rafmacalaba/datause-encoder-data huggingface dataset:
- 31 -

anywhere. Kerosene, on the other hand, must be bought in minimum quantities of a quart (1.14 liters), must be burned in a stove that costs the local currency equivalent of US$30, and is not available at all times. Also, many consumers complain that food cooked on kerosene stoves smells and tastes of kerosene. For these reasons, kerosene cannot be seen as a perfect substitute for charcoal. 11/

# Wood Replacement Cost

2.36 In densely populated rural and urban areas of many countries, the wood raw material is being depleted due to indiscriminate harvesting. Depletion of wood resources can take place over a number of years if the wood supply in the beginning is large in relation to demand. But, if nothing is done to correct the situation, supplies will eventually dwindle. By the time the critical point of scarcity is reached, it is too late to start planting trees to meet current demande As the average maturity period is ten to twelve years, people may be forced to switch to other fuels such as crop residues, animal dung or oil-based fuels.

2.37 Stumpage values can be calculated on the basis of the cost of replacing the lost wood resources. Planners calculate the cost of reforestation programs and arrive at an estimated stumpage price high enough to cover costs and give a specified return on investment. Governments may then try to raise the existing stumpage fees gradually to cover the full cost of growing the trees.

2.38 Table 2.5 from the 1986 World Bank Second Wood Energy Project Staff Appraisal Report for Malawi, 12/ shows how a stumpage estimate is derived from the cost of growing plantation trees for fuelwood. In this table, existing government costs are projected over a thirty year period. In the example, it was proposed to plant 3,000 hectares over a six-year period at an average rate of 500 ha/year. After the first crop had been felled, the trees would be allowed to grow from suckers for another three rotations, so that each crop cycle would be 24 years. A discount rate of 12 percent was used to discount both the costs and the volume. Discounted costs were then divided by discounted yield to arrive

- 11/ Sociological and traditional values also enter into household fuel choice. These factors may also affect the substitutability of fuels.

- 12/ In several World Bank reports and assessments, it has been stated that stumpage fees should be fixed so as to cover the long run marginal cost of growing the wood. This is a sound statement in theory but in practice it is almost impossible to undertake marginal cost analysis. To undertake rigorous marginal analysis, all cost and growth functions for tree production must be known. A better solution is to use average incremental costs (AIC), as in the Malawi example. AIC should not be confused with average costing procedures.
Heckman, J.J. 2007. "The economics, technology, and neuroscience of human capability formation. ." _Proceedings of the National Academy of Sciences_ 104(33): 13250-13255.

- Heckman, J.J., S.H. Moon, R. Pinto, P.A. Savelyev, and A. and Yavitz. 2010. "The rate of return to the HighScope Perry Preschool Program." _Journal of Public Economics_ 94 (1-2): 114-128.

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- Hedges, L.V., E. Tipton, and M.C. Johnson. 2010. "Robust variance estimation in meta‐regression with dependent effect size estimates." _Research Synthesis Methods_ 1 (1): 39-65.

- Hendren, N., and B. Sprung-Keyser. 2020. "A unified welfare analysis of government policies." _The Quarterly Journal of Economics_ 135 (3): 1209-1318.

- Hidrobo, M., J. Hoddinott, N Kumar, and M. Olivier. 2018. "Social protection, food security, and asset formation." _World Development_ (101): 88-103.

- Horowitz, J.L., and C.F. Manski. 2000. "Nonparametric analysis of randomized experiments with missing covariate and outcome data." _Journal of the American Statistical Association_ 95 (449): 77-84.

Ioannidis, J.P. 2008. "Why most discovered true associations are inflated." _Epidemiology_ 640-648.

Ioannidis, J.P., T.D. Stanley, and H. Doucouliagos. 2017. "The power of bias in economics research."

- Jara‐Ettinger, J., E Gibson, and Kidd, C. Piantadosi, S. 2016. "Native Amazonian children forego egalitarianism in merit‐based tasks when they learn to count." _Developmental Science_ 19 (6): 11041110.

