rafmacalaba/data-use-mentions-extended huggingface dataset:
. Reduction in labor productivity and supply due to higher temperatures 4. The impact of pollution on the labor force and 5. The impact of flooding on capital stock.

Before accounting for damages the standard MFMod production function is modified to include energy as a factor of production. In the revised form potential GDP is given as below, as a function of total factor K ∗ productivity (TFP or ) ), capital stock ) , energy � and structural employment ) . tt tt EE,tt tt (AA (KK �YY (CC ∗ K αα ββ γγ Where is the income share of capital, YY is labor’s share in total income and tt = AAttKKtt CCtt YYEE,tt is the share of energy, where the values of each of these parameters is drawn from the input-output table for Pakistan and set ∗ αα ββ γγ to 0.2, 0.7, and 0.1, respectively. represents total factor productivity, is productive capital and the equilibrium employment level and is determined as: tt tt tt AA KK CC ∗ ∗ P 1564 ∗ is the equilibrium unemployment rate, derived from a NAWRU (non-accelerating wage rate of tt tt tt tt CC = (1 −DDCCRR )PPRR PP unemployment) specification, is the participation rate while P 1564 is the working age population, tt DDCCRR which (like the total population) is exogenous and taken from the UN Population forecasts. The structural tt tt PPRR PP 41
significantly more likely to export. It is only within this select group,<sup>1</sup> of most likely highly productive firms, that productivity has no predictive power.

A second important insight is that the globalization pattern tends to be somewhat different for manufacturing firms than for agriculture or mining firms. In particular, there is a much longer tail of smaller exporters and these firms focus, not surprisingly on trade with neighboring countries. More remarkable is that also the type of products traded by small exporters tend to differ from product categories that dominate the export bundle of larger exporters. As a result, the revealed comparative advantage of Rwandan manufacturing firms is somewhat different between small and larger firms.

We obtain many more detailed insights along the way, but it is hard to summarize them succinctly. The key contribution of this report is to offer a first glimpse of the GVC engagement of firms that represent an exhaustive sample of formal firms in a developing country. This cross-industry overview provides a useful complement to the literature that contains many case studies looking at individual industries or segments within an industry in particular countries.<sup>2</sup> The remainder of this paper is organized as follows. In Section 2 we present a brief overview of the economy of Rwanda, followed by a discussion of the administrative data in Section 3. The presentation of the results, in Section 4, is organized around eight insights which are discussed in separate sub-sections. In the concluding Section 5, we summarize those insights.

# **2. Overview of the Rwanda economy**

## **2.1 General**

Rwanda is a small, landlocked country in East Africa, neighboring the Democratic Republic of Congo (to the west), Tanzania (east), Kenya (north), and Tanzania (south). It is a densely populated country with 12.2 million inhabitants in 2017. Its economy has performed quite well over the last 10 years, driven to a large extent by important
[7 groups], while we also estimate specifications with country fixed effects for robustness (143 groups).<sup>14</sup> In additional specifications, we also include control variables for financial inclusion (having ownership of an account at a bank or formal financial institution), having an outstanding housing loan (as a proxy for home ownership), and generic savings by country (having any savings during the last year, as a proxy for cultural factors shaping the propensity to save). In additional regressions, we also estimate multinomial models of saving for old age by financial inclusion status, and type of savings (formal versus informal/semi-formal<sup>15</sup> ).

Next, we estimate regressions that incorporate country-level macroeconomic characteristics to our baseline specifications. From the World Development Indicators, we include control variables for: the logarithm of life expectancy, the logarithm of per capita GDP (2011 PPP-constant levels), GDP per capita growth, the real interest rate (e.g. James and Song, 2001), and the WDI legal rights index. In additional specifications, we also include control variables for legal origin (La Porta, et al. 2008), the housing-affordability index (as a proxy for potential home ownership<sup>16</sup> ) and indicators for explicit deposit insurance and the safety net/moral hazard index (Demirgüç-Kunt, et al. 2014).

