rafmacalaba/datause-encoder-data huggingface dataset:
Table A.2: Robustness: Alternative specifications, estimation techniques, and placebo tests

||Reported gr<br>controlling li|owth while<br>ghts growth|Reported<br>growth<br>|Predicted<br>growth|Lights<br>growth|Targe<br>year_t_|t of<br>+ 1|Ove|r-reporting||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)<br>Region-<br>specific|(2)<br>Province-<br>specific|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)<br>System<br>GMM|
|PS YearsInOffice|0.254<sup>∗∗∗</sup><br>(0.072)|0.252<sup>∗∗∗</sup><br>(0.072)|0.221<sup>∗∗∗</sup><br>(0.070)|0.000<br>(0.000)|0.221<br>(0.390)|0.023<br>(0.058)|−0.036<br>(0.054)|0.101<sup>∗∗∗</sup><br>(0.033)|0.104<sup>∗∗∗</sup><br>(0.029)|0.156<sup>∗∗∗</sup><br>(0.037)|
|PS YearsInOffice<sup>2</sup>|−0.024<sup>∗∗</sup>|−0.023<sup>∗∗</sup>|−0.019<sup>∗</sup>|−0.000|−0.033|−0.011|−0.007|−0.009<sup>∗</sup>|−0.009<sup>∗∗</sup>|−0.015<sup>∗∗∗</sup>|
||(0.011)|(0.011)|(0.011)|(0.000)|(0.057)|(0.008)|(0.007)|(0.005)|(0.004)|(0.005)|
|Reported growth|||||||0.274<sup>∗∗∗</sup><br>(0.035)||||
|Target growth||||||||0.174<sup>∗∗∗</sup><br>(0.022)|||
|L.Over-reporting|||||||||0.141<sup>∗∗∗</sup><br>(0.034)|0.183<sup>∗∗∗</sup><br>(0.036)|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|City FE|||Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|# of Observations|3642|3642|3642|3642|3642|2887|2887|2890|3361|3361|
|# of Cities|281|281|281|281|281|281|281|281|281|281|
|Adj_R_<sup>2</sup>|0.56|0.56|0.65|0.72|0.78|0.74|0.78|0.65|0.62||

_Notes_ : This table presents several robustness checks for the political cycle of GDP manipulation. The dependent variable is the difference between reported real GDP growth and predicted real GDP growth imputed based on the nighttime lights data of prefecture-level cities. City controls include city-level timevarying economic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights (except for columns 4 and 5)) and leader controls are the same as the ones displayed in the table of baseline results. Standard errors clustered at the city level are reported in parentheses. ***, ** and * denote significance at 1%, 5% and 10%, respectively.

53
<!-- Start of picture text --> Formal tradables<br>1989 1994 2000<br>©<br>9<br>st<br>2)<br>—E *<br>a= o costenaunstgnsnastanuanstag eal enaae neice lies ik<br>3 0 30 60 90 120 150180210<br>xo]<br>= 2005 2010<br>s@<br>=<br>Fo<br>+<br>“<br>= 0 30 60 90 120150180210 Sanna!0 30nina60Sans90 ens!120150 inna anna180210anna<br>Firm size (bins of 5)<br>Graphs by Year of majority coverage<br><!-- End of picture text --> <!-- Start of picture text --> Formal tradables<br>1989 1994 2000<br>co<br>os<br>a<br>2)<br>—*<br>SsS ————<br>no} 0 10 20 30 40 50 60<br>S 2005 2010<br>B<br>t=S<br>=<br>~<br>“<br>= —T tT &xT &T TTT<br>0 10 20 30 40 50 60 0 10 20 30 40 50 60<br>Firm size (bins of 5)<br>Graphs by Year of majority coverage<br><!-- End of picture text --> <!-- Start of picture text --> Formal non-tradables<br>1989 1994 2000<br>2<br>oO<br>on<br>=<br>a a SS a a ay ay rea ae<br>° 0 30 60 90 120150180210<br>xo}<br>= 2005 2010<br>n2<br>3<br>(= o<br>wo<br>o 0 30 60 90 120150180210 a0O 30 60 90ae120150a aC180210ca<br>Firm size (bins of 5)<br>Graphs by Year of majority coverage<br><!-- End of picture text --> <!-- Start of picture text --> Formal non-tradables<br>1989 1994 2000<br>=<br>°<br>7) Pre)<br>E<br>=o a)DD a oo a<br>Ss<br>5 0 10 20 30 40 50 60<br>= 2005 2010<br>oe<br>=o<br>ar)<br>wo<br>S —T tT tT &T TTT<br>0 10 20 30 40 50 60 0 10 20 30 40 50 60<br>Firm size (bins of 5)<br>Graphs by Year of majority coverage<br><!-- End of picture text -->
Policy Research Working Paper 5831

