rafmacalaba/datause-encoder-data huggingface dataset:
pump. The Nigerian Petroleum Products Pricing Regulatory Agency (PPPRA) determines the daily and monthly subsidy rates by estimating the gap between the expected price of imported petroleum products, including margins, and the pre-established, regulated, domestic price.<sup>8</sup> The subsidy paid by the government on imported petroleum products varies positively relative to changes in two factors: world prices and/or demand for these products. In recent years, rising prices of petroleum products have damaged Nigeria’s fiscal health by increasing the Nigerian fuel subsidy (Rice, 2012; BBC 2012a, 2012b). For 2012, the subsidy was estimated at US$ 5.6 billion, being equivalent to about 20% of the total public budget (Umar and Umar, 2013). Thus, the fuel subsidy contributes strongly to the deterioration of Nigeria’s government balance which has changed from a surplus equal to 7.3 percent of GDP in 2008 to a deficit of 8.2 percent and 6.5 percent in 2009 and 2010 respectively (IMF, 2013).

Though the fuel subsidy helps the poor by keeping Nigerian fuel prices lower than world prices, it is not an efficient policy for poverty reduction and development for several reasons:

1. The biggest beneficiaries have been importing companies and local wholesalers that smuggle some of the subsidized fuel into neighboring countries selling it at higher prices (The Economist, 2011; 2012). Illegal trade is not well recorded in official trade statistics, which makes it difficult to analyze. According to the BBC (2012a, b), the Nigerian government paid the subsidy on 59 million liters of fuel a day in 2011, although domestic consumption in Nigeria was only 35 million liters a day. Thus, petroleum product importers were paid billions of US dollars to import fuel that was never delivered to the Nigerian people with the subsidy leaking to traders and foreign consumers.

2. The share of poor households in total fuel consumption, and thus the total subsidy volume, is lower than the share of rich households. Umar and Umar (2013) find the richest 20% of households in Nigeria benefitting more than four times as much from the fuel subsidy in Nigeria than the poorest 20%. This analysis, though, is static and limited to the first order effects of price changes without taking into account behavioral responses and changes in income to labor and capital. This is confirmed by data from the 2006 Nigerian SAM (Nwafor et al., 2010), which reports that rural households in Nigeria, which constituted 53.6 percent of the total Nigerian population in 2006 (World Bank, 2013), consumed only 33 percent of total household consumption of petroleum products in Nigeria compared to 67 consumed by the wealthier urban households. In addition, even in relative terms, poor

> 8 The PPPRA follows the import parity principle in its price determination, which includes: a) landing cost of products; b) margins for the marketers, dealers, and transporters; c) jetty-depot through-put; and d) other charges and taxes.

4
Davidson, R., and J. Y. Duclos. 1997. “Statistical inference for the measurement for the incidence of taxes and transfers.” _Econometrica_ 65: 1453-66.

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Gakidou, E., and G. King. 2000. “An Individual-Level Approach to Health Inequality: Child Survival in 50 Countries.” Global Programme on Evidence for Health Policy Discussion Paper No. 18. World Health Organization.

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Gwatkin, D. R. 2000. “Health inequalities and the health of the poor: what do we know? What can we do? [see comments].” _Bulletin of the World Health Organization_ 78(1): 3-18.

Gwatkin, D. R., S. Rustein, K. Johnson, R. Pande, and A. Wagstaff. 2000. “Socioeconomic Differences in Health, Nutrition and Population.” HNP Poverty Thematic Group, World Bank, Washington, D.C. Processed.

Heltberg, R., K. R. Simler, and F. Tarp. 2001. “Public Spending and Poverty in Mozambique.” DP 2001/63. WIDER Institute.

Illsey, R., and J. LeGrand. 1989. “The measurement of inequality.” In Williams, ed., _Health and Economics_ . London: Macmillan.

Jenkins, S. 1988. “Calculating Income Distribution Indices from Micro-data.” _National Tax Journal_ XLI(1): 139-42.

Kakwani, N., A. Wagstaff, and E. van Doorslaer. 1997. “Socioeconomic inequities in health: Measurement, computation, and statistical inference.” _Journal of Econometrics_ 77: 87-103.

Kakwani, N. C. 1977. “Applications of Lorenz Curves in Economic Analysis.” _Econometrica_ 45: 719-27.

