rafmacalaba/datause-encoder-data huggingface dataset:
**Compute the counterfactuals** The system of equations that define the general equilibrium (Eqs. 3.12, 3.7 and 3.9) can be used to undertake model-based counterfactuals for new trade costs, new productivity or new amenity levels that uses only parameters and the values of enˆ dogenous variables in the initial equilibrium. We use the hat notation such that _x_ = _x_<sup>′</sup> / _x_ with _x_<sup>′</sup> the counterfactual value. The system of equations for the counterfactual equilibrium (Eqs. 3.12, 3.7 and 3.9) can be re-written as follows:

where _Yi_ = _ωiLi_ denotes labor income and _λn_ = _Ln_ / _L_<sup> ̄</sup> denotes the population share of each location in the initial equilibrium. Given the observed variables in the initial equilibrium { _λn,Yn,πni_ } and an assumed comparative static for trade costs.

# **4 New transport and trade investments**

## **4.1 Calibration of the model**

The information needed to calibrate the model comes from traditional data sources and satelite images. Four types of data at the state and district level are used: residential land size, labor force, wages, and transport costs. Labor force includes active population as calculated by GHSL for 2015 (Figure A5 in the Appendix). All wages are monthly and expressed in dollars based on estimates from Tilottama et al. (2010) (Figure A7 in the Appendix). Land covers urban areas using CIESIN data (Figure A6 in the Appendix).

We assume labor mobility within country but not across countries. Following Redding (2016) and the residential choice Equation 3.9, workers can move in a location with higher wages, lower consumption and housing prices depending on their initial preferences across the amenities that are offered across locations. Such assumptions smooth the movement of workers across locations, otherwise all workers would move to the locations with the highest purchasing power.

Transport costs are measured as a function of the travel time to reach other locations from the center of each location. The travel time, which is calculated using GIS network techniques, is the shortest time given all the possible routes in the road network (Figure A8 in the Appendix). Travel times are based on categories of roads from Open Street Map (primary, secondary, ter9
_potential ωlA_<sup>_j_</sup> _,_

- _(iii) Keeping upside potential ωlA_<sup>_j_</sup> _fixed, the adverse interest rate effect is more pronounced for entrepreneurs whose advanced projects are more risky in the sense of a mean-preserving spread via an increase in A_<sup>_j_</sup> _and a decrease in ωl such that ρ_  ̄ _≡_ [ _ωl_ + (1 _− ωl_ ) _ρ_ ] _increases,_

- _(iv) The adverse interest rate effect is more pronounced for entrepreneurs with higher pledgeable income γ,_

The intuition behind the adverse interest rate effect established in part (i) of Proposition 1 is straightforward. Unlike the basic technology, the advanced technology is subject to idiosyncratic risk, which can be partially insured through a liquidity buffer. However, holding liquidity is subject to an opportunity cost given by the nominal interest rate. Higher interest rates therefore decrease the demand for liquidity,<sup>13</sup> and in consequence also the relative attractiveness of advanced projects. Hence, condition (12) becomes tighter, and the composition of investment is tilted towards the basic technology. That is, the adverse interest rate effect manifests in terms of an investment composition effect.

The following three parts of Proposition 1 establish that the relative attractiveness of advanced projects is more sensitive to variations in the nominal interest rate when they are potentially more profitable (part (ii)), more volatile (part (iii)), or characterized by higher pledgeable income (part (iv)). The underlying mechanism rests on the fact that higher expected profitability _ωlA_<sup>_j_</sup> or higher pledgeable income _γ_ allows for a higher debt capacity, see (8). Given the unit investment scale, an increasing share of the initial debt is held as liquidity, see (7).<sup>14</sup> The role of

> 13Appendix A.1 formally derives the comparative static effects on the demand for liquidity in (10).

