rafmacalaba/datause-encoder-data huggingface dataset:
protests by poor farmers about inadequate compensation and claims of misconduct by local officials in charge of the conversion process (see, for example, Nguyen, 2004a).

Heterogeneity in impacts can be expected. Historical differences entailed that the market economy was more developed in the Mekong Delta and the South-East (the region around Ho Chi Minh City) than the north.<sup>9</sup> More deeply entrenched traditions of collectivized agriculture in the north are likely to have created lower initial inequality in some key dimensions than found in the south. The distribution of land was more equal in the north.<sup>10</sup> It is also likely that non-land inputs to farming were more dependent on own wealth in the south (Akram-Lodhi, 2005). The pressures toward further land consolidation are likely to be stronger in the south.<sup>11</sup> Labor markets also differed. For this study, a key stylized fact is that farmers in the north are likely to have faced more remunerative options for supplying skilled labor than found in the south. The wage regressions reported by Gallup (2004) (for 1998) indicate appreciably higher returns to schooling in the rural north than the rural south. There is a corresponding schooling gap between the north and the south.<sup>12</sup> We calculate (from the surveys described later) that mean years of schooling in 1993 was 7.3 years for farm-households in the north’s Red River Delta (RRD) versus 4.3 years in the south’s Mekong Delta (MD).<sup>13</sup> It is also likely that aggregate demand for agricultural labor has been stronger than in the north, given the south’s larger farms and higher agricultural growth rate in the 1990s (Benjamin and Brandt, 2004).

> 9 After re-unification in the mid-1970s, farmers in the MD had resisted collectivization, and by the time the country de-collectivized in the late 1980s, less than 10% of all of the region’s farmers had been organized into collectives.  By contrast, virtually all of the crop land in the north and the south's Central Coastal region was collectivized by that time (Pingali and Xuan 1992; Ngo 1993).

> 10 In 1992/93, the coefficient of variation in the log of annual agricultural land was 8.3% in the North’s Red River Delta, versus 15.3% in the south’s Mekong Delta (Ravallion and van de Walle 2004).

> 11 This is consistent with the observations made by Taylor (2004), based on field work in the MD.

> 12 Across regions of Vietnam, the rate of return to schooling rises with mean schooling (Gallup,

2004). 13

> This difference persisted; in 2004 the corresponding mean years of schooling were 8.0 and 4.8.

7
than across different RTAs. This heterogeneity in the effect of RTAs means that a sound estimation of globalization dynamics requires controlling for the country-specific effects of RTAs. An analysis that estimates the different effects of the existing RTAs in the world economy is out of the scope of this paper, but I focus on one interesting example to show how to empirically disentangle the role of RTA from the overall globalization dynamics. I use the example of Spain’s integration into the European Union, going from autarky toward the end of the 1950s to the current high levels of trade integration. Taking into account that RTAs tend to have a phase-in effect over several years, the empirical strategy, in this case, is as follows: where I disentangle the globalization dynamics of the Spanish economy from the world average, and at the same time, I disentangle the dynamic effects of the RTAs signed by Spain with other countries. In equation (7), when the terms _RTAij_<sup>_ESP_</sup> , _t_ are not included, the overall globalization dynamics for Spain are obtained. When the terms _RTAij_<sup>_ESP_</sup> , _t_ are included, the globalization dynamics for Spain are obtained but without including the RTA’s effect. Equation (7) shows how to disentangle the role played by RTAS for one specific country, but it also shows how to do the same for any other countries and economic factors of interest.

# **2.3 Data**

One of the main objectives of this paper is to cover a period as long as possible to understand where globalization stands today. Also, as discussed in the previous sections, using both international and domestic trade flow data is key for measuring globalization. Additionally, covering most countries in the world economy is a priority to contribute to economic policy both in developing and developed economies. The best way to do this is to use international input-output tables. I use three input-output tables databases to cover an extended period, starting in 1965, and with as many countries as possible.

I use the Long-Run World Input–Output Database or long-run WIOD (Woltjer et al., 2021) to go back in time as much as possible. The long-run WIOD provides annual time series of world input-output tables covering the period 1965-2000 (Woltjer et al., 9
21 creatlon cost) and (ii) tracking progress made by an Institution In reducing Its subsidy dependence overtime.

