rafmacalaba/datause-encoder-data huggingface dataset:
_Figure9: Pollution and City Competitiveness in terms of Employment Growth – South Asia and Sub‐Saharan_ <!-- Start of picture text --> PM 2.5 Concentration and City Employment Growth 2000−2015<br>Africa and South Asia<br>9 Kabul ●<br>Lusaka ●<br>●<br>● Kigali<br>Ouagadougou<br>6 WHO Guideline<br>Surat<br>KhartoumLilongweBlantyre City ●● Abuja ● ● BujumburaKannur ● Pondicherry ●<br>30 WindhoekNouakchott ● ● ●●●●●●●●●●●●●●●● Bamako ●●●● DakarYamoussoukro ●●●●● ● ● ● ●● ●●● BengaluruMonrovia ●● ● ●●●●●●●●● Port Harcourt ●●●●●●●●●●●●● ● ● Coimbatore ●●● Kozhikode ●●●●● ●●●● ●●●● Tiruppur ●● ●●●●●●● Jaipur ● ●●●● ● ●●●●●●●● Bhilai NagarRaipurFaisalabad ● Ranchi ●●●●●● Kathmandu ●● Amritsar ● Bhubaneswar ●●●●● ● Gwalior ● ● Khulna ● CuttackJalandhar Lahore ● ● DhakaJamshedpur ●●●● Bokaro ●● AsansolAgraLudhianaDhanbad ●● Rajshahi ● Allahabad ● Kanpur ●● Moradabad ● Meerut ●● Ghaziabad ● ● Aligarh ●● Delhi ● ● BareillyKolkataVaranasiLucknowFaridabad ● Gorakhpur ● Patna ● Regionsaa ●● SARSSA<br>● ● ●<br>● Chandigarh ●<br>●<br>0 25 50 75 100<br>PM 2.5 in 2015<br> Percent, 2006−2015<br>City Employment Growth per capita compared to National Growth<br><!-- End of picture text --> Looking back into cities with higher levels of pollution, we can also find examples of competitive cities. These cities are developing fast and generating jobs, but they are failing at managing pollution levels. As a consequence, the quality of life in these cities is decreasing, and they may be failing to attract cleaner firms and qualified workers that can boost development. In turn, this could lower the city’s growth, and threaten its competitiveness.

Patna is a good example of a competitive city that may not be taking the necessary actions to tackle air pollution. With an annual average PM 2.5 level of 95.5 μg/m<sup>�</sup> in 2015, Patna is the city with the highest annual average concentration of PM 2.5 in our sample. Besides issuing health warnings on critical days, the government has no long‐term plan to handle this problem. Recently, a voluntary organization (The Center for Environment and Energy Development) has urged the government to develop a clean air action plan. However, there is no action plan in place yet, only a set of recommendations to follow on particularly 27
the economy. This is a significant third lesson from the DSGE analysis.

# **4  Conclusions**

In this paper, we have analyzed the economic implications of various budget rules to manage public revenue from natural resources in a developing country. We developed a 1-2-34 (for one country, two types of capital, three sectors, and four goods) model by introducing uncertainty in the dynamic 1-2-3 model. We captured the externality of public capital on firms’ technology. After defining as resource windfalls the difference between current and the steady state levels of royalties on resource exports, we considered four regimes for the use of the windfalls: All-consuming, All-investing, All-wise and Balanced regimes. We then analyzed the economic implications of the volatility of the resource export price in the case of a temporary shock and in the case of persistent but uncertain shocks in all periods.

Our simulation results suggest three policy lessons or rules of thumb. When a resource price change is positive and temporary, one should bank the revenue windfall in a sovereign fund during the temporary price increase. Drawing interest income from the sovereign fund to raise consumption over time through government transfers to households will provide the largest welfare gains. This is because in the All-investing case, public investment is only partially effective (50 percent) in raising output, as shown in prior empirical estimates. In the presence of a negative temporary resource price change however, the best strategy is to cut public investment rather than trim government transfers to households, which reduces consumption directly, or draw down the sovereign fund or borrow, which incurs an interest premium as debt rises. In the presence of shocks that occur in all periods, the best strategy is to combine both, that is, a balanced regime that provides a natural insurance against both types of price shocks, positive and negative, which are occurring over time. Combining interest income from the sovereign fund, transfers to households, and output growth brought about by public investment presents the best protective mechanism to smooth consumption over time in response to changing resource prices.

