rafmacalaba/data-use-mentions-extended huggingface dataset:
US$4.7 billion a year. Improving the reliability of electricity supply would add to these gains. According to IHDS 2012 data, the average duration of power outages of households who have less than 24 hours of power supply is about 12.5 hours a day. Increasing the supply of electricity to 24 hours a day would lead to an estimated income gain for the rural population of US$6.5 billion annually.

The Indian government plans to achieve 100 percent electrification in rural areas by 2017, at a total estimated cost of US$4.4 billion between 2015 and 2017. Its plans call for both connecting the villages that remain without electricity and improving the reliability of power supply in villages that are already connected. Assuming a 20‐year project lifespan, the annual average investment cost is about US$200 million. Even without accounting for other benefits associated with better health and education outcomes, our estimates suggest that the net income gain from providing reliable electricity to rural households would reach almost US$11 billion a year.

Based on quantile regression analysis, we also find that while every income group benefits from electricity, the benefits are larger for households in lower‐income quantiles. This suggests that the current rural electrification policy in India is progressive.

Overall, the findings from our analysis suggest that merely ensuring connectivity is not enough; increasing the reliability of service is essential to realizing greater benefits from electrification. While the availability of electricity is important, it is only one of several factors that matter to the impact of electrification. Others include the frequency of power disruptions, fluctuations in voltage, and the ability to simultaneously use multiple appliances, including high‐capacity ones. Ongoing initiatives are adding to our knowledge on these issues. One set centers on a multidimensional energy access matrix recently developed by the Sustainable Energy for All (SE4ALL) program, i.e. the Multi‐tier Framework (MTF) of energy access. The Energy Sector Management Assistance Program (ESMAP) at the World
forward interest rates for government debt securities denominated in the local currency, the foreign private holdings ratio _푓푝푖,푡_ −1 is measured as the foreign private holdings share of government debt securities denominated in the local currency.<sup>15</sup>

> 13The zero-coupon interest rate can be obtained from Bloomberg. To deflate the nominal interest rate into the real interest rate, we use two-year-ahead projections of inflation E _푡 휋푖,푡_ +2 due to data availability. The robustness check examines the nominal interest rate as well.

> 14Moreover, real-time vintages of data could affect long-term interest rates because fiscal data have been revised largely (De Castro et al. (2013)).

> 15While Arslanalp and Tsuda (2014b) provide foreign holdings share of central government debt securities denominated in local currency for emerging markets,Arslanalp and Tsuda (2014a) do not have the corresponding ratio in advanced economies but provide total general government debt securities, including debt denominated in both local and foreign currency. Hence, assuming that only foreign investors hold government debt securities denominated in foreign currency (data that can be obtained from the Bank

11
**Table 2:** Steady-state Values in percentage of total output

|**Steady-state rati**|**os**<br>**Description**|**Values**|
|---|---|---|
|_Y _<sup>_O_</sup>_/Y_<br>|Oil revenues to GDP ratio in steady state|0.072|
|_I_<sup>_/_</sup>_Y_<br>|Total investment to GDP ratio in steady state|0.1468|
|_Y _<sup>_T_</sup>_/Y_<br>|Tradable to GDP ratio in steady state|0.329|
|_Y _<sup>_N_</sup>_/Y_<br>|Tradable to GDP ratio in steady state|0.665|
|_L_<sup>_N_</sup>_/L_<br>|Share of employment in non-tradable sector|0.583|
|_L_<sup>_T_</sup>_/L_|Share of employment in tradable sector|0.417|
|_C_<sup>_N_</sup>_/C_<br>|Non-tradable goods consumption to C|0.316|
|_G_<sup>_C_</sup>_/Y_<br>|Government consumption to GDP ratio in steady state|0.53|
|_G_<sup>_I_</sup>_/Y_|Public investment to GDP ratio in steady state|0.32|
|_F _<sup>_∗_</sup>_/GDP_|Sovereign wealth fund to GDP ratio in steady state|0.08|

# **4.1 All-investing and saving in a sovereign wealth fund**

In response to an increase in the oil production, oil output and total output rise gradually, and this drives up the oil
significant. On impact, an additional USD of public capital expenditure results in an increase of GDP by 0.62 USD. Over time, additional benefits drive up the cumulative effect close to 1 USD. Our results hence suggest that positive impact of public spending on economic activity in South Asia comes entirely from capital expenditure. These results are very much in line with the findings of Ilzetzki et al. (2012) for developing countries in general. They find an insignificant multiplier for government consumption and a significant impact of public investment. The latter is 0.6 on impact and 1.6 in the long run. While we find nearly the same initial impact, the long-run effect seems lower in South Asia compared to other developing countries.