22
<!-- Start of picture text --> Distribution of PM 2.5 levels by Region<br>EAP ECA LAC<br>0.10<br>0.05<br>0.00<br>MENA OECD SAR<br>0.10 Year<br>2006<br>0.05 2015<br>0.00<br>0 25 50 75 100 0 25 50 75 100<br>SSA<br>0.10<br>0.05<br>0.00<br>0 25 50 75 100<br>PM 2.5<br>Density<br><!-- End of picture text --> Second, we compare the distribution for each region between 2006 and 2015. We find all three different possibilities. First, the distribution between 2006 and 2015 shows little or no change. This is the case for example in East Asia Pacific. Second, the distribution of pollution in 2015 has shifted to the left or more concentrated in lower values, showing an improvement in pollution levels. This is true in Latin America (slight shift to the left) and Europe and Central Asia (concentrated in lower values) regions. Third, the distribution in 2015 has shifted to the right or has become flatter, showing that pollution conditions have worsened in that region. Examples of this are Middle East and North Africa (shift to the right), South Asia (flatter), and Sub‐Saharan Africa (slight shift to the right). A first approach to identify which regions have worsened their pollution distribution is useful to guide the analysis and help focus on recognizing interesting city examples in those regions.

# Competitive Cities and Pollution

Cities that are developing fast are often identified as competitive cities. Recent work in the World Bank (Competitive Cities for Jobs and Growth, 2016) defines competitive cities as those that “successfully facilitates their firms and industries to create jobs, raise productivity and increase the incomes of citizens over time”. These cities are growing at a faster pace than their nations, attracting foreign direct investment, and constituting engines that push national growth. They often face challenges that are a direct consequence of this rapid growth, such as congestion and pollution management. How they will manage those challenges has a direct impact on their ability to keep growing and remain competitive.

7
capital, higher public borrowing will mainly comprise domestic borrowing and this will only be possible if there is macroeconomic and fiscal space, an issue which is addressed in section 3.

It is also interesting to note how the economic crisis facing the low income CIS economies compares with the crisis facing middle income transition economies in Eastern Europe and Central Asia (ECA). There are both similarities and differences. Both low and middle income economies in the ECA region face a large external shock to which they must adjust by reducing absorption, but the specific nature of the external shock differs, as does the scope for policy responses.<sup>6</sup> The fuel exporting economies, which include two classified as low income (Azerbaijan and Uzbekistan) have suffered a large adverse terms of trade shock as a result of fall in the oil price in 2008. The non fuel exporters, both low income and middle income, have experienced much less volatility in their external terms of trade. The middle income transition economies (including the fuel exporters) are much more vulnerable to a capital account crisis than the low income economies. The former have much higher levels of short term commercial external debt, with the average levels of short term debt exceeding their international reserves. The latter have, on average, both lower external debt to GDP ratios than the middle income economies and very little short term commercial debt, in part because they lack access to international capital markets. Also in contrast to the middle income transition economies, the low income CIS economies have very little external debt owed by their banks and non financial private sectors. Consequently, the external shock facing many of the middle income transition economies arises primarily from a reversal of commercial external capital flows (a “sudden stop”), whereas the main contributor to the external shock facing the low income CIS economies is the fall in remittance incomes, as discussed above.

> 6 These paragraphs draw on the data and analysis in the _economics_ note prepared by the Office of the Chief Economist, Europe and Central Asia region, World Bank, entitled “The crisis in Europe and Central Asia in perspective”, 2009.

11
<mark>Lee, K., & Kang, S. M. (2007). Innovation types and productivity growth: Evidence from Korean manufacturing firms.</mark> _<mark>Global Economic Review</mark>_ <mark>,</mark> _<mark>36</mark>_ <mark>(4), 343-359.</mark> Lööf, H., Mairesse, J., & Mohnen, P. (2017). CDM 20 years after. _Economics of Innovation and New Technology_ , _26_ (1-2), 1-5.

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<mark>Porter, M. E., & Millar, V. E. (1985). How information gives you a competitive advantage.</mark> _<mark>Harvard Business Review, 63</mark>_ <mark>(4), 149-160</mark> <mark>Syverson, C. (2011). What determines productivity?.</mark> _<mark>Journal of Economic Literature</mark>_ <mark>,</mark> _<mark>49</mark>_ <mark>(2), 326-65.</mark> 20
data and evidence generated to affect midcourse corrections in policy implementation as well as to influence scale-up or -down decisions.

