rafmacalaba/datause-encoder-data huggingface dataset:
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _40_ than the standard GARCH. Shifts in the volatility process may stem for example from price controls. However, the unconditional variance is undefined in this model which is theoretically difficult to conceive. The fractionally integrated GARCH that results under values 0 _< d <_ 1 allows the GARCH process to have hyperbolic memory in the volatility process such that the volatility process shifts gradually. Such long-memory volatility features have been observed widely in both agricultural commodities (Chang et al., 2012) and general inflationary shocks (Baillie et al., 2002).

Finally, the log likelihood requires specifying the remainder parameters in _λ_ . The model is estimated using the Generalized Error Distribution: with _θ_ = ( _μ, λ, ν_ ) as the parameter vector that define location, scale and shape. The distribution is symmetric and unimodal and so the location parameter defines both the mode, median and mean of the distribution. The distribution generalizes the Normal Distribution, when _ν_ = 2, but also allows for higher or lower kurtosis. For example, when _ν_ decreases, the distribution flattens. When _ν_ = 1, the distribution follows the Laplace distribution, while it tends to the Uniform distribution when _ν →∞_ .

The conditional volatility estimates can be used to calculated Expected Shortfall by integrating under the Value-at-Risk distribution with _V aRa_ being the (1 _− α_ ) quantile of the estimated returns distribution. Since the conditional return distribution is time-varying and fully specified by the model in B9, the time-varying Expected Shortfall can be estimated by calculating time-varying _V aRα,t_ = _μt_ + _σ_ ˆ _t|t −_ 1 _G_<sup>_−_1</sup> ( _a_ ) , where _G_<sup>_−_1</sup> is the inverse PDF function of the Generalized Error Distribution. The quantity E∆ _αPt_ is then estimated by the empirical equivalents of _ESα,t_ . The algorithms are implemented following (Ghalanos, 2020). The autoregressive order of both the ARMA and GARCH processes are kept at 1, the moving average orders are selected using the AICc allowing up to three lags.
In addition to the main reservoir, many medium-sized reservoirs (the small triangles in figure 2) and hundreds of small ponds supply irrigation water to farmers. The medium-sized reservoirs are owned by different local authorities and are managed independently from the ZIS. Most of the small ponds are owned and operated by individual farmers or a few farmers.

In 2008, the International Rice Research Institute and Wuhan University conducted a survey right after the harvesting of rice. Two neighboring townships in the ZIS, Tuanlin and Wenjiaxiang, were selected for this study (figure 2). Note that the irrigation water demand is the residual demand after rainfall and thus depends on the amount and timing of rain. Therefore, we selected two sites within a relatively close area with similar rainfall patterns that are confirmed by meteorological stations at each site (see figure S1 for rainfall data for each site in the supplemental appendix).

The WUGs, which are formed on the basis of irrigation water boundaries, are autonomous local institutions that are responsible for the management of surface irrigation. One village typically includes several WUGs, each consisting of twenty to forty farmers. We randomly sampled sixty-seven WUGs from the study site that were stratified on the basis of size and pricing method and then randomly sampled at least two farming households from each WUG, which generated a total sample of 135 households.<sup>2</sup> The household interview gathered detailed data on irrigation activities, input and output data for rice production, rice plot characteristics, and household demographic data. All sampled farmers primarily derive their income from rice cultivation. The WUG-level interview with the key informants, including a village head, helped collect data on the irrigation infrastructure and social capital endowments. The key informants referred to official statistics when they were available. To capture each WUG’s topography, we generated geographical information systems (GIS) data from satellite images.<sup>3</sup>

> 2 Regarding WUG sampling, we believe that there is little attrition after the reform ( _fei gai shui_ ) because we were able to interview the WUGs that no longer used reservoir water and relied solely on private irrigation. Even in this case, the names of WUGs remained, and we were able to include them in our sample. Regarding the household level sample, one farmer was removed in the data cleaning process because this farmer showed a level of water use that was about 8 times higher than the average.