We have three suggestions for future research. In the face of uncertainty and volatility of price shocks, a balanced approach seems ideal as a simple budget rule to follow. The optimal distribution between All-savings and All-investment will likely be different from the 50-50 split simulated here under different values of parameters such as the elasticity of output to public capital, trade elasticities, the interest rate premium for rising foreign debt, etc. Hence, determining the optimal rule for a range or combination of these parameters is one avenue for analysis. A second area of research is to model the stop and go costs of public investment, so 29
# **BOX** V2: The Budget Process in **Colombia**

In Colombia, the central govermnent has traditionally had little power and few legal or institutional mechanisms with which to manage public expenditure. The 1989 Colombia PER focuses on the budgetary process--why i, does not work well and how to gain control of it. After addressing the manageability of public expenditure and the earmarking of revenues, it discusses the three sectors (power, health, and education) that it finds require the most attention. It includes these sectors not only because they absorb large shares of government spending, but also because the budget process itself has impeded the implementation of the government's stated goals for the sector.

The annual budget process is criticized as a short-term exercise with little relationship to long-termn planning. Numerous rigidities--a constitutional requirement that the budget not exceed the previous year's budget by more than 10 percent, a requirement that the budget be formally balanced, and the extensive system of earmarked taxes--have contributed to the budget's irrelevance as an allocation and planning tool. As a result, planned income and expenditures often have little relationship to actual financing and spending.

From 1986 to 1988, total education expenditures (both capital and recurrent) not only failed to meet the planned targets, but actually fell in real terms. Although investment in primary education doubled over the period, it fell far short of the planned level, as did overall spending on primary education. On the recurrent side, nonsalary spending fell 45 percent in real terms in spite of government recognition of the importance of nonsalary education spending. Since 91 percent of education financing comes from the central government (with 4 percent from external credits and 5 percent from all other sources), the large shortfall in education financing is directly attributable to central government financing. (In 1988, education accounted for 19.1 percent of the central government budget rather than the planned 24.6 percent.) Although problems of fiscal planning and management remain, steps have been taken to rationalize the process. The report reviews these and suggests additional procedural and institutional measures. It also links a) macroeconomic and sectoral priorities with one another and with the institutional context, and b) the budget process and the actual fiscal outcome.

jaj: 'Colombia Public Expenditure Review," Report No. 7891-CO (green cover).

48
-1-

Behaviour of Firms

# Introduction

At the outset of 1992 much was made of the change in regime that was to be associated with price liberalization, reform of property rights and a new-found intolerance by the monetary and fiscal systems to financial indiscipline. The heart of these changes had necessarily to be the enterprise sector - the bedrock of the planned economy. By early 1993, however, aggregate data indicate that the regime break was less evident than might have expected, that price liberalization had transiated into-persistent and high increases in monthly inflation aad that structural changes remained restricted. Moreover, we have shown elsewhere that despite very significant negative shocks to output over 1992, changes to employment and hence to unemployment have been less than might have been predicted **2.** Further, the shift in the stance ,f the central monetary authority in mid-1992 validated the accumulation of large inter-enterprise arrears that had been accumulated through the first half of 1992 and, at the sane time, sent a qualitatively - different signal to firms regarding the viability of thei: claims impiicit and explicit - on the budget and/or banking system.

The response of Russian firms to the price shocks of 1992 and to the large and, in some cases, random disturbances transmitted through the combination of trade and political changes is clearly of key importance. In particular, we need to know how those shocks have distributed themselves with respect to branches and the subsequent actions taken by firms in response to those shocks. This, however, is easier said than done. One cost of disintegration has been considerable disruption of the statistical system and of the reporting protocols that characterised earlier practice. One consequence has been the ncid to rely on new data collection primarily centred on the firm.

