rafmacalaba/datause-encoder-data huggingface dataset:
|RY-Integrated Urban Development Project(IUDP)|Sabine W. Beddies|
|---|---|
|RY-Integrated Urban Development Project(IUDP)|Sabine W. Beddies|
|RY-Integrated Urban Development Project(IUDP)|Sabine W. Beddies|
|RY-Integrated Urban Development Project(IUDP)|Sabine W. Beddies|
|RY-Integrated Urban Development Project(IUDP)|Sabine W. Beddies|
|RY-Integrated Urban Development Project(IUDP)|Sabine W. Beddies|
|RY-Integrated Urban Development Project(IUDP)|Sabine W. Beddies|
|RY-Integrated Urban Development Project(IUDP)|Sabine W. Beddies|
|RY-Integrated Urban Development Project(IUDP)|Sabine W. Beddies|
|Second Program 135 Phase 2 Support|Son Thanh Vo|
|Second Program 135 Phase 2 Support|Son Thanh Vo|
|Street LightingEnergyEfficiency|William Nicholas Bowden|
|Street LightingEnergyEfficiency|William Nicholas Bowden|
|Street LightingEnergyEfficiency|William Nicholas Bowden|
|Sri Lanka: Avian and Human Influenza Control and Preparedness|Norman BentleyPiccioni|
|Sri Lanka: Avian and Human Influenza Control and Preparedness|Norman BentleyPiccioni|
|Sri Lanka: Avian and Human Influenza Control and Preparedness|Norman BentleyPiccioni|
|Sri Lanka: Avian and Human Influenza Control and Preparedness|Norman BentleyPiccioni|
|Sri Lanka: Avian and Human Influenza Control and Preparedness|Norman BentleyPiccioni|
|Kabul Urban Roads Improvement Project|Mesfin Wodajo Jijo|
|Kabul Urban Roads Improvement Project|Mesfin Wodajo Jijo|
|Kabul Urban Roads Improvement Project|Mesfin Wodajo Jijo|
|Kabul Urban Roads Improvement Project|Mesfin Wodajo Jijo|
|Kabul Urban Roads Improvement Project|Mesfin Wodajo Jijo|
|Kabul Urban Roads Improvement Project|Mesfin Wodajo Jijo|
|Kabul Urban Roads Improvement Project|Mesfin Wodajo Jijo|
|Kabul Urban Roads Improvement Project|Mesfin Wodajo Jijo|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
|BO PICAR CommunityInvestment in Rural Areas|GarryCharlier|
out in different markets over time. At the outset, mobile phone operators prioritized urban centers and a town’s proximity to an international border.<sup>4</sup> As a result, the capital city and regional capitals received coverage during the first three years of mobile phone rollout, followed by a quasi-experimental pattern in later years.

Aker (2010) shows the spatial rollout of mobile phone coverage by market and by year between 2001 and 2008. Mobile phone coverage increased substantially during this time, with 90 percent of the weekly markets having access to mobile phone service by 2008. The greatest increase in mobile service into more remote rural areas occurred between 2008 and 2010, after our study period.

While landlines have existed in Niger for some time, Niger has the second-lowest landline coverage in the world (World Bank 2006), and the number of landlines remained relatively stable during our study period (Figure 1).<sup>5</sup> Among all of the agricultural markets in our study, only one received new landline coverage between 1999 and 2008.

Despite a large increase in mobile phone coverage, Niger had the lowest adoption rate in Africa in 2008. There were an estimated 1.7 million mobile phone subscribers in 2008, representing 12 percent of the population (authors’ calculations from Wireless Intelligence). Yet grain traders, especially wholesalers, were some of the first adopters: A trader survey conducted in 2006 showed that approximately 30 percent owned a mobile phone and used it for their trading operations (Aker 2010; Tack and Aker 2014). In contrast, less than 5 percent of farm households owned a mobile phone at that time.<sup>6</sup>

# **Conceptual Framework**

To clarify the way in which we expect mobile phone coverage to affect producer price dispersion, we present a model of agricultural markets visited by farmers and itinerant traders. The model is based on a fair but stylized description of how agricultural traders operate in sub-Saharan Africa (Fafchamps, Gabre-Madhin, and Minten 2005).<sup>7</sup> Because each market operates for a limited duration on different days and markets are often far apart, it is difficult for farmers or traders to visit more than one market in a given day. In the absence of widespread communication devices, farmers and traders must visit a market to obtain price information 5
three months of the U.S. M1 is used to forecast the percent change in oil prices over the next three months. Global PMIs (composite and manufacturing), and China’s manufacturing PMI are used in levels and lagged _h_ months. In total, each commodity has six forecasted values for each horizon. The final forecast is an average of all statistically significant forecasts. Equations with nonsignificant results are discarded. The exercise is conducted with monthly data.

