rafmacalaba/datause-encoder-data huggingface dataset:
Table 2 – (continue) Uncompensated own-price elasticities

|**Authors**|**Hoang, 2018**|**Gibson and Kim, 2019**|**Gibson and Kim, 2019**|**Gibson and Kim, 2019**|**Bairagi et al.,**<br>**2020**|**Bairagi et al.,**<br>**2020**|**Vu, 2020**|**Vu, 2020**|**Vu, 2020**|**Vu, 2020**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Demand System**|QUAIDS|AIDS|AIDS|AIDS|QUAIDS|QUAIDS|LA/AIDS|LA/AIDS|LA/AIDS|LA/AIDS|
|**Price method**|CW|UM|Deaton|SPM|Deaton|Deaton|UV|MP|CW|Deaton|
|**VHLSS wave**|2010|2012|2012|2012|2012-2014-2016<br>(rural)|2012-2014-2016<br>(urban)|2006|2006|2006|2006|
|Rice|-0.48|-0.11|-0.19|-0.62|-0.41|-0.33|-0.89|-0.73|-0.80|-0.69|
|Other cereals|.|.|.|.|-0.88|-0.89|.|.|.|.|
|Roots and tubers|.|-0.32|-0.86|-1.04|.|.|.|.|.|.|
|Starch crops|.|.|.|.|.|.|.|.|.|.|
|Protein crops|.|.|.|.|.|.|.|.|.|.|
|Fruits|-0.90|.|.|.|-0.42|-0.65|-0.93|-0.93|-0.94|-0.88|
|Vegetables|-0.90|.|.|.|-0.79|-0.81|-0.97|-0.99|-1.00|-0.88|
|Pork|-0.85|-0.28|-0.66|-0.70|-0.65|-0.64|-0.79|-0.79|-0.83|-0.55|
|Beef|.|-0.21|-0.97|-1.00|.|.|.|.|.|.|
|Chicken|.|-0.10|0.56|-0.59|-0.26|-0.13|-1.09|-1.08|-1.07|-0.09|
|Fish|.|-0.15|-0.71|-1.01|-0.74|-0.81|-0.94|-0.99|-0.99|-1.24|
|Other meets and seafood|-0.83|.|.|.|.|.|-0.94|-0.83|-0.95|-1.04|
|Eggs|.|.|.|.|-0.25|-0.14|.|.|.|.|
|Milk|.|.|.|.|-0.22|-0.52|.|.|.|.|
|Spice|.|.|.|.|.|.|.|.|.|.|
|Staples|.|.|.|.|.|.|-0.75|-0.74|-0.75|-0.73|
|Noodle|.|.|.|.|-0.64|-0.63|.|.|.|.|
|Sugar|-0.72|.|.|.|-0.43|-0.31|.|.|.|.|
|Dairy|.|.|.|.|.|.|.|.|.|.|
|Fats and cooking oil|.|0.01|-0.83|-0.92|-0.29|-0.19|.|.|.|.|
|Cooking sauce|.|-0.04|-0.61|-0.97|-0.41|-0.41|.|.|.|.|
|Other food|-1.65|.|.|.|.|.|-1.07|-1.01|-1.01|-0.89|
|Food away from home|.|.|.|.|.|.|-1.11|-2.65|-2.03|.|
|Drinks|-0.87|-0.87|-0.82|-0.90|-0.89|-0.89|-1.01|-1.03|-1.00|-1.01|
|Alcoholic drinks|.|.|.|.|-0.25|-0.33|.|.|.|.|
|Beer<br>Non food|.<br>.|-0.14<br>.|-0.93<br>.|-0.98<br>.|.<br>.|.<br>.|.<br>.|.<br>.|.<br>.|.<br>.|
|Leisure|.|.|.|.|.|.|.|.|.|.|

