rafmacalaba/datause-encoder-data huggingface dataset:
<mark>the “Government Effectiveness” measure from the Worldwide Governance Indicators;</mark> and the <mark>annual rate of inflation (Consumer Price Index).</mark><sup>5</sup> Given the dearth of literature on which firm characteristics best capture the broader determinants of bribery mentioned above, we use several proxy measures. We begin with control for industry choice, as this can significantly affect firms’ profitability and growth (Rand and Tarp 2012). We do so by using dummy variables for the industry to which the firm belongs (Industry fixed effects). In our baseline sample, there are 29 different industries defined as the 3-digit ISIC Rev 3.1 level. Next, we control for (log of) number of full-time workers employed at the end of the last fiscal year ( _Firm Size_ ) and (log of) age of the firm. Larger and older firms may be more susceptible to paying bribes due to their greater visibility/exposure, higher ability to pay bribes due to bigger profits and better access to finance, and fewer outside options due to larger sunk investments. Countering these effects, larger and older firms may be less reliant on publicly provided utilities and services, have stronger political connections and managers who are more skillful in negotiating with the public officials. The net effect of these forces will determine how the age and size of the firm affects the level of corruption it experiences.

A firm’s profitability and, therefore, its ability to pay bribes depends on several additional factors such as management quality, exporting status, foreign ownership of the firm, financial condition, infrastructure availability, and the quality of institutions. Financial condition of the firm and the experience of its top management could affect its ability to deal with corrupt public officials as well its outside options. Exporting firms and firms that have foreign ownership may be more prone to paying bribes, as these firms tend to be more productive and profitable, more visible,

> 5 All the country-level controls described here are lagged by two years to allow for their full effects on corruption. The only exception is primary education. Due to missing data, we use average values of primary education over the last two years (prior to the date of ES) for which data are available.

14
been limited to national-level analysis. The analysis presented here points to the immense potential inherent in disaggregated analysis. The model presented here is well suited to be extended to test hypotheses regarding the availability of `lootable resources', regarding patterns in the distribution of ethnic groups and/or the geographical distribution of income, or the impact of the location of refugee camps or of lootable resources.

# **6 References**

Alesina, Alberto & Enrico Spolaore, 2003. _The Size of Nations_ . Cambridge, MA: MIT Press.

Beck, Nathaniel, Katz, Jonathan N., and Richard Tucker, 1998. `Taking Time Seriously in Binary Time-Series Cross-Section Analysis', _American Journal of Political Science_ 42(4):1260-1288.

Buhaug, Halvard & Päivi Lujala, 2005. Accounting for Scale: Measuring Geography in Quantitative Studies of Civil War. _Political Geography_ 24(4): 399–418.

Buhaug, Halvard & Jan Ketil Rød, 2006. `Local Determinants of African Civil Wars, 1970-2001', _Political Geography_ 25(3): 315–335.

Buhaug, Halvard and Scott Gates, 2002. `The Geography of Civil War', _Journal of Peace Research_ 39(4): 417–33.

Clapham, Christopher, 1985. _Third World Politics_ . London: Croom Helm.

Collier, Paul, 2000. `Doing Well Out of War: An Economic Perspective', in Mats Berdal & David M. Malone, eds, _Greed & Grievance: Economic Agendas in Civil Wars_ . Boulder, CO: Lynne Rienner (91–111).

Collier, Paul & Anke Hoeffler, 2002. `Greed and Grievance in Civil War', _CSAE Working Paper 2002/01. URL:_ http://www.csae.ox.ac.uk/workingpapers/pdfs/2002-01text.pdf Collier, Paul & Anke Hoeffler, 2004. `Greed and Grievance in Civil War', _Oxford Economic Papers_ 56(4):563–595.

Fearon, James D. & David D. Laitin, 2003. `Ethnicity, Insurgency, and Civil War', _American Political Science Review_ 97(1): 75–90.

34
- 17 **-**

In both of these case-, the improvement in the central government's control of the local enforcement agency is obtained at the cost of assigning an instrument which is otherwise less efficient than a tax. However, when enforcement is costly and government objectives diverge, total costs may be lower.

# (iii) **The** enforcement budget as a control device

**In** part (ii) above we considered the use of a simple implicit transfer mechanism to stimulate local enforcement activities, and the consequences for instrument choice. We now consider how, for a given instrument, the implicit transfer might be used to improve the enforcement incentives of local governments.

