rafmacalaba/datause-encoder-data huggingface dataset:
# **Annex 4: Sensitivity analysis**

# **<u>Sensitivity analysis: Basic model</u>**

|**Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance**<br>**board)**|**(+50% acros**|**s the**||
|---|---|---|---|
||Coeff.|Std.<br>Error|Sig.|
|(Constant)|6.672|0.288|0.0%|
|LN full-time employees|0.144|0.029|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.208|0.022|0.0%|
|Share of activities completely outsourced|-0.175|0.087|4.4%|
|Share of activities partially outsourced|0.551|0.105|0.0%|
|Adj. R2|0.261|||
|Mean of the dependent variable|9.990|||

**Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (+40% across the board)**

||Coeff.|Std.<br>Error|Sig.|
|---|---|---|---|
|(Constant)|6.599|0.288|0.0%|
|LN full-time employees|0.145|0.029|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.208|0.022|0.0%|
|Share of activities completely outsourced|-0.120|0.087|16.5%|
|Share of activities partially outsourced|0.578|0.105|0.0%|
|Adj. R2|0.259|||
|Mean of the dependent variable|9.940|||

**Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (+30% across the board)**

|(Constant)|Coeff.<br>6.520|Std.<br>Error<br>0.287|Sig.<br>0.0%|
|---|---|---|---|
|LN full-time employees|0.145|0.029|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.209|0.022|0.0%|
|Share of activities completely outsourced|-0.061|0.086|48.2%|
|Share of activities partially outsourced|0.608|0.105|0.0%|
|Adj. R2|0.257|||
|Mean of the dependent variable|9.886|||

|**Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance**|**(+20% acros**|**s the**||
|---|---|---|---|
|**board)**||||
||Coeff.|Std.<br>Error|Sig.|
|(Constant)|6.435|0.287|0.0%|
|LN full-time employees|0.146|0.029|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.209|0.022|0.0%|
|Share of activities completely outsourced|0.004|0.086|96.3%|
|Share of activities partially outsourced|0.641|0.105|0.0%|

34
# **7. References**

Adhvaryu, A., A. Chari and S. Sharma, 2013. “Firing Costs and Flexibility: Evidence from Firms' Employment Responses to Shocks in India.” _Review of Economics and Statistics_ , 95(3): 725–40.

- Aghion P., R. Burgess, S. Redding, F. Zilibotti, 2008. “The Unequal Effects of Liberalization: Evidence from Dismantling the License Raj in India.” _American Economic Review_ , 94(4):1397‐1412.

- Almeida, R. and P. Carneiro, 2012. “Enforcement of Labor Regulation and Informality.” _American Economic Journal: Applied Economics_ , 4(3): 64–89.

- Almeida, R. and P. Carneiro, 2007. “Inequality and Employment in a Dual Economy: Enforcement of Labor Regulation in Brazil.” IZA DP No. 3094.

- Ashwin S. and S. Clarke, 2002. Russian Trade Unions and Industrial Relations in Transition. Basingstoke and New York: Palgrave.

Autor, D., W. R. Kerr and A. D. Kugler, 2007. “Does Employment Protection Reduce Productivity? Evidence from U.S. States.” _The Economic Journal_ , 117(521): 189–217.

Bentolila, S. and G. Saint‐Paul. 1992. “The Macroeconomic Impact of Flexible Labor Contracts, With an <u>Application to Spain.”</u> _European Economic Review_ 36(5): 1013–47.

- Blanchard, O. and J. Wolfers, 2000. “The Role of Shocks and Institutions in the Rise of European Unemployment: The Aggregate Evidence.” _Economic Journal,_ Royal Economic Society, 110(462): 1–33.

- Boeri, T. and J.F. Jimeno, 2005. “The Effects of Employment Protection: Learning from Variable <u>Enforcement.”</u> _European Economic Review_ , 49(2005): 2057–77. (Last accessed June 15, 2015).

- Botero, J., S. Djankov, R. La Porta, F. López‐de‐Silanes and A. Shleifer, 2004.  “The Regulation of Labor.” _The Quarterly Journal of Economics_ , 119(4): 1339–1382.

Brown, J.D. and J.S. Earle, 2002. “Job Reallocation and Productivity Growth under Alternative Economic <u>Systems and Policies: Evidence from the Soviet Transition.”</u> Institute for the Study of Labor Discussion Paper, IZA DP No. 644. (Last accessed June 4, 2015).