Monthly data are available for 2004-2022, constrained by the availability of China’s Manufacturing PMI. All data are from Haver Analytics.

# **3.3 Bayesian vector autoregression model**

A reduced-form Bayesian VAR model is used to forecast oil prices. In the estimation step, to ensure compatibility between prediction and scenario analysis, we impose sign-restrictions on the VAR model. The BVAR employs quarterly data for commodity production, commodity prices, and global real GDP growth. Forecasts are conditioned on realized data or the June 2022, _Global Economic Prospects_ report (GEP).

The BVAR model of the global oil or metal market is written as: 44 The BVAR model of the global oil or metal market is written as: 44 tt ii tt−ii tt YY = �AA YY + uu ii=11 where is the vector of endogenous variables, is a sequence of serially uncorrelated random vectors with mean zero and covariance matrix .. denotes the coefficient tt tt matrices. Endogenous variables are YY , uu , ]<sup>′</sup> , where , , denote the loguu ii differences of metal or oil productions, GDP growth rates and metal or oil prices.ΣΣ AA , ii= 11, 22, 33, 44<sup>12</sup> Since GDP tt tt tt tt tt tt tt growth is only available on a quarterly basis, the exercise is conducted with quarterly averages of YY = [ΔΔqq ΔΔyy pp ΔΔqq ΔΔyy pp monthly data. Other variables that have been used in forecasting commodity prices include indicators of global economic conditions, world output gap, capacity utilization, industrial production and exchange rates (Baumeister, Korobilis and Lee 2022; Dées et al. 2007; Kaufmann et al. 2004; Tang and Hammoudeh 2002; Zamani 2004; Lalonde, Zhu and Demers 2003; Ye, Zyren and Shore 2006). In the prediction step, 1-step-ahead prediction at the time origin , , and the associated forecast error, are: hh hh hh YY<sup>�</sup> (11) ee (11) 44 , . hh ii hh+11−ii hh hh+11 YY<sup>�</sup> (11) = �AA YY ee (11) = uu ii=11 Foodstuffs include butter, cocoa beans, corn, cottonseed oil, hogs, lard, steers, sugar, and wheat. It is developed by the Commodities Research Bureau.

> 12 We adopt a log-level commodity price model following Kilian and Murphy (2014). As they suggested, it is not clear whether commodity prices should be modeled in log-levels or log-differences. The advantage of the level specification is that impulse responses of log-level price models are consistent with sign-restrictions while, in many cases, logdifference price models create meaningless impulse responses that would not be extended to scenario analysis in practice. The disadvantage of not imposing unit roots in estimation is a loss of asymptotic efficiency.

12
likely may have a global component, as countries tend to “specialize” in the same form of sovereign theft at the same time. In the next section we explore a number of such potential factors more systematically.

# **5.  Correlates of Sovereign Theft Episodes**

In this section we investigate in more detail a set of potential correlates of sovereign theft episodes that have been discussed in the literature on defaults and expropriations. Our starting point is the core empirical specification in Kraay and Nehru (2006), who investigate the correlates of "debt distress", defined as episodes of debt servicing difficulties marked by exceptional financing in the form of recourse to the Paris Club or the IMF, as well as arrears accumulation. In a large sample of developing countries they find that debt distress is more frequent in countries with high levels of external debt, with weak policy performance, and in countries experiencing adverse macroeconomic shocks. One immediate difference however is that Kraay and Nehru (2006) study debt servicing difficulties vis-a-vis both private and official creditors, while the dataset we study in this paper covers only defaults against private creditors.<sup>5</sup> We begin by considering how expropriation and sovereign default events are related to the stock of external debt owed to private creditors and the stock of FDI in a country. Data on these are taken from the Sovereign Wealth of Nations dataset by Lane and Milesi-Ferretti (2007). We also measure policy performance using the World Bank's Country Policy and Institutional Assessment (CPIA) data, which covers all World Bank borrowers since 1978. Finally, we use as proxy for macroeconomic shocks real per capita GDP growth.

