rafmacalaba/datause-encoder-data huggingface dataset:
medium-sized Indian firms. As previously mentioned this dataset was already used in several studies on the performance of Indian firms.<sup>22</sup> The dataset covers the period 1989-1997 and the information varies by year. It provides quantitative information on sales, capital stock, income from financial and non financial sources, consumption of raw material and energy, compensation to employees (wage-bill) and ownership group.<sup>23</sup> This dataset allows us to estimate firm total factor productivity (TFP) using the Levinsohn and Petrin (2003) methodology. The Prowess database provides detailed information on imports by category of goods: finished goods, intermediate goods and capital goods. In our main empirical specification, we use imports of capital goods (machinery and equipment) to measure foreign technology. Although we are not able to test directly for the impact of imported capital goods depending on the country of origin since the Prowess dataset does not include the origin country of imported goods (e.g developed vs. developing countries), one realistic assumption for the case of a developing country like India is that most imports of capital goods are sourced from more advanced economies and thus, they are a good proxy of a modern and high-technology. Looking at imports of capital goods at HS6 product level of India by country of origin reveals that about 70% of their imports came from developed countries in the period 1989-1997.<sup>24</sup> Input-trade liberalization might also allow firms to access to high-quality inputs. Using detailed firm-product level data for Colombia, Kugler and Verhoogen (2009) compare the price of domestic and imported inputs and provides evidence that higher-quality inputs may be relatively more available internationally. Due to data constraints, we are not able to look at the effects of input-liberalization on quality up-grading. The Prowess database does not provide any information on quantities to compute unit values as a proxy of quality of intermediate goods. Despite that we can not observe the quality of intermediate goods, for imported capital goods we can infer that they are more advanced or of a higher

> 22See Topalova and Khandelwal (2011), Topalova (2004), Goldberg et al. (2010), Goldberg et al. (2009), Alfaro and Chari (2009), DeLoecker et al. (2016).

> 23Variables are deflated with industry-specific wholesale price indices from India’s national accounts statistics.

> 24We used the BACI database provided by the CEPII as well as the Broad Economic Categories (BEC) classification of HS6 products by intermediates, capital goods and consumption goods.

25
When the _salaries of in-house accountants increase_ (+10%, +20%, etc.) the benefits of full outsourcing become even more pronounced. For example, in the case of the Basic model, if the cost of internal accountants would go up by about 21% it would become cheaper to completely outsource tax compliance work than to have an accountant on payroll, i.e. coefficient for the share of complete outsourcing variable becomes negative (though this variable would be insignificant in the model until the cost would go up by 50%). In the case of the Base model, the pattern is about the same – when the in-house cost goes up by about 27%, the coefficient for the complete outsourcing variable changes its sign. In the case of the restricted model, higher salaries for in-house accountants do not change the fact that complete outsourcing of tax compliance work is cheaper than partial outsourcing, and both types of outsourcing are cheaper than doing tax compliance work fully in house, though for obvious reasons the relative difference between full and partial outsourcing is getting smaller.

In the case of the polarized model, an increase in the cost of in-house accountants makes full outsourcing more and more attractive compared to doing tax compliance work fully in-house. At the same time, a reduction of the cost of in-house accounting by about 17% would make it cheaper to do all tax compliance work in-house than to outsource it completely (and when the in-house cost goes down by about 35%, the relevant dummy variable again becomes significant in the polarized regression model).

# **4. Use of outsourcing at the post-filing stage of tax compliance procedures**

A related set of questions about outsourcing of tax compliance work concerns post-filing issues such as queries from SARS, disagreement over assessments, inspections, and appeals. Since only about 6% of respondents reported any such experiences, the data were insufficient for rigorous testing of hypotheses. Nevertheless, analysis of the available data was suggestive.

When it comes to disagreeing with SARS assessments, it seems that outsourcing has no significant influence on a business whether to disagree with an assessment or not.

