rafmacalaba/data-use-mentions-extended huggingface dataset:
compounded impact on economic activity, incomes, and the government’s fiscal position. The median GDP outcome is still negative, with a larger downside risk to the agriculture sector than previously estimated (Figure 20). The combined impact of climate and macroeconomic shocks occurring at historical rates is estimated to lower annual GDP on average by 0.5 percent (compared to 0.4 percent when exogenous macroeconomic shocks are not incorporated), and downside risks roughly double, demonstrating the compounding effect of multiple shocks occurring at once. Lower annual GDP is driven by agricultural value-added falling on average by 2.3 percent.

**External and fiscal accounts are also subject to larger swings as a result** . The range of possible outcomes roughly double with respect to the climate shock scenarios. The fiscal balance can deteriorate around 0.8 percentage points of GDP in the face of a combination of negative shocks of the type that occur roughly once every six years. Negative combined shocks that occur once every forty years worsen fiscal accounts 1.5 percentage points of GDP. Compound negative shocks affect exports between 6 and 12 percent of GDP for the periodic shocks described above, respectively.

> 13 The simulated shocks are obtained from a Vector Autoregression model with 1 lag on the deviations from the Hodrick-Prescott trend of international interest rates (in levels), export prices, import prices and external markets ́ demand (all in logs). The VAR is estimated using annual data from 1995 to 2022. Simulated shocks are obtained assuming all variables are normally distributed.

27
matching of children to parents allows us to estimate both sibling and intergenerational correlations for the same sample of children. The second advantage of NFHS is that education data are more detailed. Instead of reporting education level in discrete intervals as is done in the case of NSS, NFHS collected information on years of schooling for both parents and children, which facilitates more precise estimates of sibling and intergenerational correlations.

To define the estimation sample, we follow the literature and restrict our sample to closely spaced young adult siblings between the age of 16 and 27 years. The argument for estimating sibling correlations from closely spaced siblings rests on the fact that there may be important changes in the family structure as well as shocks to family life over a longer time horizon diluting the already conservative estimate of family background on children‘s outcome. To check the sensitivity of our results, we report the estimates of sibling and intergenerational correlations for other age groups also.

While the sample for estimation of the sibling correlations should ideally focus on coresident children, it may bias the estimation of intergenerational link in education between parents and children. If, for example, among older children, the best educated ones tend to leave household earlier than less educated children, it may bias intergenerational regression coefficient β downward, but may not necessarily bias the estimate of intergenerational correlation. Because such exit of better educated children from the household would also reduce the variance in children‘s education, thus offsetting the decline in the intergenerational regression coefficients.

The problem of not observing all of the children as co-residents in the household is more prevalent in the case of older age cohorts, particularly for women who usually leave their natal household upon marriage. In the case of women, if educated women delay marriage and we have better probability of observing them as co-resident children, then estimate of intergenerational correlations from our sample may be biased upward. On the other hand, if marriage timing follows birth order and there is a substantial birth order effect as reported in Black, Deverux and Salvanes (2005
1

# **Introduction**

There is by now a substantial empirical literature documenting the ‘resource curse’ or ‘paradox of plenty.’<sup>1</sup> Resource-rich countries should enjoy an advantage in the development process, and yet these countries experienced lower GDP growth rates post-1970 than less well endowed countries. A number of plausible explanations for this phenomenon have been suggested: inflated currencies may impede the development of the non-oil export sector (‘Dutch disease’); easy money in the form of resource rents may reduce incentives to implement needed economic reforms; and volatile resource prices may complicate macroeconomic management, exacerbating political conflicts over the sharing and management of resource revenues.

In the most extreme examples, levels of welfare in resource-rich countries are lower today than they were in 1970 – development has not been sustained by Pezzey’s (1989) definition. The Hartwick Rule (Hartwick 1977) offers what Solow (1986) termed a ‘rule of thumb’ for sustainability in exhaustible resource economies – a maximal constant level of consumption can be sustained if the value of investment equals the value of rents on extracted resources at each point in time. For countries dependent on such wasting assets this rule offers a prescription<sup>2</sup> for sustainable development, a prescription that Botswana in particular has followed with its diamond wealth (Lange and Wright 2004).