“ ‐ Brown, J.D. and J.S. Earle, 2006. <u>The Microeconomics of Creating Productive Jobs: A Synthesis of Firm Level Studies in Transition Economies.”</u> World Bank Policy Research Working Paper No. WPS 3886. (Last accessed April 11, 2014) Brown, J.D. and J.S. Earle, 2008. “Understanding the Contributions of Reallocation to Productivity ‐ ” <u>Growth: Lessons from a Comparative Firm Level Analysis.</u> Institute for the Study of Labor Discussion Paper, IZA DP No. 3683. (Last accessed June 4, 2014).

17
|**13**|5.15|3.31|(0.06)|||
|---|---|---|---|---|---|
|**12**|5.55|0.01|(0.92)||ot point<br>heee-|
|**11**|5.24|1.02|(0.312)<br>|my variable|depemdent, n<br>variables are t|
|**10**|5.21|0.15|(0.69)|square dum|ommodity<br>emocracy|
|**9**|5.16|4.22|(0.04)|V = least|tem, low c<br>990). All d|
|**8**|5.16|0.45|0.5009)|rrors. LSD|dential sys<br>sm (after 1|
|**7**|5.38|1.85|(0.17)<br>(|hite) standard e|h) country, presi<br>nism to capitali|
|**6**|5.19|2.73|(0.09)|t (Huber/W|NAO (ric<br>m Commu|
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|**4**|5.56|0.91|(0..34)|ons are ru|itted cate<br>countries t<br>ulation**3**.|
|**3**|5.66|0.79|(0.37)|All regressi|an Africa.Om<br>variable for<br>able in form|
|**2**|5.58|1.87|(0.17)|rackets.|-Sahar<br>ummy<br>of vari|
|**1**|5.95|2.71|(0.1003)|between b|always sub<br>mmy is a d<br>exception|
|||=LAC dummy|<br>lue)<br>|evels of significance|ion. Africa dummy is<br>r. Transition years du<br>gged averages with the|
||SE|ica|<br>va|e: L|ress<br>orte<br>r la|
||RM|Afr|<br>F(p|Not|reg<br>exp<br>yea|
- Ambuehl, S., A. Ockenfels, and C. Stewart. 2018. “Attention and selection effects.”. Working paper.

- Anderson, J. R. 2007. “Agricultural advisory services.” _Background paper for the World Development Report 2008_ .

- Anderson, J. R., and G. Feder. 2007. “Agricultural extension.” _Handbook of Agricultural Economics_ 3: 2343–2378.

- Bandiera, O., R. Burgess, E. Deserrano, R. Morel, I. Rasul, and M. Sulaiman. 2018. “Social ties and the delivery of development programs.”. Working paper.

- Bandiera, O., and I. Rasul. 2006. “Social networks and technology adoption in Northern Mozambique.” _Economic Journal_ 116 (514): 869–902.

- Banerjee, A., and S. Mullainathan. 2008. “Limited attention and income distribution.” _American Economic Review_ 98 (2): 489–493.

- Bardhan, P., and C. Udry. 1999. _Development Microeconomics_ . Oxford University Press.

- Bartoˇs, V., M. Bauer, J. Chytilov ́a, and F. Matˇejka. 2016. “Attention discrimination: Theory and field experiments with monitoring information acquisition.” _American Economic Review_ 106 (6): 1437–1475.

- Beaman, L., J. Magruder, and J. Robinson. 2014. “Minding small change among small firms in Kenya.” _Journal of Development Economics_ 108 (2014): 69–86.

- Benin, S., E. Nkonya, G. Okecho, J. Randriamamonjy, E. Kato, G. Lubade, and M. Kyotalimye. 2011. “Returns to spending on agricultural extension: The case of the National Agricultural Advisory Services (NAADS) program of Uganda.” _Agricultural Economics_ 42 (2): 249–267.

- Benjamin, D. 1992. “Household composition, labor markets, and labor demand: Testing for separation in agricultural household models.” _Econometrica_ 60 (2): 287–322.

- Bick, A., N. Fuchs-Sch ̈undeln, and D. Lagakos. 2018. “How do hours worked vary with income? Cross-country evidence and implications.” _American Economic Review_ 108 (1): 170–99.

