 55, 565–613.

- Boot, Arnoud and Matej Marinc (2008): “The Evolving Landscape of Banking.” _Industrial and Corporate Change_ 17, 1173-1203.

- Boyd, John H., Stanley L. Graham and R. Shawn Hewitt (1993). “Bank Holding Company mergers with Nonbank Financial Firms: Effects on the risk of failure.” _Journal of Banking and Finance_ 17, 43-63.

- Chinn, Menzie and Hiro Ito (2006): “What Matters for Financial Development? Capital Controls, Institutions and Interactions,” _Journal of Development Economics_ 81, 163-92.

- Claessens, Stijn, and Luc Laeven (2004): “What Drives Bank Competition? Some International Evidence.” _Journal of Money, Credit, and Banking_ 36: 563-83.

- De Nicolo, Gianni (2000). Size, Charter Value and Risk in Banking: An International Perspective. International Finance Discussion Papers No. 689, Washington, DC: Board of Governors of the Federal Reserve System.

- Demirgüç-Kunt, Asli and Ross Levine (2001): “Bank-based and Market-based Financial Systems: Cross-Country Comparisons.” In: Demirgüç-Kunt, Asli and Ross Levine (Eds.):: _Financial Structure and Economic Growth: A Cross-Country Comparison of Banks, Markets, and Development_ , Cambridge, MA: MIT Press.

- Demirgüç-Kunt, Aslı, and Vojislav Maksimovic (2002): “Funding Growth in Bank-Based and Market-Based Financial Systems: Evidence from Firm Level Data.” _Journal of Financial Economics_ 65(3): 337-63.

- Demirguc-Kunt, Asli, Baybars Karacaovali and Luc Laeven (2005): “Deposit Insurance Around the World: A Comprehensive Database.” World Bank Policy Research Working Paper 3628.

- Freund, Caroline L. and Nikola Spatafora (2008): “Remittances: Transaction Costs, Determinants, and Informal Flows.” _Journal of Development Economics_ 86, 356-366.

- Hannan, Timothy H. and Gerald A. Hanweck (1988). Bank Insolvency Risk and the Market for Large Certificates of Deposit. _Journal of Money, Credit and Banking_ 20, 203-11.

- Honohan, Patrick (2008): “Cross-Country Variation in Household Access to Financial Services.” _Journal of Banking and Finance_ 32: 2493-2500.

Honohan, Patrick and Thorsten Beck (2007): _Making Finance Work for Africa_ . The World Bank

- International Monetary Fund (1984): “ A Guide to Money and Banking Statistics in International Financial Statistics”. Washington, D.C.

18
# **REFERENCES**

- Adams, R., Fleming, R., Chang, C., McCarl, B., Rosenzweig, C., 1995. A Reassessment of The Economic-Effects of Global Climate-Change on US Agriculture. _Climatic Change_ , 30(2):147-167.

- Alexandratos, N. 1996. China's projected cereal deficits in a world context. _Agricultural Economics_ , 15:1-16.

- Brown, L. 1995. _Who will feed China? Wakeup Call for a Small Planet_ . New York: W. W. Norton.

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- CIA, 2007. _World Fact Book_ . Found on https://www.cia.gov/library/publications/the-worldfactbook/print/xx.html.

- Eid, H., Marsafawy, S., and Ouda, S., 2007. Found on http://wwwwds.worldbank.org/external/default/WDSContentServer/IW3P/IB/2007/07/31/00015834 9_20070731143402/Rendered/PDF/wps4293.pdf.

- Fleischer, A., I. Lichtman, and R. Mendelsohn. 2007. Climate Change, Irrigation, and Israeli Agriculture: Will Warming Be Harmful? _Ecological Economics_ (forthcoming).

- Fan, S. and M. Agcaoili-Sombilla, 1997. _Why Do Projections on China’s Future Food Supply and Demand Differ_ ? EPTD Discussion paper 22. Washington DC: International Food Policy Research Institute.