**Table 5** Multipliers of current and capital expenditures

|**Multiplier **|**Current**<br>**expenditure**|**Capital**<br>**expenditure**|
|---|---|---|
|Impact|0.12|0.62**|
|Peak|0.15|0.96*|
|Cumulative (4 years)|0.02|0.96*|

**Note:** Both models are balanced panel VAR models with variables current and capital expenditure respectively, instead of the total expenditure. The model with 3 lags is estimated using the annual data from 1990 to 2017. ***, **, * denote significance at the 1, 5 and 10 percent levels respectively.

**Sources:** ADB, IMF, World Bank, and own calculations.

# **7. Robustness checks**

In the following, we present some robustness checks that provide additional support for the findings discussed above. We present an alternative measure of cyclical components for the correlation exercise, a different estimation procedure for the error-correction model, and a couple of alternative PVAR specifications.

12
Detailed data exist from the surveys of enterprises and their workers conducted in the manufacturing sector of Ghana by the Research Program of Enterprise Development (RPED) of the World Bank. As already mentioned we have the data in three waves between 1992 and 1994. This material will be used to investigate the variation in labor earnings by size of establishment in the manufacturing sector of Ghana, since the sampled enterprises covered the entire spectrum from the micro to the very large. The topic is particularly important for assessing the extent of wage difference between the informal (small) and the formal (large) sectors in manufacturing, and the role of institutional factors in causing theses differences.

# **4.1 Analysis by Teal**

We have already referred to the work by Frances Teal on earnings function using the RPED data. His estimation of the function reproduced in Table 3.6 above, showed that, after controlling for human capital characteristics of the worker and profitability and capital-intensity of the enterprise, earnings increased by -firm size in a rather substantial way. Teal in these equations used the logarithm of firn size as the explanatory variable. The high significance of this variable showed that earnings increased with firm size a in a non-linear way. The elasticity of earnings with respect to firm size varied from 0.07 to 0.13.

It is more interesting to have an idea of the way labor earnings increase for different size groups of firms. For this, one needs to use dummies of different size groups of firms in the earnings function. In another piece of analysis Teal does just this (Teal 1994). Data for all workers for the first two waves are pooled and an earnings function is estimated with the following variables: age; highest level of education; occupation; location; industry; unionization-along with firm size dummies, and a dummy to control for the change between the two years. Teal summarizes the results of our interest as follows: _"Standardizing for skill, workers in small firms, which are
of the policy interventions differ in counterfactual scenarios of a fully credible commitment to net zero, an improved rule of law, and facilitated access to green bank finance.

We address these research questions using an empirical dynamic stochastic open-economy macro framework similar to the European Commission’s _E-QUEST_ model presented by Varga et al. (2022) but shifts the focus on an emerging-market context.<sup>2</sup> The model is designed for data-driven policy analysis and the simulation of low-carbon transitions. Renewable and fossil public investment can go into energy production or infrastructure. Infrastructure improves the marginal product of private capital but only up to a satiation point. To allow the model to capture deep transformations such as the low-carbon transition, it includes two critical features: a _learning-by-doing externality_ in the generation of renewable energy (Mercure 2012, Rubin et al. 2015); and the distinction between _short- and long-term elasticities of substitution between renewable and fossil energy_ (Guerrieri et al. 2008). On the labor market, the model allows for the possibility of _dis-equilibrium unemployment_ at the steady state, which provides flexibility regarding model closures and minimizes the impact of theory on the results for the long run.<sup>3</sup> The model features various financial instruments, bank lending constraints, and a financial accelerator mechanism along the lines of Kiyotaki and Moore (1997). The interested reader is referred to Appendix A, which presents the details of the model. We fit the model to Turkish data using standard Bayesian techniques as outlined in Herbst and Schorfheide (2015). The dataset covers the period from 2000Q2 to 2022Q3 and includes 31 time series in mixed frequency. Appendix B reports the details of the model estimation. We run all estimations and simulations in _Dynare_ 5.3 (Adjemian et al. 2022).

