rafmacalaba/datause-encoder-data huggingface dataset:
# Figures and tables

## **Figure 1. Aggregate infrastructure stock and quality, 1991–2005**

<!-- Start of picture text --> a. Aggregate index of infrastructure stock (IAK1)<br>(First principal component: main lines and mobiles, electricity-generating capacity, total roads)<br>2.5<br>2.0<br>1991–5 2001–5<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>Western Europe EAP7 Africa North Africa West Africa East Africa Southern Africa Central Africa<br>b. Aggregate index of infrastructure stock (IAK2)<br>(First principal component: main lines, electricity-generating capacity, total roads)<br>2.5<br>2.0<br>1.5<br>1991–5 2001–5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>Western Europe EAP7 Africa North Africa West Africa East Africa Southern Africa Central Africa<br>Median (logs)<br>Median (logs)<br><!-- End of picture text -->
national poverty lines and note that six countries among the poorest in this sample (Bangladesh, Indonesia, Kenya, Morocco, Nepal and Tanzania) were all within one dollar of a poverty line of USD 31 per person per month at 1985 PPPs. The similarities in the value of the national poverty line for these six countries then served as the basis for the original “dollar-a-day” global poverty line.<sup>9</sup> The resulting global count of the poor for 1985 was 1.1 billion persons based on data for 22 countries (with extrapolation models for an additional 64 countries).

With new household-survey data and expanding country coverage, global poverty estimates were regularly updated, with some substantial modifications over time. These modifications typically occurred in response to each new ICP price data collection exercise, and the subsequent release of new PPP exchange rates, or conversion factors. The first major revision to the dollar-a-day line came when Chen and Ravallion (2001) updated the line based on the then more recent 1993 PPP data. They re-estimated the global poverty line following the same basic idea of basing the global poverty line on a sub-sample of national poverty lines, but used the median national poverty line from the ten lowest national poverty lines from the RDW sample of national poverty lines. This resulted in a revised global poverty line of $1.08 a day in 1993 PPP prices.

After the 1993 PPP data, the ICP did not release a significant update to the PPP data until the 2005 benchmark year. In part due to the very long gap in data updates, the release of the 2005 PPP data had significant implications for both the global poverty line and global poverty estimates. In preparing an update that incorporated this change in PPPs, Ravallion, Chen and Sangraula (2009) also compiled a new sample of national poverty lines from 74 countries and used it to re-estimate the global poverty line. They observed a positive association between the value of the poverty line and national per capita consumption for the large majority of countries, but found that the relationship was flat for the fifteen poorest countries in their sample.<sup>10</sup> The

> 9 The national poverty lines for six countries (Indonesia, Bangladesh, Nepal, Kenya, Tanzania, and Morocco) were within one dollar of the USD 30.42 per month line. The Philippines and Pakistan were also close to this value. For the purposes of comparison, India’s poverty line was, using 1985 PPP terms, USD 23 per month. If the India line had been used, the resulting global poverty line would have been about 76 cents per day, rather than the ‘dollar-a-day’ line that was selected.

> 10 More specifically, they use threshold estimator proposed by Hansen (2002) to identify where there is a break between the flat part and upward sloping part of the bi-variate regression of poverty line on the log of per capita consumption.

6
# **2 Literature review**

A common approach to testing the convergence hypothesis is the _β_ -convergence that relies on whether poorer countries grow faster. It can be tested by using country-level cross-sectional or panel data: where _y_ represents per capita income in country _i_ and time _t_ ,<sup>2</sup> _τ >_ 0, and _ε_ is an error term. Empirically, testing for convergence entails conducting a test of the null hypothesis _β_ = 0 against the alternative _β <_ 0 (Johnson and Papageorgiou 2020). This test presumes that initial per capita income contains sufficient information to capture countries’ initial conditions. If the null hypothesis is rejected, this provides support to the _β_ -convergence hypothesis. Specifically, it implies the occurrence of unconditional convergence in that there is no effect of initial conditions in the long run.