- Huang, J., S. Rozelle, and M. Rosegrant, 1997. _China’s Food Economy to the 21st Century: Supply Demand and Trade_ .  2020 Vision Discussion Paper 19. Washington DC: International Food Policy Research Institute.

- Huang, J., S. Rozelle and M. Rosegrant, 1999.  China’s Food Economy to the 21st Century: Supply, Demand and Trade, _Economic Development and Cultural Change_ , 47: 737-766

- IPCC, 2007. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Summary for Policy Makers.

18
outcomes are similar across the different treatment groups (see Appendix Table A1). The followup surveys (described below) put in place strict protocols to ensure consistency in which household member was interviewed, ensuring the main remittance receiver responsible for household financial decision-making in the migrant’s absence was interviewed. Table 1 shows the balance across treatment groups in the individual characteristics of the respondents at the time of the follow-ups.

Household respondents in the study are on average 41 years old at baseline, with 33 percent female, and 85 percent married. They are typically the spouse, parent, or sibling of the migrant. Education levels are lower than the migrant’s levels on average: 14% have not completed primary school, 36% have only completed primary schooling, 28% secondary schooling, 19% senior high school, and 3 percent have a higher degree. Average household income is approximately US$150 per month.

Only half of all households report having any savings at baseline, and only 3 percent record income and expenditure. Despite 92 percent of respondents saying they discuss financial issues with family, only 40 percent had heard of the term financial budgeting, only 39 percent knew what an exchange rate was, and only 29 percent had heard of the TKI insurance that all legally registered migrant workers must have. These baseline levels suggest potential scope for financial literacy training to build knowledge and develop savings behaviors.

# **2.4 The Financial Literacy Intervention**

Training was generally conducted at the Singosari Training Center in Malang, which is a complex managed by the Malang’s Manpower & Transmigration Office. Aside from classrooms, workshops, meeting halls and dining halls, it also rents out rooms. Training sessions were also conducted directly at the offices of the PPTKIS when an individual PPTKIS had sufficient workers about to embark. These locations were very convenient for the migrant workers, since they typically live in facilities run by the PPTKIS for two to six months prior to departure, and the PPTKIS arranged transportation from their locations to the Training Center. Family members typically lived further away. The Regional Economic Development Institute (REDI) coordinated with the PPTKIS to invite family members to this training, and family members were provided with a transportation allowance and one night’s accommodation in order to facilitate their access to these training sessions.

Financial literacy training sessions for migrants and for their family members were conducted separately. The training sessions for the migrant workers lasted two full days of 9 8
financial products. These questions are also supplemented by others regarding the respondent’s attitudes towards financial institutions, risk, and coping strategies in times of economic hardship, among other issues.

By contrast, the most comprehensive effort to use the modular approach to measure usage, the LSMS, tends to ask broad, generic questions about ‘credit’ or ‘accounts’ or dealings with types of institutions such as banks. Another important difference between the FinScope and LSMS approaches is that the LSMS finance modules track household usage of financial services, whereas FinScope randomly selects individuals from the population to provide information only on their own use.

In light of these differences in approach, the purpose of this paper is to provide evidence from a randomized experiment that tests whether measured usage of financial services is similar when respondents are asked detailed product-based questions (the FinScope approach) as opposed to more generic, institution-based questions (the LSMS approach). To foreshadow our results, the two approaches yield similar estimates for basic products such as savings accounts with banks or other formal providers, but not for others such as insurance or credit provided by banks and other institutions.

These comparisons are potentially important because the expense of stand-alone surveys makes it unlikely that they will be rolled out throughout the developing world any time soon. Our results provide guidance on the product- and institution-based questions that yield similar estimates of usage, and they suggest ways that generic, institutions-based questions used in finance modules could be modified to produce similar estimates of usage for products such as insurance and formal credit.

6