The simulation results suggest that T ̈urkiye needs to employ a dynamic policy mix to achieve the steady decline of 12 million (mn) tonnes of CO2 emissions per year required for net zero by 2053
Table 5: Estimation of Labor Supply across sectors

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Outcome:|OLS<br>∆_t,s_ln_Ls_|OLS<br>∆_t,s_ln_Ls_|IV1<br>∆_t,s_ln_Ls_|IV1<br>∆_t,s_ln_Ls_|IV2<br>∆_t,s_ln_Ls_|IV2<br>∆_t,s_ln_Ls_|
|_κ_|0.179**|-0.383***|1.142***|1.516***|1.490***|2.387***|
||(0.078)|(0.102)|(0.142)|(0.222)|(0.351)|(0.643)|
|Observations|3,192|3,192|3,192|3,192|3,192|3,192|
|Adjusted R-squared|0.237|0.296|0.247|0.302|0.238|0.295|
||||FS1|FS1|FS2|FS2|
|Outcome:|||∆_t,s_ CMA|∆_t,s_ CMA|∆_t,s_ CMA|∆_t,s_ CMA|
|∆_t,s_CMA|||1.981***<br>(0.093)|1.407***<br>(0.070)|||
|_Ti_|||||0.047***<br>(0.004)|0.028***<br>(0.003)|
|Observations|||
We estimate the effect of radio access on how many bed nets households report, including how many they purchased and how many they received for free. A serious problem of inference would arise if anti-malaria media campaigns were focused on those areas targeted for paid bed nets and avoided areas where free bed nets were distributed. There are numerous reasons to conclude that this issue does not arise in the Benin context.

First, policy documents indicate that donors and government exercise little real control over the pricing of bed nets, beyond issuing the general instruction of free distribution, everywhere. Second, even if they did, no documents that mention communications campaigns indicate that donors or governments modulate the campaigns according to their intended pricing strategies. On the contrary, the documents emphasize donor interest in both increasing demand for bed nets and promoting their correct usage: donors prefer to support programming wherever bed nets are distributed. For example, USAID, a major provider of both bed nets and communications programs, contracted with Population Services International to provide communications services. USAID emphasized in its request for proposal only the objective of increasing demand and proper usage, making no mention of pricing issues.<sup>8</sup> Finally, third, evaluations of the anti-malaria campaign in Benin suggest that donors have difficulties reliably implementing and coordinating their communications strategies, which would make coordination with pricing strategies even more difficult (Rwagacondo 2009).

Both the success of these distribution programs in reaching far-flung households, and the ability of local officials to charge for bed nets, are evident in our sample. The analysis is based on our survey of 20 randomly selected households in each of 210 villages across northern Benin. These 4,200 households responded to a detailed bed net module.<sup>9</sup> It asked respondents to list, for each bed net currently owned by the household, precisely how it was acquired. The options were: purchased from private sellers, purchased from government organizations, received as a donation from government, received as a donation
Scaling-up treatment of depression and anxiety: a global return on investment analysis." The Lancet Psychiatry **3** (5): 415-424.

Christian, C., L. Hensel and C. Roth (2018). "Income shocks and suicides: Causal evidence from Indonesia." Chuang, Y. and L. Schechter (2015). "Stability of experimental and survey measures of risk, time, and social preferences: A review and some new results." Journal of Development Economics **117** : 151-170. Das, J., Q.-T. Do, J. Friedman and D. McKenzie (2009). "Mental health patterns and consequences: results from survey data in five developing countries." The World Bank Economic Review **23** (1): 31-55. `‐` Dinkelman, T. (2017). "Long run Health Repercussions of Drought Shocks: Evidence from South African Homelands." The Economic Journal.

Druckman, J. N. and J. W. Holmes (2004). "Does presidential rhetoric matter? Priming and presidential approval." Presidential Studies Quarterly **34** (4): 755-778.

Eaton, J., L. McCay, M. Semrau, S. Chatterjee, F. Baingana, R. Araya, C. Ntulo, G. Thornicroft and S. Saxena (2011). "Scale up of services for mental health in low-income and middle-income countries." The <u>Lancet</u> **378** (9802): 1592-1603.