- Johnson, R.C., and C.K. Jackson. 2019. "Reducing inequality through dynamic complementarity: Evidence from Head Start and public school spending." _American Economic Journal: Economic Policy_ 11 (4): 310-49.

- Kline, P., and C.R. Walters. 2016. "Evaluating public programs with close substitutes: The case of Head Start." _The Quarterly Journal of Economics,_ 131 (4): 1795-1848.

- Knudsen, E.I. 2004. "Sensitive periods in the development of the brain and behavior." _Journal of Cognitive Neuroscience_ 16 (8): 1412-1425.

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- Kremer, M., and A. Holla. 2009. "Improving education in the developing world: what have we learned from randomized evaluations?" _Annu. Rev. Econ._ 1 (1): 513-542.

- Krueger, A.B. 1999. "Experimental estimates of education production functions." _The Quarterly Journal of Economics_ 114 (2): 497-532.

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Lipsey, M.W., and D.B., Wilson. 2001. _Practical meta-analysis._ SAGE publications, Inc.
31

**Table 1. Global Trade Reduction Indexes, by Commodity, 1960 to 2004 (percent)**

||**1960-64**|**1965-69**|**1970-74**|**1975-79**|**1980-84**|**1985-89**|**1990-94**|**1995-99**|**2000-04**|
|---|---|---|---|---|---|---|---|---|---|
|**Grains and tubers**|**24**|**36**|**20**|**17**|**19**|**28**|**26**|**19**|**10**|
|Rice|49|50|58|42|41|58|53|32|43|
|Wheat|15|15|1|1|9|28|20|11|4|
|Maize|4|8|4|9|-3|9|10|2|3|
|Cassava|na|na|23|0|8|15|10|13|10|
|Barley|37|33|5|-13|-1|36|32|10|4|
|Sorghum|117|55|65|42|15|24|9|18|6|
|Millet|67|66|29|1|-14|-31|-114|-32|-22|
|Oat|16|10|-5|-1|-10|-2|-2|13|9|
|**Oilseeds**|**8**|**6**|**8**|**5**|**3**|**17**|**14**|**10**|**3**|
|Soybean|0|1|0|6|8|11|8|6|6|
|Groundnut|24|17|49|33|16|38|-12|-7|-10|
|Palmoil|20|28|12|-5|-11|-1|14|13|-3|
|Rapeseed|-1|19|9|4|10|39|28|7|12|
|Sunflower|-8|-5|-10|-2|-12|36|21|15|13|
|Sesame|48|60|62|65|55|43|41|45|32|
|**Tropical crops**|**26**|**35**|**23**|**35**|**37**|**28**|**28**|**16**|**11**|
|Sugar|89|143|27|40|47|56|44|41|55|
|Cotton|9|2|13|14|1|13|4|9|-4|
|Coconut|29|24|8|3|12|21|35|23|9|
|Coffee|18|30|31|37|46|33|13|12|2|
|Rubber|30|33|7|19|21|17|14|-4|-3|
|Tea|35|36|27|26|23|22|23|20|17|
|Cocoa|27|40|39|53|45|30|26|27|33|
|**Livestock products**|**23**|**27**|**24**|**34**|**42**|**36**|**33**|**21**|**22**|
|Pigmeat|27|37|28|25|47|25|11|9|8|
|Milk|81|83|79|133|131|125|63|53|45|
|Beef|24|20|17|18|32|47|32|33|32|
|Poultry|23|22|29|26|24|27|27|18|18|
|Egg|-9|-5|-6|11|8|13|11|11|7|
|Sheepmeat|58|70|96|139|83|68|45|24|20|
|Wool<br>|0|0|-6|-4|-7|-3|-4|0|0|
|**All of the above**<br>**28 commodities**|**23**|**29**|**20**|**23**|**27**|**28**|**27**|**17**|**12**|