29. When such an Interventlon In the fInancial market Is considered warranted, It Is essentlal to ensure that the "Infant Industry" Is supported on a transitory bails. This would avoid unwarranted prolonging of the Intervention given that market forces are assumed to catch-up and resolve the transitory Issue of Inadequate allocatlon of funds to priority sectors/activities. The "Infant Industry" approach, by definition, conveys the message that an Intervention should eventually be phased out.

30. Given the poor state of affairs In the agricultural credit systems of many cilent countrlos, many rural credit operatlons will need to be conditloned on, and some may Include as a component, a significant financial sector reform.<sup>24</sup> The reform may be speciflc to agricultural credit, or deal more generally with the financlal system, depending on the nature of problems faced. The aim of the reform would be to ellminate obstacles to the officlent functloning of the rural credit market In the broadest sense under compotitive conditions such as removal of monopoly positlons In access to government rediscount facilitles, olimination or tighter targeting of subsidies, and deregulatlon of Interest rates.

2 4 This appills to credt programs in all rgiom, m fr exampl, 'Review of FNances Services in Sub-Saharan Africa', Agricultr Dihbn of Afrkc Tecnnial Deparment 1969; and 'Latn Ameia- New Ditons for Agrkultural Credit Projects and Ruwal Fiaci PolIcis, Trade, Finance ad Industry Division, Latin Anerica Techncl Depa0nwit 1989.
**Table 2.3. Projections for GDP, population, and labor force for GEM‐Core applied to macro SAM**

||2015|2016|2017|2018|2019|2020|2021|2022|2023|2024|2025|2026|2027|2028|2029|2030|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Growth in GDP at factor cost(%)||4.13|4.49|4.79|4.88|4.94|4.99|5.01|5.01|5.01|5.01|5.01|5.01|5.01|5.01|5.01|
|Populationgrowth(%)||2.71|2.69|2.67|2.64|2.62|2.60|2.57|2.55|2.52|2.50|2.47|2.45|2.42|2.40|2.37|
|Population 15‐64years(%)*|53.8|54.0|54.2|54.5|54.8|55.1|55.3|55.6|55.8|56.1|56.4|56.7|56.9|57.2|57.5|57.8|
|Labor forceparticipation rate(%)**|79.5|79.5|79.5|79.5|79.5|79.5|79.5|79.5|79.5|79.5|79.5|79.5|79.5|79.5|79.5|79.5|

*Population aged 15‐64 years as percent of total population.

**Total labor force as percent of population aged 15‐64.

Note: The simulation period is 2015‐2030.

‐13‐
over the period) and then go up. Between 1999 and 2006, the Theil index rises by 7.4%, almost three times its coefficient of variation over the period 1963-99.

<!-- Start of picture text --> Figure 3<br>OECD import concentration, 1963-2006<br>All imports Extra-OECD imports only<br>105<br>120<br>100<br>110 Herfindahl 95<br>Theil<br>90<br>100<br>85<br>90 80<br>75<br>80 Theil<br>70 Herfindahl<br>70 65<br>60<br>60<br>1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 1963196619691972197519781981198419871990 19931996199920022005<br><!-- End of picture text --> Note: base 100, 1963; simple averages of indices over all products. Data from COMTRADE The trend reversal is unmistakable as far as imports from non-OECD countries are concerned. However, it takes place quite late in the sample period. In order to verify whether it is statistically significant, and that it is not a pure composition effect between products (i.e. a sectoral shift away from widelyprocured products toward narrowly-procured ones), we now turn to regressions of concentration indices on time and its square using fixed (product) effects. Results are shown in Table 2 . Columns (1)-(2) show results with concentration indices (the dependent variable) calculated over all imports (including intraOECD) whereas columns (3)-(4) show results for extra-OECD imports only (a more interesting measure from a developmental perspective).

The within estimator confirms the convex time trend, as both time and its square are significant with opposite signs.

18
- 14 -

## **Table 1. Regional crop** **<u>prices of selected commodities</u>**

|Food crop|$/ton|Export crop|$/ton|
|---|---|---|---|
|Maize|260|Coffee|3,192|
|Rice|856|Tea|1,506|
|Bananas|536|Cotton|1,429|
|Plantain|240|Tobacco|1,365|
|Cassava|294|||

Source: Calculated based on FAOSTAT.