3 A digital elevation model (DEM) for the study area was acquired from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image through the Warehouse Inventory Search Tool (WIST). ASTER is a remote sensory device on board the Terra satellite (http://asterweb.jpl.nasa.gov/gdem.asp, http://en.wikipedia.org/wiki/ Advanced_ Spaceborne_Thermal_Emission_and_Reflection_Radiometer). WIST can be found online 5
There is significant variation in β<sup>ˆ</sup> over the different consumption modules (Table 3). The coefficient on log per capita consumption in the benchmark module is -0.056 (in panel A, with demographics and household head characteristics excluded). In three of the other modules, β<sup>ˆ</sup> is statistically significantly more negative, ranging from -0.084 to -0.100. The hypothesis of equal β<sup>ˆ</sup> across all eight modules is strongly rejected (p=0.003). In contrast, there is no module effect on γ ,ˆ which ranges between -0.051 and -0.073 with no statistically significant differences. The significant impact on β<sup>ˆ</sup> and lack of impact on γ ˆ strengthens when covariates are included (Table 3, panel B).<sup>11</sup> Except for module 1, all other modules give a significantly more negative β<sup>ˆ</sup> than the benchmark, with p-values that range from 0.000 to 0.032. In contrast, there are no statistically significant effects on γ .ˆ What type of measurement errors are consistent with this pattern of a significantly more negative β<sup>ˆ</sup> and little impact on γ ˆ ? The simulation results in Figure 1 show that it requires errors in food consumption to be more strongly (negatively) correlated with true values than are the measurement errors in nonfood consumption.<sup>12</sup> This combination of errors causes the β<sup>ˆ</sup> estimated from the error-ridden module to be more negative than the β<sup>ˆ</sup> estimated with the benchmark survey, while leaving γ ˆ largely unchanged. For example, a simulated error process where errors in food consumption are generated from a regression on true food consumption with a coefficient of φ = − 3.0 while errors in non-food are generated from a regression on true nonfood consumption whose coefficient is just ψ = − .005 gives results like those observed in the ˆ ˆ Table 3 regressions, with β = − .0082 and γ = − .0068.

> 11 We include three demographic ratios (the share of children aged less than six, children aged six to fifteen, and elders aged over 65), and the age, education, gender and marital status of the household head.

> 12 Appendix A describes the Monte Carlo experiments yielding the simulations illustrated in Figure 1.

12
In a recent study, Albornoz et al. (2009) suggest that firms discover their profitability as exporters only after actually engaging in exporting. According to what the firms learn about themselves, they adjust quantities and decide whether to exit and whether to serve new destinations. Edwards (2007) discusses the impacts of trade liberalization in a situation of search costs and matching of producers and buyers in overseas markets. Once a search process has been undertaken for a long period a proportion of firms and distributors will be matched and will be less sensitive to new potential trade partners. This would explain the negative duration dependence found by Besedes and Prusa (2006) in US import data; once a flow has been established for a period of time the probability of failure becomes much smaller. Established relationships are likely to be less sensitive to changes in the relative prices of different suppliers than for firms and distributors that are still looking for a satisfactory partner. This suggests time dependency and that inappropriate sequencing of trade policy reforms may have long run adverse impacts. For example, preferential import liberalization may lock in trade diversion (due to information costs) even after subsequent multilateral reduction of tariffs has removed tariff preferences.

Information asymmetries and formal institutions for contract enforcement will tend to be more important for buyers searching amongst suppliers providing products of different quality. In other words, the matching of exporters and buyers will tend to be easier for standard and homogeneous products. Much of the discussion concerning export diversification in developing countries centers on increasing exports of differentiated products, especially manufactures, and reducing the importance of homogenous products, particularly commodities. Rauch (1999) presents, albeit tentative evidence, that search costs are higher and matching more difficult for differentiated products and that proximity and common language and colonial links are more important for differentiated than for homogenous products that are traded on organized international exchanges.