Our discussion is based on the work of Jones and Scotchmer (1990), who consider the implementation of a regulatory design or performance standard. The transfer mechanism available to the central government is defined in terms of the local authority's enforcement budget. The budget can be affected by the use of direct lump-sum payments (i.e. categorical grants), which govern its overall size. In addition, the central government can use a transfer conditional upon the enforcement policy chosen by the local authority. In particular, a proportion of th: collected fines is used to finance the enforcement budget.

In the model, the objectives of the central and local administrations differ with respect to the weight given firms' costs of compliance (e.g. costs of abatement, costs of adopting the required technology, etc.). In particular, the local government does not take these into account, and seeks to maximize the compliance rate.'<sup>6</sup> This is despite the fact that it has some knowledge, in the form of a signal, of tfie compliance costs. The signal is costless for the local authority to observe, and allows it to identify which firms, _ceteris paribus,_ are likely to comply.

'Shis could arise because the firms are owned by interests in other parts of the country.
6.3.2 Alternative Measure of Exposure to GBV In this robustness check, we examine the effect of the CCT program on an alternative measure of GBV outcome. In the primary analysis (described in Section 4), we defined GBV incidence as a dummy variable, which took value 1 if the respondent experienced ANY act of violence, and 0 otherwise. For instance, to measure physical IPV, if the respondent had experienced ANY of the following 7 acts – (i) push/shake, (ii) slap, (iii) punch, (iv) kick/drag, (v) strangle or burn, (vi) twist arm/pull hair, or (vii) threaten with knife/gun – then the outcome will take value 1 (irrespective of how many violent acts the respondent experienced). In the alternative measure, we utilize a continuous measure of exposure and evaluate the sum of all acts that the respondent may have experienced. Thus, in the above example, the alternative measure of physical IPV could take discrete values ranging between 0 to 7. Tables 15-18 present the estimates using this alternative measure of GBV and we find that the results are consistent with the primary analysis. Like before, we find that the CCT program significantly reduced incidence of emotional NPDV.

_Table 15: Effect of CCT on Incidence of IPV (continuous measure of exposure to violence)_

|(1)<br>Incd. of physical<br>IPV: Sum of 7<br>answers|(2)<br>Incd. of sexual<br>IPV: Sum of 3<br>answers|(3)<br>Incd. of<br>emotional IPV:<br>Sum of 3<br>answers|(4)<br>Incd. of<br>economic IPV:<br>Sum of 4<br>answers|
|---|---|---|---|
|RD Effect<br>-0.027|0.025|0.016|-0.059|
|(0.88)|(0.29)|(0.83)|(0.46)|
|FDR q-val<br>0.88|0.88|0.88|0.88|
|Observations<br>562|562|562|562|
|Control mean<br>0.208|0.025|0.158|0.201|
|P-values in parentheses: * p<0.1 ** p<0.05 **|* p<0.01|||

Note: All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all IPV regressions in the table.

_Table 16: Effect of CCT on Incidence of NPDV (continuous measure of exposure to violence)_

||(1)<br>Incd. of physical<br>NPDV: Sum of 7<br>answers|(2)<br>Incd. of sexual<br>NPDV: 1 answer|(3)<br>Incd. of<br>emotional NPDV:<br>Sum of 3<br>answers|(4)<br>Incd. of<br>economic NPDV:<br>Sum of 4<br>answers|
|---|---|---|---|---|
|RD Effect|-0.029|-0.003|-0.156**|-0.088*|
||(0.37)|(0.29)|(0.03)|(0.06)|
|FDR q-val|0.37|0.37|0.13|0.13|
|Observations|564|564|564|564|
|Control mean|0.057|0.000|0.154|0.089|
|P-values in parentheses:|* p<0.1 ** p<0.05 **|* p<0.01|||

Note: All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all NPDV regressions in the table.

24
this fixes Norway’s output at current levels and caps those of the U.S. and Switzerland in respectively the last 4 and 5 years of the projection (final 9 and 10 years in the high growth scenario.).