Source: Authors’ calculations based on NRA and CTE estimates in Anderson and Valenzuela (2008).
A similar pattern is observed for the data regulation variables. A one standard deviation shock to each of these variables appeared to raise, on average, digital payment usage in the static model by 6.6 p.p. and 8.8 p.p., respectively, for the enablers data regulations and the data safeguards. In sharp contrast, the respective estimates fall to much smaller absolute values, with the estimated impact of the enablers data variables turning negative to about -0.5 p.p. and the impact of the data safeguards regulations falling to about 0.6 p.p. in the dynamic model but rising slightly to 0.9 p.p. in the dynamic model with interactions. Further, the negative impact of data enablers is plausible because this variable captures regulatory restrictions on the use of digital payments, whereas the safeguards data variables capture the effect of regulations that safeguard data privacy, thus enhancing public trust in digital transactions. These considerations add credence to our interpretation of the dynamic model and the model with interactions as producing estimates that are closer to the true causal effects.

Overall, if we put more emphasis on the results from the dynamic models with interactions with the size of the COVID-19 shock, the largest average effects come from the variables related to digital infrastructure, namely pre-COVID-19 internet coverage with a 3.7 p.p. effect and mobile subscriptions per capita with a 3.2 p.p. effect. These are followed by pre-COVID-19 educational attainment with an effect of 2.6 p.p.

# **_5. Discussion_**

The key findings from the base results in Table 4 and Table 5, which are again our best attempts to uncover causal relationships, center on the importance of infrastructure and connectivity and the capabilities of users in facilitating greater usage of digital payments during the crisis. The patterns underscore the importance of policies that promote widespread access to reliable electricity and access to high-speed broadband signals. Recent research also shows, for 22
compiled by the United Nations, which track actual movements of capital within given annual intervals. Moreover, in fDi Markets, missing figures for capital expenditure and employment are often estimated by the publisher, and are not attributed proportionally according to the equity participation of foreign investors. A recent evaluation of alignment with official sources has shown that aggregates produced from fDi Markets have high levels of correlation with actual flows (Shekhar Aiyar & Presbitero 2023). Regularly used in combination with official FDI statistics by international organizations (United Nations 2023, World Bank Group 2020) to analyze investment project dynamics, both sources may however have incomplete coverage of smaller countries at the low end of the income spectrum (Casella et al. 2023). We address this issue by using a minimum threshold of investors per year received by a host country to be included in our analysis.

# **3 Measurement and empirical framework**

**FDI Concentration.** Two measures are used to evaluate FDI concentration levels at a given time: i. Concentration Ratio corresponding to the share of the _N_ largest investing firms in total FDI flows and ii. The Herfindahl-Hirschman Index (HHI), based on the sum of the squares of each investing firm’s share in total FDI flows. While both have been widely used in the empirical literature, the former is less affected by measurement error or truncation at the bottom of the distribution, and generally preferred for its simplicity and ease of interpretation. Regional and income group aggregates correspond to cross-country averages weighted by the value of FDI flows in each country belonging to the group.

**Country and regional trends.** Trends over time are presented conditional on country and region-year fixed effects, in a panel data specification. Let _Yit_ denote a measure of FDI concentration (the concentration ratio of the top 5 investing firms or the Herfindahl-Hirschman concentration index) in host-country _i_ in year _t_ . Then, our trend equation is:

where _Trendt_ indicates a linear time trend and _Developingi_ indicates whether country _i_ is a developing economy.<sup>1</sup> _δi_ are country fixed effects and _θrt_ are region-by-year fixed effects. We include this set of fixed effects to net out time invariant conditions, regional dynamics and year-specific global economic conditions, allowing us to more accurately ascertain the evolution of FDI concentration in developing as opposed to high income economies. A proper analysis of variance (ANOVA) is used to investigate the sources of variation in FDI concentration - at the country, sector, and year levels as well as interactions of the three - based on the methodology outlined in Gamst et al. (2008).

> 1Developing countries are defined as all countries that are not “high income” according to the World Bank’s income classification for the year 2003.

