rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 1120

# **Abstract**

This study examines the impact of temperature uncertainty on firm fixed capital growth using a unique dataset that merges extensive firm-level financial data with detailed gridlevel weather data. The analysis reveals a strong negative relationship between temperature uncertainty and fixed capital growth. Furthermore, the impact varies significantly across industries with differing levels of investment irreversibility and among countries with varying income levels. Firms in industries characterized by high investment irreversibility and those operating in higher-income countries experience more pronounced declines in fixed asset growth due to temperature uncertainty.

This paper is a product of the Development Policy Team, Development Economics. 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 j634yang@uwaterloo.ca and cschoder@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
269. These ECs are under the domain of the National Electrification Administration (NEA) and while NEA is not a regulator in the strict sense of the word, it is tasked with providing financial and technical support to the ECs, and monitoring their performance. These non-stock ECs will be the focus of this paper.

ECs are owned by their members/consumers on payment of a nominal fee, with the members electing a board of directors who are responsible for the overall policy setting of the organization. The mode of elections is specified in guidelines issued by NEA. The board, which numbers between 5 and 15 depending on the size of the EC, is elected on the basis of one-member one vote, with a once consecutive renewable term of three years, and with a third of the board up for election each year. The geographical area of the cooperative is demarcated into districts which usually encapsulate one or more municipalities, with each director representing a district. Aside from citizenship and membership of the cooperative, the only major restriction on eligibility for standing for the board elections is that the candidate cannot be holding any elective government office except that of barangay captain.

The board, which among themselves selects a president, vice president, secretary, and treasurer, is de jure responsible for managing the overall business of the EC, and can adopt the necessary bylaws for this purpose. It is meant to be a policy-making body, with actual day-to-day management the responsibility of a general manager. The board is required to hold an annual general members meeting, with a quorum being 5% of members or a 100 whichever is less, and any additional members meetings as deemed necessary. For example, major policy decisions, such as the consolidation of two or more cooperatives, or a merger, need a two-thirds approval of the members of each of the concerned cooperatives. Similarly, the conversion of the EC into a

> 1 The description of the sector is based on data available circa 2008-09.

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difference in Poland was closer to 4 percentage points, thus being in line with the relatively flat tax profile of that country.

# **Figure 7. Different reactions of the tax system to job polarization: Average tax and social contribution rate by occupational group, 2007-2014**

_Source:_ Authors’ calculations based on EUROMOD H1.0+.

_Note:_ Occupational groups are the following: non-routine, cognitive task intensive occupations (ISCO 08 major groups 1, 2 3); routine task intensive occupations (ISCO 08 major groups 4, 7 8); non-routine, manual task intensive occupations (ISCO 08 major groups 5, 6, 9). The light blue bar indicates the average tax an social contributions rate, calculated as a share of gross income, for 2007. The navy color bar indicates the same variable for 2014. The blue bar indicates the counterfactual rate in 2014 had the tax and transfer system been the same as that of 2007.

The evolution of the average tax rate over time reveals different patterns. In Germany, the average tax rate has slightly fallen for all three occupation groups, but more for the highest paid group with non-routine, cognitive task intensive occupations. This regressive change is, however, a combination of market forces going in one direction, thereby making tax rates converge across occupations, and active changes in the tax system going in the opposite direction, leading to a divergence in tax rates. Had the system not changed, the average tax rate would have decreased the most for non-routine, cognitive task intensive occupations, with comparatively lower effects on routine task intensive and non-routine, manual task intensive occupations. Active changes in the tax system led to a 3.3 percentage point reduction in average tax rate for the routine task intensive occupations, a 2.8 percentage point reduction for the non-routine manual task intensive 18
# **Public Perceptions of the Seriousness of the Climate Change Problem**

19. How serious a problem do citizens of Tajikistan perceive climate change to be? The answer to this question depends upon how the issue is posed to them. Respondents in the P-LITS 2 were asked a series of questions regarding the extent to which they think climate change is a very serious problem confronting the world. First, they were presented a list of various issues, and were asked which of these they considered to be “very serious problems” facing the world.