The importance of quality introduces an additional set of institutions that may be important in influencing bilateral trade. Poor quality metrology, testing and conformity 11
different picture: for poorer households, monetization of utility loss makes up the largest source of losses at 44%, since they usually cannot afford self-generation.

Unreliable access to electricity negatively affects the welfare of households. Frequent outages limit households’ ability to engage in productive, educational, and recreational activities during nighttime (Lenz et al., 2017). For instance, with regular access to electricity, children can study at night, thereby improving their educational outcomes (Dasso, Fernandez, & Ñopo, 2015). An unreliable power network also impact women’s welfare: in Bangladesh, Zhang (2019) finds that long power outages are associated with a decrease in women’s labor force participation, likely because the lack of electricity is associated with an increase in the time needed for domestic work (Figure 3): time has to be spent collecting or buying fuel and making fires to cook and heat water; and, due to lack of refrigeration, to shopping on a daily basis (Chant, 1996). Outages also have an impact through access to and income from jobs. In Africa, for instance, outages reduce the probability of employment by approximately 35 percent, and probability of nonfarm employment by 55 percent (World Bank, 2019).

**Figure 3: Difference between grid-connected households with and without at least 20 hours of electricity daily in Bangladesh**

<!-- Start of picture text --> 10<br>8.50<br>8<br>6<br>4<br>2<br>0<br>-2<br>-1.50<br>-1.92<br>-4 -2.53<br>-3.30 -3.31<br>-6<br>-8<br>-7.26<br>-10<br>Kerosene Per capita Per capita Girls' study Women’s labor Women can Women can<br>consumption income expenditure time force decide alone on decide alone on<br>participation children’s the use of own<br>healthcare income<br>Percentage difference<br><!-- End of picture text --> _Source: Zhang (2019). Note: Each regression estimate refers to the estimated change based on econometric analysis._ Poor quality electricity network can also affect public health: during power disruption, levels of air pollution increase. Farquharson, Jaramillo, & Samaras (2018) estimate the change in CO2, PM2.5, NOx and SOx due to backup power generation in firms in Sub Saharan Africa. PM2.5 have a documented impact on health, and emissions increase by between 0.01 and 0.15 kilograms per person with electricity access per year when replacing grid electricity with backup diesel generators during power outages. As a point of comparison, baseline PM2.5 emissions range from 0.01 to 0.32 kilograms per person with electricity access per year in Sub-Saharan Africa. Moreover, these estimates are likely to be underestimating the impact of 7
_-51_ -

# ANNEx 3.L **BASC** EARNIGS **FuNCIONS**

# Brazil 1979

|||Gen|der|Sec|tor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.114|0.118|0.089|n.a.|n.a.|
|Experience (EX)|0.071|OD70|0.053|n.a.|n.a.|
|Experience-squared(EX2)|-0.0011|-0.0011|-0.0009|n.a.|n.a.|
|Log Hours (H) Worked|-0.174|-0.270|-0.301|n.a.|n.a.|
|Constant Term|8.066|8.510|8.605|n.a.|n.a.|
|R<sup>2</sup>|0.34|0.37|0.28|n.a.|n.a.|
|N|6,655|5,529|1,126|n.a.|n.a.|
|Means||||||
|Earnings (Y)|10,244|10,943|6,813|n.a.|n.a.|
|S|5.3|5.2|_5.5_|n.a.|n.a.|
|EX|21|21|18|n.a.|n.a.|
|H|52|53|47|n.a.|n.a.|
|Var (LnY)|0.651|0.632|0.578|n.a.|n.a.|

Source: _Pesquisa Nacional por Amostra de Domicillos._ Notes: The earnings variable (Y) is in cruzeiros per month. H is the weekly hours worked.