Finally, in the third stage of our analysis we incorporate control variables accounting for country pension-system characteristics, in addition to the baseline individual and macroeconomic variables. We utilize two sources of data capturing country pension systems. Specifically, we incorporate the following variables from the Pension Watch/HelpAge's Social Pensions Database (2014):<sup>17</sup> (i) the logarithm of the benefit (in PPP international dollars), (ii) the benefit as a percentage of GDP per capita, and (iii) the benefit as a percentage of the $
XL-2/2-1-88 37 -

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in 1973 did not fall below 250Z (annualized) **at** the end of this period. This sluggishness of inflation was accompanied by a dramatic rise in unemployment(from 4.6Z in 1973 to 16.8Z in 1975) while real GNP fell 14.4 Z in 1975 (see Table 7a).

These results should be clearly very disturbing for the monetarists. Surely the severe cut in the budget deficit cannot be denied but some doubts were raised with respect to the existence of a monetary crunch in view of the fact that the nominal money supply continued to rise at high rates [Harberger (1981)]. However, the relevant question is whether monetary growth was contractionary relative to the force of inflationary inertia.

The data on the ratio of M2 to CNP can throw some light on this question. Table 7a shows that this ratio fell dramatically in the 1974-76 period. Note also that during this period the ratio did not change much in spite of the fact that inflation fell gradually. A fall in inflation leads to an increase in money demand. This constitutes indirect evidence of a monetary crunch.

It should also be noted that when prices were deregulated in the last quarter of 1973 consumer prices rose (on a quarterly basis) by 128.5% while M2 rose only 45.7% (Fozley (1983), Table 8]. *Thus, there can be little doubt that monetary policy was highly non-accommodative from the start relative to the sluggish inflation. This is also the evaluction of Foxley (1983) who attributes the recession which developed in 1974 towards the beginning of 1975 to the foregoing monetary crunch (Foxley, op. cit., p. 53).

- 1/ The contractionary policies which increased unemployment were motivated not only by disinflation but also by the deterioration in the external position in the end of 1974.
related to purchase of energy by fuel type and by economic activity. Aggregate information from SIEN is provided between 2009 and 2018, whereas firm-level information from the DGII is available between 2007 and 2021. Our analysis refers to the combined dataset relative to the period 2009–2018. Summary statistics at the activity level are reported in Appendix B.1.

**Estimation of CO2 emissions.** Form 606 provides the link between DGII firm data and SIEN emissions data, which enables us to estimate energy consumption and CO2 emissions by firm. Form 606 is a monthly report submitted by companies to DGII that provides information on the purchase of goods and services that include a tax receipt number (NCF). To quantify the energy consumption and CO2 emissions by firm, we leverage on the information on purchases of different fuels for energy generation as well as electrical energy purchases. The energy activities reported in Form 606 are displayed in Appendix B.2.

**Emissions from Electricity Consumption.** A firm’s expenditure in electricity is equated to a firm’s expenditure reported in Form 606 in sectors 4011 (electric energy generation), 4012 (transportation of electric energy), and 4013 (distribution and sale of electric energy). Using a firm’s location, it is possible to assign a price per KWh to each firm based on the average price reported by the Dominican Republic’s electricity distribution companies: EDESUR, EDENORTE, and EDEESTE (EDEs). Each of the EDEs serves a specific region in the country, and there is variation in the average electricity price across regions. Dividing the firm’s electricity expenditure by its location’s average electric price, we estimate the firm’s energy consumption in kilowatt-hour (kWh). To estimate CO2 firms’ emissions from electricity consumption, it is necessary to estimate a conversion factor from kWh consumed to kilogram (kg) of CO2 emissions. BNE provides information on the total Gigawatt hour (GWh) of electricity produced, with the SIEN representing nearly
issues: simultaneity of input choices and selection biases. These two issues will generate biased estimates of and , and therefore biased estimates of the TFP. A variety of techniques have been suggested to address these issues. We use the widely-used method kk ll ββ ββ proposed by Olley and Pakes (1996). We call this TFP-OP. As another key measure of TFP, we use a straightforward index number approach, which does not require estimating any parameters. To implement, the industry-specific wage share in the output is used to measure . One minus this share is used to measure . Here the assumption is that a cost-minimizing firm will make sure that the relative factor price ratio equals the local elasticity of ll kk ββ ββ substitution between the inputs of the production technology. To avoid potential bias from the cost shares using our own data—the cost shares in our data might reflect the ownership restructuring directly—we rely on the estimates of the factor shares at the two-digit industry level from SaintPaul and Bentolila (2003), as in Bloom, Sadun and Van Reenen (2012). We call this TFP-IN.