17
# **A Appendix: Production Function Estimation**

The literature on production function estimation has focused on the potential correlation between the unobserved productivity term _ωit_ and the choice of inputs, referred to as simultaneity and selection biases. Olley and Pakes (1996), Levinsohn and Petrin (2003) and Ackerberg et al. (2015) have proposed several solutions to overcome the simultaneity and selection biases. Nonetheless, consistent estimation of the production function requires all inputs and output to be in physical quantities. Because of the lack of data on quantities, a common practice is to deflate the variables with industry-level price indices. The Ghanaian dataset contains _firm-specific_ input and output price indices, thus alleviating the difficulty of having to make additional assumptions on potential deviations between industry-level and firm-level prices.

However, firm-specific prices are subject to factors such as differences in quality of inputs, the location of the firm and the firm’s market share. It is therefore essential to avoid picking up price differences in the estimation of the production function to recover output elasticities. Recent developments in production function estimation has emphasized that failure to account for price differences in the estimation process will lead to biased estimates of the inputs coefficients (Foster et al., 2008; De Loecker, 2011; De Loecker and Goldberg, 2014). The present study follows a recent approach proposed by De Loecker et al. (2016) to control for simultaneity, selection, and input price biases.

Firm _i_ produces output _Q_ at time _t_ according to the following production function: where _Lit_ , _Mit_ , and _Kit_ represent vectors of labor, intermediate materials, and capital inputs, respectively, and _ωit_ is the firm-specific productivity term. Upon rewriting the production function (A.1) in terms of logarithms and allowing for log-additive measurement errors and/or unanticipated shocks, the expression becomes where _qit_ is the production level of firm _i_ at time _t_ ; **x** _it_ is a vector of inputs, specifically, of labor, materials, and capital;
to value added, employment and number of establishments. The various combinations of these strata generate the cells for each industrysizeregion. Notice the lack of 4-digit stratification, feature to which we shall return in the subsection below.

As a first look at the data, we assess the comparability between the manufacturing censuses and ES using the Quantile-Quantile (QQ) plot. Figure 8 plots the quantiles of the size distribution of firms in the ES against the quantiles based on manufacturing census data. The size quantiles are given in a logarithm of employment. If the points in the plot more or less lie on the same line, then we expect the ES data to reflect the size distribution in the national censuses. Departures from this relationship indicate that the size distribution in the two datasets are<sup>18</sup> <!-- Start of picture text --> Cote d’Ivoire ES 2009 Ethiopia ES 2011<br>2 4 6 8 2 3 4 5 6 7<br>Employment(Census) Employment (Census)<br>Ghana ES 2007 Ghana ES 2013 Kenya ES 2007 Kenya ES 2013<br>2 3 4 5 6 7 2 3 4 5 6 7 2 4 6 8 10 2 4 6 8 10<br>Employment (Census) Employment (Census) Employment (Census) Employment (Census)<br>10 10<br>8 8<br>6 6<br>Employment (ES 2009) 4 Employment (ES 2011) 4<br>2 2<br>8 10 10<br>7<br>6 8 8<br>6<br>5<br>6 6<br>4<br>4<br>Employment (ES 2007) Employment (ES 2013) Employment (ES 2007) Employment (ES 2013)<br>4 4<br>3<br>2 2 2 2<br><!-- End of picture text --> Figure 8
done by computing CPI inflation as 100 times the year-on-year inflation rate, ie., as 100 _×_ (ln( _CPIit_ ) _−_ ln( _CPIit−_ 4)).<sup>20</sup> The real interest rate is then calculated as the lending rate minus yearon-year inflation. The alternative cost of borrowing variable, which captures the cost of borrowing in foreign currency, is calculated as the country specific lending rate minus the world interest rate minus the in the rate. The year-on-year change exchange exchange rate is defined as the number of local currency units per one US dollar, so that a declining value indicates an appreciation of the respective country’s currency against the US dollar. To avoid unnecessary volatility in the exchange rate series, we use quarterly averages rather than end-of-quarter values. For the US, we use (the inverse) of the Trade Weighted Exchange Index of major currencies to get a measure of the exchange rate impact on credit.<sup>21</sup> The world lending rate is approximated by the US dollar lending rate.