<!-- Start of picture text --> Global population weighted GDP scenario Global population weighted poverty projection<br>Historical<br>Pessimistic<br>BAU<br>Opportunistic<br>Historical<br>Pessimistic<br>BAU<br>Opportunistic<br>2000 2005 2010 2015 2020 2025 2030 2000 2005 2010 2015 2020 2025 2030<br>Year Year<br>Forest cover weighted % tree cover loss, Population weighted PM25 concentrations,<br>projection for the world projection for the world<br>Historical<br>Pessimistic<br>BAU<br>Opportunistic<br>Historical<br>Pessimistic<br>BAU<br>Opportunistic<br>2000 2005 2010 2015 2020 2025 2030 2000 2005 2010 2015 2020 2025 2030<br>Year Year<br>Total carbon emission output,<br>projection for the world<br>Historical<br>Pessimistic<br>BAU<br>Opportunistic<br>2000 2005 2010 2015 2020 2025 2030<br>Year<br>25000<br>20<br>20000 15<br>Poverty rate<br>GDP per capita<br>15000 10<br>10000 5<br>0.65 36<br>0.60 34<br>0.55 32<br>0.50<br>30<br>PM25 concentration<br>Annual % Tree cover loss 0.45<br>28<br>0.40<br>80<br>70<br>60<br>50<br>CO2 Billion Tons 40<br>30<br>20<br><!-- End of picture text --> Figure 6: Projection for global environmental outcomes. Table 6 contains aggregate statistics of the BAU line and highlights the distributional changes across income.

Figure 6 presents our projections at the global level made by aggregating all the countrylevel results and assuming that the average in-sample trend scales appropriately with missing areas. Results are also available for income segments in table 6. In the average scenario, global extreme poverty falls below 7.4% of the global population. The poorest 20% countries in our sample have stronger successes and go from 45% poverty to just over 33%.

While poverty reductions and GDP increases may improve livelihoods through eco27
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Growth

Calderón, César. 2009. ―Infrastructure and Growth in Africa,‖ Policy Research Working Paper 4914, World Bank, Washington, DC.

- Escribano, Alvaro, J. Luis Guasch, and Jorge Pena. 2010. ―Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa.‖ Policy Research Working Paper 5191, World Bank, Washington, DC.

- Yepes, Tito, Justin Pierce, and Vivien Foster. 2009. ―Making Sense of Africa’s Infrastructure Endowment: A Benchmarking Approach.‖ Policy Research Working Paper 4912, World Bank, Washington, DC.

# Information and communication technologies

Ampah, Mavis, Daniel Camos, Cecilia Briceño-Garmendia, Michael Minges, Maria Shkaratan, and Mark Williams. 2009. ―Information and Communications Technology in Sub-Saharan Africa: A Sector Review.‖ AICD Background Paper 10, Africa Region, World Bank, Washington, DC.

- ATRPT (l'Autorité Transitoire de Régulation des Postes et Télécommunications), 2008. Tableau de Bord de la Couverture du Territoire en 2008. Available at:

http://www.atrpt.bj/Observatoire/statistiques%20secteur%20telecom/Tableau%20de%20Bord%2 0Couverture%20GSM.pdf

- ———. June 2009.

- http://www.atrpt.bj/Observatoire/telecom2009/internet/TABLEAU%20DE%20BORD%20INTE RNET%20JUIN%202009.pdf

- ———. 2009. Analyse de l’evolution des marches du mobile, du fixe et de l’Internet au cours du premier semestre 2009. Available at:

- http://www.atrpt.bj/Observatoire/statistiques%20secteur%20telecom/ANALYSE%20EVOLUTI ON%20DU%20SECTEUR%20S01%202009.pdf

- Gillwald, Alison, and Christoph Stork. 2008. Towards Evidence-based ICT Policy and Regulation: ICT access and usage in Africa. Available at: http://www.researchictafrica.net/index.php/riapublications

- Global Insight. 2007. Beninese Regulator Withdraws Operating Licences from MTN and Moov. 16 July. Available at: http://www.globalinsight.com/SDA/SDADetail9935.htm

INSAE. 2010. Les Entreprises des TICS au Benin. Available at: http://www.insae-bj.org/ Lohento, Ken. 2000. L'Internet au Bénin de 1995 à 2000. Available at: http://www.reseauafricanet.org/anais/ARTICLES/ARTICLE93.HTML

- Mayer, Rebecca, Ken Figueredo, Mike Jensen, Tim Kelly, Richard Green, and Alvaro Federico Barra. 2009. ―Connecting the Continent: Costing the Needs for Spending on ICT Infrastructure in Africa.‖ AICD Background Paper 3, Africa Region, World Bank, Washington, DC.