4
||Food|insecurity|pre and dur|ing-CO|VID-19||||
|---|---|---|---|---|---|---|---|---|
|Wave3 (2021)||49.1%||26.6|%|$.8%|o|e|
|Wave7(2078)||50.9%|||34.6%||B.A%|on|
|0%|10%<br>20%|40%|40%<br>50%|60%|Fo%|60%|80%|100%|
||Never|Seldom|sSometmes|eOften|=Always||||
|GENCO-I (JPCL)|0.81|0.81|0.81|
|---|---|---|---|
|GENCO-II (CPGCL)|0.70|0.80|0.55|
|GENCO-III (NPGCL)|0.78|0.80|0.85|
|GENCO-IV (LPGCL)|0.20|0.20|0.20|
|Rousch Power|0.96|0.96|0.96|
|Lalpir Power|0.97|0.97|0.97|
|Uch Power|0.94|0.94|0.94|
|Liberty Power|0.97|0.97|0.97|

Source: State of Industry Report 2016.

This huge shortage of supply, relative to peak demand, has had large impacts on the reliability of the power sector. Load shedding has been substantial for several years, by all the unbundled sector DISCOs, as shown in table 6. The sole private sector firm (KE) was among the better performers, indicating that its policies for purchasing power from various IPPs may have been better matched to demand.

**Table 6: Average daily load shedding (hours) for distribution utilities**

||**2011-12**|**2012-13**|**2013-14**|**2014-15**|**2015-16**|**2016-17**|
|---|---|---|---|---|---|---|
|Peshawar - PESCO|4.9|4.8|4.8|2.5|2.3|3.2|
|Tribal - TESCO|-|-|10.5|8.0|7.7|-|
|Islamabad - IESCO|5.6|5.6|5.0|4.0|4.0|3.3|
|Gujranwala- GEPCO|3.6|3.2|3.2|4.0|4.0|3.3|
|Lahore - LESCO|2.1|0.7|3.5|2.3|1.7|2.0|
|Faisalabad - FESCO|7.0|7.8|7.3|4.3|3.5|3.2|
|Multan- MEPCO|9.0|9.0|10.0|4.3|3.2|3.4|
|Hyderabad - HESCO|3.8|7.3|3.8|4.0|3.3|4.5|
|Sukkur - SEPCO|4.0|2.0|2.0|1.0|1.0|2.3|
|Quetta - QESCO|10.5|11.1|10.5|3.4|2.8|3.9|
|KE|2.0|2.4|2.3|1.1|1.3|2.5|

_Source: NEPRA, State of Industry Report 2016_ In addition to these announced and unannounced cuts in supply, information was collected by the NEPRA to calculate the System Average Interruption Frequency Index (SAIFI), and the System Average Interruption Duration Index (SAIDI) for each of the DISCOs. Only the Islamabad (IESCO) utility was able to meet the allowed limits set by the regulator (SAIFI = 13 per annum; SAIDI = 14 minutes). However, the report published by the regulator<sup>27</sup> indicates that there were considerable inaccuracies in the collection of these data by the DISCOs and that these statistics cannot be taken as realistic.

> 27 _Performance Evaluation Report of All Distribution Companies for 2015-2016_ . National Electric Power Regulatory Authority.

27
# **1.Introduction and Motivation**

The way budgets are managed has direct implications on a government’s ability to maintain a prudent fiscal stance and efficiently deliver services. Public financial management (PFM) is the vehicle that enables governments to implement policy. This is broadly recognized by the literature and practitioners alike [1-6] and consequently governments with the assistance of the development community have invested considerable time and resources into PFM reforms. The OECD estimates that every year oversees development assistance to PFM amounts to about US$ 1.6 billion globally. [7] Financial management information systems (FMIS) play a central role in PFM, as they facilitate the budget execution process. [8-10] At their core, FMIS systems enable processing of financial transactions and have embedded a set of controls that ensure the PFM act is implemented. As such, the adequate use of an FMIS should ensure budgetary and commitment control and thereby contribute to adherence to fiscal rules. The repository of financial transactions carried out in government provides the basis for financial accountability of the executive to the legislature and citizens and is thus a fundamental building block in democracy. Conversely, inadequate and opaque capture of government expenditure data introduces opportunity for fraudulent conduct and distances the executive from citizens. FMIS systems are thus central to expenditure control, transparency, and service delivery and by extension democratic processes.