These IEs are bundled with capacity-building activities and monitoring. Government and project teams attend workshops and trainings, are exposed to international evidence and the use of analytical tools for decision making, and participate in IE discussions and analysis. The IE research and field teams accompany the projects through their life cycle, monitoring IE implementation and data collection, and often supporting the implementation of project activities (World Bank 2006). Because of the bundled nature of Bank IEs, the analysis cannot separate the effects of IE research from that of capacity building and monitoring.

Furthermore, because the Bank IEs have been subject to internal quality assurance processes since 2007 (which means they require managerial approval, peer review, and discussion), they are less likely to be purely research led and more likely to have stronger links to policy. For these reasons, the estimates may not be applicable to more research-led IEs.

# 3.B. DATA

Our panel covers the period since 2005 when the Bank formally started doing IEs (Figure 1) and includes the 1,135 projects approved during 2005–11 that are in sectors that have at least one IE. Of these, 100 have an IE.

Project data are extracted from the World Bank’s Business Warehouse. Time-invariant characteristics include project id, region, country, sector, date of approval, and closing date. Timevarying characteristics are planned disbursements (initial and possible revisions), actual disbursements, and identity of the project leader. The data include 1,252 entries that are the universe of the projects approved by the Board between the first quarter of 2005 and the last quarter of 2011, across 28 quarters. As sectors with no IEs are excluded from the analysis, the sample drops to 1,135 projects, with up to those 28 quarterly observations, corresponding to 17,784 total quarterly observations.<sup>11</sup> Impact evaluation data comes from the IE database maintained by the Development Impact Evaluation Unit (DIME) in the Research Department of the World Bank. The database includes 377 entries in the period 2005–11. Of these, 100 are for Bank projects approved in the same period, or 9 percent of the project in our sample.<sup>12</sup> Time-invariant characteristics include IE id, region, country, sector, start date, and project id. Time-varying characteristics include IE status and identity of IE leader. We use project id to merge the data. When the variable “project id” is missing, it indicates that

> 11 The number of observations for a project depends on its vintage or year of approval.

> 12 The remaining is for policy interventions not financed by the World Bank, for which implementation data is not available.

7
# **_Firm-Level Outcomes (for New Entrants)_**

So far, we have shown that for incumbent firms, city status does not necessarily lead to better aggregate firm-level outcomes, unless accompanied by commensurate capacity. Now, we study new firms that are established in newly upgraded cities. After counties upgrade to cities, they tend to attract more firms. We examine whether these new firms also outperform new firms located in similar counties. Admittedly, this exercise is fraught with selection bias. However, we remain interested in knowing if new firms in cities outperform new firms in similar counties – especially, since this finding has policy implications. Cities care about better economic outcomes – these could be generated by better performance by incumbents, or by better-performing entrants.

New firms in cities tend to operate on a larger scale than new firms in counties – in terms of operating revenues (sales) and operating costs. However, they are not necessarily more profitable, nor do they generate more or better-paying jobs. Thus, city status does not necessarily attract better and more competitive firms to locate there. We also tried to break down new firms located in high capacity cities versus low capacity cities and see whether there is a difference. Unfortunately, we do not have enough data on entrants to carry out a robustness test.

**Table 15:  Firm-level outcomes of city upgrading using PSM for new firms**

|**_New Firms_**|(1)|(2)|(3)|
|---|---|---|---|
|_Firm-level Outcome_||||
|Log main operating revenue|0.297|0.344*|0.344*|
|Log main operating cost|0.378*|0.423**|0.427**|
|Log main operating profit|0.0845|0.294|0.322|
|Log # of employees|0.113|0.154|0.140|
|Log wage per employee|-0.000390|-0.0564|-0.0572|
|Log paid-in capital|-0.111|-0.0189|-0.0180|
|Controls|Block FE|Block FE|Block FE|
|||Industry FE<br>Year FE|Industry**×**Year<br>FE|

1. Significance levels of 10%, 5%, 1% are represented by *, **, ***, respectively.

2. Please note that there are not enough observations for new firms’ vocational labor data and export value data. To avoid using an under-representative sample and generalizing results from comparing between new firms located in only a few cities and counties, we do not report the results of these two firm-level outcomes.