Drawing on a 30-year time series of resource rent data underlying the _World Development Indicators_ (World Bank 2004), we construct a ‘Hartwick Rule counterfactual’: how rich would countries be in the year 2000 if they had followed the Hartwick Rule since 1970? The results are, in many cases, striking.

Our empirical work draws upon new results<sup>3</sup> showing that the Hartwick Rule is a special case of a more general rule for sustainability. We extend the results in Hamilton and Withagen (2004) to determine the properties of a constant net saving rule – constant positive net savings entails a path
granularity as well as focus on new policy issues (including sectoral ones) they make the problem in a way more complex, albeit in a positive way richer in information content. There is also the question of varieties of methodologies used in producing these indicators, which can be subject to limitations, thus creating uncertainty as to what are best-practice indicators.

_Absence of sector dimensions_ . A third limitation is that most tools fail to account for sector or product specific dimensions. In some cases, the sector dimension is nearly fully absent of the discussion and the focus is almost exclusively on country-level cross-cutting issues such as in Doing Business, the WEF indicators or Growth Diagnostics. Arguably, even policies that affect all sectors have different degrees of relevance for different sectors and thus priority reform order will be sector/product specific to some extent. Tools that use product and sector specific information such as gravity modeling and the GIFF are also not really designed to evaluate for sector specific policy dimensions. An early attempt to find the relevance and priority of policies at the sector level can be found in Pathikonda and Farole (2016). Another relevant development has been the use of sectoral gravity models to acknowledge the vastly different nature of trade for different products and industries (see Yotov et al., 2015, for a review).

In conclusion none of the current tools provides clear guidance on the choice of the relevant set of public policy reforms that could lead to improved outcomes. Because of their partial nature, existing tools while helping identify relevant policies offer little guarantee that reform will lead to change in outcomes, because other binding constraints may be missing from the diagnostic. Second because of the multiplicity of indicators, identification tends to be biased by how these indicators are constructed and it might be difficult also to distinguish between closely related indicators and which convey better information. Also, by failing to account for the sector specific responses existing tools lack focus. Arguably, the identification of constraints at the country level will reflect the constraints that affect sectors that are
regional value chains in support of this scaling up. It would also have the potential to deliver powerful demonstration effects on the benefits of integration and to help entrench the integration process.

The practical challenges to implementation of this proposal are also substantial – in particular the political economy challenges. The largest of them is the issue of “credible commitment” – the challenge of managing the investment risk by maintaining the commitment of the host government(s) and other partners over the long term.

The paper offers a number of potential solutions to address these challenges. They include commercial and financial mechanisms that can establish an effective incentive system from the outset; formal, institutional approaches to ensuring commitment such as legal constraints and enforcement mechanisms; and informal approaches to align mutual interests of stakeholders. Effectively addressing commitment risks will likely require making use of all three mechanisms.

# **References**

Acemoglu, D. and Robinson, S.H.. 2006. “Unbundling institutions.” _Journal of Political Economy_ . University of Chicago Press. 113(5): 949-995.

Auty, R. 2010. “Early Reform Zones: Catalysts for Dynamic Market Economies in Sub-Saharan Africa.” Mimeo. World Bank.

Boyenge, J.P.S. 2007. ILO Database on Export Processing Zones, Revised. ILO: Geneva.

Brulhart, M. 2009. “An Account of Global Intra-Industry Trade: 1962-2006.” _The World Economy_ . 32(3): 401-459.

Crescenzi, R Rodríguez-Pose, A. and Storper, M. 2007. "The geographical processes behind innovation: A Europe-United States comparative analysis." _Working Papers 2007-13_ . Instituto Madrileño de Estudios Avanzados (IMDEA) Ciencias Sociales.