**Table 2: Respondent Opinions about Major Problems Currently Facing the World**

|**Issue:**|**Percentage of**<br>**consider the iss**<br>|**respondents in the**<br>**ue to be a very ser**<br>|**country that**<br>**iousproblem**<br>|
|---|---|---|---|
||**EU27 countries**|**Kazakhstan**|**Tajikistan**|
|Poverty, lack of food and drinking water|70|66|76|
|The spread of an infectious disease|24|70|84|
|International terrorism|55|50|47|
|**Climate change**|**64**|**36**|**41**|
|A major global economic downturn|25|47|38|
|Proliferation of nuclear weapons|24|33|29|
|Armed conflicts|39|53|28|
|The increasingworldpopulation|20|12|17|

Source: Authors calculations based on P-LITS 2 data Special EU Barometer 300/wave 69.2 20. Three main findings are noteworthy (Table 2): (i) “poverty, lack of food and drinking water” and
and electronics firms. Given that China’s aggregate exports are dominated by processing exporters in the electronics sector with FDI, these results suggest that the overall increase in China’s DVAR in Figure 1 is largely due to these firms gain access to local suppliers through HSR expansion.

**Substitutability of Imported Inputs** The impact of HSR expansion on firms’ input sourcing decisions also hinges on the substitutability between domestic and imported inputs. To capture this, we interact CI with _Diffi,t_ 0—a firm-level, import-weighted differentiation index that reflects the degree of product differentiation in the firm’s import bundle. This interaction term examines the different impacts of HSR on firm DVAR, based on the complexity of the imported inputs of the exporters:

where the variable _Diffi,t_ 0 _≡_<sup>�</sup> _j_<sup>_sjit_</sup> 0<sup>_×Rauchj_measuresthedegreeofdifferentiationinthe</sup> products imported by firm _i_ at the initial period. Here, _sjit_ 0 represents the share of product _j_ (classified at the HS 6-digit level) in the total imports of firm _i_ during its first observed year, _t_ 0. The term _Rauchj_ refers to the product differentiation indicator from Rauch (1999), which assigns a value of 1 for differentiated products and 0 for homogeneous products.18 Firms importing a larger proportion of differentiated products will have a higher value of _Diffi,t_ 0.

> 18Rauch (1999) classifies products at the 4-digit level of the Standard International Trade Classification (SITC, version 2). Homogeneous goods include those traded on organized exchanges and reference-priced goods, while the remaining are categorized as differentiated products. The correspondence between the HS 2007, HS 2012, and SITC 4 classifications is provided by the UN Trade Statistics (UN Trade Statistics Correspondence Tables).

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proportion rural. The large t-statistic on the FONCODES index in columns (i) and (ii) suggests that it is important that the relationship between expenditures and the index be specified correctly. To account for possible non-linearities in this relationship, I include a Fourier senres expansion of the FONCODES index in column (iii).<sup>22</sup> In addition, this regression includes numerous socio-economic controls listed at the foot of the table, and a dummy variable for border provinces. This border dummy is included as a control because FONCODES may have spent differently in areas near the volatile Ecuadorian border. Finally, the specification in column (iii) includes 25 department-level dummies. FONCODES' regional offices generally correspond to departments, and these dummies are meant to capture differences in institutional capacity across offices, as well as any other possible department-level differences.

|**Table 3: The impact of the second-r**<br>**capita**FONCODES**expenditures on c**|**ound 1990 presidential el**<br>**ommunity-based projec**|**ection on the dist**<br>**ts, December 199**|**ribution of per**<br>**1-October**1993|
|---|---|---|---|
||(i)|(ii)|(iii)|
|Percent vote for Fujimori|0.023***<br>(2.59)|0.028***<br>(3.02)|0.023**<br>(2.07)|
|Abs. value (%/. for Fujimori-50)|-0.002<br>(0.24)|-0.010<br>(0.94)|-0.030**<br>(2.11)|
|In (
**Figure 10**