However, several studies show that initial per capita income is not a sufficient statistic to determine convergence, and therefore, other factors such as geography, human capital, and institutions need to be accounted for in characterizing income growth.<sup>3</sup> This is called conditional convergence and can be tested as in equation 1 after introducing controls ( _Ci,t−τ_ ) and country fixed effects ( _λi_ ): Another approach to testing for convergence is the _σ_ -convergence depending on whether crosscountry dispersion in per capita income falls over time. That is, between two different points in time _t_ and _t_ + _τ_ where _τ >_ 0, the presence of _σ_ -convergence yields _σt_<sup>2</sup><sup>_>σ_</sup> _t_<sup>2</sup> + _τ_<sup>,where</sup><sup>_σ_2isthecross-</sup> country dispersion in per capita income. It is important to note that _β_ -convergence is a necessary but not sufficient condition for _σ_ -convergence.<sup>4</sup> Empirically, testing for convergence is commonly done using linear models (as in equations (1) and (2)). Studies have also used non-linear models to allow for possibilities of club convergences and divergence. Johnson and Papageorgiou (2020) categorize these models into those clustering the data

> 2 Although the specification in equation 1 is based on per capita income, it can be generalized to productivity or any other economic development indicators.

> 3 See, for example, Durlauf and Johnson (1995), Fiaschi et al. (2019), and Tan (2010).

> 4 Using U.S. county-level data on real per capita income, Young et al. (2008) show that _β_ -convergence does not necessarily imply _σ_ -convergence.

4
# **Appendix Table A2: Effect of the information intervention on number of SEBRAE services used**

|Num|ber of SEBRAE|services used|within|
|---|---|---|---|
|3 months|6 months|12 months|18 months|
|(1)|(2)|(3)|(4)|
|_Panel A: Pooled treatment effect_||||
|Any information sheet<br>0.114|0.146|0.170|0.204|
|(0.120)|(0.162)|(0.251)|(0.342)|
|R2<br>0.222|0.302|0.273|0.232|
|Control mean<br>0.518|0.732|1.435|2.155|
|_Panel B: Effects for each treatment group_||||
|(1) No motiv, no pre-enroll<br>0.047|0.106|0.259|0.305|
|(0.149)|(0.194)|(0.314)|(0.478)|
|(2) No motiv, pre-enroll<br>0.004|0.079|0.120|0.046|
|(0.157)|(0.207)|(0.350)|(0.451)|
|(3) Motiv, no pre-enroll<br>0.105|0.040|-0.185|-0.133|
|(0.162)|(0.198)|(0.300)|(0.409)|
|(4) Motiv, pre-enroll<br>0.299*|0.361|0.495|0.606|
|(0.164)|(0.222)|(0.349)|(0.455)|
|R2<br>0.226|0.305|0.277|0.235|
|F-test p-value (2=1)<br>0.785|0.887|0.697|0.607|
|F-test p-value (3=1)<br>0.723|0.723|0.152|0.356|
|F-test p-value (4=1)<br>0.133|0.233|0.505|0.556|
|F-test p-value (1=2=3=4)<br>0.327|0.469|0.208|0.367|
|<br>_Panel C: Heterogenous treatment effects by baseline b_|_usiness practice_|_s (BPs)_||
|Any information sheet*Low BPs<br>0.275*|0.387**|0.591*|0.800*|
|(0.153)|(0.195)|(0.324)|(0.419)|
|Any information sheet*High BPs<br>-0.101|-0.156|-0.350|-0.547|
|(0.197)|(0.280)|(0.418)|(0.586)|
|R2<br>0.237|0.313|0.283|0.241|
|F-test p-value (Low BPs=High BPs)<br>0.139|0.119|0.085|0.069|
|Control mean low BPs<br>0.279|0.430|0.919|1.395|
|Control mean high BPs<br>0.797|1.089|2.051|3.063|
|N<br>866|866|866|866|