Overall, these three approaches yield quite similar results. The correlations of these productivity measures are fairly high (see Table C1 in this appendix): that between TFP-OLS and TFP-IN is 0.955; that between TFP-IN and TFP-OP is 0.937. Thus, it is not surprising that our results do not hinge on how we measure productivity.

Our index function approach relies on factor shares from Saint-Paul and Bentolila (2003). This has the virtue of not relying on the cost shares estimated from our own data. After all, the cost shares from the dataset itself might reflect the allocation effects of ownership restructuring itself, and thus bias the TFP measurements based on the cost shares from the data. However, it is also possible that the cost share based on our own data might better capture the underlying technologies and using them might result in more accurate measurements of TFP. We thus also experimented with the `
results to the assumed lower limit of nonhealthcare consumption for an individual but, again, we are not aware of any empirical evidence that suggests this is adequate.

Finally, we use reported healthcare expenditure information, but we know nothing about the severity of the illness or injury that drove people to seek healthcare in the first place, the impact that has on respondents’ welfare, or the effect that has on respondents’ decisions to seek care and willingness to pay for it.

21
7 Finally, log(city GDP) / log (city pop.) is our income measure instead of log (city GDP / city pop.). We believe that the latter measure does not properly capture city incomes due to non-linearities in the relation between lights and population (see Appendix Section C3. below).

Appendix Figure C.3 shows a positive volume-income relation. For a given income level, there is more variation across cities than across countries. For example, volume per capita dramatically varies across U.S. cities despite them having similar incomes when compared to other world cities (e.g., 311m<sup>3</sup> per resident in New York vs. 569 in Dallas).

Note that this analysis indirectly controls for the effect of city population size. In the U.S., city incomes and sizes strongly correlate (Appendix Fig. C.5). That is also true globally conditional on country indicator variables (see Appendix Section C4. below). Indeed, within the same economy, higher-wage cities attract workers. Conversely, larger cities are denser, which generates productivity-enhancing agglomeration economies (Combes and Gobillon, 2015).

Based on the same analysis as at the country level, heights explain between two thirds and three fourths of the volume-income relation (vs. 65% at the country level) (Appx. Section C5. and Appx. Fig. C.4-C.6). Low-rises and high-rises account for 88% and 12% of the heights-income relation, hence 65% and 10% of the volume-income relation (25% for areas). At the country level and including rural areas, we found 60% for high-rises, 5% for low-rises, and 35% for areas.

**C3. City per capita income measure** To study how city stocks vary with respect to income, we use the 2015 city GDP data from Chen et al. (2022). More precisely, they provide high-resolution “real
of litigation in Brazil. If we assume that the South African ―underprivileged‖ come from the bottom 40<sup>th</sup> income percentile (in fact, they are probably even less ―privileged‖ than that), then South African SE litigation is twice as pro-poor as the Brazilian model.

To put this in the same terms as we used for Brazil we would have to calculate the distribution of, for example, HIV/AIDS patients by income decile. We have been unable to find any such study for South Africa. Instead, we use the results of surveys and studies of the characteristics of people with a particular illness. For example, according to a household survey, the truly poor appear to be overrepresented among households with an HIV/AIDS patient by a factor of at least 1.4 (69% of HIV/AIDS households earn under $132/month, while only about half of all households in the overall population fall below that threshold). The other cases suggest even more of a pro-poor bias – they involve prisoners, for instance, or subsidies to low income school children. We summarize our calculations (described more fully in the Appendix available from the authors) in the following table.