# **Industrial Activity Index in Mexico and USA, 1980-2009**

(Seasonal Adjusted Data, Mexico 1993=100, USA:1994=100) <!-- Start of picture text --> 180<br>160<br>NAFTA<br>140<br>120 México<br>USA<br>100<br>80<br>60<br>Sources: INEGI and Federal Reserve<br>1980/01 1981/01 1982/01 1983/01 1984/01 1985/01 1986/01 1987/01 1988/01 1989/01 1990/01 1991/01 1992/01 1993/01 1994/01 1995/01 1996/01 1997/01 1998/01 1999/01 2000/01 2001/01 2002/01 2003/01 2004/01 2005/01 2006/01 2007/01 2008/01 2009/01<br><!-- End of picture text --> Source: Esquivel (2011) Economic growth also places increasing pressure on common resources like the environment. Given demographic imbalances between countries, and the threat of larger-scale environmental disruptions in the future, migration also looms as a growing issue. How should global arrangements be reformed to ensure that these challenges are widely seen as important and legitimate, and that international responses provide a robust and supportive framework for sustainable development?

New challenges can also be expected for food security. Demand for food is expected to grow substantially over the next half-century in response to population growth and rising incomes, especially among the large and populous emerging economies. Agricultural productivity growth is needed, given constraints on the use of additional land or water, but in recent decades productivity growth appears to have slowed, in part because of past cutbacks in support for public agricultural research and development. Further sharp spikes in food prices, as in 2007-08, are expected when adverse shocks coincide with low stock levels. One of the 21
Incidence of COVID-19 and Connections with Air Pollution Exposure It is well known that models with a high number of variables can over-fit data sets that contain only a modest number of observations. Model 2 estimates the same linear regression but uses step-wise variable selection following the AIC, Model 3 uses the selected variables in a spatial error model that controls for spatial dependence in the residuals ( _λ_ parameter), Model 4 estimates a spatial autoregressive model that allows for dependence on neighboring observations ( _ρ_ parameter), Model 5 allows for unique spatial autocorrelation and spatial residual correlation parameters. For compactness, table 1 only lists Model 1 estimates for variables selected by the AIC, even though all regressors are included. Finally, PM10 correlates (.95) strongly with PM2 _._ 5 and the AIC favored PM2 _._ 5. Replacing it with PM10 in the regressions below led to a small deterioration in measures of fit, indicating that PM2 _._ 5 is a statistically preferred predictor, although the main conclusions do not depend on this. For simplicity, the focus remains on the PM2 _._ 5 data.

Table 1: Dependent Variable: Confirmed COVID-19 cases <u>per</u> 100,000 inhabitants.

|**Variable**|**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 5b**|
|---|---|---|---|---|---|---|
|(Intercept)|-359.58|-402.51***|-202.58***|-200.63***|-207.50***|-185.43***|
||(218.20)|(80.61)|(76.06)|(64.
. And indeed in Figure 1, the poorest person is from the middle-income country, while her compatriot is the second richest (the second tallest) in our group of ten individuals.

But moving from Concept 2 to Concept 3 inequality is not easy. The chief difficulty comes from the fact that to calculate the latter we need access to household surveys with data on individual

> 2 Gini coefficient is a statistical measure of inequality  which takes  its  name from the Italian statistician and economist Corrado Gini. The Gini index is the most frequently used measure of inequality, ranging from 0 – when everybody has the same income - to 1, or 100 (expressed as a percentage or an index), when one person gets the entire income of a city (province, nation, world)—whatever is the relevant population over which we calculate inequality.