- Birkhaeuser, D., R. Evenson, and G. Feder. 1991. “The economic impact of agricultural extension: A review.” _Economic Development and Cultural Change_ 39 (3): 607–650.

- Birner, R., and J. R. Anderson. 2007. “How to make agricultural extension demanddriven? The case of India’s agricultural extension policy.” _IFPRI Discussion Paper 00729_ .

- Bold, T., K. Kaizzi, J. Svensson, and D. Yanagizawa-Drott. 2017. “Lemon technologies and adoption: Measurement, theory and evidence from agricultural markets in Uganda.” _Quarterly Journal of Economics_ 132 (3): 1055–1100.

- Bordalo, P., N. Gennaioli, and A. Shleifer. 2013. “Salience and consumer choice.” _Journal of Political Economy_ 121 (5): 803–843.

- Buehren, N., M. Goldstein, E. Molina, and J. Vaillant. 2019. “The impact of strengthening agricultural extension services on women farmers: Evidence from Ethiopia.” _Agricultural Economics_ 50 (4): 407–419.

28
# 3 Data and Methodology

In order to study gram sabha deliberations we bring together two di¤erent sources of information. In November 2001 we conducted a survey at the village and household level to study various aspects of GPs in South India employing a sampling methodology described in detail in the next section. One randomly chosen adult from every household in the sample was asked questions about the household’s socioeconomic status, household structure, views and use of public services in the village, and access to targeted bene...ts from the government. The respondents were also asked to provide open-ended responses rank-ordering their preference for problems in the village that needed attention. The problems were elicited from the respondent and postcoded into broader categories. From this ordering we constructed an individual preference measure: de...ned as his or her ...rst-ranked problem in the village.

Then from January to September 2003 we tape-recorded the proceedings of 38 gram sabhas in a sub-sample of the villages surveyed in the 2001 survey. This was supplemented by another round of 93 gram sabha recordings from October 2004 to February 2006 - where the 38 villages from 2003 were revisited along with an additional 55 villages, also selected from the original 2001 sample. Table 1 presents the meeting breakdown by round and state. Each transcript was divided into paragraphs, according to the natural pauses in speech. In the transcripts, all speakers were identi...ed by position (o¢ cial or villager) and gender<sup>5</sup> . A change in speaker automatically translates into a new paragraph, but a speaker can have more than one consecutive paragraph. For each paragraph the topics mentioned were recorded via two methods: First, topics were manually coded, by reading every transcript and noting the topics mentioned in each paragraph. Second, to ensure the replicability of our ...ndings, we coded the

> 5 Speaker caste is also identi...ed in some transcripts.

9
# Table 7. EFFECT OF IE ON RATE OF DISBURSEMENT

|||DID|-OLS|||DI|D-RE|||DID|-FE||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**T (quarters with IE==1)**|0.082***|0.037***|0.083***|0.037***|0.039*|0.011|0.040*|0.011|0.037|0.0091|0.038|0.0096|
||(9.21)|(3.71)|(9.27)|(3.75)|(1.69)|(0.54)|(1.73)|(0.56)|(1.50)|(0.44)|(1.54)|(0.46)|
|**D (project with IE==1)**|-0.029***|0.0044|-0.030***|0.0044|-0.0035|0.011|-0.0044|0.011|||||
||(-4.66)|(0.44)|(-4.70)|(0.43)|(-0.20)|(0.45)|(-0.25)|(0.45)|||||
|**_TTL switch dummies_**|Y|N|Y|N|Y|N|Y|N|Y|N|Y|N|
|**_TTL UPI dummies_**|N|Y|N|Y|N|Y|N|Y|N|Y|N|Y|
|**_Polynomial of sector averages_**|N|N|Y|Y|N|N|Y|Y|N|N|Y|Y|
|**_Observations_**|17784|17784|17784|17784|17784|17784|17784|17784|17784|17784|17784|17784|
|**_Outcome mean_**|0.244|0.244|0.244|0.244|0.244|0.244|0.244|0.244|0.244|0.244|0.244|0.244|
|**_Effect in %_**|33.6%|15.2%|34.0%|15.2%|16.0%|4.5%|16.4%|4.5%|15.2%|3.7%|15.6%|3.9%|

_Note:_ Standard errors are clustered (sector and quarter level). T-stats are in parenthesis. All specifications include fixed effects for quarters of project life cycle, sector, fiscal year when project became effective and country.

Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent 34
and inequality were mainly discussed in each period. The main results are shown in Appendix Table B4. This alternative method confirms the results obtained through LDA: the top frequent words used in the contextual excerpts of poverty and inequality closely match the terms that define the topics of each period.

This section set out to examine the frameworks in which poverty and inequality have been discussed over time, and whether increasing socially-oriented policy concerns can also be observed in the context of discussion. By performing LDA on the excerpts in which poverty and inequality are discussed, we identified a clear shift in the rhetoric presidents used when discussing these subjects. Specifically, the topics extracted by LDA indicate that beginning in the second half of the 20<sup>th</sup> century, presidents began to discuss poverty and inequality in a clear context of social development, a context that was not clearly visible in earlier periods.

# **6. Deeds and words**

A final question of interest is whether the extent to which poverty and inequality are discussed in presidential speeches is correlated with policy outcome variables. Particularly, we analyze the correlation between keyword prevalence and social public spending, as well as the levels of poverty and inequality of the countries. If the frequency of references to these topics is assumed to reflect the degree of concern and prioritization on the government’s side, perhaps, countries where these subjects receive greater attention have higher levels of social public spending, lower levels of poverty and inequality, or have made greater progress in their reduction. Conversely, greater attention by the government could be the consequence of enduring poverty and inequality, in which case, countries where these subjects receive greater attention may in fact be poorer and more unequal. The goal of this section, therefore, is to ascertain what the relationship, if any, might be.

To assess the relationship between prevalence of poverty and inequality in speeches on the one hand, and evolution of objective indicators on the other hand, we focus on the 2000s, a period in which the prevalence of both keywords was the highest. We get social public spending from ECLAC, and poverty and inequality data from SEDLAC (see Appendix A for more details about the data). Social public spending is calculated based on the expenditures of the central government, and is measured as percentage of the total government expenditure. The poverty rate is defined as the proportion of individuals with a per capita household income lower than $4 per person per day in 2005 purchasing parity power (PPP), while we use the Gini coefficient to measure income inequality.<sup>11</sup> We begin to explore a possible relationship by examining the levels of social spending and how such levels evolved over time. For each country, we calculate the social public

> 11 The Gini coefficient takes values between zero and one: a value equal to zero means perfect equality, while a Gini coefficient equal to one means complete inequality.

25
electric motor and thermal energy equipment in the industry sector. The implementation of these measures leads to 26% reduction of residential sector energy consumption by 2050 from that in the normal demand case. The energy efficiency measures in the non-residential buildings sector (e.g., more energy efficient space heating and space cooling) results in about 30% reduction in services energy demand. However, the size of service (or commercial) sector in the total energy consumption is small. In the case of industrial sector, the introduction of more energy-efficient technologies especially electric motors and boilers reduces leads to about 16% reduction of industrial sector energy consumption from that in the normal demand case by 2050. The design of the low demand scenario reflects Romania's commitment to EU’s energy and climate strategies.

# **3. Results**

In this section we present discussions of results produced by the TIMES model. These results include primary energy supply, electricity and heat supply, total system costs and investment requirement and CO2 emission reductions.

# **3.1. Primary Energy Supply**

Primary energy supply projections in Romania under different scenarios are presented in Figure 3a to 3f. Under the baseline scenario, Romania's total primary energy supply in 2050 is projected to increase by approximately 49% from the 2015 levels, from about 38,184 ktoe to about 56,779 ktoe. Under the low demand baseline scenario, total primary energy supply in 2050 would be 14% smaller compared to that in the baseline scenario.

Oil and natural gas are the most important sources of primary energy. While the share of oil in the baseline drops slightly from 29% in 2015 to 26% in 2050, share of gas increases by 8 percentage point, from 22% in 2015 to 30% in 2050 because Romania needs cleaner fuels to meet the EU’s GHG mitigation targets. For the same reason, share of coal drops from 16% in 2015 to 8% in 2050 in the baseline.