# **IV. ESTIMATION RESULTS AND POLICY IMPLICATIONS**

The spatial tobit (SPTobit) regression is performed for each crop and each geographic group. Since the amount of data points is significant from the computational point of view, the entire region is separated into 6 areas for food crop calculations and 3 areas for export crop calculations (Figure 9).<sup>10</sup> The three landlocked countries comprise the first group. Kenya is the second group, but the analysis is focused only on the western half of the country, because there is little variation in agricultural production in the other half.

Tanzania is a large country, which is composed of over 9,000 10 x 10 km pixels. This would cause significant computational difficulties without disaggregation of the country. Food crops are produced in many places. Therefore, the sample data is divided into four areas: “Tabora”, “Mbeya”, “Arusha”, and “Coastal.” For export crops, the production takes place in very limited places (compared to the size of the country). Thus, the sample data are trimmed down to focus on the areas where major production takes place.

All the estimation results are shown in Annex Tables. The ordinary least squares (OLS) estimates are also included as references. The results are broadly consistent with _a priori_ expectations. First of all, regarding the autocorrelation parameters, i.e., _λ_ and _ρ_ , they indicate

> 10 Major national parks are also excluded from our analysis, because these areas are environmentally protected and not supposed to be used for agriculture production purposes.
ascending trend, or at best, a plateau. In this way, over ten years, Argentina's spending on social benefits increased from 14 percent of GDP in 2008 to 18 percent in 2018.

# **4. Data and Estimation**

We analyze asymmetric cyclical behavior in emerging and advanced economies using the IMF's World Economic Outlook dataset from April 2019. We chose this dataset over newer versions due to its detailed disaggregation of government expenditure, which is no longer available in more recent publications. The frequency of the data is annual. The panel dataset spans from up to 1980 to 2018 and includes 71 countries from various regions and income classifications. Our findings contrast evidence from up to 49 emerging economies and 22 advanced countries. A comprehensive list of these countries is provided in Table A1 in the appendix. We employ a non-linear econometric model to estimate the elasticities of public consumption relative to aggregate output throughout the business cycle. Table 1 presents descriptive statistics for the growth rates of the specific categories of public spending under analysis, as well as the real GDP used in our regressions.

**Table 1: Descriptive Statistics**

|**Advanced Economies**||||||
|---|---|---|---|---|---|
|Variable|Obs|Mean|Std. Dev.|Min|Max|
|GDP Growth|836|.024|.022|-.086|.126|
|Compensation to Employees|469|.018|.028|-.166|.129|
|<br>Growth||||||
|Goods and Services Growth|471|.023|.039|-.16|.255|

|**Emerging Markets**||||||
|---|---|---|---|---|---|
|Variable|Obs|Mean|Std. Dev.|Min|Max|
|GDP Growth|1740|.038|.049|-.548|.368|
|Compensation to Employees|922|.044|.097|-1.234|.748|
|Growth||||||
|Goods and Services Growth|861|.04|.172|-.914|1.411|

Source: Authors’ calculations based on World Economic Outlook database 2019.

We can observe that, regarding GDP growth, while emerging markets show a higher average rate for the period considered, they also exhibit greater dispersion. This observation is consistent with
**Table 1: Trends in the global cement industry by continent.** The concentrationadjusted number of firms is _Nit_<sup>_⋆≡_1</sup><sup>_/Hit_,where</sup><sup>_Hit_istheHerfindahlindexofcapacitycon-</sup> centration. The sample is the balanced panel of all economies in which prices and quantities are observed in 2011 and 2017, including economies without domestic production capacity. Continent groupings follow Nunn and Puga (2012), with Fiji, the only economy in our data set in Oceania, grouped with economies in Asia.