All coefficients are statistically significant at the 5% level or better. n.a. = Not available.
Political Connections and Financial Constraints: Evidence from

# Transition Countries<sup>_∗_</sup>

Maurizio Bussolo Francesca de Nicola Ugo Panizza The World Bank The World Bank The Graduate Institute Geneva & CEPR Richard Varghese The Graduate Institute Geneva & The World Bank

## **JEL Classification** : D22, O17, P12, P14

**Keywords** : Investment, Political Connections, Corruption, Financial Constraints

> _∗_ Contact information: mbussolo@worldbank.org; fdenicola@worldbank.org; ugo.panizza@graduateinstitute.ch; richard.varghese@graduateinstitute.ch.
|BPRP funded support for capacitybuildingat the DRC PRSP unit|Franck M. Adoho|
|---|---|
|Moldova EFA FTI 3 Grant|Mariana Doina Moarcas|
|Moldova EFA FTI 3 Grant|Mariana Doina Moarcas|
|Moldova EFA FTI 3 Grant|Mariana Doina Moarcas|
|Strengtheningthe AccountingProfession in Kenya|HenryAmena Amuguni|
|Strengtheningthe AccountingProfession in Kenya|HenryAmena Amuguni|
|Namibian Coast Conservation Additional Finance|Claudia Sobrevila|
|ModernizingHigher Education Project|Diego Ambasz|
|ModernizingHigher Education Project|Diego Ambasz|
|ModernizingHigher Education Project|Diego Ambasz|
|ModernizingHigher Education Project|Diego Ambasz|
|ModernizingHigher Education Project|Diego Ambasz|
|ModernizingHigher Education Project|Diego Ambasz|
|ModernizingHigher Education Project|Diego Ambasz|
|ModernizingHigher Education Project|Diego Ambasz|
|ModernizingHigher Education Project|Diego Ambasz|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Cameroon Social SafetyNets|Rebekka E. Grun|
|Partnershipfor Market Readiness in Ukraine|Dmytro Glazkov|
|MA-PromotingAccessibilityof Persons with Limited Mobility (PLM)|Vincent Vesin|
|MA-PromotingAccessibilityof Persons with Limited Mobility (PLM)|Vincent Vesin|
|MA-PromotingAccessibilityof Persons with Limited Mobility (PLM)|Vincent Vesin|
|MA-PromotingAccessibilityof Persons with Limited Mobility (PLM)|Vincent Vesin|
|MA-PromotingAccessibilityof Persons with Limited Mobility (PLM)|Vincent Vesin|
|MA-PromotingAccessibilityof Persons with Limited Mobility (PLM)|Vincent Vesin|
|MA-PromotingAccessibilityof Persons with Limited Mobility (PLM)|Vincent Vesin|
|MA-PromotingAccessibilityof Persons with Limited Mobility (PLM)|Vincent Vesin|
|MA-PromotingAccessibilityof Persons with Limited Mobility (PLM)|Vincent Vesin|
|LS- First Growth and Competitiveness DPG|Asli Senkal|
|LS- First Growth and Competitiveness DPG|Asli Senkal|
|SUSTAINABLE CITIES|Joanna Mclean Masic|
|SUSTAINABLE CITIES|Joanna Mclean Masic|
|SUSTAINABLE CITIES|Joanna Mclean Masic|
Stiroh, 2002). Figure 5 shows the kernel regression, based on both the NSS (upper panel) and LFS (lower panel). We observe a clear tendency towards increasing spatial concentration.<sup>4</sup>  Although the service sector in India shows some similarities with the service sector in the U.S.  both exhibit agglomeration economies there is one important difference: as can be seen from comparing Figure 2 and Figure 6, in the U.S. agglomeration economies in services dominate for medium-density locations, whereas in India agglomeration economies dominate for high-density locations.<sup>5</sup> In principle, there could be two possible explanations for this finding. Either the highdensity service clusters in India have a lower density than the high-density service clusters in the U.S., or the tradeoff between agglomeration economies and congestion costs is different in both countries.