> 14The model can be generalized to allow for variable investment scale at date 0. Given the linear specification

13
Table 11. Questionnaire Survey on Price Regulations in Selected Sample Countries

||Question 1|Question 2|Question 3|
|---|---|---|---|
|||Tii|Si ii|
|Country|General tariff<br>changes|ermnaton-<br>based<br>discrimination|trategc prcng,<br>such as "family<br>plan"|
|Botswana|(b)|(c)|(b)|
|Ghana|(b) fixed;<br>(d) mobile|<br>(c)|(c)|
|Lesotho|(b)|(b)|(c)|
|Malawi|(d)|(d)|(d)|
|Mauritius|(b)|(c)|(c)|
|Namibia|(c)|(e)|(e)|
|Seychelles|(b)|(a)|(b)|
|Sierra Leone|(b)|(b)|(c)|
|Tanzania|(c)|(c)|(d)|
|Uganda|(b) fixed;<br>(c) mobile|<br>(b)|(c)|

Note that five choices are provided to Questions 2 and 3: (a) prohibited, (b) required to obtain approval from the authorities, (c) generally accepted but need to report to the authorities, (d) no control, and (e) other. For Question 1, only the last four choices are given to answerers.

Source: Author’s questionnaire survey to telecommunications regulatory authorities.

Figure 13. Termination-Based Discrimination and Regulation in Selected Sample Counties <!-- Start of picture text --> 0.20<br>Contract<br>Prepaid<br>0.15<br>0.10<br>0.05<br>0.00<br>Prohibited Required to obtain Need to report No control<br>approval<br> off-net rates for peak hours (US$)<br> Average tariff differential between on- and<br><!-- End of picture text --> Source: Author’s questionnaire survey to telecommunications regulatory authorities.

This survey result is by and large consistent with several existing data sets relevant to price regulation. First of all, at the general level, the ITU Tariff Policies database tells us that African countries are relatively likely to control mobile tariffs and unlikely to regulate fixed-to-mobile interconnection rates, compared with countries in other regions (Table 12).<sup>32</sup> About 45 percent of countries in Africa are controlling mobile prices. This

> 32 In regional comparison Asia is most likely to have price control over mobile tariffs and interconnection rates.

35
listed in Table 1, including profits, ability, management practices, number of employees, and business assets. One potential pitfall for this approach can arise from outliers, so we also included dummy variables for profits and assets in the top or bottom 5 percent to reduce the possibility that balance on means was disguising large outliers. We then took the maximum F-statistic across these 13 variables, and then choose the random assignment from among the 1,000 allocations that had the minimum maximum F-statistic. In all reported regressions, we control for the baseline measures of these variables and for the full set of strata dummies, which Bruhn and McKenzie (2009) show gives the correct size and power after re-randomizing.

For the majority of this paper we use the sample of 286 pure control enterprises and 250 enterprises assigned to the wage subsidy treatment alone. Table 1 shows that we achieved balance at baseline on a set of important observable variables: we are unable to reject the null hypothesis that these observables are jointly orthogonal to treatment status (p=0.734). In addition, we follow Imbens and Rubin (2015) in considering the normalized difference ��<sup>�</sup> � ��<sup>�</sup> ��/ ��̅�� ��̅���/2 as a <u>�</u> measure of balance, where �<sup>�</sup> � and �̅�� are the sample mean and variance of the variable for the treatment group ( _j=T_ ) and control group ( _j=C_ ) respectively. These normalized differences provide a scale-invariant measure of the difference in locations, and show good balance, with the largest differences less than 0.2 standard deviations. Appendix 3 also shows balance for the supplementary interventions.

Table 1 helps provide a descriptive picture of the owners of these firms and their businesses. The average owner is 35 years old, has finished 10 years of schooling, and works 58 hours a week in their business. Most firms do not have any paid employees, with only 11 percent having at least one paid worker, and an average of 0.17 paid workers per firm. The businesses are mostly informal (only one-third are registered for tax purposes), with 40 percent in retail (e.g. groceries, hardware, plastic products), and the remainder in manufacturing (e.g. tailoring, brasswork, carpentry, food production) and services (e.g. electricians, vehicle repair, haircutting, transportation). In 2008, mean monthly profits were 14,184 LKR (approx. US$130) on 46,434 LKR (approx. US$430) of monthly sales.<sup>6</sup>

> 6 The exchange rate averaged 108 LKR per USD in 2008, was in the 110-115 range from 2009 to 2011, and then averaged 128 LKR per USD in 2012, 129 in 2013, and 130 in 2014.