**Panel A: Prices and Quantities**

||A|verage pr|ice|Tota|l consum|ption|
|---|---|---|---|---|---|---|
|||USD/t|||Mt||
||2011|2017|∆|2011|2017|∆|
|Africa|252.26|167.09|–33.8%|67.1|81.7|21.7%|
|South America|197.45|175.60|–11.1%|74.5|64.4|–13.5%|
|Europe|163.67|145.69|–11.0%|80.6|76.5|–5.0%|
|North America|151.83|176.03|15.9%|75.6|100.8|33.3%|
|Asia|107.78|95.54|–11.4%|2605.2|3001.8|15.2%|
|Total|188.18|142.02|–24.5%|2902.9|3325.2|14.6%|

**Panel B: Entry and Capacity Expansion**

||Average|number|of frms|Aver|age capa|city|
|---|---|---|---|---|---|---|
||(concent|ration-a|djusted)||Mt||
||2011|2017|∆|2011|2017|∆|
|Africa|1.2|1.9|58.5%|3.0|5.1|70.3%|
|South America|2.1|3.6|67.6%|18.8|25.8|37.6%|
|Europe|2.5|2.6|4.5%|21.6|27.4|26.8%|
|North America|5.5|4.8|–12.4%|35.3|42.7|21.0%|
|Asia|4.2|5.4|28.9%|168.0|210.2|25.1%|
|Total|2.6|3.4|32.4%|63.4|80.1|26.3%|

36
in land are younger on average, have smaller families and a lower dependency ratio. Households renting out land are more likely to be female-headed, older and with a higher dependency ratio.

# _Land fragmentation and characteristics_

We use the LSMS-ISA data to calculate four measures of land fragmentation, summarized in Table 2a. The simplest measure is the number of parcels K held by a household. All else being equal, more parcels suggest greater fragmentation.<sup>4</sup> However this does not take into account the different size of parcels in hectares, which we denote _αk_ . One measure incorporating both parcel count and size is the Simpson land fragmentation index (FI): Where K is the number of parcels, and _αk_ their size in square meters. A score of 0 would indicate no land fragmentation, while as _K →∞FI →_ 1. This index has three properties (Demetriou, Stillwell, and See 2013):

1. Fragmentation increases proportional to n

2. Fragmentation increases when the range of parcel sizes _α_ is small 3. Fragmentation decreases as the area of large parcels increases and that of the small parcels decreases.

> 4. The Januszewski index is similar to the Simpson index in scale and composition. We also calculated it but the results were so similar to those derived from the Simpson index that we do not report them.

9
**<u>Table 11. Results from Robustness Checks – Restricted Samples</u>**

|||||De|pendent Varia|ble:||||
|---|---|---|---|---|---|---|---|---|---|
||Export|Export|Export|<br>Export|Export|Export|<br>Export|Export|<br>Export|
||Participation|Entry|Exit|<br>Participation|Entry|Exit|<br>Participation|Entry|Exit|
||Dummyc,i,k,d,t|Dummyc,i,k,d,t|Dummyc,i,k,d,t|<br>Dummyc,i,k,d,t|Dummyc,i,k,d,t|Dummyc,i,k,d,t|<br>Dummyc,i,k,d,t|Dummyc,i,k,d,t|<br>Dummyc,i,k,d,t|
||Destinat|ion‐Restricted|Sample|Year|‐Restricted Sa|mple|Produ|ct‐Restricted|Sample|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Rel_restrictivenessc,k,d,t|‐0.116<br>[0.015]***|‐0.247<br>[0.039]***|0.302<br>[0.160]*|‐0.069<br>[0.031]**|‐0.104<br>[0.044]**|‐0.148<br>[0.203]|‐0.117<br>[0.018]***|‐0.109<br>[0.040]***|0.603<br>[0.179]***|
|Log Agro_exportsc,i, t‐1||0.048|‐0.089||0.047|‐0.098||0.053|‐0.096|
|||[0.002]***|[0.003]***||[0.001]***|[0.004]***||[0.001]***|[0.003]***|
|Rel_restrictivenessc,k,d,tX||||||||||
|Log Agro_exportsc,i,t‐1||0.007|‐0.029||0.005|0.016||0.003|‐0.044|
|||[0.002]***|[0.011]**||[0.002]**|[0.013]||[0.002]|[0.013]***|
|Log 1+Tariffc,k,d,t|‐0.027|‐0.048|‐0.023|‐0.029|‐0.001|0.004|‐0.029|‐0.018|‐0.005|
||[0.008]***|[0.014]***|[0.021]|[0.007]***|[0.007]|[0.015]|[0.008]***|[0.007]***|[0.012]|
|Log GDP per capitad,t|0.225|0.192|‐0.361|0.407|0.199|‐0.052|0.387|0.185|‐0.762|
||[0.067]***|[0.077]**|[0.185]*|[0.054]***|[0.056]***|[0.140]|[0.056]***|[0.062]***|[0.141]***|
|Log Total Exportsc,k,t|0.042<br>[0.002]***|0.038<br>[0.003]***|‐0.077<br>[0.009]***|0.046<br>[0.002]***|0.040<br>[0.002]***|‐0.068<br>[0.008]***|0.054<br>[0.003]***|0.048<br>[0.003]***|‐0.082<br>[0.009]***|
|Exporting Firm‐Importing<br>Country‐Product Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
||Linear|Linear|Linear|Linear|Linear|Linear|Linear|Linear|Linear|
|Estimation method|Probability|Probability|Probability|Probability|Probability|Probability|Probability|Probability|Probability|
||Model|Model|Model|Model|Model|Model|Model|Model|Model|
|Observations|441,796|157,212|118,455|608,241|236,042|143,881|478,185|167,137|128,989|
|R‐squared|0.359|0.395|0.697|0.439|0.471|0.745|0.347|0.386|0.705|
|Notes: Robust standard errors in brack<br>confidence levels, respectively. Depend<br>equals one if firm_i_exports product_k_to<br>to do so in year_t_. Similarly, exit equals<br>in year_t-1_and continues to do so in yea|ets, clustered a<br>ent variables ar<br>destination_d_in<br>one if firm_i_doe<br>r_t_.|t the exportin<br>e defined as f<br>year_t_but did<br>s not export p|g country-imp<br>ollows: export<br>not do so in ye<br>roduct_k_to des|orting country-<br>participation e<br>ar_t-1_and zero<br>tination_d_in ye|product-year l<br>quals one if th<br>if the firm did<br>ar_t_but did so i|evel. ***, **,<br>ere is positive<br>not export pro<br>n year_t-1_and|and * indicate<br>trade between<br>duct_k_to destin<br>zero if the firm|significance<br>countries and<br>ation_d_in year<br>exported pro|at 1%, 5%, and 10%<br>zero otherwise; entry<br> _t-1_and does not start<br>duct_k_to destination_d_|