Notes: This table shows OLS estimates for the effect of receiving the information sheet on number of SEBRAE services used. The outcome variables are the number of SEBRAE services used within the stated number of months after the intervention. Panel A pools all treatment groups. Panel B shows separate treatment effects for each of the four different versions of the information sheet. Panel C shows heterogenous treatment effects for firms using below and above the median percentage of baseline business practices. BP stands for business practices. Low BP refers to below the median (50%), while high BP refers to above the median. All regressions control for number of SEBRAE services used within 12 months before the intervention and strata fixed effects. The regressions in Panel C additionally control for a dummy variable equal to one if the firm had low BPs at baseline. ***, **, and * indicate significance at the 1, 5, and 10 percent level. To deal with multiple hypothesis testing, †††, ††, and † indicate sharpened q-values statistically significant at the 1, 5, and 10 percent level.

23
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE in magnitude, with the former sourced largely from the private sector and the latter from the public sector. Investments in water and sanitation are absent.

**Figure 30. Sudan’s pattern of capital investment in infrastructure and that of comparator countries**

<!-- Start of picture text --> Investment in infrastructure sectors as percentage of GDP, by source<br>2.5 Sudan<br>Resource Rich<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>ICT Power Transport WSS ICT Power Transport WSS<br>Public ODA Non-OECD Private<br>Percentage of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2009).

_Note:_ Private investment includes self-financing by households. ODA = official development assistance; OECD = Organisation for Economic Co-operation and Development; ICT = information and communication technology; GDP = gross domestic product; WSS = water supply and sanitation; LIC = low-income country.

# How much more can be done within the existing resource envelope?

Even without increasing spending, additional resources could be directed toward infrastructure by eliminating inefficiencies. This report quantifies some operational inefficiencies based on measurable and observable performance indicators. The first type relates to operators and/or infrastructure providers, including utilities, SOEs, and special funds. For this type of estimate, providers within Sudan will be compared against well-functioning utilities in Africa and/or engineering norms. These comparisons will cover (i) underrecovery of costs: comparing effective tariffs and user fees against actual unit costs of providing the service; (ii) overstaffing: assessing the difference between average labor cost per connection of the utility evaluated against a well-functioning utility; (iii) distribution losses: assessing the difference between distribution of losses of the evaluated system against engineering norms for a system of similar age and characteristics; and (iv) undercollection: assessing the ability of the operator to collect bills within the year against a standard of full revenue collection. In addition, the analysis measures the ability of governmental agencies to execute their budget allocations for capital projects within the fiscal year.

The analysis suggests that Sudan can recover $584 million each year by reducing inefficiencies in the system (table 21). Most of the efficiency gains are associated with aligning tariffs to cost-recovery levels and reducing distribution losses in both the water and power systems. Increasing tariffs to cost-recovery levels could save $378 million in power and $15 million in water sectors annually. Distributional losses in power cost $106 million per year, and another $27 million in the water sector. Undercollection of bills for water services could save a significant $35 million. Finally, budget underexecution is a significant issue in the transport sector, leading to underspending of about $20 million of available funds annually.

> <sup>42</sup>
# FIGURE 12: SHARE OF GOVERNMENT. HEALTH. EDUCATION. MILITARY SERVICES IN VALUE ADDED: USSR AND OECD

<!-- Start of picture text --> 25%-<br>DM18WE<br>20%X DNK<br>NE D NED<br>15%-$ . e R~~~~~~~~~O<br>US8R RLUS<br>LOG OF PER CAPITA INCOME<br><!-- End of picture text -->
9 of the congestion charging scheme in London, conducted by Evans (2007) show that distance vehicles traveled across London were reduced by approximately 211 million per year with a £5 charge, and 237 million with an £8 charge. The value of CO2 emissions saved was £2.3 million to £2.5 million with £5 and £8 charges. Rich and Nielson (2007) discuss the socio-economic assessment of proposed road user charging schemes in Copenhagen. They estimate that CO2 emissions in Copenhagen can be reduced anywhere from 11, 500 tons to 154,000,000 tons annually, depending upon the type of congestion charge (see Table 2). They also find that the congestion charges, based on distance traveled (i.e., km charge) could reduce the highest amount of CO2 compared to other types of congestion charges, such as large toll ring, and small toll ring.