Recognizing the potential benefits, many donors have invested significant resources into FMIS reforms. The World Bank alone has committed over US$ 4,079 million through 134 operations across 74 countries since 1985. By the turn of the century, about six operations were approved annually, averaging about US$ 33 million each. About 28 percent of all projects mapped to the World Bank Governance practice had at least one FMIS component making up about 17 percent of its overall commitment. [10] However, the results have often been disappointing. Frequently implementation of FMIS systems has been found to be operationally difficult, prone to cost overruns and delays, and subject to change management issues,<sup>1</sup> [11] which eventually led to unsatisfactory progress. It was estimated that FMIS project completion times range from seven to 14 years [12], which is well beyond the stipulated five-year period for World Bank investment loans and even still an underestimation of implementation time, as it

> 1 A detailed discussion on the role of change management issues in FMIS reform can be found in Hugues et al 2017.

3
13 Since Australia’s imports pre-1970 were almost exclusively manufactures, customs revenue as a percentage of the value of all merchandise imports provides a reasonable proxy for the country’s nominal rate of tariff protection for manufacturing. Annual estimates from 1945-46 of that indicator are reported in Appendix Table 1, along with estimates referring just to dutiable imports, using data from the Australian Bureau of Statistics (ABS, previously the Commonwealth Bureau of Census and Statistics, CBCS). The latter were compiled by Lloyd (2006, 2007).

These series relate to customs duties only and so do not include assistance due to non-tariff measures, apart from anti-dumping, countervailing and revenue (“Primage”) duties. They do include an adjustment for refunds and drawbacks of customs duties though, and they include primage duties where applicable, which is important because these added significant protection to Australian producers of many goods. The first column of Appendix Table 1 also includes duties levied on excisable goods,<sup>10</sup> and refers to actual rates levied on imports, thereby combining MFN, preferential and concessional tariffs on final goods and intermediate or capital inputs. As those inputs that were not competitive with Australian-produced goods were admitted duty free under by-law and concessional import schemes, the series has long been regarded as less useful than the series of the average tariff on dutiable imports only. Thus the best available series measuring the average nominal protection due to the Australian tariff over the period to 1968-69 (after which more-comprehensive Productivity Commission estimates are

> 10 Ideally the duty collected should be adjusted to cover only the margin over the excise duty levied on like Australian-produced goods, but that was not possible.
the broader point that removal of one distortion in the presence of a second distortion is not necessarily welfare-enhancing.<sup>2</sup> There are conditions sufficient to ensure that an agreement will be welfare-enhancing. Kemp and Wan (1976) show that if external tariffs are adjusted so that the formation of a customs union does not affect trade with outsiders, the union is necessarily welfare-improving. The logic is straightforward: if tariffs are such that external trade is constant, then any additional trade between members must be trade creation. This ensures that outsiders are not hurt by the union. With appropriate lump-sum transfers, it is also possible to guarantee that all members are made better off by the union. This is a very general result. It extends to free trade areas (Panagariya and Krishna 2002), to partial liberalization contexts (Neary 1998), and to imperfect competition (Mrazova 2009).<sup>3</sup> Now, despite the theoretical and normative importance of the Kemp-Wan result, its practical importance is less clear. External tariffs are subject to political constraints, and not set to hold trade with outsiders fixed. As we will see, the endogenous changes in external tariffs following the formation of an RTA are central for the appraisal of regionalism. Moreover, even if members set external tariffs to satisfy the Kemp-Wan criterion, the optimal adjustment of tariffs by the rest of world following the formation of an RTA could leave its members worse off (see Richardson 1995a). In other words, the Kemp-Wan result is not about equilibrium

> 2 The early regionalism literature focuses on the conditions that make a CU more or less distortionary in general equilibrium settings. Much of the discussion evolves around the relative importance of trade creation and trade diversion depending on whether CU members produce substitutes or complements, and on the gains from expanded consumption possibilities versus the production inefficiencies that CUs can cause. For an early survey, see Lipsey (1960).