39
Table 13: Sequential Probit Results-Rural Areas First Stage: P1 = Probability of going to school and not woiring

||Probit<br>|Standard|Probability<br>|
|---|---|---|---|
||Coefficient|Error|Derivative|
|-||0047<br>91 2|1 0|
|_____________||_________.<br>|_.|
|Child Characteristics||||
|AGE<br>|0.0130<br>|0.1296<br>|0.26<br>|
|AGESQ|-0.0075<br>|0.0058|-0.15|
|FEMALE|-0.7652*|0.2483|-15.17*|
|Parent Characteristics<br>||||
|EDUCFA|0.0279|0.0192|0.55|
|EDUCFA X FEMALE|0.0608*|0.0305|1.21*|
|EDUCMO|0.0609*|0.0331|1.21*|
|EDUCMO X FEMALE|-0.2256*|0.0720|4.47*|
|EMPFA|-0.1217|0.1485|-2.41|
|EMPFA X FEMALE|-0.0504|0.2512|-1.00|
|EMPMO|-0.0685|0.1109|-1.36|
|EMPMO X FEMALE|-0.1198|0.1884|-2.38|
|Household Characteristics||||
|HEADAGE|0.0081|0.0236|0.16|
|HEADAGESQ|0.0000|0.0002|0.00|
|HEADFEMALE|Q0.5347*|0.2216|-10.60*|
|#BOYS 0-5|0.0188|0.0422|0.37|
|#BOYS 6-9|0.0588|0.0604|1.17|
|#BOYS 10-15|0.1141*|0.0652|2.26*|
|#BOYS 16-17|-0.0301|0.2682|-0.60|
|#GIRLS 0-5|0.0043|0.0404|0.08|
|#GIRLS 6-9|0.0857|0.0675|1.70|
|#GIRLS 10-15|0.1117|0.0755|2.21|
|#GIRLS 16-17|-0.0850|0.2261|-1.69|
|BUSINESS<br>|0.2304<br>|0.1496<br>|4.57<br>|
|POOR|-0.1452|0.1064|-2.88|
|Cost of Schooling<br>||||
|COST|-0.0086|0.0073|-0.17|
|DISTANCE 1-5|-0.1851|0.1203|-3.67|
|DISTANCE 5+|0.2888*|0.1161|5.73*|
|Location|...~|~~~~~~~~~~~~~~|~~~~~~~~~~~~|
|WFOREST|-0.2015*|0.1028|4.00*|
|SAVANNAH|-0.9786*|0.1229|-19.40*|
|Log. Likelihood|-622.2|||
|Restricted Log. Likelihood|-743.5<br>|||
|Chi-Squared|242.5*|||
|<br>% Correct Predictions|82.1|||