In addition, we consider two characteristics of the political system, both taken from the Database of Political Institutions by Beck et al. (2001). The first is the ideology of the government of in power, measured with a dummy variable taking the value one if the

> 5 Rescheduling of debts owed to official creditors (via the Paris Club in the case of bilateral creditors, and the Heavily-Indebted Poor Countries (HIPC) initiative in the case of multilateral creditors) may to some extent reflect a voluntary decision on the part of creditors to provide a resource transfer to debtors, and so are different from defaults against private creditors.  Nevertheless, as we shall see the correlates of defaults against private creditors are broadly similar to those identified in Kraay and Nehru (2006).

10
states, and these states lag behind on measures like poverty reduction and other developmental outcomes (World Bank 2011b).<sup>1</sup> Understanding the cost of public infrastructure is important in particular for these countries in which resources for public investment are often scarce. Further, public work contracts, including roads, are subject to substantial levels of corruption. According to Transparency International’s Bribe Payers Survey of over three thousand business executives worldwide, public works contracts and construction is the sector with the highest propensity of paying bribes to officials and other firms (Transparency International 2011). As this paper attempts to establish a first set of facts on differences in costs in low and middle income countries, a focus on the link between corruption and costs is a natural priority.<sup>2</sup> A review by the World Bank’s Transport Research Support Program on the roads sector in conflict countries states that _“...projects that take place in conflict settings would almost always be more costly than in other settings because of challenges such as insecurity and low government capacity”_ (Rebosio and Wam 2011). Higher costs can be due to the costs of monitoring an areas security situation, the potential for incurring substantial risks to visit the construction site, and the possibility of limitations on planning. In addition to protection of the staff working on the particular roads project, firms also risk supplies being cut off due to disruptions of transport networks.<sup>3</sup> Not only the construction but also the procurement process can be riskier in conflict countries. Rebosio and Wam (2011) and Benamghar and Iimi (2011) give evidence for these effects on risks and costs from Nepal: a government employed road engineer was killed in the Terai regions; road construction teams were constantly monitoring the security situation and adjusting their operations accordingly; in certain regions violence and intimidation were employed during the bidding process to prevent firms from submitting a bid for a profitable project.

Allegations of fraud, corruption, or collusion were made in about one fourth of the five hundred approved World Bank financed projects with a road component between 2000

> 1If road construction and maintenance costs are significantly higher in conflict countries, this is a further mechanism through which conflict negatively affects economic development.

> 2See Kenny (2007) for a discussion of corruption in the construction industry in developing countries.

> 3If conflict takes place along ethnic lines, road construction firms might need to ensure that they employ an ethnically balanced workforce, in order not to further fuel the conflict or become targets of violence themselves. Consultations with communities, while helpful, also significantly add to the costs of construction.