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Firstly, what does this finding imply for Brazil‘s innovation and growth policies? Instead of focusing on traditional instruments like the R&D tax credit, should Brazil consider widening this instrument to an ̳innovation tax credit‘ that covers a wider range of measured intangible assets where there are sufficient market failures to warrant public support, or consider a broader mix of policies targeting different types of intangible expenditures? If we were to extrapolate from growth analytics conducted for the US and UK using methodologically comparable estimates of intangible assets, then our results suggest that intangible investment has become a significant source of output growth in Brazil, implying that such polices should at least be considered. But it is possible that results from advanced economies do not translate readily to middle and low income countries. As a first step, it is therefore necessary to test the basic premise of this exercise more directly; that is, to see if measured ̳intangible investment‘ does have a statistically significant and quantifiably important impact on future productivity. We presented some positive preliminary evidence on this question in our paper. With better data, it should be possible to see how the relationship with productivity varies by type of intangible asset, which is important for policy specificity – together with considerations such as the extent of positive spillovers for different types of intangibles, and implementation challenges of specific policies, including focus, design and governance structures to mitigate rent-seeking.

The second main message of our findings is that if intangibles are so sizable in Brazil (and in China, as another recent study suggests), then their measurement should get more attention in other developing countries as well. Our work has identified two challenges in this area. The first is data availability. For instance, finding primary survey data on worker training is far more difficult in Brazil than in the US. The second issue is whether it is correct to make the same assumptions as CHS do about expenditures not observed directly. For example, new product development in finance is a major component of our intangible estimates
Figure 1: Raw Materials Inventory vs. GDP/Capita <!-- Start of picture text --> 379486 Egypt<br>Zimbabwe Peru<br>_Hcncbjres<br>El Saoho<br>Raw Guntemnl<br>Materials Bmtglade pwFs<br>Inventory _nnaad aow<br>(fraction of Phlppi VW<br>a year) Luwbou<br>Col4ibib FPi CwwSb<br>_ urk_poloysin AWR knord<br>PuLto R U10$5CW<br>o) HorigKon<br>ism ~~Us<br>.103652 Koe<br>6.93803 9.59106<br>Log of Real PPP GDP/apl<br>Final and Process Inventory vs. GDP/capita<br>.169216 - POaUlS Sweden<br>Egypt<br>Cotaft Na<br>Ecuador Fhbn<br>B M FYI knUk<br>7imbabwN Peru<br>Final and Cypnu AWhStt<br>Process PhilpWisian Twkey brw Dewsima<br>Inventory - Us Uondura<br>(fraction of a El Salv jombia Mal New ZeI Cvnda<br>year) mJalvf3w Sk9wpa<br>Chile AsM<br>Korea Vlnx<br>_Snezil Hwgwy Puguo R<br>Bedwwos Lwnmbou<br>Japan<br>Potand<br>HongKcn<br>.054599 l klnd<br>6.93803 Log of Real PPP GDPkaplta (aere 197i164) 9.59106<br><!-- End of picture text --> 15
**Table 6:  Robustness Checks, 1**

||**Removing**|**Influential**|**Observations**|**Using No**|**n-Interest**<br>**Spendin**|**Government**<br>**g**|**Using**<br>**on Gov**|**National A**<br>**ernment C**|**ccounts Data**<br>**onsumption**|**Controlli**<br>|**ng for Appr**<br>**Disbursing **|**ovals of Slow-**<br>**Loans**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Sample of Countries|**Full**|**IDA**|**Disb/G>10%**|**Full**|**IDA**|**Disb/G>10%**|**Full**|**IDA**|**Disb/G>10%**|**Full**|**IDA**|**Disb/G>10%**|
|_(Dependent variable is Change in Real G_|_DP)_||||||||||||
|**_Panel
et al. (2010), Brown (2014)) and rainfall (Funk et al. (2015)), both of which have longer time series than the newer measures. Unlike rainfall, the Normalized Difference Vegetation Index (Tucker (1979)) is a cumulative measure. With available ground data, as is the case in the United States, remote-sensed vegetation anomalies can be calibrated to historic crop yields to build models that provide accurate out-of-sample estimates (Johnson (2014)). However, when ground data are scarce, vegetation indices cannot be properly calibrated and less is known about the relationship with crop-productivity in particular contexts (Burke and Lobell (2017)). Rainfall, during the growing season, is unambiguously correlated with crop productivity. However, the influence of rainfall on yields depends on its timing and distribution. Therefore, aggregating rainfall estimates over a growing season can be misleading (Sivakumar (1992), Barron et al. (2003)).