> 19We do not provide a separate table that lists the countries included in the panel data set to conserve space, nonetheless, the _x−_ axis labels of Figure 2 show explicitly which countries are included in the panel. 20We use year-on-year values, rather than annualised quarter-on-quarter values, to reduce the volatility of

> 21This series was obtained from the FRED2 database of the Federal Reserve Bank of St. Louise. The series code is DTWEXM. The series was also aggregated to the quarterly average. We use the inverse to be consistent with the definition of a decreasing value implying an appreciation of the domestic currency for non-US countries in the cross-section.

16
accuracy of the disaggregated poverty measures could further be enhanced and the geographies in which the disaggregated poverty measures are predicted better aligned with the geography of the urban–rural catchment configurations (Engstrom, Newhouse, & Soundararajan, 2019; Jean et al., 2016). This would enable more precise estimation of the role of the urban hierarchy and their related hinterlands in poverty outcomes, using consistent city-location categories and similar poverty predictors across countries.

Other welfare outcomes such as dietary diversity and nutritional status (stunting as well as obesity) could be similarly mapped and examined across the urban hierarchy and their hinterlands. As urbanization proceeds, and dietary habits change, often towards more sugary, fat and convenient foods (Cockx, Colen, & De Weerdt, 2018), understanding how the dietary patterns and related health outcomes evolve across the urban–rural spectrum will be important to guide and target food and nutrition interventions. Important research questions include understanding if obesity is limited to large cities, or whether it also appears in their immediate hinterland or smaller towns. Or understanding if stunting improves at low-levels of urbanization and is sustained at high-levels of urbanization, where changing child feeding practices may offset the effects of improved sanitation, as observed in some African countries (Ameye & De Weerdt, 2020). Such questions can now be readily explored across multiple countries by overlaying the many existing georeferenced living standard measurement or demographic health surveys with the urban categorizations and their hinterlands.

# 4.4 Access to services

Two other policy areas that could be served by public access to the FEA and URCA databases are access to health services and education. These services have a disproportionate impact on the well21
# Zble of Contents

||FISCAL STABILIZATION AND EXCHANGE RATEIN2AS LUTTY:<br>THEORETICAL APPROACH AND SOME POLICY SIMULATIONS<br>USING MEXICAN DATA||
|---|---|---|
|I.|INTRODUCTION ..................................................|...I|
|II.|EXCHANGE RATE DETERMINATION AND THE REDUCTIONOF ....<br>......<br>GOVERNMENT SPENDING:<br>BACKGROUND<br>AND INTUITION................|.......<br>5<br>...<br>5|
|_T.|THE MODEL.<br>...............................................|........<br>8|
||A. Production ................................................|... 9|
||B. Consumption ...............................................|... 13|
||C. Budget Deficits and the Adjustment of the Exchange Rate ..
our regressions. Nonetheless, we do not see evidence of such a shock when looking at VAT collection (refer to Appendix Figure A.3) – no large changes in tax collected are observed around the same period we see the significant increase in TOT taxes, suggesting that the response we document is much more likely to be an evasion response than a real economic shock. Over time, it is possible that the higher TOT rate may have real economic implications as businesses have fewer resources to reinvest in their operations.

The responsiveness of taxpayers to a change in tax obligations is consistent with findings from the surveys showing that respondents were relatively better informed about the TOT rate they needed to pay compared to other aspects of the TOT regime. Specifically, 44.6 percent of registered small businesses in the in-person survey and 31.8 percent of TOT payers in the SMS survey were aware of the existing TOT rate. The lower level of knowledge in the SMS survey may be a result of the tax rate changes in the preceding 12-month period.

# **4.1.3 Requiring a percentage to be paid**

Thirdly, requiring taxpayers to calculate a percentage of their monthly turnover to be paid in tax appears to be challenging for some businesses, especially those that do not have extensive transaction records. Instead, it appears many small businesses rely on heuristics to determine their tax liability, as a non-trivial share consistently pays the same amount in TOT each month despite numerous policy changes altering their tax liability up and down (see Table 2).

Both surveys reveal that registered small businesses struggle to calculate percentages of turnover. The in-person survey shows that just over half (53.5 percent) of registered small businesses could calculate 1 percent of 5 million Kenyan shillings. The SMS survey illustrated that just over two-thirds (68.9 percent) of TOT payers who responded to the survey could calculate the tax obligation required from businesses earning 5 million Kenyan shillings.<sup>16</sup>

> 16In the case