Analysis of **_queries, inspections and audits_** and their relation to outsourcing reveals that business having queries have a significantly higher outsourcing index<sup>19</sup> (31% and 24%, respectively) than firms not having queries and similarly for inspections and audits<sup>20</sup> (32% and 25%, respectively).

One possible explanation for this is that some businesses have overall more complex tax accounting leading to both higher level of outsourcing and more frequent SARS

> 19 t = 2.039, significant at 5% level.

> 20 t = 1.812, significant  10%  level.

22
||**Latin**<br>**America &**<br>**Caribbean**|**South**<br>**Asia**|**East**<br>**Asia &**<br>**Pacific**|**Sub-**<br>**Saharan**<br>**Africa**|**PEN**<br>**total**|
|---|---|---|---|---|---|
|Cash share in forest income [%]|59|20|61|63|59|
|Non-forest environmental income [US$ pc/year]|379|60|113|462|343|
|Non-forest environmental cash income [US$ pc/year]|114|4|55|172|119|
|Share non-forest environmental income in total income [%]|3|2|3|15|6|
|Cash share of non-forest environmental income[%]|30|7|49|37|35|
|Wage income [US$ pc/year]|3622|219|570|175|946|
|Share wage income in total income [%]|29|7|14|6|18|
|Household size|4|5|4|6|5|
|Number of households|228|223|270|876|1596|

For the sake of completeness, Table 4 also gives the income numbers for the three middle-income quintiles, i.e. the residual group after that the top 20% and the bottom 20% have been subtracted. Various values are unsurprisingly fairly similar to the average income values expressed in Table 1; however, there are exceptions. For instance, livestock, wage and even forest incomes are for those groups on average only close to half of the overall household average, indicating some tail-skewed distributions at the upper end: high-earning groups households raise the overall average well beyond the median, which will typically be located in this middle-rage group – more so than e.g. for crop income.

**Table 4: Middle-income quintiles (20-80%): annual household incomes in PEN sites. PPP converted 2005 USD adult equivalent units (aeu)**

||**Latin**<br>**America &**<br>**Caribbean**|**South**<br>**Asia**|**East**<br>**Asia &**<br>**Pacific**|**Sub-**<br>**Saharan**<br>**Africa**|**PEN**<br>**total**|
|---|---|---|---|---|---|
|Total income [US$ pc/year]|3070|1185|1310|583|977|
|Total cash income [US$ pc/year]|2290|694|891|302|594|
|Cash share in total income [%]|75|59|68|52|61|
|Crop income [US$ pc/year]|527|343|381|206|304|
|Crop cash income [US$ pc/year]|319|108|190|72|129|
|Share crop income in total income [%]|17|29|29|35|31|
|Cash share in crop income [%]|61|32|50|35|42|
|Livestock income [US$ pc/year]|313|148|196|79|126|
|Livestock cash income [US$ pc/year]|225|68|148|44|75|
|Share livestock income in total income [%]|10|12|15|14|13|
|Cash share in livestock income [%]|72|46|75|55|60|
|Forest income [US$ pc/year]|888|163|280|101|178|
|Forest cash income [US$ pc/year]|529|36|166|38|82|
|Share forest income in total income [%]|29|14|21|17|18|
|Cash share in forest income [%]|60|22|59|37|46|
|Non-forest environmental income [US$ pc/year]|145|41|79|57|69|
|Non-forest environmental cash income [US$ pc/year]|27|7|18|12|15|
|Share non-forest environmental income in total income<br>[%]|5|3|6|10|7|
|Cash share in non-forest environmental income [%]|18|16|23|21|22|
|Wage income [US$ pc/year]|694|222|195|54|138|
|Share wage income in total income [%]|23|19|15|9|14|
|Household size|6|5|5|7|6|
|Number of households|684|668|808|2625|4786|

18
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Petrou, Stavos, and Emil Kupek. 2010. “Poverty and Childhood Undernutrition in Developing Countries: A Multi-National Cohort Study,” _Social Science and Medicine_ 71: 1366-1373. Pitt, Mark. 1983. <mark>“Food Preferences and Nutrition in Rural Bangladesh,”</mark> _<mark>Review of Economics and</mark>_ _<mark>Statistics</mark>_ <mark>LXV: 105-114.</mark> Ravallion, Martin. 1990. “Income Effects on Undernutrition,” _Economic Development and Cultural Change_ 38: 490-515.