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# **II. The Size of Tax Expenditures and Their Fiscal Effects**

# **A. The Size of Tax Expenditures**

According to published country tax expenditure data, the size of tax expenditures varies among countries. In 2003, the size of tax expenditures was over 2 percent of GDP in the Netherlands, 4 percent of GDP in Australia, and about 7 percent of GDP, in Canada and in the US. Table 1 below gives the estimates of the average size of tax expenditures in Australia, Canada, the Netherlands, and the US from 2000 to 2003.

By comparing tax expenditures to integrated tax expenditures and direct expenditures (referred to as “total expenditures”), Table 1 shows that governments have used tax expenditures to implement a significant portion of their total expenditures. The Australian government has used tax expenditures to allocate 15 percent of its total expenditures; the Canadian government has used tax expenditures to allocate about 30 percent of its total expenditures; the Netherlands government has used tax expenditures to allocate 4 percent of total expenditures, and the US government has used tax expenditures to allocate 26 percent of its total expenditures.<sup>4</sup> Like direct expenditures, tax expenditures tend to grow. Sometimes, the growth of tax expenditures is faster than that of direct expenditures. For example, according to recent released data from the Government Accountability Office (GAO) of the US, the federal government’s tax expenditures increased from about US$400 billion in fiscal year 1993<sup>5</sup> to US$850 billion in fiscal year 2004<sup>6</sup> (which represents a 113 percent increase from 1993 to 2004). Federal direct expenditures were US$1,409.5 billion in 1993 and US$2,292.2 billion in 2004 (a 62 percent increase from 1993 to 2004). There are many similar examples from other countries.

Poland (central government only), for example, has not
.3<br>access to inputs/raw 9.1 obtain skills 32.6<br>materials 19.5<br>28.3<br>19.2 household lost wage earning 26.9<br>knowledge about market<br>17.3<br>39.4<br>no other income<br>1 18.3<br>availability of electricity<br>12.3 18.2<br>other<br>18<br>10.1<br>roads & transportation 11.1 low agriculture income 4 14.6<br>10.1<br>availability of skilled labor 0<br>7.7 no accesss to agric. Land 10.6<br>29.3 18.2<br>availability of water 2.2 social/economic independence 7.2<br>0 20 40 60 80 0 10 20 30 40 50<br><!-- End of picture text --> _Source:_ Yemen RIC Survey 2010 weighted _Note:_ Each entrepreneur chose up to two obstacles.

_Source:_ Yemen RIC Survey 2010 weighted _Note:_ Each entrepreneur chose up to two motives.

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unchanged: lower oil prices hurt stock and high-yield bond indices, and help investment-grade and long-term Treasury bonds. The details of the bootstrapped regressions are shown in Appendix B.

# **On volatility**

This section examines the impacts of oil price fluctuations on uncertainty, proxied by the log of the VIX index. VIX is a popular measure of the market’s expectation of stock volatility over the next 30-day period. Table 4.3 reveals that volatility hiked by 9.09% for every 10% decline in WTI oil price. This finding reinforces the argument that oil price declines, although driven by exogenous supply shocks, can create uncertainty, flight to safety, and a deterioration of the stock market.

Table 4.3: Oil price declines and the VIX index

|VARIABLES|∆log(VIX)|∆log(VIX)|
|---|---|---|
|∆_Log (Oil Price)_|-1.005***|-0.909***|
||(0.297)|(0.290)|
|Lags|No|Yes|
|Constant|-0.000804|-0.000757|
||(0.00297)|(0.00300)|
|Observations|699|696|
|R-squared|0.054|0.068|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

# **The US Market: Breakdown by sector and asset class**

Table 4.4 presents the impact of oil price fluctuations on different asset classes (stocks, high-yield bonds, and investment-grade bonds) of different sectors. In each asset class, the sectors are sorted by the magnitude of the impacts.

About half of the sectoral stock indices are negatively affected by oil price declines. As expected, the energy sector is hit the hardest as the WTI oil
The WorldPop population estimates are based on a combination of available census data, land-cover satellite imagery and mathematical modeling. This allows higher resolution estimates than are available from other sources, and on a nation-wide basis, without regard for administrative units, which can be inconsistent, difficult to compare and do not necessarily match the analysis in the paper. Furthermore, the WorldPop data are freely usable and do not require requesting census and administrative data.