# **Abstract**

A pricing reform in Turkey increased the residential electricity tariff by more than 50 percent in 2008. The reform, aimed at encouraging energy efficiency and private investment, sparked considerable policy debate about its potential impact on household welfare. This paper estimates a short-run residential electricity demand function for evaluating the distributional consequences of the tariff reform. The model allows heterogeneity in household price sensitivities and is estimated using a national sample of 18,671 Turkish households. The model also addresses the common problem of missing data in survey research. The study reveals a highly skewed distribution of price elasticities in the population, with rich households three times more responsive in adjusting consumption to price changes than the poor. This is most likely because the poor are close to their minimum electricity consumption levels and have fewer coping options. In addition, the welfare loss of the poorest quintile—measured by the consumer surplus change as a percentage of income—is 2.9 times of that of the wealthiest.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. 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://econ.worldbank.org. The author may be contacted at fzhang1@worldbank.org.

_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 findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Nevertheless, our findings are subject to a number of caveats. Most importantly, enterprise surveys cover only the formal sector, excluding the informal firms. Hence our results do not speak to informal enterprises. In addition we also do not have data on micro enterprises (less than 5 employees) in our sample. Second, we have data only on surviving firms, which probably overestimates the growth rates for very young firms given they tend to have higher failure rates. While this database is the best available at this point, we recognize these limitations.

Overall, our findings contribute to the emerging literature on what types of firms create jobs. Our findings on size and employment share are consistent with the findings in Haltiwanger, Jarmin and Miranda (2010a, 2010b) who find that in the US, large (and mature firms) have the largest share of employment. However we also find that in developing countries, the SME sector as a whole is comparable to large firms in contributing to employment. On job creation, the US evidence suggests that small mature firms have net job losses whereas in developing countries we find that small firms have the largest share of job creation. Moreover, in countries that have had net job losses in the economy as a whole, it is only the small firms that have net job gains.<sup>4</sup> Our data does not allow us to comment on the contribution of start-ups and young firms that have been shown to be important in the US.

The remainder of the paper is organized as follows. In Section II we describe the data and the indicators used in this paper, and present summary statistics. In Section III, we discuss in

> 4 Haltiwanger et al. (2010a) also find that there is no systematic relation between firm size and growth once age is controlled for. Specifically, they argue that the "systematic inverse relationship between firm size and net growth rates in prior analyses is entirely attributable to most new firms being classified in small size classes."  Since surviving new firms grow much faster than older firms in the US, this classification may make it seem that firm size is a determinant of firm growth.  By contrast, in our sample of developing countries, we find that small firms are significant contributors to employment growth even after controlling for age.

5
**Table 1 Main determinants of maritime transport costs identified in the literature**

||||_Imbalance_<br>_between_<br>|_Det_<br>|_erminant_||||
|---|---|---|---|---|---|---|---|---|
|_Authors_|_Period_<br>_studied_|_Distance_|_imports and_<br>_exports_|_Value-_<br>_weight_|_Container_|_Weight_|_Volume_|_Efficiency_|
|Blonigen|1991–|0.13*** –0.21***|Imports|0.55***|–0.04***|0.91***|0.00***|–0.09<sup>a</sup>|
|and Wilson<br>(2008)|2003||(–0.00)<br>Exports (–<br>0.00)||||||
|Fink,|1998|0.33***|||–0.07**||–0.02**||
|Mattoo, and|||||||||
|Neagu|||||||||
|(2001)|||||||||
|Micco and|1995–|0.17***||0.55***|–0.02||–0.04***|–0.07***|
|Pérez (2002)|99||||||||
|Sanchez and|2002|0.09||0.54***|–0.02|||0.03, –0.06,|
|others||||||||0.00<sup>b</sup>|
|(2003)|||||||||
|Limao and|1998|0.38**|||||||
|Venables|||||||||
|(2001)|||||||||
|Wilmsmeier,|2002|0.35***|0.00*|0.34***||–0.09***|–0.02**<sup>c</sup>|–0.38***|
|Hoffmann,|||||||||
|and Sanchez<br>(2006)|||||||||
|Clark,|1998|0.18***|–0.07***|0.55***|–0.03**||–0.04***|–0.06***|
|Dollar, and|||||||||
|Micco and|||||||||
|(2004)|||||||||