Fakhoury, I. 2011. “Realizing the Vision for Aqaba: The Creation of the Aqaba Special Economic Zone.” Presented at the AFZA Convention 2011. Dar Es Salaam, Tanzania. 11-13 May, 2011.

Ferroni, M. 2002. “Regional Public Goods in Official Development Assistance.” Occasional Paper 11.
the estimates. In terms of the proposed QLME method, both the markup and productivity effects are estimated unbiasedly. Note that the estimator is asymptotically consistent in the numerical estimation. For small _T_ and small _N_ the variance in the estimator increases. In addition, the estimator is able to pick up the production function specification - _γ_ is much larger than zero.

<u>1</u> Table 3 summarizes the results when the elasticity of substitution is _σ_<sup>_CES_</sup> = 1 _−ρ_<sup>=2.</sup> In this case, the ACF and DLW methods are unable to estimate the true coefficient of regulation on markups and productivity, respectively. In addition, the presence of correlation between error terms worsens the DLW significantly for markups. In contrast, the QLME method is able to pick up the true coefficient in the majority of cases. The small sample problem in _T_ makes inference hard in all cases. _T_ = 5 is deliberately chosen to be small to test the strength and weaknesses of the results. The small _T_ makes inference hard and thus researchers should be cautious when interpreting results on small firm-level panels.

In addition to estimating the impact of regulation on productivity and markups, researchers are typically interested to compare market power between industries, either by using the markup in levels or its dispersion within firms in a given sector. Because of the absence of price information and other problems related to endogeneity as discussed in section 2.2, we expect that any estimate on the markup level would be biased (see Bond et al. (2020)). Figure 1 presents the simulated markup distribution derived using a Cobb-Douglas production function. We compare it with the distribution of the estimated markups using the DLW and the QMLE methods. The results suggest that none of the methods can recover the precise level of markups. The DLW method is biased upwards relative to the QMLE method. Research that attempts to explain how the markup level changes over time using sales
variable was created from a merged dataset of BEA offshore employment data, trade data, and CPS monthly outgoing rotation group individual-level data, by industry and year. We calculate for each occupation its’ exposure to trade using the distribution of workers employed in this occupation _across_ _L_ industries in 1979. For each occupation _k_ and industry _j_ , we have:  _kj_  _kj_ where _Lkj_ is the total _L k_ number of workers in occupation _k_ and industry _j_ , and<sup>_L_</sup> _k_<sup>is the total number of workers across all</sup> industries in occupation _k._ We then calculate occupation-specific import penetration in year _t_ for occupation _k_ as: where IMP _jt_ is the measure of import penetration for goods in industry _j_ in year _t._ Occupationspecific measures of export shares and offshoring to low and high-income countries were also created in the same way.

This leads to a specification of the form: 1( _b_ ) _Wijkt_   0 _Zijkt_   1 _Gkt_  1   2 _TFPjt_  1   3 _PINVjt_  1   4 _REALSHIPjt_  1   5 _COMPkt_   6 _dt_   7 _I j_   8 _Occupationk_   _ijkt_ where _k_ indexes the worker’s occupation, and workers within the same _k_ occupation may be in different _j_ industries.<sup>5</sup> Our _G_ vector is now an occupation-specific measure for each worker, and we have added occupation fixed effects to absorb variation specific to time invariant features of occupations. Note that we also control for variation in computer use rates by occupation and year, which is meant to account for wage changes driven by the ability of some occupations to benefit from computer technology (Autor et al. 1998). We will estimate this specification for all occupations,