Ravallion, Martin. 1992. “Does Undernutrition Respond to Incomes and Prices?: Dominance Tests for Indonesia,” _World Bank Economic Review_ 6: 109-124.

Ravallion, Martin. 2009. “How Relevant is Targeting to the Success of the Antipoverty Program?” _World Bank Research Observer_ 24(3): 205-231.

Richard, Stephanie, Robert Black, Robert Gilman, Richard Guerrant, Gagandeep Kang, Claudio Lanata, Ka ̊re Mølbak, Zeba Rasmussen, Bradley Sack, Palle Valentiner-Branth, William Checkley, and Childhood Infection and Malnutrition Network. 2012. “Wasting is Associated with Stunting in Early Childhood,” _Journal of Nutrition: Nutritional Epidemiology_ 142: 1291–1296.

Rose, Elaina. 1999. “Consumption Smoothing and Excess Female Mortality in Rural India,” _Review of Economics and Statistics_ 81(1): 41-49.

Ruel, Marie, Harold Alderman, and Maternal and Child Nutrition Study Group. 2013. "Nutritionsensitive Interventions and Programmes: How can they Help to Accelerate Progress in Improving Maternal and Child Nutrition?" _The Lancet_ 382: 536-551.

Sahn, David and David Stifel. 2003. “Exploring Alternative Measures of Welfare in the Absence of Expenditure Data.” _Review of Income and Wealth_ 49: 463–489.

Sahn, David and Stephen Younger. 2009. “Measuring Intra-Household Health Inequality: Explorations Using the Body Mass Index.” _Health Economics_ 18: S13-S36.

Smith, Lisa and Lawrence Haddad. 2015. “Reducing Child Undernutrition: Past Drivers and Priorities for the Post-MDG Era,” _World Development_ 68: 180-204.

van de Walle, Dominique. 2013. “Lasting Welfare Effects of Widowhood in Mali.” _World Development_ 51: 1-19.

Victora, Cesar. 1992. “The Association between Wasting and Stunting: An International Perspective.” _Journal of Nutrition_ 122(5): 1105–10.

Wagstaff, Adam. 2003. “Child Health on a Dollar a Day: Some Tentative Cross-country Comparisons.” _Social Science and Medicine_ 57(9): 1529-38.

27
0-10 scale ( _Democracy_ 2). The mean value of _Democracy_ equals 0.62 and the standard deviation is 0.49.

The third measure of democracy we use is based on data from Freedom House. It equals the average of the Political Rights index and the Civil Liberty index for the year 2003 ( _Democracy_ 3). According to _Democracy_ 3, Australia, Chile, Mauritius, Norway, and United States are most democratic in our sample, while the least democratic countries are China, Eritrea, Sudan, Vietnam and Uzbekistan. The three democracy measures defined above are highly correlated with each other (Table 3).

These two sources of democracy data have been used in the previous studies in this field: Barro (1997), La Porta et al (1999), Rodrik (1999), and Glaeser et al. (2004).

# Other determinants of reform

The proclivity to reform is likely to depend on the quality of the broader economic institutions that may determine how governments behave. We check for the robustness of the democracy-reform relationship by controlling for a number of alternative proxy measures of broader economic institutions suggested in the previous literature.

We begin by controlling for differences in income levels across countries using (log of) GDP per capita in 2003 taken from Penn World Tables. The quality of democracy is typically better in higher income countries (Table 3). We may also expect higher income countries to reform more because these countries have greater capacity for reform, more checks and balances on the government that prevent the use of business regulations by politicians for generating rents, etc.