This paper provides an extended discussion of firm behaviour with particular emphasie' on the associations between output, financial performance and wage and employment decisions. In addition, we provide a first attempt at systematically quantifying the values and costs of non-monetary wage components. The main objective of the paper is thus to get some empirical handle on a fluid and diverse set of changes in the environment facing firms and structuring their decision rules.

'1: The Survey The paper is largely organized around the results from a small survey of 41 firms that was carried out in and around Moscow in the last two weeks of November 1992. The firms were randomly selected and covered ten branches, including trade and services. Appendix Tables 1 and 2 give the breakdown - of the sample by size in terms of employment and assets, as well as by branch and property type. As 2 Commnder, LibeMan ad YeMtSov (1993a)
<!-- Start of picture text --> Management Score<br>Sectors facturing Tech Tech Intensive Intensive<br>Q) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)<br>Log(Employees) 0.047*** 0.055*** 0.064*** 0.042*** 0.053°** 0.073°** 0.037°* 0.044***<br>(0.007) (0.008) (0.011) (0.010) (0.019) (0.014) (0.015) (0.015)<br>Log(Age) —0.017** —0.030*** | —0.019* -0.034*** -0.019 —0.021 —0.020 —0.044***<br>(0.007) (0.007) (0.011) (0.009) (0.017) (0.014) (0.013) (0.013)<br>DeEzporter 0.025** 0.024° 0.039** 0.004 0.075** 0.034* —0.015 0.024<br>(0.013) (0.013) (0.017) (0.018) (0.030) (0.020) (0.028) (0.025)<br>DuighEdu 0.036"*(0.015) 0.048"**(0.014) 0.041°*(0.021) 0.056°°*(0.020) —0.017(0.031) 0.066°°(0.028) (0.040)0.046 0.057**(0.024)<br>DFamity—owned —0.003(0.013) (0.013)0.015 0.0003(0.020) (0.017)0.025 (0.040)0.016 —0.0002(0.022) (0.021)0.029 (0.025)0.020<br>R-Square 0.12 0.09 0.08 0.09 0.08 0.17 0.18 0.18 0.27 0.18 0.13 0.22<br>N 727 724 727 727 727 724 313 411 101 212 204 207<br>Region Dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes<br>Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes<br>Notes: Columns (1) — (12) use management score of a firm as the dependent variable. ‘Log(Employees)’ is the total number of employees ofa firm.<br>‘Log(Age)’ is the age of a firm. ‘Dgzzporter’ takes value 1 when a firm’s export values are greater than zero. ‘Dyighzdu’ takes value 1 if more than 20%<br>workers of a firm have a higher education degree. ‘DFamily—owned’ takes a value 1 if a firm is family-owned and run by the owner or a family member<br>of the owner. Robust standard errors are in the parenthesis. Intercepts included but not reported. *,~*.*** denotes 10%, 5% and 1% level of<br>significance, respectively.<br><!-- End of picture text -->
# Openness and Growth: A Time-Series, Cross-Country Analysis for Developing Countries

# by Ann Harrison*

# Table of Contents

|I.|An Overview of the Literature on Openness and Economic<br>Growth|3|
|---|---|---|
|I I.|Data and Correlations Between Openness Variables|10|
|III.|Basic Results|14|
|IV.|Sensitivity Tests: Country Size and Macroeconomic<br>Policy|18|
|V.|Conclusion and Directions for Further Research|ao|
|Bibli|ography|23|
|Table|s|26|
|Figur|e|34|

* I would like to thank members of the _World Development Report 1991,_ Lant Pritchett, and Bill Easterly for many useful discussions.
# 8 Appendix: Data

Table A1 lists all the countries in our main sample of Figure 1. The total is 189 countries, comprising 152 EMDE and 37 AE or 94 commodity-exporters and 95 noncommodity exporters.