11
# **3. Violent Conflict and Oil**

24. A large number of quantitative studies have examined the role of mineral wealth in general, and petroleum wealth in particular, on the onset, duration, and intensity of violent conflict.<sup>9</sup> A country‘s petroleum wealth – measured as the value of oil and gas produced per capita – is strongly correlated with the likelihood that a conflict will begin. The relationship between oil and conflict risk is non-linear, as Collier and Hoeffler (2004) have suggested: while modest sums of petroleum wealth can increase a country‘s conflict risk, exceptional oil wealth – enough to raise per capita incomes above about $5000 – makes countries sufficiently rich that their conflict risk begins to drop. To put it differently, oil wealth only raises the risk of conflict in countries with relatively low incomes; if they are rich enough – even if they became rich through their oil – insurgencies become less likely.<sup>10</sup> Fjelde (2009) notes that political corruption may not necessarily be associated with higher conflict risks in oil-rich states, because this is inherent to the stability of the regimes themselves. But political corruption in turn generates particular symptoms and outcomes that pose challenges for more ―virtuous transitions.‖ 25. To describe conflict trends globally and in the MENA region (Section 4), we use the Armed Conflict Dataset (ACD), originally produced by Gleditsch et al. (2002) and updated on the website of the Peace Research Institute of Oslo; it is the most transparent and complete list of armed conflicts around the world.<sup>11</sup> It records both ―minor‖ conflicts (which cause from 25 to 1000 battle-related deaths in a calendar year) and ―major‖ conflicts (which cause more than 1000 battle-related deaths). Table 4 displays some simple cross-tabulations, showing the annual conflict rate among countries in different categories.<sup>12</sup> Figure 5 shows the same results in a bar chart. Among countries at all income levels since 1960, the conflict rate in the oil states has been about 40 percent higher; since the Cold War ended in 1992, it has been almost 50 percent higher; among lower-income countries since 1960, it has been roughly 75 percent higher; and among lower-income countries since 1992, it has been more than twice as high.<sup>13</sup>

> 9 For surveys see Ross (2004b; 2006); McNeish (2010).

> 10 Basedau and Lay (2009) come to similar conclusion but through a different logic.

> 11 - The most recent version (v4 2009) can be downloaded at: http://www.prio.no/CSCW/Datasets/Armed <u>Conflict.  For conflicts in 2009, we use Harbom and Wallensteen (2010).</u>

> 12 We define ―oil producers‖ as states that generate at least $100 per capita in oil and gas wealth (using constant 2000 dollars) in a given year.

> 13 In defining ―low-income countries‖ as those below the $5000 per capita threshold, we also excluded states like Libya, Kuwait and Saudi Arabia, whose per capita income from oil alone exceeds $5000.

1
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Ocean Shipping Consultants, Inc. 2009. ―Beyond the Bottlenecks: Ports in Africa.‖ AICD Background Paper 8, Africa Region, World Bank, Washington, DC.

- Teravaninthorn, Supee, and Gael Raballand. 2008. ―Transport Prices and Costs in Africa: A Review of the Main International Corridors.‖ Working Paper 14, Africa Infrastructure Sector Diagnostic, World Bank.

# Water supply and sanitation

- Banerjee, Sudeshna, Vivien Foster, Yvonne Ying, Heather Skilling, and Quentin Wodon. ―Cost Recovery, Equity, and Efficiency in Water Tariffs: Evidence from African Utilities.‖ AICD Working Paper 7, World Bank, Washington, DC.

- Banerjee, Sudeshna, Heather Skilling, Vivien Foster, Cecilia Briceño-Garmendia, Elvira Morella, and Tarik Chfadi. 2008. ―Ebbing Water, Surging Deficits: Urban Water Supply in Sub-Saharan Africa.‖ AICD Background Paper 12, Africa Region, World Bank, Washington, DC.

- Keener, Sarah, Manuel Luengo, and Sudeshna Banerjee. 2009. ―Provision of Water to the Poor in Africa: Experience with Water Standposts and the Informal Water Sector.‖ AICD Working Paper 13, World Bank, Washington, DC.