8
# **Performance of Renewable Energy Auctions: Experience in Brazil, China and India**

Gabriela Elizondo Azuela<sup>1(a)</sup> , Luiz Barroso<sup>(b)</sup> , Ashish Khanna<sup>(a)</sup> , Xiaodong Wang<sup>(a))</sup> , Yun Wu<sup>(a)</sup> , Gabriel Cunha<sup>(b 2</sup>

- (a) The World Bank, Washington DC

- (b) PSR, Brazil

**Keywords:** Renewable Energy; Energy Auctions; Energy Policy **JEL Codes:** Q20, Q42, Q48

> 1 Corresponding author: gazuela@worldbank.org.

> 2 The paper was peer reviewed by Morgan Bazilian (Lead Energy Specialist), Luiz Maurer (Principal Industry Specialist, International Finance Corporation), and Katharina Gassner (Senior Economist, International Finance Corporation). The authors thank Vivien Foster for her useful comments and direction.
performance being above the planned target.

These solid performers, not surprisingly, still fulfill their targets. This is shown in columns 5 to 8, where the indicator of fulfilling the target (i.e., whether the reported growth surpasses the target) is the dependent variable. After controlling for a continuous function of true over-performance (i.e., predicted true GDP growth – target), the solid performers are more likely to fulfill their target.

A potential concern is that the city growth target, albeit predetermined, is not exogenous since the PS might influence the determination of the target for future manipulation. While the provincial government exerts an overwhelming influence in setting the growth target for cities in its jurisdiction, the city PS could influence the process. For example, the PS could lobby for a lower target so that the true growth just exceeds the target by a small margin. To address this concern, we repeat the above RDD exercise while restricting the sample to secretaries who had served in the current position for less than two years. Since a newcomer to a city is less likely to influence its target, the target and the running variable in this sample are more likely to be exogenous. The results shown in Panel B of Table 10 are similar to those in Panel A, again confirming our finding of ratchet effects.

# **7.2.2 Scapegoating**

Another explanation is that a new PS would under-report when he could scapegoat his predecessor in the turnover year. The new PS would report a low GDP number and scapegoat his predecessor for poor performance, making his performance benchmark low and thus his future years shine in terms of growth.<sup>48</sup> However, blaming the predecessor needs pre-conditions: (1) in the calendar year the city was mostly under the oversight of the predecessor; (2) the predecessor must be demoted so that scapegoating him would incur no political costs. After all, if his move is a lateral move or promotion, shifting blame is more subject to challenges and could cause future revenge. We thus expect that in city leader turnover years and when the departing PS is demoted, the new PS would under-report performance more.

To this end, we create an indicator _Demoted_ indicating that the departing PS was demoted, and another indicator _Scapegoatable_ indicating that in most of the calendar year the predecessor was in charge of the city. We then regress the indicator of under-reporting (i.e., the reported GDP growth being lower than the predicted true growth) on the turnover dummy, _Demoted_ , _Scapegoatable_ , and their two-way and three-way interactions. Our hypothesis only has implications for the three-way interaction: _Turnover_ × _Demoted_ × _Scapegoatable_ should positively predict under-reporting. In essence, this is again the ratchet effect at work when the PS could lower targets conveniently. We also

> 48 Such turnover effect is documented in Cohen, Coval, and Malloy (2011) who show that chairman turnover in the U.S. congress has an impact on state-level federal expenditures.

29
**Table 4. Datt-Ravallion decomposition, 2017-2022**

||Baseyear 2017|Baseyear 2022|Average effect|
|---|---|---|---|
|Poverty rate (P0)|28.0|11.6||
|Change in P0|-16.39|-16.39|-16.39|
|Growth component|-4.00|-3.47|-3.74|
|Redistribution component|-12.92|-12.39|-12.66|
|Interaction component|0.53|0.53|0.00|

Note: Expenditures underlying the decomposition are expressed in 2022 prices, using the implicit deflator implied by the new 2017 and 2022 upper bound poverty lines.