**Table 2. Annual Reductions in External Effects in 2005**

|**External effect**|**Km**<br>**charge**|**Cordon**<br>**charge**|**Large toll ring**|**Small toll ring**|
|---|---|---|---|---|
|Reduced CO2tonnage (×1000)|154,000|138,000|58,000|11,500|
|Reduced accidents (number)|330|155|298|100|
|Reduced noise (1000 SBT)|2.7|2.7|1.2|0.2|
|Reduced wear damage (million<br>DKK)|12|11|4|1|

Source: Rich and Nielsen (2007) A congestion charge not only reduces traffic congestion, but also reduces carbon monoxide (CO) and volatile organic compound (VOC) emissions proportionately more than other types of emissions (Abbott et al., 1995; Acutt and Dodgson, 1997). Daniel and Bekka (1999) estimate the impacts of congestion pricing on emissions of CO, nitrogen oxide and hydro carbons for an actual metropolitan highway network calibrating Delaware’s household travel demand, and highway traffic count data in EPA’s mobile 5a model. They find that vehicle emissions could be reduced as much as 10% in aggregate and 30% in highly congested areas through the use of a congestion charge.
# **TABLES AND FIGURES**

# Table 1 Descriptive statistics of variables by country: Mean 1996-2006.

|**country/mean**|**Y**|**K**|**L**|**E**|**AVA**|**EVI**|**FDI**|**IVA**|**MT**|**R&D**|**SVA**|**RRD**|**SET**|**LFT**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Argentina|85,000,000,000|46,940,000,000|16,400,000|60,904.36|7.42|113.75|2.96|31.07|26.28|0.43|61.51|733.17|58.53|27.76|
|Bolivia|8,775,000,000|1,410,000,000|3,683,423|4,819.09|15.17|138.04|6.19|30.46|41.38|0.30|54.37|92.70|36.61|16.27|
|Brazil|665,400,000,000|108,300,000,000|84,800,000|195,304.73|5.96|131.97|2.97|27.40|18.86|0.87|66.63|409.85|19.15|7.16|
|Chile|79,300,000,000|17,990,000,000|6,280,403|26,002.64|5.42|131.20|6.47|39.76|53.06|0.57|54.83|495.63|41.33|25.20|
|Costa Rica|16,500,000,000|3,003,000,000|1,703,393|3,393.27|10.10|99.56|3.88|30.02|77.70|0.33|59.88|120.31|19.66|16.49|
|Ecuador|17,650,000,000|4,346,000,000|5,112,170|9,023.91|7.05|130.22|3.54|35.82|48.23|0.07|57.14|63.98|21.06|25.45|
|El Salvador|13,380,000,000|2,215,000,000|2,324,244|4,148.27|11.11|96.87|2.31|30.74|56.80|0.08|58.15|28.31|18.41|23.80|
|Guatemala|19,840,000,000|3,226,000,000|3,627,581|7,065.18|17.71|100.14|1.37|23.87|46.92|0.03|58.42|30.53|9.25|5.45|
|Honduras|7,570,000,000|1,879,000,000|2,332,121|3,416.09|16.14|105.08|3.79|30.28|110.45|0.05|53.57|70.03|15.72|5.45|
|Mexico|572,900,000,000|115,400,000,000|40,100,000|155,317.73|4.50|96.43|2.95|30.25|54.99|0.41|65.25|299.77|22.32|19.75|
|Nicaragua|4,003,000,000|926,500,000|1,871,618|2,899.00|20.63|101.84|5.56|28.59|62.18|0.06|50.78|70.03|17.77|5.45|
|Panama|12,080,000,000|2,265,000,000|1,307,084|2,551.45|7.37|99.68|7.19|17.79|34.61|0.32|74.84|107.21|43.28|18.10|
|Paraguay|7,499,000,000|1,327,000,000|2,536,386|4,052.64|18.93|137.76|1.52|22.25|56.01|0.09|58.82|81.36|21.06|25.45|
|Peru|56,480,000,000|11,610,000,000|12,200,000|12,417.64|8.12|141.56|3.22|31.38|29.31|0.11|60.51|227.77|33.12|30.46|
|Uruguay|22,490,000,000|3,050,000,000|1,567,053|2,879.18|8.85|114.26|2.21|26.00|31.69|0.27|65.15|290.09|34.43|16.98|
|Venezuela, RB|19,600,000,000|24,710,000,000|10,400,000|57,239.00|4.50|97.75|2.90|50.03|44.50|0.37|45.46|157.55|39.74|30.46|
|Total|119,279,187,500|21,787,343,750|12,265,342|34,465|11|115|4|30|50|0|59|205|28|19|

