rafmacalaba/datause-encoder-data huggingface dataset:
## **5.6 Testing for different size of forecasting windows**

The IMF produces 5-year forecasts in each WEO edition. We use this five yearly forecast as our default testing horizon, since it captures the maximum information regarding a country’s growth trajectory in any given year. In order to check the robustness of our results we can narrow this forecast window by excluding outer years from our variables and re-run our main specification.

Table 16 below presents our results using forecast windows of 1, 2, 3 and 4 years in addition to our baseline estimates using the 5 year window. For windows shorter than 4-years we fail to find a significant effect. This suggests that shorter windows we are unable to measure with precision our negative effect on the forecast error. The magnitude and significance is maximized at the longest window available in the WEO forecasts.

||(1)<br>Dif 1y|(2)<br>Dif 2y|(3)<br>Dif 3y|(4)<br>Dif 4y|(5)<br>Dif 5y|
|---|---|---|---|---|---|
|Lag disc dum||||||
|(disc in past 2 years (excl. t=0))|-0.599|-0.527|-0.350|-0.597<sup>_∗∗_</sup>|-0.833<sup>_∗∗∗_</sup>|
||(0.509)|(0.381)|(0.305)|(0.295)|(0.308)|
|Dep var|1 yr window|2 yr window|3 yr window|4 yr window|5 yr window|
|Fixed efects|CFE YFE|CFE YFE|CFE YFE|CFE YFE|CFE YFE|
|Sample|Full|Full|Full|Full|Full|
|Obs- Country Year)|4522|4335|4148|3964|3781|
|R-sq|0.06|0.07|0.07|0.06|0.06|
|F-test|15.20|12.41|11.89|12.82|13.78|

Time trend, Standard errors clustered at country level _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Table 16: Forecast error results, all sample, different time windows

# **6 Conclusions**

We find that in the years following a giant oil or gas discovery, growth forecasts increase significantly while actual growth performance does not. Following a major discovery in countries with weaker prior institutional quality, actual growth performance is negative relative to the same years prior to the discovery. These results add up to a systematic growth differential reflecting growth under-performance relative to a counter-factual anticipated growth path. Our results increase in magnitude in the case of supergiant discoveries. Furthermore when we limit our analysis to only oil discoveries (excluding gas) the magnitude also increases.

We interpret our growth disappointment estimates as causal evidence that discoveries may trigger growth under-performance relative to expectations in countries with weaker governance. We find these effects set in following major discoveries but prior to production starting; a phenomenon we term the _presource curse_ .

28
in Kimball (1990), demand for precautionary savings is governed by the prudence of the utility function, which depends on the third derivative. Therefore,<sup>_argmax_</sup> _γ A_ ( _γ_ ) will unambiguously increase with risk aversion only if prudence is also weakly decreasing. Under CRRA preferences, relative risk aversion and relative prudence are positively correlated, meaning that under CRRA an increase in risk aversion has ambiguous effects on<sup>_argmax_</sup> _γ A_ ( _γ_ ).

The model also does not provide a clear indication of how<sup>_argmax_</sup> _γ A_ ( _γ_ ) changes with the discount factor. As _β_ increases, consumers would show increasing appetite for savings and insurance, but the net effect on<sup>_argmax_</sup> _γ A_ ( _γ_ ) is ambiguous. The explicit insurance coverage through the WISA and self-insurance through (loadingfree) savings could be substitutes. The experimental analysis below measures comparative statics with respect to risk and time preferences, among other objectives, to provide insight where the theory is not conclusive.

In summary, the main prediction of the theoretical model is that for any individual, demand for a mixture of savings and insurance (0 _< γ <_ 1) should be greater than demand for pure savings, pure insurance, or both. The model does not allow demand for a mixture to be less than demand for both pure savings and both insurance. However, the effect of risk and time preferences on the relative demand for savings and insurance are ambiguous.

# **3 Experimental Design**

## **3.1 Procedures**

To learn more about savings and insurance decision-making, and to pilot WISA features, we recruited 322 male farmers from rural areas surrounding Ahmedabad, India, for a laboratory experiment in April-May 2010. Sessions were conducted in the Ahmedabad office of the Centre for Microfinance. During the computer-based sessions implemented using Qualtrics software, subjects participated in tasks designed to elicit preferences about risk, time, savings, and insurance. The savings and insurance products used in the exercise were real, and participants had the opportunity to leave the session with rainfall insurance policies or vouchers for delayed payments 13
# **Introduction**

When estimating returns to schooling using a Mincer type earnings function, the disturbance term will capture individual unobservable attributes and effects which, in general, tend to influence the schooling decision, hence resulting in a correlation between schooling and the error term. Cognitive (as well as non-cognitive) ability is such an unobservable. If schooling is endogenous, then estimation by Ordinary Least Squares (OLS) will yield biased estimates of the return to schooling.

The return to investing in education, based on past empirical studies, is known to differ among individuals with different cognitive skills. For example, evidence from the United States (Ingram and Neumann 2005) shows that over the past decades, individuals with college education but without specific skills experienced the lowest benefits from investing in education. Therefore, individuals with low ability may not benefit as much from investing in education, compared to individuals in the upper part of the ability distribution. For the latter, ability is expected to interact positively with education resulting in higher benefits from education investments.