3. Analysis is restricted to new firms opened after 1997, i.e. after the upgrading was finished.

20
show potential for growth, while others still resemble the state owned enterprises and reduce employment.<sup>16</sup> The superior employment performance of private firms compared with public firms is not surprising and is what one would have expected in a transition economy. The problem is however that the size of the job creating private sector is small in Moldova, which severely limits its employment generation potential. In the non-agricultural business sector the share of private firms in employment is only 11 percent. The difference between this figure and the high share of the private sector reported in official statistics is made up by the “collectively owned” (privatized) firms, which are officially categorized as private firms. However, as we have just seen, in terms of job creation they are very different from the genuinely private firms: the privatized firms tend to reduce employment while private firms increase it. Thus, the point is valid that the size of the dynamic “new” private sector is small. The increase in the share of this sector requires a higher pace of the formation of new firms, a point that already has been raised and to which we will return later when discussing barriers to job creation.

# **_Firm size_**

Job turnover also varies substantially by firm size. Smaller firms are much more dynamic than larger ones.<sup>17</sup> The job creation rate sharply decreases with firm size. In contrast, the variation in job destruction rates is much smaller. As a result, only the small firm sector (consisting of firms employing up to 50 workers) is a net creator of jobs and increases its employment. The large firm sector is shrinking. Consequently, the small firm sector accounts for a disproportionately large part of job creation (Figure 3).

> 16 In fact, employment reductions can have different economic meaning.  There may be _active_ employment reduction aimed to reduce costs and improve the firm’s competitiveness, and _reactive_ reductions reflecting worsening economic conditions faced by the firm.

> 17 Variation in the job turnover rates by size is not independent of variation by ownership since small firms tend to be privately owned while larger firms are either state owned or privatized.

20
# Table B3. Distribution of Manufacturing Establishments Power Outage by Employment Size, Nigeria

OUTAGE (AVERAGE NO. OF OUTAGES/wek) FIRhSIZE (EMPLOYEES _per_ FIRN) <!-- Start of picture text --> Frncuwy<br>Percent<br>now Pct<br>Cot Pct 1-19 120-49 150-99 1100-199 1200-499 .500-999 11000 £<br>-I I I I lover TotaL<br>................. ........ ........ ....... +_., ... ----- ----------- ....... .<br>La"s than 5f ok | 0| 13 1 3 50<br>e 2.791 5.S9 7.261 4.47 2.79 3.35 1.68 27.93<br>1100 co 20.00 26.00 16.00' 10.00 12.00' 6.00<br>31.25 28:57 1 29.55 1 22.86 119.23 I 40:00 37:5O<br>.. ... .. ............. ..... .. .............. .<br>5 - 10 / week 9 20 1 22 19 isI 4I 2 91<br>5.03 11.17 1 12.29 10.61 8 38 ' 2.23 1.12 ' 50 S4<br>9.89 21.981 24.18 20.88 16:481 4.400 2.20;<br>56.25 5 57.14 1 50.00 1 54.29 I 57.69 1 26.67 1 25.00 I<br>......... ... 4...4................. ........... ....<br>11 & wore / week 0 * ' 9 7 8' 1 31 30<br>o.oo 2.23 3.1 4.4 2.234 2.23 .68 16.76<br>0.00 I 13.33 23.33 26.67 1 13.33 I 13.33 1 10.00 1<br>0.00 I 11.43 1 15.91 22.86 1 15.38 1 26.67 37.50 1<br>..... .. .. ..- ...... .. ..- .. ... -......<br>N/A 1 21. 11 21 01 21 1 0 a<br>1.12 I 0.56 1.12 0.00 1.12 I 0.56 0.001 4.47<br>1 25.00 1 12.50 1 25.00 I 0.00 25.00 1 12.50 0.00<br>1 12.50 I 2.86 4 .55 I 0.00 I 7.69 1 6.67 1 0.00 1<br>. ................. ........<br>Total 16 35 U 35 26 15 8 179<br>8.94 19.55 24.58 19.55 14.53 8.38 4.47 100.00<br><!-- End of picture text -->
**Appendix Table B: Log Value of Yield Within-Estimator**