60
- 30 -

# Table 3.4. Proportion of Water Supply from Own Boreholes by Total Water Consumption Category For Firms with Own Boreholes

|**Total**Water<br>Corsumpti<br>on#|Nig|oria|Total Water<br>Coneuupt ion_I_|Indo|no Ia|Tha|iland|
|---|---|---|---|---|---|---|---|
|(1000<br>gall*ra)|Nuier of|Percent||l0uc<br>(1000cubic|ur<br>of<br>MFirm|Percent|Nuwber of<br>Ffirm|Percent|
|1-99|4|100.0|<br>1-9|90|95.5|26|92.5|
|100 - 499|20|74.0|10- 49|50|92.8|16|92.1|
|500-<br>9|8a|89.8|S0 - 9|13|9 .2|6|8 .1|
|1000 - 499|24|75.9|100 - 499|12|90.2|11|67.5|
|5000- 999|9|78.7|500-W99|3|100.0|4|96.7|
|10000&**more**|14|71.4|1000<br>more|6|100.0|9|9.9|
|All<br>||<br>79<br>||<br>72.9|All|174|984|_n_|99.4|

NOTE: From Appendix Tables R4 C4 and D4.

A/ Total water corsun ption **consists** of water supply from own boreholes and public source.

Source: NIDE/18 Project Establfshmant Survey for Nigeria 1988. World Bank Research Project (RPO 676-71) Establishment Survey for **Indonca** 1992.
# constraints.<sup>13</sup>

For the annual dataset used in this regression, the event years are defined as the years of the onset of the oil price collapse from its pre-collapse peak (1985, 1990, 1997, 2000, 2008, and 2014). The results are robust to defining them based on the year in which oil prices bottomed out. For the baseline results, only the oil price collapses of 1985 and 2014 are considered supply-driven, the other results are considered demand-driven. The robustness of the results to this assumption is tested in the robustness section.

The regression sample includes 153 EMDEs for 1970-2018, of which 34 EMDEs are considered energy exporting (oil, gas, or coal), defined as in World Bank (2020b). Annual data on real GDP are available from the World Bank’ World Development Indicators.

# 3.3 Baseline results

The model estimates the response of EMDE output to the six oil price plunges before 2020 over the following five years. It distinguishes between demand-driven (1990-91, 1997-98, 2000-01, 2008-09) and supply-driven oil price plunges (1985-86, 2014-15). Table 3 presents the results.

Oil price collapses were associated with significant EMDE output losses up to five years after the collapse ( Table 3, first column ).<sup>14</sup> The output response in the first year was insignificant, reflecting the fact that all oil price collapses straddled the turn of a year. However, from the following year, when oil prices reached their trough, the effect becomes statistically significantly negative. Five years after the oil price collapse, EMDE output was still 2.7 percent below baseline.

These output losses associated with oil price collapses were broad-based, affecting both energy-exporting and other EMDEs to broadly similar degrees. To test for differential impacts on energy-exporting and other EMDEs, a dummy for energy exporter status and an interaction term between this dummy and the oil price collapse dummy are added ( Table 3 , last three columns ). The main coefficient on oil price collapses, which now reflects the output response of non-energy-exporting EMDEs, remains statistically significantly negative; the coefficient on the interaction term, which reflects any differential impact in energy-exporting EMDEs, is statistically insignificant until the last year. The overall response of EMDE energy exporters’ output to oil price collapses is statistically significantly negative (as the probability of the corresponding F-test shows in the last column of Table 3 ).

> 13 This is also consistent with Ramey and Zubairy (2018) who use four lags for quarterly data, i.e. also one year.

> 14 Note that the coefficients reported in different tables are cumulative impulse responses. Hence, they already take into account any rebound in prices and economic activity that has taken place during the forecast horizon.

9