_Note_ : a. The first result compares the Port of Oakland with the most efficient port in the United States (RichmondPetersburg), where port charges are about 9 percent lower. The second result compares the Port of Rotterdam with the most efficient port outside the United States in the sample considered by the authors (Zeebrugge, Belgium), where port charges are 6 percent lower.

b. Results are related to time inefficiency, productivity, and stay per vessel, respectively.

c. Result is for bilateral trade.

* Significant at the 10 percent level, ** Significant at the 5 percent level, *** significant at the 1 percent level.

Trade asymmetries have a large and significant effect on trade costs. Greece, for example, imports 60 percent more than it exports, whereas Ireland exports 40 percent more than it imports (Baldwin and Taglioni 2006). If country 1 exports to country 2 while importing nothing from country 2, ships may haul empty containers on one leg of the journey and return with full containers. The exporter that receives the empty containers and sends the containers back packed often bears a higher cost. For example, India has a favorable trade balance with the 4
These projections indicate that under the baseline assumptions population aging would have strong effects on fiscal balances in the long run. Annual education expenses would stabilize below 5.5 percent of GDP; total annual expenses on social benefits would remain below 8 percent GDP. The exogeneity of social spending (rather than its direct linkage to the productivity or real GDP growth in the model) reflects the fact that social spending can often be driven by demand or political discretion.

**Table 2. Baseline projections of public expenditures on selected items**

(percent of projected GDP)

|Expense|1995/96|2000/01|2010/11|2030/31|2050/51|
|---|---|---|---|---|---|
|NZS|5.7|4.7|5.1|8.8|10.2|
|Health|5.9|5.5|6.0|9.0|11.8|
|Education|5.6|5.3|5.4|5.3|5.7|
|Unemployment|1.4|1.1|1.1|1.1|1.1|
|<br>Domestic purposes|1.5|1.5|1.5|1.5|1.5|
|<br>Other social welfare|5.0|4.8|4.7|5.1|5.3|
|Defense|1.1|1.0|0.9|0.9|0.9|
|Debt interest|4.0|1.5|1.3|1.2|1.2|

Source: New Zealand Treasury fiscal model.

**Table 3. Baseline projections of public pension spending in selected industrial countries** (percent of projected GDP)

|Country|1995|2000|2010|2030|2050|
|---|---|---|---|---|---|
|United States|4.4|4.3|4.2|7.4|7.7|
|Japan|5.7|6.5|7.5|8.9|10.7|
|Germany|10.0|11.1|11.0|18.4|18.7|
|France|12.5|12.0|12.6|19.4|21.3|
|Italy|16.0|17.1|15.2|23.3|25.7|
|United Kingdom|4.4|4.3|4.6|4.7|3.4|
|Canada|4.4|4.5|4.9|7.5|7.1|
|Sweden|8.5|8.2|8.1|9.2|7.4|
|New Zealand|5.7|4.7|5.1|8.8|10.2|
|Average|7.9|8.1|8.1|12.0|12.5|

Source: Estimates by IMF staff and the New Zealand Treasury fiscal model.