11
reforms including macroeconomic stabilization, exchange rate liberalization, fiscal restructuring, removal of maize subsidies, decontrol of agricultural prices, privatization of agricultural marketing, and trade and industrial policy. For a more detailed description of the reforms, see World Bank (1994), McCulloch et al. (2001) and Litchfield and McCulloch (2003).

# **1.1 Cotton Reforms**

The cotton sector was significantly affected by the agricultural reforms adopted by Zambia during the 1990s.<sup>5</sup> Before 1994, intervention in cotton markets was widespread and involved setting prices for sales of certified cotton seeds, pesticides, and sprayers, providing subsidized inputs to producers, facilitating access to credit, etc. From 1977 to 1994, the Lint Company of Zambia (Lintco) acted as a nexus between local Zambian producers and international markets. Lintco had a monopsony in seed cotton markets, and a monopoly in inputs sales and credit loans to farmers.

The reforms of the mid-1990s eliminated most of these interventions and markets were liberalized. Since Lintco was sold to Lonrho Cotton in 1994, a domestic monopsony developed early after liberalization. As market opportunities arose, several firms (private ginners such as Swarp Textiles and Clark Cotton) entered the Zambian cotton market. This initial phase of liberalization, however, did not succeed in introducing much competition in the sector. This is because the three major firms segmented the market geographically. In consequence, liberalization gave rise to geographical monopsonies rather than national oligopsonies.

At that moment, Lonrho and Clark Cotton developed an outgrower scheme with the Zambian farmers. This scheme allowed ginners to expand production and take advantage of economies of scale and idle capacity. In these outgrower programs, firms provided seeds and

> 5For more details on cotton reforms in Zambia, see Food Security Research Project (2000) and Cotton News (2002).

4
the baseline (Figure 8).<sup>14</sup> An exception to this is the case of Botswana, where the employment ratio convergence of males and females to the OECD average values (63 and 51 percent, respectively) leads to lower income per capita growth. This is because male and female employment ratios for Botswana are higher in the benchmark year, at 69 and 56 percent, respectively, and so a convergence to OECD ratios in the _ER convergence_ scenario implies a slower growth of their labor supply relative to the baseline, and hence slower real GDP growth.

**Figure 8: The key factor for acceleration of income per capita growth varies across countries**

Real GDP per capita growth rates, 2015-50 (average annual, percent) <!-- Start of picture text --> Baseline Faster productivity growth<br>Education convergence Mobilized savings<br>ER convergence ER convergene + gender parity<br>All interventions<br>5<br>4<br>3<br>2<br>1<br>Botswana Namibia South Africa Lesotho and<br>Swaziland<br><!-- End of picture text --> Source: Authors’ simulation results.

While progressing in all policy areas leads to the greatest realization of demographic dividends, a few patterns emerge when comparing the magnitude of specific policy outcomes across countries. Improving educational quality – reflected through faster labor productivity growth – has greater impact in the countries with higher educational attainment rates than improving attainment. The labor forces of Lesotho and Swaziland have the lowest shares of skilled workers in the benchmark year, and skilling up the labor forces even to that of lower-middle income countries would lead to a greater boost to growth than accelerating the productivity of a less skilled labor force ( _education convergence_ versus _faster productivity growth_ scenarios). Improving educational attainment in Lesotho and Swaziland has even greater impact on growth per capita than mobilizing savings. In other SACU economies, mobilizing savings for faster investment capital accumulation leads the _mobilize savings_ scenario to have greater impact than education sector interventions.