When a true measure of ability is an omitted variable in the earnings equation, one of three different approaches have been used in the empirical literature to capture the “true” return to education. The first approach uses twins, to arrive at a measure of the causal return to education. For example, Ashenfelter and Rouse (1998) and Rouse (1997) using data from the United States, have compared the earnings of twins with different educational levels, and reported an estimate of the return to education that is about 30 percent smaller than the OLS
<!-- Start of picture text --> (% population below the poverty line)<br>60<br>50.1<br>50 453<br>41.8<br>40 37.2<br>33.8 31.8 598<br>30 25.7 25.7<br>20<br>13.7<br>10<br>0<br>Rural Urban National<br>1993-04 m2004-05 =2009-10 m 2011-12<br><!-- End of picture text -->
Paradoxically, families who earn barely enough to survive spend vast amounts of money on celebrations. In the rural South Indian data' I analyze in this paper, a typical household spends approximately seven times its annual income on a daughter's marriage - on celebrating the wedding and on dowry transfers to the groom's family. Moreover, fifteen per cent of its expenditures, on average, are spent on celebrating village festivals. Poor households tend to find such large sums of money by going severely into debt at interest rates that range from 100-300 per cent per year which leads to chronic indebtedness and, sometimes, bonded labor. Despite the importance of these expenditures the Economics literature has largely ignored them, perhaps because they fall into the realm of "non-rational" behavior. However, given their pervasiveness, and the dominant role accorded to Economics in the formulation of policy, this omission can have serious consequences for the design of policy interventions. Money spent on celebrations is, after all, money not spent on food, education, health, and other productive inputs.

What explains this paradox? Are celebrations wasteful? In this paper I will examine the factors underlying expenditures on festivals and weddings with qualitative and quantitative data from South India. In this introductory section I sketch a general conceptual model of publicly observable events and the role they play in Indian village life. I will then illustrate this conceptual framework by examining aspects of wedding and festival related behaviors. While the paper relies on fieldwork from the South Indian state of Karnataka, I believe that the analysis is easily generalized to other parts of South Asia. With changes in structure and context, the basic points are perhaps also relevant to human behavior in other non-Western societies. Working within a context with which I am relatively familiar, however, allows me to provide a more nuanced perspective on an important aspect of the cultural economics of behavior in poor households<sup>2</sup> .

In rural Indian life, celebrations tend to mark important transitions: A wedding marks the transition of a woman from daughter to wife and is the central event in her life-cycle (Fruzzetti, 1990), festivals mark important events in the life-cycle of a village - a religious event, or a harvest (Fuller, 1992). They are rituals - marking events that shape identity and meaning. They are also, necessarily, public. If they were private, the change in status would not be recognized because others would not observe the ritual. The public nature of celebrations thus provides an l The data are from two different samples in Kamataka state.

2For an elaboration on this metlhodology see Rao (1997) 2
information from the Thomson Reuters Worldscope database. One advantage of the matched SDCWorldscope data set is that it covers a longer time span, including the 1990s. However, due to the more limited Worldscope coverage, the matched Worldscope data set contains a smaller set of firms (38,622 firms) than the matched Orbis data set and does not include the United States. A comparison of the sample of firms in Orbis and Worldscope suggests that the Worldscope sample is biased toward larger firms. As with the analysis using the M&A data, the results using the Worldscope data are mentioned in the text but not reported.

# **3. Capital Market Growth: The Intensive and Extensive Margins**

Capital markets have grown markedly since the early 1990s in both developed and emerging economies (Figure 1). The median developed country’s equity market expanded from an average of 35% of GDP over the period 1991-1995 to an average of 84% over 2006-2011.<sup>8</sup> Even more pronounced growth patterns are observed in emerging countries, where markets grew from 17% to 59% of GDP over the same period in the median country. Corporate bond markets also grew, especially in emerging economies where they increased more than 6-fold, albeit from a low base. In the median developed country, corporate bond markets expanded from an average of 27% of GDP in 1991-1995 to 41% in 2006-2011. As a comparison, private credit by deposit money banks increased from 81% to 117% (27% to 36%) of GDP in the median developed (emerging) country during the same period.

The issuance data show that the expansion in primary capital market activity has also been sizeable (Figure 2, Panel A). For the median country in our sample, the per annum amount of new

> 8 Throughout the paper, we use different median values to describe our results. In each case, the median is taken for the variable we are describing in that instance. For example, in this paragraph we compute the median country according to the level of financial sector development (alternatively, equity market capitalization, corporate bond market capitalization, and bank credit). In the next paragraph, the median is computed using the amount raised.

10
National Statistical Office (NSO) [Malawi], and ORC Macro. 2005. 'Malawi Demographic and Health Survey 2004'.

- Nel, A., Z. Mabude, J. Smit, P. Kotze, D. Arbuckle, J. Wu, N. van Niekerk, and J. van de Wijgert. 2012. 'HIV incidence remains high in KwaZulu-Natal, South Africa: evidence from three districts', _PLoS One_ , 7: e35278.