- Morella, Elvira, Vivien Foster, and Sudeshna Ghosh Banerjee. 2008. ―Climbing the Ladder: The State of Sanitation in Sub-Saharan Africa.‖ AICD Background Paper 13, Africa Region, World Bank, Washington, DC.

# Other

- Briceño-Garmendia, C., and V. Foster. 2009. ―Republic of Congo: Prioritizing Infrastructure Investments—A Spatial Approach.‖ Unpublished paper, World Bank, Washington, DC.

- Collier, P, and S. O’Connell. 2006. ―Opportunities and Choices in Explaining African Economic Growth.‖ Center for Study of African Economies, Oxford.

- IMF. October 2007. _Regional Economic Outlook: Sub-Saharan Africa_ . Washington, DC: IMF.

- IMF. February 2009. _Republic of Congo: Selected Issues_ . Country Report No. 09/72. Washington DC: IMF.

- World Bank. 2009. ―Investment Climate Assessment for Congo.‖ Financial and Private Sector Development, West and Central Africa (AFTFW), Washington, DC.

40
signs, statistical significance, and approximate magnitude regardless of which individual sector or year is dropped.<sup>9</sup> In addition, it is possible that the inclusion or exclusion of extreme values may be driving our observed results. To account for this possibility, we reintroduce potentially valid markup observations that had been dropped (i.e., those markups greater than three, although markups less than zero remain excluded as they are not plausibly valid) and experiment with different levels of winsorization and then rerun our preferred specifications. Again, the regression coefficients largely preserve their signs, statistical significance, and approximate magnitudes across specifications, as seen in Table 6 and Table 7.

_Table 6. Impact of FDI presence on markups (different levels of winsorization)_

|VARIABLES|(1)<br>Markup<br>(CD)|(2)<br>Markup<br>(TL)|(3)<br>Markup<br>(CD)|(4)<br>Markup<br>(TL)|(5)<br>Markup<br>(CD)|(6)<br>Markup<br>(TL)|
|---|---|---|---|---|---|---|
|FDIs,t−1|-0.127|-0.257**|-0.145|-0.261***|-0.156*|-0.267***|
||(0.120)|(0.0992)|(0.100)|(0.0904)|(0.0771)|(0.0797)|
|EXPs,t−1|-0.192|-0.449***|-0.256|-0.488***|-0.296*|-0.454***|
||(0.261)|(0.156)|(0.195)|(0.147)|(0.150)|(0.121)|
|FDIs,t−1∗EXPs,t−1|0.239|0.419|0.299|0.470*|0.343*|0.546**|
||(0.264)|(0.274)|(0.217)|(0.255)|(0.185)|(0.206)|
|MNEi,s,t|0.155***|0.150***|0.126***|0.129***|0.0910***|0.111***|
||(0.0543)|(0.0493)|(0.0355)|(0.0294)|(0.0221)|(0.0241)|
|Constant|1.254***|1.324***|1.244***|1.331***|1.219***|1.311***|
||(0.0793)|(0.0521)|(0.0630)|(0.0498)|(0.0467)|(0.0424)|
|Level of winsorization|None|None|1%|1%|5%|5%|
|Firm fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|103,283|98,051|103,283|98,051|103,283|98,051|
|R-squared|0.654|0.793|0.799|0.822|0.826|0.834|

Robust standard errors clustered at the industry level in parentheses. Dependent variable displayed at the top of each column: CD denotes markups derived assuming a Cobb-Douglas production function, while TL denotes markups derived assuming a translog production function. *** p<0.01, ** p<0.05, * p<0.1 _Table 7. Impact of_ _<u>foreign control on markups (different levels of winsorization)</u>_

|VARIABLES|(1)<br>Markup<br>(CD)|(2)<br>Markup<br>(TL)|(3)<br>Markup<br>(CD)|(4)<br>Markup<br>(TL)|(5)<br>Markup<br>(CD)|(6)<br>Markup<br>(TL)|
|---|---|---|---|---|---|---|
|MNEi,s,t|0.107**|0.140***|0.0955**|0.123***|0.0757**|0.104***|
||(0.0493)|(0.0456)|(0.0436)|(0.0314)|(0.0313)|(0.0269)|
|log (Output)i,s,t−1|-0.0209|0.00286|-0.0154*|0.00302|-0.0144*|0.00265|
||(0.0124)|(0.00336)|(0.00827)|(0.00285)|(0.00756)|(0.00236)|