Given that we have used the same scaling to draw Figure 2 and Figure 6 it is clear that for similar high-density service clusters, congestion dominates in the U.S. whereas agglomeration economies dominate in India. According to our findings, agglomeration economies in the U.S. service sector peak at a density of between 50 and 150 employees per square kilometer. Three of the main hightech counties in the U.S. fall within that range: Santa Clara, Calif. (Silicon Valley), Middlesex, Mass. (Route 128) and Durham, NC (Research Triangle). In contrast, in India, agglomeration economies increase in the upper tail of the distribution, in places, such as Hyderabad and Chennai, with service employment densities reaching into the thousands. For those levels of density, U.S. locations exhibit substantial congestion.

In as far as the allocation of activity across space is efficient in the U.S., this suggests that there are forces restricting growth in medium-density places in India, making the high-density locations _relatively_ more attractive. In other words, it might be the case that the high-density clusters in India are more successful, not because of the lack of congestion in the mega-cities but because of the absence of agglomeration economies in medium-sized locations. Certain policies or frictions, such as a lack of general infrastructure, may prevent these medium-sized cities from growing faster.

In that sense it may be suggestive to compare India’s experience not just to the U.S., but also to another large emerging economy, China. Figure 7 compares India and the U.S., whereas Figure 8 compares India and China. Before discussing the results, a word of caution about the data we use for

> 4 To make all the figures comparable, the scale of the horizontal axis is always the same (i.e. observations below 0 are not shown). If we were to show smaller places, we would find evidence of convergence in low-density districts.

> 5 Our regressions for the U.S. take counties as the unit of observation. To make the definition of services as similar as possible to the one in the U.S. we are using the sum of transport & utilities and other services from the BEA. Using broader definitions of services by including, say, retail and wholesale, do not change the findings.

8
# **From Middle Class to Poverty:**

# **<mark>The Unequal Impacts of the COVID-19 Pandemic on Developing</mark> Countries**

Mariana Viollaz<sup>a</sup> Daniel Duque<sup>b</sup> Carolina Diaz-Bonilla<sup>c</sup> David Newhouse<sup>c</sup> Michael Weber<sup>c</sup> JEL codes: C36, D31, O20 Keywords: Microsimulation, economics shocks, distributional impact a CEDLAS-FCE-UNLP b Norwegian School of Economics c World Bank Group
Value‐added trade measures come from Wang et al. (2012) and are based on the World Input Output data set (WIOD) for the years 1995‐2011. Value‐added trade provides a more accurate measure of GVC involvement. It also allows us to investigate the impact of deep trade agreements on both goods and services trade and across industries with different levels of value added. The information on value‐added trade, however, covers a limited sample of countries (40). Trade in parts and components records gross trade flows, which can be subject to double counting, but has the advantage of being available for the full set of countries and years covered by the new data set on PTAs. Having the whole sample of countries allows us to investigate how the effect of deep PTAs varies with the level of development of countries involved in an agreement. It also provides insights on whether certain types of provisions included in PTAs are more relevant for agreements between countries with different levels of development.

We first study how the depth of PTAs affects GVC integration in goods and services. We look at total domestic value added (DVA) in gross exports and foreign value added (FVA) in gross exports. The main finding is that deep PTAs increase the domestic value‐added content of exports mainly through GVCs. Adding a provision to a PTA boosts domestic value added of intermediate goods and services exports (i.e. forward GVC linkages) by 0.48 percent, while an additional provision in a PTA increases foreign value added of intermediate goods and services exports (i.e. backward GVC linkages) by 0.38 percent. We also find evidence that deep trade agreements improve forward linkages particularly for more complex GVCs, i.e. GVCs where exported intermediates cross borders two times or more, while we do not find a significant impact of deep trade agreements on domestic and foreign value added of final goods and services exports. Estimations performed separately for services and goods show that the impact of deep trade agreements is usually higher for value‐added trade in services compared to value‐added trade in goods.

We also analyze whether the impact of deep trade agreements on GVC integration is heterogeneous across industries. Specifically, we estimate a set of sectoral regressions. where we include an interaction term between the depth of an agreement and the share of value added that a particular sector has in overall production. The results suggest that deep trade agreements are particularly relevant for GVC integration in high value‐added industries. Not surprisingly these 4