Given these challenges, we employ the following strategy. First, we focus on shocks at a particular spatial scale: a country’s first administrative level (Hijmans et al. (2019)) — 7
3. Data sources and stylized facts from the data

# **_Data sources_**

This paper uses monthly series spanning the period from July 1998 to September 2020. The data are obtained from the National Bank of Ethiopia (NBE), except for the import prices which are sourced from World Bank Commodity Price Data (The Pink Sheet) and the Federal Reserve Bank for the U.S. manufacturing producer price index (as a measure of import prices of non-food items other than oil). The fiscal variables are transformed into monthly frequency from annual series using interpolation. The list of variables used in the regressions as well as the data sources are provided in Table A1 in the Appendix.

Before moving to the empirical methodology, some highlights from the data and salient relationships among the key variables of interest are provided below.

# **_Trends of monetary aggregates_**

The trends of money stocks show three phenomena. The first is a divergence of broad money (M2) from narrow money (M1) and base money (M0), which became increasingly pronounced starting from 2010/2011 as M2 grew faster than the two other aggregates. The rapid growth of M2 was driven by rapid growth in savings deposits, which grew from 35.4 percent of M2 in 1997 to 60.7 percent in 2020, and it is also reflected in an increase in the money multiplier from 2011.

The second and third phenomena are trends in the velocity of money, both of which reflect important developments in Ethiopia’s financial system. Specifically, the second phenomenon is an increase in the velocity of money starting in 2002 (Figure 1), which reflects the acceleration of monetization of the economy. The velocity of money was declining for all three money stocks until mid-2002 (end 2004 for M0). The velocity of M0 and M1 then rose steadily, while that of M2 peaked in October 2008 then went on a downward trend. The third phenomenon is a decrease in the velocity of broad money starting from the end
Banks Statistical Performance Indicators, a measure of statistical data quality, in all GEP regressions.<sup>19</sup> Adding this variable did not change any previous results in a significant way.

3.3 What mediates the effect of economic growth on household welfare? As a final step in the analysis, we assess which factors mediate the effect of economic growth (measured by GDP per capita or HFCE per capita) on household welfare, as measured by household consumption or income per capita from surveys. In the absence of a strong theoretical framework on how the GEP is determined, we draw on the vast (and now largely defunct) cross-country growth literature and the more recent and country-specific microeconomic analysis on the determinants of household welfare to identify a list of variables that potentially mediate the relationship between economic growth and household welfare. The cross-country growth literature, particularly popular in the end of the 1980s and in the 1990s, used cross-country growth regressions to search for empirical linkages between long-run growth rates and a variety economic policy, institutional and political indicators.<sup>20</sup> While this literature has identified over 50 variables that are significantly correlated with growth in at least one specification, ranging from human capital and investment to levels of democracy and ethno-linguistic fractionalization, sensitivity analyses have shown that only a limited number of variables are robustly and systematically correlated with economic growth (Levine and Renelt, 1992): Human and physical capital (measured as a share of investment in GDP) and initial income levels. While the literature on the determinants of household welfare is less straightforward to summarize given substantial country heterogeneity and different data sources, robust correlates of household welfare include the level of education of household members, urban vs rural location (the location effect usually persists even after controlling for other socioeconomic factors), health status of household members and access to basic health services, presence of basic infrastructure (roads, electricity, schools, health centers, etc.) and exposure to conflicts and violence.<sup>