9 Regression results from these specifications are not reported in this paper but are available from the paper’s authors upon request.

15
30

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Appendix Table 21 Firm Indebtedness and Use of Public Programs, Including Risk

||∆Debt / S|ales (2019)|∆Guarante<br>Sales|ed Debt /<br>(2019)|∆Non-Gua<br>Sale|ranteed Debt /<br>s (2019)|
|---|---|---|---|---|---|---|
||(1)<br>With<br>Previous<br>Loans|(2)<br>Without<br>Previous<br>Loans|(3)<br>With<br>Previous<br>Loans|(4)<br>Without<br>Previous<br>Loans|(5)<br>With<br>Previous<br>Loans|(6)<br>Without<br>Previous<br>Loans|
|Use Credit Program _×_ Risk|0_._195|0_._083|0_._067|0_._053|0_._128|0_._030|
||(0.017)|(0.021)|(0.006)|(0.015)|(0.016)|(0.013)|
|Risk|_−_0_._176|0_._003|0_._014|0_._022|_−_0_._190|_−_0_._019|
||(0.013)|(0.006)|(0.002)|(0.002)|(0.012)|(0.006)|
|Use Credit Program|0_._129|0_._121|0_._133|0_._113|_−_0_._004|0_._009|
||(0.002)|(0.003)|(0.001)|(0.002)|(0.002)|(0.002)|
|Use Employment Program|0_._009|0_._002|0_._001|0_._000|0_._008|0_._001|
||(0.002)|(0.001)|(0.000)|(0.000)|(0.002)|(0.001)|
|Use Credit Program _×_ Employment Program|_−_0_._014|_−_0_._016|_−_0_._004|_−_0_._009|_−_0_._010|_−_0_._007|
||(0.003)|(0.003)|(0.001)|(0.002)|(0.003)|(0.002)|
|Increase in Sales Dummy|0_._019|0_._004|_−_0_._000|0_._001|0_._019|0_._002|
||(0.003)|(0.001)|(0.001)|(0.000)|(0.003)|(0.001)|
|Decrease in Sales Dummy|0_._015|0_._002|_−_0_._002|0_._000|0_._017|0_._001|
||(0.003)|(0.001)|(0.001)|(0.000)|(0.003)|(0.001)|
|Dependent Variable Mean|0.054|0.028|0.070|0.020|-0.016|0.008|
|Dependent Variable Std. Dev.|0.172|0.082|0.087|0.055|0.148|0.054|
|Number of Firms|62,950|51,729|62,950|51,729|62,950|51,729|
|R<sup>2</sup>|0.193|0.359|0.629|0.645|0.021|0.019|
|Industry FE and Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|

This table reports linear regressions of the change in firms’ leverage on a set of firm-level characteristics, separately for firms with and without previous loans, for the credit program eligible firms sample. Firms with previous loans are those that either have bank credit outstanding in December 2019 or receive a bank loan over the period 2012–2019. Conversely, firms without previous loans are those with no credit records in the banking system throughout the same period. The dependent variable is the change in the debt outstanding between December 2020 and December 2019, relative to 2019 sales. Risk corresponds to the fitted values of the regression specifications reported in Columns 4 and 8 of Table 1 for firms without and with previous loans, respectively. Use credit program and employment program are dummy variables for program participation. Increase (decrease) in sales dummy is equal to one if sales growth is greater (lower) than or equal to 2% (-2%), and zero if sales growth is between -2% and 2%. All columns include industry and municipality fixed effects. Standard errors, shown in parentheses, are clustered at the industry and municipality level. For change in debt over 2019 sales and change in guaranteed debt over 2019 sales, observations in the top and bottom 1% of the distribution are winsorized. Change in non-guaranteed debt over sales is constructed as the difference between change in debt outstanding and the change in guaranteed debt, over 2019 sales.