Figures 1 through 16 plot the country correlations between the HP filtered cyclical components of real government spending and real GDP (WEO, IMF, April 2022). The sample period is 1980-2020, but the panel is unbalanced since some countries do not have data for the whole sample period. The institutional variables (political risk, bureaucracy quality, control of corruption, government stability, and law and order) are taken from the International Country Risk Guide (ICRG). The threshold to distinguish countries with better institutions from those with worse institutions was computed by taking an average score for each country between 1984-2020 and comparing it to the cross-country median score. For capital controls, the Chinn-Ito index was used to calculate the financial openness of an economy for the period 1980-2019. The Ilzetzki et al. (2021) database was used to calculate the average exchange rate regime of an economy between 1980 and 2019.

In the regressions of Section 3, the size of the public sector is defined as government spending as a proportion of GDP, from WEO (IMF). WEO is also the source for GDP per capita. For openness (defined as exports plus imports as a proportion of GDP), the source is the World Integrated Trade Solution (World Bank). In the regressions of Section 4, fiscal rules are taken from the fiscal database in Davoodi et al. (2022). This database distinguishes between budget balance rules, debt rules, expenditure rules, and revenue rules. The variable fiscal rules is a dummy-variable that takes the value 1 if any rule is being enforced and 0 if none is. We then take the average over the country sample. Sovereign wealth funds (SWF) is a dummy-variable that takes the value 1 if a fund is in existence and 0 if it is not. We then take the average over the country sample.

36
- [2] Andersson, M. (2007). "Entry costs and adjustments on the extensive margin - an analysis of how familiarity breeds exports." Royal Institute of Technology of Stockholm, CESIS Working Paper 81.

- [3] Baganha, M. (2003). "From closed to open doors: Portuguese emigration under the corporatist regime." e-Journal of Portuguese History, 1(1): 1-16.

- [4] Baldwin, R. and Harrigan, J. (2011). "Zeros, quality and space: Trade theory and trade evidence." American Economic Journal: Microeconomics, 3(2): 60-88.

- [5] Bartelsman, E., Haltiwanger, J. and Scarpetta, S. (2008). "Cross country di¤erences in productivity: The role of allocative e¢ ciency." University of Maryland, mimeo.

- [6] Bastos, P. and Silva, J. (2010). "The quality of a ...rm’s exports: Where you export to matters." Journal of International Economics, 89(2): 99-111.

- [7] Bernard, A., Jensen, J.B., Redding, S. and Schott, P. (2007). "Firms in international trade." Journal of Economic Perspectives, 21(3): 105-130.

- [8] Bustos, P. (2011). "Trade liberalization, exports and technology upgrading: Evidence from the impact of MERCOSUR on Argentinean ...rms." American Economic Review, 101 (1): 304-340.

- [9] Cabral, L. and Mata, J. (2003). "On the evolution of the ...rm size distribution: Facts and theory." American Economic Review, 93(4): 1075-1090.

- [10] Chaney, T. (2008). "Distorted gravity: The intensive and extensive margins of international trade." American Economic Review, 98(4): 1707-1721.

- [11] Crozet, M. and Koenig, P. (2010). "Structural gravity equation with extensive and intensive margins." Canadian Journal of Economics, 43(1): 41-62.

- [12] Crozet, M., Head, K. and Mayer, T. (2011). "Quality sorting and trade: Firm-level evidence from French wine." Review of Economic Studies, forthcoming.

- [13] Eaton, J., Kortum, S. and Kramarz, F. (2011). "An anatomy of international trade: Evidence from French ...rms", Econometrica, 79(5), 1453-1498.

- [14] Guiso, L., Sapienza, P. and Zingales, L. (2009). "Cultural biases in economic exchange?" Quarterly Journal of Economics, 124(3), 1095-1131.

- [15] Gould, D. (1994). "Immigrant links to the home country: Empirical implications for U.S. bilateral trade ‡ows." Review of Economics and Statistics, 76(2): 302-316.

17
8 Data analysis was conducted in STATA version 15.1.<sup>22</sup> We first descriptively explored demographic characteristics of participants and self-reported sexual behavior and sexual health history, including number and type of clients, income, condom use, prevalence of STIs, and previous STI and HIV testing. Characteristics between treatment groups and baseline and endline participants were compared.