Labor market interventions that enhance employment ratios are most important for South Africa, given that the country has the highest working-age population share for the longest period in 2015-50 and also has the lowest employment ratios of all SACU economies. As such, South Africa’s labor market is potentially the most mature for a demographic dividend while also having the greatest room for improvement. South Africa’s National Development Strategy recognizes the under-performance of the labor market relative to its potential, and its growth

> 14 Figure A1 includes an alternative version of this figure, where the _all interventions_ scenario does not consider an improvement in savings mobilization.

18
_Table 3 Sources for understanding inequality in each stage of the digital civic engagement process for the four case studies_

|**Platform**|**Digital**<br>**divide**|**Participant profile**|**Nature of**<br>**demand**|**Impact on citizens**|
|---|---|---|---|---|
|Participatory<br>budgeting|Official<br>statistics|Spada et al. (2016)<br>Mellon, Peixoto and<br>Sjoberg (2017)<br>New analysis|Mellon, Peixoto<br>and Sjoberg (2017)<br>Halkin, Sjoberg and<br>Mellon (2017)|Mellon, Peixoto and Sjoberg<br>(2017)<br>Lorenz and Bremen (2005)<br>Halkin, Sjoberg and Mellon<br>(2017)|
|Crowdsourced<br>constitution|Official<br>statistics|Hudson (2018)<br>Helgadóttir (2014)|New analysis|Hudson (2018)|
|Change.org|Official<br>statistics||Mellon et al. (201|7)|
|Fix My Street|Official<br>statistics|Cantijoch, Galandini<br>and Gibson (2016)|N|ew analysis|

# **Case study 1: Participatory budgeting in Rio Grande do Sul**

Our first case study is the Rio Grande do Sul (Brazil) regional participatory budgeting (PB) vote. This vote was the second and final stage of the PB process, which defines part of the allocation of the state’s budget. In the first (deliberative) stage, through multiple in-person meetings across regions of the state, citizens were able to propose, deliberate, and come-up with a pre-selection of capital improvement projects to be submitted to vote. In the second and later stage, the final voting takes place, which can be cast either in-person or online.

The PB system was framed as a redistributive effort to give disadvantaged people more say in how money was spent to help them (Halkin, Sjoberg and Mellon, 2017). It would therefore undermine the 11
# **Table 4.  Economic Growth and Infrastructure – Individual Effects**

_Sample: 78 countries, 1961-2005 (5-year period observations) Estimation Method: System GMM_

||**Depend**|**ent Variable: G**|**DPper capita Gr**|**owth**|
|---|---|---|---|---|
||**[1]**|**[2]**|**[3]**|**[4]**|
|_Infrastructure Variables:_<br>|||||
|Electricity Index<sup>1</sup>|1.539 ***||||
||[6.436]||||
|Transportation Index<sup>2</sup>||2.45 ***<br>[5.631]|||
|Telecommunication Index<sup>3</sup>|||1.476 ***||
||||[6.687]||
|Transportation & Telecommunication Index<sup>4</sup>||||2.81 ***<br>[7.171]|
|_Control Variables:_|||||
|Initial GDP per capita|-1.592 ***|-2.072 ***|-1.512 ***|-2.688 ***|
|in logs|[-5.175]|[-5.900]|[-7.133]|[-7.576]|
|Education|0.949 **|1.008 ***|0.239|0.367|
|secondary school enrollment rate, in logs|[2.424]|[2.973]|[0.813]|[1.186]|
|Financial Depth|0.403 **|0.719 ***|1.206 ***|1.075 ***|
|private credit/GDP, in logs|[2.114]|[4.226]|[7.165]|[5.925]|
|Crisis Volatility|-1.876 ***|-1.734 ***|-1.937 ***|-1.761 ***|
|std dev of GDP per capita growth<sup>5</sup>|[-15.070]|[-15.400]|[-20.300]|[-16.120]|
|Government Burden|-0.919 *|-0.224|-0.274|0.102|
|government expenditure/GDP, in logs|[-1.957]|[-0.429]|[-0.611]|[0.213]|
|Inflation|-0.227|-2.033 ***|-3.036 ***|-2.841 ***|
|1+Growth rate of CPI, in logs|[-0.362]|[-3.189]|[-5.071]|[-4.561]|
|Trade Openness|4.221 ***|2.062 ***|1.287 **|1.586 ***|
|(exports+imports)/GDP, in logs|[9.487]|[4.358]|[2.432]|[3.504]|
|Growth rate of Terms of Trade|0.038 ***|0.035 ***|0.046 ***|0.045 ***|
|log differences of terms of trade index|[3.294]|[2.942]|[4.167]|[4.019]|
|Constant|0.733|16.826 ***|21.379 ***|26.997 ***|
||[0.208]|[3.624]|[5.036]|[5.750]|
|Observations|522|522|522|522|
|Number of Countries|78|78|78|78|
|Number of Instruments|58|58|58|58|
|Arellano-Bond test for AR(1) in first differences|0.000|0.000|0.000|0.000|
|Arellano-Bond test for AR(2) in first differences|0.064|0.0517|0.134|0.072|
|Hansen test of overidentifying restrictions|0.182|0.357|0.471|0.435|