4
**Table A13. Government transfers, in real terms (2014q1 set to 100)**

|||201|4|||20|15|||201|6|||20|17||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|1|2|3|4|1|2|3|4|1|2|3|4|
|||||||Pla|ce of re|sidence|||||||||
|Minsk|100.0|103.7|105.4|109.4|105.9|102.9|103.9|106.5|104.6|105.6|98.7|104.0|111.1|108.2|108.5|111.3|
|Large cities|100.0|103.5|102.4|107.8|108.0|104.6|107.1|106.7|103.3|101.6|100.3|102.6|102.6|99.9|99.4|102.2|
|Small cities|100.0|100.8|102.0|106.0|105.9|104.5|106.7|110.1|101.7|100.3|98.3|100.2|105.3|103.6|104.4|104.4|
|Rural area|100.0|99.5|102.9|104.6|99.2|98.4|100.9|102.4|95.6|94.9|93.8|93.9|96.0|95.3|95.3|96.4|
||||||||Age||||||||||
|Child|100.0|108.1|105.5|105.3|104.9|102.1|106.1|103.5|104.3|103.1|94.1|99.5|106.9|105.0|103.4|106.4|
|working age|100.0|99.7|96.7|99.8|106.3|102.8|103.1|103.4|99.3|97.8|91.2|93.8|101.1|95.5|93.5|96.9|
|Retiree|100.0|100.6|102.6|105.9|100.6|100.1|100.2|101.7|96.3|95.0|94.5|94.1|93.1|91.9|92.5|93.6|
|||||Em|ployme|nt statu|s for w|orking|age pop|ulation|||||||
|Employed|100.0|99.4|98.9|102.5|104.6|101.4|100.1|102.4|100.1|96.8|91.4|95.1|98.7|93.9|90.6|94.0|
|Partly employed|100.0|112.7|75.3|97.2|113.6|91.6|97.4|126.7|75.1|129.5|98.6|113.5|98.1|94.8|81.9|93.3|
|Nonemployed|100.0|104.5|105.0|109.7|107.0|106.3|109.6|109.4|109.1|106.4|103.2|106.3|112.8|109.0|109.5|112.4|
|||||E|ducatio|n level|for wor|king ag|e popul|ation|||||||
|high education<br>secondary|100.0|96.5|91.8|94.2|105.8|107.7|109.2|108.2|99.4|100.4|90.4|91.7|104.7|100.1|94.6|101.6|
|specialized|100.0|101.0|98.1|100.4|102.6|95.6|96.3|97.5|106.0|102.1|95.0|101.6|101.2|95.9|97.1|97.1|
|Other|100.0|100.8|98.6|103.1|108.4|104.2|104.1|102.9|94.3|92.6|88.7|89.3|98.2|91.7|89.8|92.7|
|||||||Type o|f house|hold (H|H)||||||||
|HH without|||||||||||||||||
|children|100.0|101.0|103.5|107.7|103.7|103.7|103.8|106.3|100.5|99.5|98.5|100.1|101.9|99.9|100.6|103.2|
|Retiree HH<br>HH with|100.0|97.5|99.1|102.3|99.3|98.0|97.4|98.1|93.2|91.6|90.2|90.3|90.4|88.7|88.5|91.3|
|children|100.0|105.3|101.3|103.1|103.5|102.0|103.3|101.5|101.5|99.7|90.5|95.3|105.3|102.2|99.4|103.7|
|1 child|100.0|94.6|91.5|94.9|99.7|99.7|97.8|96.5|94.1|94.1|84.6|86.4|100.3|93.9|87.9|95.0|
|2 children<br>3 and more|100.0|117.0|109.6|110.5|105.7|101.1|102.7|102.5|110.9|102.6|92.2|102.5|111.3|113.0|116.4|116.3|
|children|100.0|132.4|132.2|126.3|123.3|120.2|147.1|135.2|123.6|131.8|128.6|136.9|121.4|126.4|125.1|125.0|
|singleparent|100.0|106.7|103.9|96.9|106.3|103.8|108.9|102.2|105.8|106.6|99.4|98.5|111.1|105.5|98.4|100.6|

Source: Authors’ estimates based on HSS data.

40
portfolio exceeding INR 1bn) and 12% for the others); or ii) the average base rate of the five largest commercial banks by assets multiplied by 2.75.<sup>31</sup> This modification should ensure that in a low cost environment, the ultimate borrower will benefit, while in a rising interest rate environment NBFC‐MFIs can continue to lend sufficiently at viable terms.<sup>32</sup> The figure above shows the different components of the interest rate cap framework in India. In the left‐hand panel it plots the development of the ceiling based on the average base rate of the five largest banks. It can be seen that, when introduced in 2014, this ceiling was higher than the previously applied 26% absolute cap. As the average base rate has declined since then the level of the cap fell from 27.7% in 2014 to 24.6% in Q1 2018. Due to the calculation of the ceiling as a multiple of the benchmark rather than as a fixed spread the percentage point reduction in the cap exceeded the percentage point reduction of the base rate and the difference between the two variables declined from 17.7% to 15.7%. The right‐ hand panel shows that the level calculated based on the average base rate is the effective upper limit for almost all small NBFC‐MFIs, as their funding costs plus 12% margin are higher and hence the lower threshold applies. In contrast, for large NBFC‐MFIs the effective cap is lower. It varied from 20% to 25.3% depending on the individual funding costs in late 2017. Average lending rates of the individual MFIs are closely below their respective caps, suggesting that the caps are binding.

**Figure 16: Cost and productivity improvements in the MFI sector**

<!-- Start of picture text --> Operating Administrative Gross loan<br>expenses/loan portfolio expenses/assets portfolio/employee<br>20% 6% 50<br>15% 5% 40<br>4%<br>30<br>10% 3%<br>2% 20<br>5%<br>1% 10<br>0% 0% 0<br><!-- End of picture text --> _Notes:_ Gross loan portfolio per employee is expressed in Rs. lakhs. _Source:_ MFIN 31 Moreover, the maximum variance permitted for individual loans between the minimum and maximum interest rate cannot exceed 4%. Processing charges are capped at 1% of gross loan amount. 32 https://rbidocs.rbi.org.in/rdocs/notification/PDFs/43BF010714FSC.PDF 31
barriers and quantify their aggregate effects using the DBI as their source. On the measurement front, we contribute to this literature by providing a complementary metric of entry taxation that is based on equilibrium conditions and is disciplined by the cross-country distribution of average firm sizes. As we show below, this complementary metric depicts a different picture about the degree of entry barriers, albeit for a narrower set of countries. On the quantitative side, by studying entry barriers and idiosyncratic distortions jointly, we can establish the relative importance of these sources of inefficiency.<sup>10</sup> Our work builds on and contributes to the idiosyncratic distortions literature. From Hsieh and Klenow [2009], we inherit the inference strategy of these distortions. An important feature of this strategy is the independence of the distribution of wedges from aggregate equilibrium variables in the economy. This independence, which emerges from the definition of misallocation as dispersion in marginal revenue products relative to the average return in the industry, enables us to estimate the elasticity between TFPR and TFPQ directly from the data and to devote the model’s equilibrium condition to pin down the entry barrier.