To illustrate the options for financing future government spending, figure 6 plots the required balanced budget tax revenue against the constant tax rate tax revenue. The constant tax rate for the tax-smoothing approach has been set (using two small tax cuts during 1998-2000) so that it allows the government to accumulate sufficient 15
Table 2: WBES-De Loecker Comparison: Non-Parametric Estimates of Exporting on TFPR (in <u>percent)</u>

|SectorDescrition|Median Productivity Premium from Exporting|
|---|---|
|p|WBES<br>De Loecker|
|Food & beverages|5.953<br>2.280|
|Textiles|4.949<br>1.980|
|Garments|3.696<br>1.660|
|Leather|-1.577<br>1.830|
|Wood|7.186<br>1.920|
|Publishing, printing and|5.732<br>4.880|
|reproduction||
|Chemicals|6.541<br>3.930|
|Rubber & plastics|6.122<br>4.500|
|Other non-metallic products|5.246<br>2.730|
|Basic metals|5.141<br>3.190|
|Fabricated metal products|6.071<br>3.320|
|Machinery and equipment|4.218<br>3.450|
|Electrical machinery|3.687<br>4.640|
|Furniture|1.862<br>1.990|

Note: Table 2 presents the median TFPR-premium from exporting following De Loecker (2013) method. The latter is based on the estimation of a value-added log-linearized Cobb-Douglas production function based on the Control Function approach by Ackerberg, Caves, and Frazer (2015) and assuming an endogenous (cubic) Markovian (AR 1) productivity process, which is a function of learning by exporting. WBES data covers a sample of manufacturing enterprises from 82 developing countries during the period 2002-2019; while De Loecker (2013) study focuses on 7,915 manufacturing firms in Slovenia during the period 1994-2000. Data for WBES come from the World Bank, while data from De Loecker (2013) come from the Slovenian Central Statistical Office. The correlation coefficient is 0.36.

# **6 Nonparametric Estimates of the Digital-Technology Adoption Effect (DAE) on TFPR**

This section presents the semi-parametric estimates of the digital adoption effects (e.g., email and website), using the approach presented in section 4. Table 3 reports the median effects, the percentage of the estimation sample with positive marginal effects, and the F-test associated with each variable of interest. Column (1) displays the results from estimating an endogenous TFPR process that is a function of learning by exporting, as in De Loecker (2013). Column (2) reports the results from estimating an endogenous TFPR process that is a function of the adoption of digital business solutions, namely email and website. Column 21
**Table 5. Optimal policy versus simple rules for** ����� **(quantities in million tons)**

|**Variable**|**Laissez‐faire**|**Optimalpolicy**|**Simple rules**|
|---|---|---|---|
|**_India_**||||
|Meanprice(Rs/t)|9,809|9,544|9,583|
|Price CV(%)|14.39|4.78|8.53|
|Mean stock|0.10|0.95|0.95|
|Mean exports|1.38|0.85|0.62|
|Mean imports|0.02|0.32|0.00|
|**_RoW_**||||
|Meanprice(US$/t)|200.5|201.9|201.7|
|Price CV(%)|20.70|22.69|22.48|
|**_India welfare_**||||
|Consumers' surplus||1.66|1.70|
|Producers' surplus||‐1.79|‐1.84|
|Total cost||0.02|0.05|
|Storage cost||‐0.09|‐0.12|
|Trade cost||0.11|0.17|
|Cost of volatility||0.57|0.49|
|Total welfare||0.46|0.40|
|**_RoW welfare_**||**_‐0.01_**|**_‐0.01_**|

The simple-rules trade policy is counter-cyclical to border price, with an optimal constant elasticity of –0.49, and adjusts similarly for exports and for imports. Compared to the laissez-faire arrangement, the simple-rules trade policy has a strong anti-trade bias, significantly reducing exports and imports. The optimal trade policy does not restrict trade as much; it reduces exports but increases imports. By design, the optimal trade policy is a function of both world and domestic availability, not just world price, as under the simple-rules policy. In consequence, instead of just insulating India from the world market, the optimal policy exploits the world market to increase domestic stability, resulting in increased imports. With this policy, imports may be subsidized to decrease the domestic price when domestic availability is low, whereas imports are subsidized under the simple-rules policy only when the world price spikes.

Despite these limitations, the simple-rules policy does very well, achieving almost all of the welfare gains offered by the optimal policy. The good performance of the simple rules is not limited to the specific value of �. Table 6 shows that for other positive weights given to price stability in the welfare function, simple rules achieve a high share of the gains from the optimal policy. Since policies under simple rules are easier to analyze and to implement than optimal policies, the remaining discussion will focus on the simple-rules policies.