The primary intent-to-treat analysis included only participants who completed the study, as the primary outcome of combined HIV/HSV2 incidence was measured at endline. Linear probability models were used to estimate unadjusted and regression adjusted risk differences (RD) with 95% confidence intervals (CI). Control variables in the adjusted models were baseline: age, education, amount of time living in Dar es Salaam, marital status, number of children, total monthly income, monthly income from sex work, social ladder ranking, location of sex work, number of clients, price received for sex with a condom, previous STI diagnosis, timing of last STI testing, preferred frequency of STI testing, previous HIV testing, perceived HIV risk, and baseline value for the dependent variable being analyzed.

In sensitivity analyses, we also report results of the adjusted models for the primary outcome after applying inverse probability of censoring weighting (IPCW) to account for systematic observed differences in participants who were lost to follow-up. The probability of participating in endline (not attriting) was generated using logistic regression as a function of the same baseline control variables listed above. Weights were calculated using the predicted probability of not attriting and were included in adjusted analyses.

Finally, for our main endpoint (combined HIV/HSV2 incidence), we also estimate unadjusted treatment effect bounds following Lee (2009),<sup>23</sup> using the _leebounds_ command in Stata. We report these upper and lower bound treatment effect estimates which vary the assumptions regarding nonrandom selection of the FSW who attrited from the endline survey. This is a nonparametric bounding that drops a set of the Treatment arm observations to match the proportion of Control arm observations not attriting. The upper (lower) bounds are computed by dropping these Treatment arm observations from the lower (upper) end of the distribution of the dependent variable and re-estimating the unadjusted treatment effects model.

# _Ethics_

The RESPECT II trial received ethical approval from the Committee for Protection of Human Subjects at University of California, Berkeley (Protocol 2015-08-7849) and the National Institute for Medical Research in Tanzania <mark>(NIMR/HQ/R.8a/Yol.IXI 2770)</mark> . All data collection was carried out by research assistants from Innovations for Poverty Action Tanzania who were trained in
Table 6: Regression results for CDM Engagement (No interaction trms)

|Equation|1|2|3|
|---|---|---|---|
|Estimationprocedure|Logit|Pr|obit|
|Dependet variable|_CDM_<br>_Engagement_|_CDM E_|_ngagement_<br>|
||||_Marginal Effect_<sup>_a_</sup>|
|_hst_Average Annual Energy Use_|2.04E-03***<br>(2.56)|1.24E-03***<br>(2.79)|0.0000495|
|_inv_Average Annual Energy Use_|8.79e-03***<br>(9.63)|4.62E-03***<br>(9.43)|0.0001845|
|_hst_Ease of Doing Business_|-9.47E-03***<br>(-3.33)|-4.59E-03***<br>(-3.06)|-0.0001833|
|_inv_Ease of Doing Business_|0.046<br>(0.59)|3.11E-02<br>(0.77)|0.000124|
|_hst_Governance_|0.077***<br>(2.77)|0.037***<br>(2.65)|0.0015142|
|_inv_Governance_|0.368***<br>(6.04)|0.185***<br>(5.93)|0.0073955|
|_hst_Renewable Energy_|1.26E-03***<br>(4.17)|6.02E-04***<br>(3.58)|0.000024|
|_inv_Renewable Energy_|-1.02E-03***<br>(-2.73)|-5.88E-04***<br>(-3.07)|-0.0000235|
|_hst_Impact Vulnerability_|4.06E-02***<br>(6.01)|1.90E-02***<br>(5.13)|0.0007592|
|_inv_Impact Vulnerability_|4.34E-02***<br>(5.18)|2.05E-02***<br>(5.13)|0.0008188|
|_Trade1_|209.61***<br>(2.70)|118.60***<br>(3.03)|4.726333|
|_Constant_|-10.05***|-5.12***||
||(-9.88)|(-9.96)||
|Observations|2771|2771||
|LogLikelihood|-461.01|-461.86||
|LR|420.91***|419.21***||
|PseudoR<sup>2</sup>|0.313|0.312||
|Overallobserved probability|||0.065|
|Predicted Probability|||0.016|

In parentheses are t-values; *** (=0.01); ** (=0.05); * (=0.10). aMarginal effect: the change in the probability for an infinitesimal change in each independent.

30