Our work also contributes to Hsieh and Klenow [2009] in proposing a rich theory of the size distribution where average size is indeed responsive to idiosyncratic distortions. Recall that in its most basic form, Hsieh and Klenow [2009] reads idiosyncratic distortions taking as given the firm size distribution, but the mechanisms in the theory are such that the average firm size is unresponsive to the existence of idiosyncratic distortions.<sup>11</sup> Thus, to be able to connect distortions to cross-country differences in average firm size, we need to construct a richer theory of the firm size distribution. Our model enriches

> 10Boedo and Mukoyama [2012] is an exception in the sense that it studies entry barriers in the context of a particular type of allocative distortions: firing taxes. Poschke [2010] also considers an interaction between entry barriers and mark-ups through a reduced-form relationship between the elasticity of substitution in and the number of producers. In both studies, the interaction between entry barriers and some form of allocative distortion exists. However, as we argue below, accounting for the full extent of misallocation is essential for minimizing biases in the identification of entry tax rates.

> 11It is straightforward to show that in a model with heterogeneous productivity without firm dynamics and exogenous exit, removing idiosyncratic distortions has no effects on the average size in the economy.

7
Over the past ten years, China’s industrial sector has made substantial reductions in energy intensity through the implementation of market reforms. Fisher-Vanden et al. (2004) point out that there has been a nearly 70% decline in Chinese energy intensity during the 1980s and 1990s, attributing market-oriented reforms as one of the main reasons behind this decline. Fan, Liao, and Wei (2007) estimate changes in own-price elasticity and elasticities of substitution between energy, capital and labor, and find that accelerated market-oriented reforms have contributed significantly to the decline in energy intensity since 1993. He and Wang (2007), using panel data on energy intensity across 30 Chinese provinces from 1998–2005, examine the relationship between economic transition—including market liberalization, decentralization, and globalization—and the decline in energy intensity among Chinese enterprises. Their empirical results show that economic transition made a substantial contribution to the decline in energy intensity over this time period.

Rising energy prices are another important factor. Fisher-Vanden et al. (2004) suggest that rising energy prices have contributed significantly to intensity declines across several energy types. Hang and Tu (2007) estimate energy-price elasticities to evaluate the effects of price changes on aggregate energy intensity and find, for each energy type, rising energy prices have led to a decline in energy intensity.

Research and development activities have also contributed to declines in industrial energy intensity. Since the late 1990s, the Chinese government has undergone the process of privatizing R&D institutes. As a result of these policies, commercial R&D expenditures as a share of China’s total R&D expenditures has risen from 32% in 1994 to 60% in 2000 (Fisher-Vanden 2009). It is expected that this increase in R&D expenditures will lead to more efficient 10
# **TABLE 9. Average wage. Productivity channel**

||F<br>|E<br>|FE‐<br>|2SLS<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Productivity|0.041***|0.034***|0.53*|0.50|
||(0.0096)|(0.0085)|(0.30)|(0.32)|
|Productivity|0.037***|0.032***|0.11*|0.10|
||(0.011)|(0.0098)|(0.065)|(0.072)|
|Prod * High Prod|0.016|0.011|0.14**|0.14**|
||(0.014)|(0.013)|(0.059)|(0.061)|
|Productivity|0.035***|0.030***|0.34*|0.31*|
||(0.0088)|(0.0085)|(0.18)|(0.18)|
|Prod * Skills|0.017|0.013|0.043|0.041|
||(0.018)|(0.016)|(0.030)|(0.028)|
|Observations|3,160|3,151|3,160|3,151|

Dependent variable: change in log average wage. Regressors: change in labor productivity, and change in labor productivity interacted with firm‐level indicator variables defined as in Table 5. Instruments as in Table 3. Robust standard errors in parenthesis.

33