39
The widely preferred estimator of program impact in this context is the **differencein-differences estimator (DID)** . Assuming that the aggregate effect, summarized by the estimate of β _y_ , is identical for both treatment and control groups, one may obtain an estimate of the impact of a program that is free of any of the biases that are likely to contaminate the BA and CS estimators. For example, the DID may be viewed as removing the undesirable components from the CS estimate of program impact in 2003 ( β _T_ and [ _E_ (μ( _s_ ) | _T_ = 1 ) − _E_ (μ( _s_ ) | _T_ = 0 )] in eq. 9) by subtracting from it the cross-sectional differences between the same groups in 2000. Along similar lines, the DID may be thought of as removing the aggregate or trend effect β _y_ from the BA estimate of program impact described in equation (9). Using the specification of equation (2), the DID estimate of the program impact can be summarized by the single parameter β _Ty_ , i.e.

DID estimates using OLS with many years of data may suffer from severe serial correlation, as Bertrand et al. (2004) demonstrate using placebo laws on U.S. states. The use of few time periods with many groups partly addresses the concern. Furthermore, we focus on discussing nonparametric propensity score matching over the OLS results due to this and other aforementioned reasons.

Mexico’s education data generally suggest that trends in education outcomes are non-negligible; hence, a before-after estimator is unlikely to reflect PEC’s true impact. Since PEC is a voluntary program, PEC schools likely differ from non-PEC schools in factors that will affect both levels and trends in outcomes. Hence, conventional cross section or before and after comparisons are unlikely to capture PEC’s true effect.

Even though the DID estimate of program impact is the preferred estimator for the reasons outlined above, we also present and discuss briefly the other impact estimators as a 18
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Heckman, James. 1979 “Sample Selection Bias as a Specification Error” _Econometrica: Journal of the Econometric Society_ , 153–161.

29
**_Energy demand models for policy_** **_<u>formulation</u>_**

lnEt = ln a + b ln Yt + c ln Pt + d lnEt-1 The short run and long-run price and income elasticities are obtained here.

(f) log-linear model of price and other demographic variables lnEt = ln a + b ln Pt + c ln EMPt + d lnPOPt (g) log-linear model of energy, price, income, fuel share and economic structure variables lnEt = ln a + b ln Pt + c ln Yt + d ln Ft + e ln St (h) dynamic version of the above model lnEt = ln a + b ln Pt + c ln Yt + d ln Ft + e ln St + f ln (Et-1) (i) linear relation between per capita energy and income Et/POPt = a + b Yt/POPt (j) Log linear relation between per capita energy and income ln(Et/POPt) = ln a + b ln (Yt/POPt) (k) log-linear relation between energy intensity and other variables ln(Et/Yt) = ln a + b ln Pt + c ln Ft + d ln St (l) Dynamic version of log-linear energy intensity relation ln(Et/Yt) = ln a + b ln Pt + c ln Ft + d ln St + e ln (Et-1/Yt-1) Source: Westoby and Pearce (1984).

Ibrahim (1985) has reviewed the energy demand forecasting efforts in Arab countries and their performance. He noted that time series, single equation models, and aggregated approach were commonly used in most of the earlier studies. His review suggested that most of the efforts were quite primitive at that time and did not meet the requirements of policy analysis. Similarly, Chern and Soberon-Ferrer (1986) analyzed the structural changes in energy demand in developing countries.