45
18 <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Leave children Return after 5<br>Work Work outside Work if married Work w/ men<br>w/ relative pm<br>SE 42% 37% 30% 22% 17% 6%<br>SN 62% 65% 65% 54% 57% 51%<br>PB 93% 86% 78% 64% 56% 40%<br>BC 79% 83% 96% 82% 69% 77%<br><!-- End of picture text --> Next, we look at how working women are different from non-working women across the three themes shown in Table 4 (women working in general, publicness and mixing, gender roles). This analysis provides key insights in terms of how working women and their counterparts think differently from households where women are not working. It tells us how social norms and beliefs vary across the thematic areas between households with and without working women. Put differently, changing these perceptions in households with non-working women may be the first step towards greater acceptability and effort in women joining the labor force.

For regression analysis, since several questions were asked for each thematic area as shown in Table 4 above, we first aggregate the responses and standardize to create indices.<sup>15</sup> Separate indices were constructed for variables under each of the themes, as well as components of our measurement: social empirical and normative expectations, personal beliefs, and expectations of counterpart. For specification (ii) below we also construct separate indices by gender.

We run equation the specification below separately for women and men to better isolate the influence of male counterpart perceptions, and at the individual level.

_Yi= βo + β1SE_indexi + β2SN_indexi+ β3PB_indexi + β4IH_indexi + βiXi + Ɛ i_ where Yi is the work decision for individual i (i.e. whether the female respondent or female counterpart of male respondent is working or wants to work), _SE_indexi_ is an index of relevant

> 15 This helps to reduce multiple testing, and we test if the broader theme is different across working women and non-working women. This is also a simpler way to show the reliability and validity analysis of these indices as shown in Appendix Section 7.
We note that all the variables used in the regressions below are taken from Enterprise Surveys. Summary statistics of all the variables used are provided in Table 2. Correlations between the various explanatory variables used are provided in Table 3.

# 2.1 _Dependent variable_

Our dependent variable is a dummy variable equal to 1 if the top manager of the firm is female and 0 otherwise ( _Female_ ). In the full sample, 18.8 percent of the firms have a female top manager. The percentage figure varies between 0.16 (Yemen) and 37.1 (St. Vincent and the Grenadines). In the remainder of the paper, we will use the term female managers interchangeably with the likelihood of a firm having a female manger or the proportion of managers that are female for a specified set of firms.

# 2.2 _Main explanatory variable_

Our main explanatory variables include dummy variables indicating the sector to which the firm belongs. We begin by analyzing the presence of female managers in services as a whole vs. manufacturing sector. To this end, we define a dummy variable equal to 1 if the firm belongs to the service sectors and 0 otherwise ( _Services_ ). Next, we analyze how the individual service sectors compare with manufacturing and retail sectors as far as the proportion of female managers are concerned. For this purpose, we introduce a dummy variable equal to 1 if the firm belongs to the retail sector and 0 otherwise ( _Retail_ ), a dummy variable equal to 1 if the firm is engaged in wholesale activity and 0 otherwise ( _Wholesale_ ), a dummy variable equal to 1 if the firm belongs to the construction services sector and 0 otherwise ( _Construction_ ) and a dummy variable equal to 1 if the firm belongs to any of the remaining service sectors (i.e., service sectors 6
upon both employment shares and fraction of plants. Small industry clearly dominates the poorest regions of Brazil.<sup>5</sup> In the lowest-decile areas, 90% of manufacturing facilities are small plants. This declines to 63% in the highest-income municipios. The small-plant employment share drops steadily from 83% in decile 1 to 31% in decile 9, and then plummets to 19% in decile 10. The share of employment in large plants climbs steadily through 45% in decile 9 before jumping to 62% in the highest-income municipios.<sup>6</sup>

# 5. **Results: Total Emissions and Health** Damage

The results in Section 4 are consistent with the three hypotheses at the end of Section 2. The Mexican data, especially combined with the air dispersion parameters, show that small plants are far more pollution-intensive than large ones. In Brazil, small plants play a much larger role in the industrial economies of poor regions. Furthermore, these poorer regions of Brazil exhibit a greater concentration of plants in the dirtiest sectors. In contrast to poor areas, Brazil's richest municipios are the heartland of large enterprises in relatively clean sectors.

Yet, paradoxically, confirmation of these hypotheses does not imply that pollution damage is actually greater in poor regions. Economic development also promotes two countervailing trends. The first is an increase in the _scale_ of industrial activity, which may lead to greater pollution even if production shifts toward larger plants in cleaner sectors. The second is _urbanization,_ which increases the size of populations exposed to industrial pollution. A larger exposed population will suffer greater aggregate health damage from pollution, even if industrial emissions remain constant. In this section, _5_ The database provided by IEBGE is more comprehensive than any comparable information source we have seen. Nevertheless, it is entirely possible that small enterprises are undercounted in the database. For this exercise, what matters is the effect of income level on the propensity to undercount. Data-gathering is probably less efficient in poor regions, so undercounting of small plants should be more serious there. Thus, the results in the text probably _underestimate_ the decline in small enterprise share as income increases.