||**(1)**|**(2)**<br>|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
||**Raw Gap**|**Fixed**<br>**Effects**<br>**Only**|**Plot**<br>**Characteristics**|**Plot Manager**<br>**Characteristics**|**Cropping**<br>**Choices**|**Inputs**<br>**Applied**|
|Managed by Female|-0.175***|-0.232***|-0.382***|-0.433***|-0.409***|-0.355***|
||(0.029)|(0.042)|(0.040)|(0.059)|(0.054)|(0.053)|
|15 Min - 30 Min|||0.024|0.026|0.036|0.007|
||||(0.056)|(0.056)|(0.053)|(0.051)|
|30 Min - 60 Min|||0.058|0.062|0.074|0.029|
||||(0.061)|(0.060)|(0.058)|(0.056)|
|1 Hour - 2 Hours|||0.051|0.052|0.052|0.008|
||||(0.091)|(0.090)|(0.087)|(0.085)|
|Over 2 Hours|||0.057|0.057|0.089|0.091|
||||(0.140)|(0.139)|(0.148)|(0.143)|
|Soil Quality: Fair|||-0.114***|-0.114***|-0.134***|-0.104**|
||||(0.044)|(0.044)|(0.043)|(0.041)|
|Soil Quality: Poor|||-0.084|-0.088|-0.146*|-0.115|
||||(0.084)|(0.085)|(0.082)|(0.082)|
|Hilly Parcel|||<br>0.017|<br>0.020|<br>0.035|<br>0.007|
||||(0.079)|(0.078)|(0.076)|(0.072)|
|Gentle Slope Parcel|||0.133***|0.135***|0.143***|0.110**|
||||(0.047)|(0.048)|(0.045)|(0.045)|
|Steep Slope Parcel|||0.010|0.031|0.075|0.022|
||||(0.175)|(0.172)|(0.155)|(0.143)|
|Valley Parcel|||-0.062|-0.059|-0.027|-0.038|
||||(0.127)|(0.128)|(0.122)|(0.112)|
|Irrigated Parcel|||-0.387***|-0.374***|-0.286**|-0.299***|
||||(0.121)|(0.123)|(0.122)|(0.111)|
|Swamp/Wetland Parcel|||0.362***|0.361***|0.430***|0.371***|
||||<br>(0.129)|<br>(0.130)|<br>(0.136)|<br>(0.125)|
|Planted Acres (log)|||-0.439***|-0.441***|-0.447***|-0.265***|
||||(0.023)|(0.024)|(0.023)|(0.028)|
|Rented Parcel|||<br>0.134***|<br>0.133***|<br>0.170***|<br>0.159***|
||||(0.052)|(0.052)|(0.053)|(0.050)|
|Manager Is Under 30 Years of Age||||0.447**|0.450**|0.436**|
|||||(0.217)|(0.210)|(0.206)|
|Manager Is 30-40 Years of Age||||0.346**|0.348**|0.345**|
|||||(0.176)|(0.172)|(0.167)|
|Manager Is 40-50 Years of Age||||0.325**|0.353**|0.330**|
|||||(0.142)|(0.142)|(0.138)|
|Manager Is 50-60 Years of Age||||0.229**|0.280**|0.254**|
|||||(0.113)|(0.111)|(0.110)|
|Manager Received NAADS||||<br>0.145**|<br>0.131**|<br>0.115**|
|<br>Extension Services||||(0.057)|(0.056)|(0.055)|
|Manager Received Other Extension||||<br>0.061|<br>0.056|<br>0.024|
|<br>Services||||(0.070)|(0.071)|(0.068)|
|Education Years of Manager||||<br>-0.001|<br>-0.003|<br>-0.004|
|||||(0.010)|(0.010)|(0.009)|
|% Planted with Bananas|||||<br>0.170**|<br>0.284***|
||||||(0.075)|(0.073)|
|% Planted with Coffee|||||<br>0.139|<br>0.312***|
||||||(0.112)|(0.110)|
|% Planted with Fruits|||||0.366*|0.509**|
||||||(0.208)|(0.214)|

25
# Table 4: Developing Country Specific Effects

||||||**Simulation Modeling**<br>||
|---|---|---|---|---|---|---|
|**DEVELOPING COUNTRY**<br>**SPECIFIC IMPACTS**|**Case**<br>**studies**|**Micro‐econ.**<br>**Studies**|**Macro‐econ.**<br>**Studies**|**Fixed**<br>**employment**|**Full employment**|**Non‐full**<br>**employment**|
|**Developing country labor markets**|1,2|1,2|||||
|What characteristics typify<br>developing country labor markets?<br>Rural‐urban? Informal labor markets?|||||||
|**_Developing country labor market_**<br>**_institutions_**|1,2|||||1,2|
|_How does the lack of unemployment_<br>_insurance affect developing country_<br>_labor markets?_|||||||
|**Climate policy‐ developing country**<br>**labor market interactions**|||1|1||2,3|
|How do climate policies affect labor<br>markets in developing countries?|||||||
|Do climate policies mitigate or|||||||
|exacerbate developing country labor<br>market distortions?|||||||

44