> 5 For workers outside of manufacturing, the control variables for TFP, PIINV, and REALSHIP are not available and are therefore assumed constant.
use in last 12 months<br>(% of population age 18 and older)|Percentage of population age 18 and older using inpatient care in the last 12 months|DHS, ECHP,<br>ENAHO,<br>Eurobarometer,<br>EHIS, ISSP, LSMS,<br>MCSS, US-NHIS,<br>SLC, SUSENAS, UK-<br>GHS, WHS|
|||Treated for high blood pressure (%<br>of adultpopulation)|Percentage of adult population being treated for high blood pressure (age-range may vary)|Eurobarometer,<br>EHIS|
|||Treated for raised blood glucose or<br>diabetes (% of adult population)|Percentage of adult population being treated for raised blood glucose or diabetes (age-range may<br>vary)|DHS,<br>Eurobarometer,<br>EHIS|
# **Energy Prices, Energy Intensity, and Firm Performance**

Reyes Aterido, Mariana Iootty, and Martin Melecky _World Bank_

**Keywords** : World Bank Business Pulse Survey Data, Firm-level Panel Data, Energy Prices, Energy Intensity of Industries and Firms, Firm Performance, Productivity, Sales, Employment.

**ion** : D22, L25, Q41, Q43, O13.
individuals.

# _III.4 Substitution across goods and inflation inequality_

The other well-known shortcoming of the standard CPI stems from its use of a Laspeyres framework of fixed base-year weights, which amounts to ignoring substitution across goods and hence leads to overstating inflation. This makes the CPI an upper bound for a true cost-of-living index; see e.g., Diewert (1998).

In this regard, the Boskin report identified two types of substitution bias. The first, estimated to raise measured inflation by 0.25 percentage point annually, is _lower-level_ substitution bias and occurs when consumers substitute between similar items within a category (e.g., substituting between pippin and gala apples). The second type, estimated to boost inflation by 0.15 percentage point annually, is called _upper-level_ substitution bias and occurs when consumers substitute between items from different categories (computers for television sets, for example) in response to price changes.

From the perspective of inflation inequality, the overstatement of inflation implied by the substitution bias is a concern if it differs between rich and poor households. This is not entirely implausible; for example, if the rich enjoy broader upper-level (i.e., inter-category) substitution possibilities than the poor (because their consumption basket includes a lower share of hard-toreplace items such as food, say) then the CPI would exaggerate the inflation rate faced by the rich more than that faced by the poor. In a similar fashion, lower-level (i.e., intra-category) substitution could broaden the gap between inflation of poor and rich because the latter might have access to a wider variety of within-category substitutes. Under these conditions, our finding above of anti-rich inflation could be partly an artifact of the use of a Laspeyres index.

An indirect way to assess this issue is to compute a Paasche index, whose weights are given by the current consumption basket, rather than that of the initial period, thereby capturing the effects of substitution across goods on households’ consumption patterns. Following the notation in (1
sub-national governments, typically lacking access to the most productive tax bases, have continued to employ tax systems that include large numbers of distorting, unnecessary and overlapping taxes, and offer substantial discretion to tax collectors engaging in face-to-face interactions with taxpayers. While this has been a means for local governments lacking adequate revenue sources to maximize receipts, it has sometimes been accompanied by enormous complexity, horizontal inequities and opportunities for abuse and informal arrangements. The Democratic Republic of Congo presents a particularly extreme case: while the national tax system resembles those of Western countries, local governments officially have more than 400 types of taxes, levies and fees. Unsurprisingly, recent evidence suggests that this has contributed to high levels of extraction from taxpayers, with very little of that revenue reaching the government budget, and low levels of trust in government.<sup>72</sup> While facilitation reforms initially adopted a technical focus on reducing the costs of compliance, they have evolved to include an interest in building taxpayer trust. Efforts to expand transparency, promote taxpayer education and create new points of engagement between taxpayers and officials are in part designed to increase understanding of tax obligations, and thus ease compliance. But they also reflect a belief that if taxpayers have a clearer understanding of what they need to pay,

> 70 World Bank. (2019). _Doing business 2019: Training for reform._ Washington, DC: The World Bank.