_Numbers in brackets are the corresponding t-statistics. * significant at 10%; ** significant at 5%; *** significant at 1% Period fixed effects were included (coefficients not reported)._ 1 First principal component of two indicators: power loss (as % of electricity output) & electricity generating capacity (in MW per 1000 workers, in logs).

2 First principal component of two indicators: share of paved roads in the overall road network & length of roads (in km, sqrt of per 1000 workers*surface area, in logs).

3 First principal component of two indicators: main telephone lines per 1000 workers, in logs & main telephone lines per 1000 workers, in logs.

4 First principal component of two indicators: Transportation index & Telecommunication index. 5 Crisis volatility is the portion of the standard deviation of GDP per capita growth that corresponds to downward deviations below the world-wide 1-std-dev threshold.

29
5 For example, Aghion et al. (1992) concluded that elimination of barriers to imports by the EU would increase exports of Czechoslovakia, Hungary and Poland by 40 percent. In the event, exports increased by 95 percent during 1989-93. Agricultural exports were expected to expand by 75 percent; they actually fell slightly. Mastropasqua and Rolli (1994), using the SMART partial equilibrium simulation model, predicted that EU liberalization would increase 'traditional' exports in goods with export values above $3 million (at the 8-digit level) in 1988 by $245 million, $175 million and $115 million, for Poland, the Czech and Slovak Republics, and Hungary, respectively. In fact, exports of such goods increased by $850 n'.llion for Poland and $215 million for the Czech and Slovak Republics, and declined by $220 million for Hungary.<sup>9</sup> As discussed at greater length below, the importance of 'traditional' exports had fallen dramatically by 1993.

# **3. Disaggregated Measures of Change in Export Composition**

Restructuring occurs at the level of the firm. The trade data that have been discussed so far are highly aggregated and only suggestive regarding the magnitude of the changes that have been occurring at the finn/industry level. Although trade statistics are not reported at the level of the finn, detailed statistics are available, for about 10,000 product categories imported by the EU. These data provide a valuable source of information on the changes in production for export in the CEECs and the FSU.

A useful indicator of the change in the structure of exports is the sum of the absolute changes in the value of exports of individual commodities between 1989 and 1993, divided by the 1989 value. This measure is reported in Appendix Table 2. It treats increases and decreases symmetrically, and is intended to give an impression of 'how much' change occurred in exports. For ease of comparison, the figures are

**Flgure 4: Structural change In exports** relative to **sample** mean, 1989-93

<!-- Start of picture text --> F1pl-n C Uin<br>3owth .. wr °hph<br>10*<br>srn<br>.-.<br>.0.<br>s*%<br>e0.<br>_0%<br>,0-<br>20%<br>10%<br>oI<br>.40%<br>4 0;<br>40X<br>Hunu n Ptmd RAS u U aFMd<br><!-- End of picture text --> expressed as a function of the mean change in trade structure for the sample of selected countries.'" As can be seen in Figure 4, the structural changes in exports are much higher for the CEECs than for other countries. This suggests substantial restructuring is occurring. The absolute value of the change between 1989 and 1993 is particularly high for the Czech and Slovak Republics." The relatively high number for Bulgaria reflects in part the small initial value of trade with the EU.