> 6 These correlations are not just an artifact of Brazil's separation into poor and rich regions. Regressions for the municipios of Rio de Janeiro and Sao Paulo States reveal similar relationships between dirty-sector share, industry scale and development.

12
the relationship between the outcome variable and revenue is approximately linear. The specification in Column 3 controls for a quadratic function of revenue to allow the relationship to be non-linear.

The shaded rows give the estimated impact of the reform in terms of new firms registered. The estimates in Panel A suggest that the micro tax reform increased the number of new firms in 2010 below the threshold by 2.2 to 3.4 firms, depending on the specification used. This effect is statistically significant at least at the 5-percent level. The size of the effect corresponds to an 18 to 30-percent increase in the number of new firms below the GEL 30,000 cutoff<sup>29</sup> . In contrast, the results in Panel B show no significant increase in firms registered in 2011 due to the reform.

**Table 8: Basic RDD to estimate the effect of the micro firm tax regime around GEL 30,000 on formal**

**firm creation**

||(1)|(2)|(3)|
|---|---|---|---|
|Sample with revenue between|25,000 and 35,000|20,000 and 40,000|10,000 and 50,000|
|Observations(intervals)|100|200|400|
|Panel A - D<br>|ependent variable: Nu<br>|mber of new firms in 201<br>|0<br>|
|2011 Revenue < 30,000 dummy|3.420**|2.256**|2.642***|
||(1.332)|(0.932)|(0.882)|
|2011 Revenue|0.401|-2.384***|-58.692***|
||(2.050)|(0.811)|(16.364)|
|Constant|6.737|16.124***|68.628***|
||(6.692)|(2.858)|(10.212)|
|R-squared|0.226|0.380|0.773|
|Panel B - D<br>|ependent variable: Nu<br>|mber of new firms in 201<br>|1<br>|
|2012 Revenue < 30,000 dummy|1.514|0.093|-0.033|
||(1.131)|(0.810)|(0.785)|
|2012 Revenue|0.108|-2.737***|-38.352**|
||(1.889)|(0.720)|(15.538)|
|Constant|6.849|16.050***|50.786***|
||(6.112)|(2.519)|(9.975)|
|R-squared|0.065|0.248|0.717|

Note: Revenue is in intervals of 10,000s of GEL. The regression in Column 3 additionally controls for a quadratic function of revenue (coefficients not reported to save space). Robust standard errors in parentheses. Statistical significance levels: *10 percent, **5 percent, *** 1 percent.

Figures 16a and16b show the jump in number of new firms around the GEL 100,000 threshold in the post-reform period, using 2011 and 2012 data. In both years, the number of new firms is clearly higher just below the threshold than it is just above the threshold. However, as discussed above, at the GEL 100,000 cutoff, there was already a jump in newly registered firms in the pre-reform period since

> 29 The average number of new firms registered in the 50 revenue intervals below the GEL 30,000 cutoff in the estimation sample in Column 1 (i.e. in the revenue intervals that fall between GEL 25,000 and 30,000) was 12 in 2010. That is, an increase in the number of firms by 2.2 corresponds to 2.2/12 = 18 percent (similarly for 3.4).

25
Figure **1** Number of Telephones in Service (CTC), 1934-91 <!-- Start of picture text --> Thousands of telephones<br>1,600<br>1,200 - - I<br>1,000 _ I<br>300_<br>600 - _ - -<br>400-- -<br>-<br>200 _ _<br>20 - - - ---------------- - - - - - - - - - - -<br>r I I I I I , I I I I I , I I I I<br>1934 1937 1940 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991<br><!-- End of picture text --> Figure **2** Basic Network Expansion **(CTC), 1960 - 91** <!-- Start of picture text --> Ttosnds Of mnes<br>1.400<br>IAW~~~~~~~~~~~~~~~~~~~~~~~~~<br>1,200<br>1,000 -- - - - - - - - - - - - -Lns ----<br>800 -_ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -L69i ~~~~ 7<br>I<br>800v<br>600-== - ess<br>200-- - -- - - -.----<br>.._ .............. -<br>1I I I I .. I 1 1 1 I 1 1 I I<br>1960 1982 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990<br><!-- End of picture text -->