> 71 Devas, N., & Kelly, R. (2001). Regulations or revenues? An analysis of local business licenses, with a case study of the single business permit reform in Kenya." _Public Administration and Development_ , 21:381-391.

> 72 Paler, L., Prichard, W., Sanchez de la Sierra, R., & Samii, C. (2017). Survey on total tax burden in the DRC, Final Report. Kinshasa: Department for International Development.

21
<!-- Start of picture text --> Quarterly Poverty Estimates<br>Ww<br>N<br>N<br>10<br>fos)<br>o OSA<br>2000 2002 2004 - 2006 2008 2010<br>—— Urban: 2007 model m—— Rural: 2007 model<br>———_ Urban: 2001 model ——— Rural: 2001 model<br><!-- End of picture text -->
convolutional neural network, are less transparent and interpretable than the geospatial features used in the main specification. Furthermore, since machine learning methods already have the flexibility to incorporate interactions, interacting features with a measure of urbanity has little systematic positive impacts on accuracy.

These findings primarily contribute to a newer literature using new types of data to estimate economic statistics of interest, especially welfare. In the past decade, there has been a proliferation in the use of satellite imagery to estimate poverty and welfare (Jean et al., 2016; Yeh et al., 2020; Engstrom et al., 2022; Newhouse et al., 2025; Chi et al., 2022). However, processing raw imagery is typically computationally intensive and requires specialized skills. In comparison, other types of data are easier to use, like mobile phone call data records (Aiken et al., 2023; Blumenstock et al., 2015), but these can be more difficult to access due to privacy concerns and also raise issues related to representativeness. The satellite indicators we use can be obtained from publicly available sources relatively easily and are much smaller in size.<sup>3</sup> We also contribute to a related literature on small area estimation, which grew out of the statistics literature in the 1970s (Efron and Morris, 1973; Carter and Rolph, 1974; Fay III and Herriot, 1979; Battese et al., 1988). Earlier work proposed the use of census data for prediction (Elbers et al., 2003) and the empirical best predictor (Jiang and Lahiri, 2006; Molina and Rao, 2010; Tzavidis et al., 2018) is now one of the most common implementations of small area estimation. One reason the EBP model is preferred in many applications is its transparency; a nested-error regression model allows for a straightforward estimation of linear coefficients with random effects specified at the target area level. A simple table of coefficients indicates exactly how each variable is related to the measure of
secondary school-age population in each host country.<sup>1</sup> These population estimates are then combined with refugee gross enrollment rates (GERs), compiled by UNHCR (2023c), to estimate the refugee population accessing education in each country. Host country GERs are taken from UNESCO’s Institute for Statistics, which provides national GERs for primary and secondary education only (UNESCO, n.d.).

# Financing data

possible. To improve the quality of data on financing for refugee education, the authors have provided more detailed estimates on all major sources of education financing through either collecting new primary data or re-examining existing data to produce more accurate financing information. The methods used to produce these estimates are explained below.

# (i) Bilateral and multilateral ODA for refugee education

The analysis of ODA presented in this paper is based on data from the OECD’s 2023 _Ad hoc Survey on Development Finance for Refugee Situations in Low- and Middle-Income Countries, 2020-21_ .<sup>2</sup> Unlike its predecessors, the 2020-21 refugee financing survey captured ODA by area/sector, making it possible to estimate ODA for refugee education for the first time. However, the OECD’s classification only allows financing to be categorized into one area/sector, limiting the accuracy of its sector level analysis. As such, this paper presents new estimates for refugee education financing for all LICs and MICs by reviewing the descriptors in each line of ODA to ensure that all financing for education purposes are categorized as such. For example, in several instances financing was classified in the OECD dataset as ‘emergency response’, but was used entirely for education purposes, and as such should be incorporated into refugee education financing estimates. To ensure that all education financing was captured in this paper and the accompanying dataset, ‘education identifiers’<sup>3</sup> were used to detect potential misallocations. Each potential misallocation was then reviewed in detail, with only financing allocated entirely for education purposes categorized