As mentioned earlier, the change indicator gives equal weight to positive and negative changes. Both are equally relevant as far as restrucring is concerned. Figure 4 also shows that the Czech and Slovak Republics have very few declining export sectors: over 80 percent of the dhange reflect commodities where export growth occurs. Romania lies at
rates, and as primary childcare givers are more exposed to the risk of ill health of their children. As a result the agricultural insurance product, in insuring only one of the risks they face to their income stream, poses larger basis risk to women than to men. As a result, the value, and thus demand, for this product is lower among women.

**Table 6: Determinants of amount insured and saved**

||(1)<br>Insurance|(2)<br>Insurance|(3)<br>Savings|(4)<br>Savings|(5)<br>Savings|(6)<br>Extended<br>savings|
|---|---|---|---|---|---|---|
|Male|570.66|472.79|-150.54|-137.55|-613.27|-13.55|
||[241.80]**|[200.39]**|[212.10]|[214.26]|[356.44]*|[355.00]|
|Burkina Faso|319.27|-1,268.25|847.97|1,584.40|1,557.92|166.83|
||[210.06]|[352.64]***|[209.29]***|[261.93]***|[266.06]***|[348.19]|
|Group leader|397.05|352.32|319.15|401.89|410.56|-273.63|
||[257.56]|[250.41]|[198.79]|[197.05]**|[195.57]**|[419.38]|
|Insurance discount|25.90|7.09|||||
||[39.19]|[32.22]|||||
|Day of offer|138.34<br>[37.90]***||||||
|Senegal*Day of offer||0.33<br>[47.34]|||||
|Burkina Faso*Day of offer||237.59<br>[28.28]***|||||
|Agricultural savings|||-178.78|406.06|200.44||
||||[214.06]|[470.04]|[473.55]||
|Agricultural savings * male|||||702.91<br>[316.25]**||
|Low commitment savings|||242.74|1,058.83|1,000.70||
||||[315.37]|[372.30]***|[381.84]**||
|Burkina Faso * Low commitment|||||||
|savings||||-2,220.17<br>[376.42]***|-2,148.35<br>[379.89]***||
|Interest|||11.65|||29.39|
||||[9.70]|||[12.08]**|
|Emergency savings* Interest rate||||22.47|23.62||
|||||[13.40]|[13.98]*||
|Ag savings * Interest rate||||1.00|0.48||
|||||[10.00]|[9.72]||
|Sample|T1|T1|T2,T3,T4|T2,T3,T4|T2,T3,T4|T4|
|Observations|201|201|603|603|603|200|
|R-squared|0.25|0.34|0.08|0.16|0.17|0.07|
|Robust standard errors in brackets *|** p<0.01, **|p<0.05, * p<0.1|||||

Secondly, in contrast to the experimental literature that shows a high price elasticity of insurance demand (Cole et al. 2013, Hill et al. 2013, Karlan et al. 2014), we find no demand response to the price. The right 16
<!-- Start of picture text --> Comparative Terms of Trade Loss (Jan 2003 - May 2008)<br>20.0<br>10.0 96 9.1<br>.Q 90 Li02 | so1.0 04os04 2.0 -0,9 0.6 2.7<br>o 10.0 5A<br>8 -10.1 A28 -10.5<br>Sg -20.0 i<br>@ -30.0 ae<br>-40.0 at<br>-50.0<br>South Asia East Asia Sub-Saharan —_ LatinAmerica Europe and Middle East and<br>and Pacific Africa and Carribean Central Asia North africa<br>Source: DECPG, The World Bank AllCommodities 9 Food Energy<br><!-- End of picture text -->