Source: Authors‘ calculation from the World Bank‘s World Development Indicators (WDI).

16
# **Appendix 3. Descriptive Statistics**

_Data in country averages; periods vary by informality measure_

## (a) Univariate (regression sample)

|**Variable**|**Obs.**|**Mean**|**Std. Dev.**|**Minimum**|**Maximum**|
|---|---|---|---|---|---|
|Schneider Shadow Economy index<br>(% of GDP)|84|32.960|14.735|8.550|68.200|
|Heritage Foundation Informal Market index<br>(range 1-5: higher, more informality)|86|3.055|1.251|1.000|5.000|
|Self Employment (% of total employment)|57|26.204|12.028|7.132|59.335|
|Non-contributor to Pension Scheme<br>(% of labor force)|70|53.198|33.482|1.450|98.000|

## (b) Univariate (full sample)

|**Variable**|**Obs.**|**Mean**|**Std. Dev.**|**Minimum**|**Maximum**|
|---|---|---|---|---|---|
|Schneider Shadow Economy index<br>(% of GDP)|145|34.838|13.214|8.550|68.200|
|Heritage Foundation Informal Market index<br>(range 1-5: higher, more informality)|159|3.409|1.201|1.000|5.000|
|Self Employment (% of total employment)|86|25.158|12.118|1.119|59.335|
|Non-contributor to Pension Scheme<br>(% of labor force)|110|55.999|31.905|1.450|98.500|

## (c) Bivariate Correlations between Informality Measures

(upper triangle for regression sample (in italics) and lower triangle for full sample)

|**Variable**|**Schneider**<br>**Shadow Economy**|**Heritage Fndn.**<br>**Informal Market**|**Self**<br>**Employment**|**Non-contributor**<br>**to Pension**|
|---|---|---|---|---|
|Schneider Shadow Economy index|1.00|_0.68***_|_0.71***_|_0.72***_|
|(% of GDP)|145 |_84_|_83_|_55_|_70_|
|Heritage Foundation Informal Market index|0.65***|1.00|_0.88***_|_0.90***_|
|(range 1-5: higher, more informality)|132|159 |_86_|_57_|_70_|
|Self Emloment (% of total emloment)|0.65***|0.79***|1.00|_0.89***_|
|py py|69|76|86 |_57_|_51_|
|Non-contributor to Pension Scheme|0.59***|0.77***|0.88***|1.00|
|(% of labor force)|104|107|57|110 |_70_|

Notes:

1. Sample sizes are presented below the corresponding coefficients.

2. *** denotes significance at the 1 percent level.

33
Table 3: Impacts on reported physical health, cognitive health, and preferences

||(1)<br>Days Healthy|(2)<br>Cognitive Index|(3)<br>Risk Tol.|(4)<br>Time Pref.|
|---|---|---|---|---|
|Work|0.789|0.231|0.239|-0.032|
||(0.397)|(0.079)|(0.104)|(0.112)|
|Cash|0.107|0.071|-0.014|-0.013|
||(0.467)|(0.088)|(0.120)|(0.117)|
|Shrp. q-val Work|0.059|0.018|0.047|0.453|
|Test Work=Cash|0.075|0.011|0.009|0.822|
|Shrp. q-val Work=Cash|0.081|0.028|0.028|0.300|
|Observations|726|726|726|726|

**Notes:** This table reports the treatment effect of the employment and cash arms on physical, cognitive, and risk and time preference outcomes. Each column shows the OLS estimates of Equation (1) including dummies for each treatment arm and controlling for the baseline measure of the dependent variable (ANCOVA), camp and enumerator fixed effects, and sociodemographic controls as determined through a double-selection LASSO procedure (Belloni, Chernozhukov, and Hansen, 2014). Each row presents the coefficients of the specified treatment relative to the control group (which received a small weekly cash payment).