# **3.2 The synthetic panel approach**

We closely follow the methodology of Dang and Lanjouw (2023) which, based on standard assumptions rooted in asymptotic theory, delivers point estimates and related standard errors of a set of probabilities that provide different measures of poverty dynamics, across both survey periods. These probabilities can be defined as follows, where the ~ symbol indicates either the greater sign (>) or the smaller or equal sign (≤), and the letter _z_ denotes the comparable poverty lines for each survey year (as in section 3.1): first, we are interested in the _joint_ probabilities _P_ ( _yi_ ,1 ~ _z_ 1 and _yi_ ,2 ~ _z_ 2 ), that is, for example, the probability that a household is poor in both survey periods, considered together; secondly, we are interested in the _conditional_ probabilities _P_ ( _yi_ 2 ~ _z_ 2 | _yi_ 1 ~ _z_ 1 ), that is, the probability that, for instance, household _i_ remains poor in the second period, _given that_ it was poor in the first period.

As pointed out by Dang and Lanjouw (2023), these probabilities can also be interpreted as estimates of population shares: for instance, _P_ ( _yi_ ,1 < _z_ 1 and _yi_ ,2 < _z_ 2 ) estimates the share of individuals that are poor in both years, as a proportion of the total population observed in _both_ survey periods, considered jointly; on the other hand, _P_ ( _yi_ ,2 < _z_ 2 | _yi_ ,1 < _z_ 1 ) estimates the share of individuals that are poor in the second period, as a proportion of the number of individuals that were poor in the first period.

9
**Graph 11: Trends in advance payments (2006-2011)(in percentage of total yearly road allocation)**

<!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>19% 18%<br>15%<br>10%<br>8% 7%<br>5%<br>3% 3%<br>0%<br>2006 2007 2008 2009 2010 2011<br><!-- End of picture text -->

# **5. Conclusion**

The main findings of contract analysis are the following:

- **The growing incidence of unplanned roads**<sup>**16**</sup> **in the RDA’s Work Plan poses a risk to proper project selection and procurement.** Roads are meant to be included in the Work Plan based on a clear and agreed, objective criteria. Once the Plan is published, it should, in theory be very difficult to insert additional roads, unless a strong justification can be given for the importance. The extent to which inclusion of _additional_ roads in the RDA Work Plan appears to be common practice is a strong indication of external influence on the RDA, since these roads have been added without the agreed selection criteria.

- **The negative correlation between support for the major opposition party (PF) at the time and road allocations may indicate that project selection is influenced by political party support.** This would provide support to the contention that roads have previously been used as a source of political patronage in Zambia. For the road sector to have been used in this way, it would indicate that the RDA is not well-insulated from a political agenda. This should also raise questions about the willingness to alleviate the existing weaknesses in RDA of those who have benefited from them.

- **Though open tendering continues to be the main procurement method in Zambia, a significant amount of road project funds are being single sourced.** This is of concern, since single source procurement is particularly susceptible to corruption, given that it allows specific firms to be awarded the contract without a close eye to value for money. Although on paper the actual percentage of contracts being single sourced seems small, the actual _value_ is significant.

> 16 Increase in the share of unplanned roads comes from interviews of staff. We have not been able to analyze databases at the beginning of the 2000s.

18
-48-

# GRAPH 2b: ACTUAL COMHODITY PRICE CHANGES a LICPAG FORECASTS AND ERROR BARS

<!-- Start of picture text --> &LICPAG FORECAST=<br>.4CM<br>.900<br>.299<br>.me~~~~~~~~~~~~~~~~~~~~~~~~~~~~~P0<br>-,2300<br>1979 1986 1981 1982 £9838 984 1985 198!se 1987 £988 1989<br><!-- End of picture text --> Forecasted Sample Period: 1979-1988