> 3 Mrazova (2009) shows in particular that a lower degree of competition among oligopolistic firms reduces the potential for trade diversion, which results in higher Kemp-Wan external tariffs. Thus, less market competition can presumably make it easier (from a political-economy perspective) to meet the Kemp-Wan requirement.

6
Figure 5: Gender ratios improve due to the<sup>reform</sup> F/M ratlos of PV of lifetime benefits,<sup>Mexico</sup> <!-- Start of picture text --> t2 _ - L, . . - _ =<br>08 -o-- New -DC+SQ+joint annuity (FC)<br>-a-o-Old-ow n+w idow (FC)<br>- ~~~~~~~~~~~~~New -DC+SQ+joInt annuity (av.)<br>06 ~~~~~~~~~~~~~-#4- Oil-ow n+w idow (av)<br>j -r' -- -+--- New-DC+SQ+joint annuity (10 yr)<br>S - - - ._ 's -e COd-own+widow (10 yr)<br>0 4~~~~~4<br>02 4<br>0<br>*ucn 2d1 - ,proprimay to postsec'ndcry) -<br><!-- End of picture text -->

**69**
**Table A6 – Share of households living in usufruct or free of charge in European countries** _(percentages)_

|||||**Age c**|**lass *(aver**|**age across**|**years)**|
|---|---|---|---|---|---|---|---|
|**Country**|**2014**|**2017**|**2021**|**Up to 40**<br>**years**|**41-50**<br>**years**|**51-65**<br>**years**|**More than**<br>**65years**|
|**Austria**|7.0|7.2|7.4|6.8|2.0|2.7|15.3|
|**Belgium**|3.1|2.3|2.3|1.2|2.8|1.0|5.0|
|**Cyprus**|17.7|19.0|18.8|18.6|6.3|12.3|39.2|
|**Czechia**|-|-|5.3|7.9|3.1|1.5|8.7|
|**Germany**|4.5|4.0|3.9|4.0|2.5|2.0|7.6|
|**Estonia**|12.7|13.0|11.1|14.8|7.6|9.4|15.4|
|**Spain**|7.9|7.7|7.1|11.9|7.2|5.3|7.1|
|**Finland**|1.1|1.3|1.0|1.3|0.4|0.8|1.8|
|**France**|2.8|2.8|3.2|4.0|3.2|2.3|2.4|
|**Greece**|6.5|7.7|7.0|12.6|7.0|4.4|5.8|
|**Croatia**|-|9.2|12.9|21.4|14.3|7.2|5.7|
|**Hungary**|6.5|5.1|4.8|8.3|3.6|3.4|6.6|
|**Ireland**|1.2|1.8|1.9|1.6|-|-|-|
|**Italy**|11.1|11.1|8.1|16.5|11.6|6.7|9.1|
|**Lithuania**|-|4.2|3.0|5.0|3.9|3.3|2.5|
|**Luxembourg**|5.2|2.8|2.9|5.7|1.7|2.9|3.3|
|**Latvia**|7.6|8.8|5.4|10.2|3.3|4.0|10.9|
|**Malta**|2.5|2.3|5.5|3.6|-|-|-|
|**Netherlands**|0.8|0.7|0.4|1.0|1.0|0.1|0.5|
|**Poland**|13.2|12.4|-|14.7|10.4|10.5|15.7|
|**Portugal**|7.3|7.7|7.7|10.5|8.1|5.9|6.8|
|**Slovenia**|14.2|13.8|13.3|30.3|12.5|7.0|11.2|
|**Slovak**<br>**Republic**|4.4|5.9|4.3|6.7|3.1|2.4|8.2|
|**Total**|6.3|6.2|5.0|7.1|5.6|3.9|6.9|

Source: Our elaboration on HFCS data.

* Age of the reference person, defined according to the UN/Canberra definition.