‘Days Healthy’ is a self-reported measure of the number of days the respondent reports feeling healthy (not sick) in the previous 30 days. ‘Cognitive Index’ is an inverse covariance-weighted index of forward and backwards digit span tests and two arithmetic questions. ‘Risk Av.’ represents an incentivized risk-preference elicitation game in which higher values represent greater aversion to risk, while ‘Time Pref.’ represents an incentivized time-preference elicitation game in which higher values represent greater patience.

Standard errors are clustered at the block level. The row labeled “Shrp. q-val Work” reflects p-values, referred to as ‘sharpened q-values,’ adjusted for multiple hypothesis testing using Anderson (2008) for all outcomes in this table. The row labeled “Test Work=Cash” reflects the unadjusted p-value for the test of equality between the Work and Cash arms, while the row labeled “Shrp. q-val Work=Cash” reflects the adjusted p-value for this test of equality.

36
rate against imports from China. Similarly, the variable ∆ _τd,usht_<sup>_US_(∆</sup><sup>_τ EU_</sup> _d,euht_<sup>) designates the magnitude</sup> of the contemporaneous change in the U.S. (EU) tariff rate against imports from country _d_ .

When implementing the model on a sample of data, we choose India as ‘country _d_ ’ for a number of reasons. As we detail below, India has considerable similarities with China when it comes to export structure (both by commodity and by destination market), export growth during this time period, and it is also an important target of both U.S. and EU use of antidumping.<sup>16</sup> The coefficients _β_ 1 _j_ and _β_ 2 _j_ for _j_ = _t −_ 2 _, t −_ 1 _, t_ identify trade deflection associated with U.S. and EU antidumping duties. If the imposition of a U.S. antidumping duty against China is associated with an increase in China’s exports relative to India’s (country _d_ ’s) exports, we expect that _β_ 1 _j_ will be greater than zero. Similarly, if an increase in the U.S. antidumping duty against India induces Indian trade deflection, we expect India’s exports to market _i_ to rise relative to China’s exports to _i_ , yielding a positive coefficient on _β_ 1 _j_ . The same reasoning implies that trade deflection associated with an EU antidumping measure will yield an estimate of _β_ 2 _j_ that is positive.

Note, however, that one implication of this particular difference-in-difference approach is that we cannot identify _β_ 3 _j_ and _β_ 4 _j_ - i.e., trade depression - from equation (2). We therefore introduce a framework for estimating trade depression separately in the next section.

Finally, while equation (2) forms our baseline specification, as a robustness check we also estimate a variant of the model to examine the possibility of “aggregate deflection” by China and India (country _d_ ) to _all_ markets other than the U.S. and EU. Specifically, in this particular sensitivity analysis we sum Chinese exports to China’s top 41 trading partners (see again table 1) less the U.S., EU and India (country _d_ ) for each product in year _t_ ( _x_<sup>_row_</sup> _cht_<sup>).Similarly,inaccordancewithourdifference-in-</sup> difference strategy, we sum India’s (country _d_ ’s) exports to those same 38 trading partners (China’s top 41 less the U.S., EU, and India) for each product _h_ in each year _t_ ( _x_<sup>_row_</sup> _dht_<sup>).Wethenestimatean</sup> aggregated analog to equation (2) given by

> 16While India did undertake a substantial unilateral trade liberalization episode during the 1991-1997 period, we do not include information on India’s import tariff changes in the estimation. While changes to India’s import tariff structure could feed through into changes into its exports, making this link would require a highly disaggregated inputoutput mapping that is beyond the scope of this paper. In unreported results we have introduced controls for India’s own use of antidumping against China and the estimates we report below are unaffected.

10