Ishiguro and Akiyama (1995) have analyzed energy demand in five Asian countries, namely China, India, South Korea, Thailand and Indonesia both at the aggregate level and the sector level using a simple econometric model. They have used the model for forecasting energy demand in these countries up to 2005. Their main focus was to analyze the effect of different policies on energy demand growth. The analysis was presented for the base case and two alternative scenarios (high GDP growth and high energy price). Al-Azam and Howdon (1997) apply a dynamic OLS approach to forecast energy demand in Jordan. The objective of this study is to show whether modern 45
**<u>Annex Table 5. Marginal Effects from probit regressions. Dependent variable: Participating in school, ages 6 to 17</u>**

||Burundi 2000|Cambodia<br>1999|Cambodia<br>2000|Indonesia<br>2003|Jamaica 1998|Jamaica<br>2000|Mongolia<br>2000|Myanmar<br>2000|Romania<br>1995|Sierra Leone<br>2000|
|---|---|---|---|---|---|---|---|---|---|---|
|Disabled (0,1)|-0.179|-0.452|-0.263|-0.675|-0.279|-0.294|-0.279|0.014|-0.533|-0.108|
||(3.19)***|(6.27)***|(5.91)***|(18.73)***|(6.79)***|(3.13)***|(7.68)***|(0.17)|(9.56)***|(0.96)|
|Age (years)|0.509|0.457|0.507|0.269|0.036|0.003|0.548|0.237|0.340|0.104|
||(20.98)***|(32.72)***|(59.36)***|(75.51)***|(11.89)***|(1.64)*|(38.25)***|(21.20)***|(51.76)***|(7.32)***|
|Age (years) squared|-0.023|-0.019|-0.023|-0.012|-0.002|-0.000|-0.023|-0.012|-0.014|-0.005|
||(19.22)***|(31.98)***|(60.33)***|(80.30)***|(15.55)***|(3.26)***|(36.61)***|(21.56)***|(51.43)***|(8.31)***|
|Male (0,1)|0.092|0.085|0.120|-0.007|-0.012|-0.004|-0.071|-0.001|-0.007|0.099|
||(6.70)***|(6.91)***|(15.76)***|(2.30)**|(4.93)***|(3.56)***|(5.94)***|(0.18)|(1.31)|(7.84)***|
|Urban (0,1)|0.191|0.098|0.044|0.059|-0.001|0.001|-0.087|0.123|0.095|0.073|
||(4.40)***|(7.59)***|(3.19)***|(16.25)***|(0.27)|(0.97)|(5.29)***|(13.74)***|(15.68)***|(3.80)***|
|Quintile 2|0.006|-0.044|0.034|0.044|0.014|0.003|0.056|-0.000|0.068|0.022|
||(0.24)|(2.23)**|(3.09)***|(11.52)***|(4.90)***|(2.51)**|(3.09)***|(0.01)|(10.73)***|(1.08)|
|Quintile 3|0.126|-0.009|0.093|0.077|0.018|0.003|0.145|0.019|0.091|0.093|
||(5.54)***|(0.44)|(8.37)***|(19.89)***|(6.52)***|(2.61)***|(8.19)***|(1.90)*|(14.65)***|(4.49)***|
|Quintile 4|0.164|0.065|0.196|0.103|0.021|0.003|0.198|-0.010|0.091|0.172|
||(7.27)***|(3.36)***|(17.83)***|(25.83)***|(7.66)***|(3.43)***|(10.73)***|(0.98)|(14.14)***|(8.10)***|
|Quintile 5|0.266|0.232|0.321|0.134|0.023|0.004|0.219|0.002|0.101|0.271|
||(10.91)***|(10.61)***|(25.50)***|(30.42)***|(9.13)***|(3.60)***|(10.33)***|(0.13)|(15.21)***|(11.19)***|
|Observations|5834|10880|23719|64136|6952|1547|7593|25796|13777|7339|

Absolute value of t statistics in parentheses. *, **, *** indicates significant at 10%, 5%; and 1% levels. Models include region dummies 21