As mentioned in section 3.2, in order to provide a comparable geographic unit among cities, we relied on a uniform study area of the metropolitan region based on access patterns, rather than administrative units - which are not comparable between cities. TABLE 1 summarizes the geographic and population scope of each study area – as defined by the uniform metropolitan boundary described previously. This is in comparison with the UN 2015 World Urbanization Prospects (UN DESA 2015) estimated metro population in the next column – which utilize administrative boundaries to define the urban area. Cities whose study area populations fall far below the UN Population estimates may have underperforming transport systems in terms of reach, while those that are above it have more of their peripheral population with access to the city.

||**Area in km sq**|**Population Within**<br>**Study Area**|**UN Pop Estimate**<br>**2015**|
|---|---|---|---|
|**Nairobi**|822|5,685,117|3,914,000|

10 www.worldpop.org. Note that supplementary work could investigate how sensitive our results are to using other global data sources such as Landscan or GHS-Pop.

9
irrigated croplands and rain-fed croplands. _Share of Irrigable Area_ denotes the fraction of the rain-fed area within the grid where the crop’s water demand can be sufficiently met with the crop’s water supply.<sup>9</sup> It equals one if the crop’s water supply is greater than or equal to the crop’s water demand; otherwise, it is set as the crop’s water demand divided by the crop’s water supply. This variable can be thought of as a discount on the achievable production gain, where we scale down the gain in production to consider only the share of the grid where groundwater availability can fill in existing crop water deficits or the extent to which the existing crop water deficits can be filled using groundwater.

## **5.2.1 Estimating Water Supply**

We overlay water-volume gridded data with gridded agricultural data (referred to as GAEZ grids). Subsequently, we estimate the volume of local shallow groundwater in each GAEZ grid _g_ using equation (8).

> 8To compute this ratio, we divided the total water use by the mean recharge rate of each grid. For each country, we divided total water use across grids within the country weighted by the GAEZ grid’s area and multiplied by, _φ_ , the share of water use that is attributable to groundwater use (=67.5%) (Rodella et al., 2022)).

> 9In the water-supply calculations, we only consider groundwater availability after accounting for sanitation needs.

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of political will emerges as the most compelling explanation.

The increasingly-recognized centrality of politics in bringing about tax reforms is consistent with its role in other aspects of public sector performance. The World Development Report (WDR) 2017, _Governance and the Law,_ argues that asymmetries of power—which lead to exclusion, capture, and clientelism—lie at the root of a wide range of development challenges. Tax reform inevitably creates winners, losers, and confronts strong vested interests. It cannot succeed without political support—but broad-based political support is frequently absent owing to perceptions that the system is unfair, and to the absence of clear reciprocity and benefits from expanded tax

> 25 Cummings, R. G., Martinez-Vazquez, J., McKee, M., & Benno, T. (2009). Tax morale affects tax compliance: Evidence from surveys and artefactual field experiment. _Field Experiments in Economics_ , 70 (3):447-457.

> 26 Alm, J., Jackson, B., & McKee, M. (1993). Fiscal exchange, collective decision institutions, and tax compliance _. Journal of Economic Behavior and Organization_ 22: 285–303.

> 27 Lago-Peñas, I., & Lago-Peñas, S. (2010). The determinants of tax morale in comparative perspective: Evidence from European countries. _European Journal of Political economy_ , 26(4), 441-453; Torgler, B. (2005). Tax morale in Latin America. _Public Choice_ , 122(1-2): 133-157; Chan, H. F., Supriyadi, M. W., & Torgler, B. (2017). Trust and tax morale. In _The Oxford Handbook of Social and Political Trust_ . Oxford University Press; NESG. (2019). A survey of Nigerian households and small businesses: What do Nigerians think about taxes? Lagos: The Nigerian Economic Summit Group.

> 28 Torgler, B. (2003). Tax morale, rule-governed behaviour and trust. _Constitutional Political Economy_