As expected the primary energy supply mixes in the green and super-green scenarios are different from that in the baseline scenario because of substitution of fossil fuels with renewables and nuclear. For example, in the green scenario, the shares of renewables (hydro, wind and solar) in 2050 increases to 23% from 21% in the baseline. The reverse would happen in the case of natural gas, where its share decreases from 30% in 2015 to 24% in 2050. The share of biomass increases 8
Environment and Enterprise Performance Survey (BEEPS) and AMADEUS—in order to address the aforementioned shortcomings of the earlier studies. For the measurement of the business environment, the analysis draws on firm-level data from the BEEPS, which was conducted by the World Bank in conjunction with the European Bank for Reconstruction and Development (EBRD) and covers all countries of Central and Eastern Europe, the former Soviet Union, and Turkey. In an effort to track changes of evolving business environments and benchmark the effects of reforms, BEEPS was conducted in 2002 and again in 2005, asking an identical core set of questions (covering 367 variables) in both rounds to ensure comparability across countries and years. For the estimation of firm productivity, the analysis uses data from the May 2006 edition of the AMADEUS database, a comprehensive, pan-European commercial database compiled by Bureau van Dijk. For each firm, the database includes up to ten years of accounting data. The manufacturing sector of these two large databases are merged on four criteria—country, subnational location, firm size, and year—producing a large 4-year panel of 22,004 manufacturing firms in 8 countries, Bulgaria, Croatia, the Czech Republic, Estonia, Poland, Romania, Serbia, and Ukraine. This unique dataset enables us to measure the effect of changes in the business environment on firm-level productivity growth over the period 2001 to 2004.

Second, in order to mitigate the problems of multicollinearity in the full model regression, a new set of robust indicators is constructed, using principal component analysis on quantitative variables from the BEEPS manufacturing dataset, that summarizes the following five distinct aspects of the business environment: (a) infrastructure quality, (b) financial development, (c) governance, (d) labor market flexibility, and (e) labor quality. Variable selection for PCA is guided by the preference for quantitative over qualitative indicators for two reasons. First, quantitative responses link directly to objective, actionable policy actions, as opposed to firm perceptions. Second, there are numerous statistical and measurement problems associated with the use of perception-based data, such as Likert-scale survey responses.<sup>2</sup> The construction of each indicator meets the three variables per component minimum threshold recommended for exploratory factor analysis (Thurstone, 1935; Kim and Mueller, 1978b). Furthermore, all synthetic indicators are given by the first principal component of their

> 2 For example, based on data from Enterprise Surveys in 33 African and Latin American countries that used instruments similar to those in the BEEPS, González, López-Córdova, and Valladares (2007) show that perceptions adjust slowly to firms’ experience with corrupt officials and hence are an imperfect proxy for the true incidence of graft.

2
monitoring experiment to be described below.<sup>2</sup> We choose 180 retail trade firms, intended to be similar in size to the retail firms surveyed in the SLMS. A baseline survey was carried out from August 22-26, 2005, collecting July data for these firms. August data was collected in the first 10 days of September, and September data was collected throughout the month for some firms, and in the first few days of October for others. Data for 174 firms was available for all three months.

Table 1 provides summary statistics for both surveys. The firms in both surveys are quite similar in many respects, although more of the SLMS firms are operated by women. The firms have generally been in operation for some time, with a mean age of 6 years in Kandy and 10 years in the SLMS. Firm owners average 9 to 10 years of schooling, and there is near universal literacy and numeracy. However, few firms keep business records: 86 percent of firms in Kandy and 73 percent of firms in the SLMS keep no business records. Moreover, over 90 percent of firms in both surveys do not pay taxes, removing another potential source of business accounting information. Firms in Kandy are more likely to be registered with the local level administration (Pradeshiya Saba) or District Secretariat (D.S.), but this higher level of formalization still does not lead to more record keeping.

The lack of record keeping observed among these small firms suggests that reported profits may be subject to a range of different types of measurement error. The lack of record-keeping appears to be a general phenomenon among small businesses in developing countries. For example, 22 percent of Mexican microenterprises with less than US$1000 of non-land capital investment keep records, according to the 1998 ENAMIN survey.<sup>3</sup> Studies during the 1980s found only 17 percent of small industry owners kept records in Sierra Leone, and less than 10 percent in Jamaica and rural Bangladesh (Liedholm, 1991). It is therefore of general interest to determine what can be done to elicit reasonable information on profits from small firm owners.

> 2 Fieldwork was carried out by the Kandy Consulting Group. 3 See McKenzie and Woodruff (2006) for more details on this survey.

5