28
comparable data from other ex-CIS countries, so do not know whether the South Caucasus has experienced greater breakdown of trust than the others.

# **_3.2  Gradual reconstruction_**

The 1990s were the period of the worst chaos, as shown by many indicators. Since then, there has been gradual reconstruction of basic services and economies. Basic services have been gradually re-established and improved over time. The DHS and RHS survey reports document these improvements for maternal and child health services.

Economic conditions are improving. Estimated GDP per capita rose to levels well above their pre-1991 levels in Azerbaijan and Armenia, but not in Georgia which also experienced the sharpest initial decline (Figure 5).<sup>13</sup> However, job growth has lagged economic growth. This requires better matching of training with needed job skills, and stronger institutional support for growth of businesses (World Bank 2013, 2014d, Onder 2013). This may change — for example, Georgia ranked 15 out of 189 countries (just below Germany) in the World Bank’s 2015 rankings of the ease of doing business.

Pension payments have risen. In 2000, average pensions were low, constituting only 19% of average wages in Armenia, 25% in Georgia, and 32% in Azerbaijan (World Bank 2003: Table 7.6). In 1999, the average pension in Armenia made only 50 percent of the food poverty line (World Bank 2003:133).

Pension systems and other mechanisms for social protection have been rebuilt and payments increased ( <mark>Falkingham and Vlachantoni 2012). For example, i</mark> n 1999 Armenia reformed its social assistance system into a well-administered program, targeting the poorest 28% of households (World Bank 2003:127). In <mark>Georgia the Targeted Social Assistance (TSA) program was set up in 2006, and payments doubled in 2009 (World Bank 2011:14). A</mark> zerbaijan used its oil resources to fund extensive social protection programs, including a well-targeted TSA program set up in 2006, and raised pension payments by 52% between 2008 and 2009 (World Bank 2010:103-105, Onder 2013). These generous social transfers helped reduce the overall poverty headcount in all three countries, and most steeply in Azerbaijan (Table 4).

That these programs work well is also indicative of improvements in governance. Other specific examples of reform include the police reforms in Georgia (Kakachia and O’Shea 2012), which is part of the reforms that have helped raise the country’s ranking in the ease of doing business. Azerbaijan established a sovereign wealth fund to invest its oil revenues and diversify its income sources.<sup>14</sup> In sum, conditions are improving. There has been economic growth. Pension and social protection systems have been substantially strengthened, and poverty levels have fallen. Governance also shows signs of improvement, though much remains to be done.

8
Combining goods and service liberalizations, the results in table 4c predict that exports from the U.S. to India and those from India to the U.S. may expand by 90 percent and 20 percent respectively. The impacts on total trade for the United States are 0.6 percent and 0.7 percent increases in import and export values respectively whereas the corresponding figures are 3.5 percent and 4.1 percent for India. In terms of trade values, the resulting increase in bilateral trade (imports plus exports) is estimated to be $29 billion (0.21 percent and 2.4 percent of the U.S.’s and India’s initial GDP respectively).<sup>7</sup> Overall, the U.S.’s total trade and that of India may increase by $22 billion (0.16 percent of initial GDP) and by $19 billion (1.6 percent of initial GDP) respectively.

Table 4d presents the results of a scenario in which India first signs a free trade agreement with the United States and then liberalizes with all its other trading partners on an MFN basis. Using an India-US FTA for further liberalization may be particularly suitable for India relative to using mechanisms under the World Trade Organization (WTO). This is because India is not required to lower its tariff rates under the WTO as India’s bound tariff rates are far higher than its applied rates; and an India-US FTA is likely to become a comprehensive, deep and symmetrical agreement while India has little obligation to reduce its protection at the WTO because of the special and differential treatment principle (Lawrence and Chadha 2004). The simulation results show that total Indian imports and exports both expand substantially by 26 percent and 29 percent respectively; and India’s exports to the United States increase disproportionately relative to other countries (45 percent) since India continues to receive preferential market access in the US market. The rise in India’s imports is especially large in the

> 7 These estimates are based on the initial trade values in 2007, which is the reference year of GTAP 8. As trade between the U.S. and India has continued to grow since 2007, the magnitude of the trade effect is likely to be larger if an FTA takes effect at a later time.

26