- Peterson, L., K. Nanda, B. K. Opoku, W. K. Ampofo, M. Owusu-Amoako, A. Y. Boakye, W. Rountree, A. Troxler, R. Dominik, R. Roddy, and L. Dorflinger. 2007. 'SAVVY (C31G) gel for prevention of HIV infection in women: a Phase 3, double-blind, randomized, placebo-controlled trial in Ghana', _PLoS One_ , 2: e1312.

- Program, National AIDS and STI Control. 2010. "Sentinel surveillance for HIV and Syphilis Among Pregnant Women, 2010." In.

- Ramjee, G., S. Kapiga, S. Weiss, L. Peterson, C. Leburg, C. Kelly, B. Masse, and Hptn Study Team. 2008. 'The value of site preparedness studies for future implementation of phase 2/IIb/III HIV prevention trials: experience from the HPTN 055 study', _J Acquir Immune Defic Syndr_ , 47: 93-100.

- Rehle, T. M., T. B. Hallett, O. Shisana, V. Pillay-van Wyk, K. Zuma, H. Carrara, and S. Jooste. 2010. 'A decline in new HIV infections in South Africa: estimating HIV incidence from three national HIV surveys in 2002, 2005 and 2008', _PLoS One_ , 5: e11094.

- Republic of Namibia Ministry of Health and Social Services. 2010. "United Nations General Assembly Special Session (UNGASS) Country Report " In.

- Rice, B. D., J. Batzing-Feigenbaum, V. Hosegood, F. Tanser, C. Hill, T. Barnighausen, K. Herbst, T. Welz, and M. L. Newell. 2007. 'Population and antenatal-based HIV prevalence estimates in a high contracepting female population in rural South Africa', _BMC Public Health_ , 7: 160.

- Ruzagira, E., S. Wandiembe, A. Abaasa, J. Levin, A. Bwanika, U. Bahemuka, M. A. Price, and A. Kamali. 2011. 'Prevalence and incidence of HIV in a rural communitybased HIV vaccine preparedness cohort in Masaka, Uganda', _PLoS One_ , 6: e20684.

- Seeley, J., J. Nakiyingi-Miiro, A. Kamali, J. Mpendo, G. Asiki, A. Abaasa, J. De Bont, L. Nielsen, P. Kaleebu, and Chivtum Study Team. 2012. 'High HIV incidence and socio-behavioral risk patterns in fishing communities on the shores of Lake Victoria, Uganda', _Sex Transm Dis_ , 39: 433-9.

- Shisana, O , T Rehle, LC Simbayi, K Zuma, S Jooste, V Pillay-van-Wyk, N Mbelle, J Van Zyl, W Parker, NP Zungu, S Pezi, and SABSSM III Implementation Team. 2009. "South African national HIV prevalence, incidence, behaviour and communication survey 2008: A turning tide among teenagers? ." In.

- Shisana, O, T Rehle, Simbayi LC, K Zuma, S Jooste, Zungu N, D Labadarios, and D Onoya. 2014. "South African National HIV Prevalence, Incidence and Behaviour Survey,

- 2012." In. Cape Town.

Shisana, O, T Rehle, LC Simbayi, W Parker, K Zuma, A Bhana, C Connolly, S Jooste, and V Pillay. 2005. "South African national HIV prevalence, HIV incidence, behaviour and communication survey, 2005." In.

SIDA, Comité National de Lutte contre le. 2008a. "Rapport De Situation Nationale Pour La Republique Du Congo À L'Intention De L'UNGASS " In.

77
24

**Table 12. Growth accounting** **<u>simulation of the welfare loss</u>**

|Country<br>(1)|r<br>Overall<br>social r,<br>full<br>method<br>average<br>(%)<br>(2)|(Ie/Y)<br>inertia<br>ed. exp.<br>as % of<br>GDP<br>(%)<br>(3)|r*(Ie/Y)<br>Inertia<br>educ.<br>contrib.<br>growth<br>points<br>(4)|Enhanced<br>education<br>contri-<br>bution<br>growth<br>points if<br>(Ie/Y) + 1<br>(5)|Enhanced<br>-inertia<br>difference<br>in growth<br>points<br>(6)|Actual<br>inertia<br>Growt<br>h rate<br>(7)|Enhanced<br>to inertia<br>growth rate<br>(%)<br>(8)|
|---|---|---|---|---|---|---|---|
|Argentina|7.7|4.6|0.35|0.43|0.08|4.1|1.9|
|Brazil|20.7|4.0|0.83|1.04|0.21|2.9|7.1|
|Chile|11.1|3.9|0.43|0.54|0.11|5.3|2.1|
|China|12.9|2.5|0.32|0.45|0.13|8.0|1.6|
|Colombia|15.1|3.1|0.47|0.62|0.15|3.2|4.7|
|Ecuador|12.4|1.3|0.16|0.29|0.12|2.0|6.2|
|Ethiopia|13.7|3.9|0.54|0.67|0.14|5.3|2.6|
|Ghana|15.8|4.7|0.74|0.90|0.16|4.8|3.3|
|Israel|10.0|6.6|0.66|0.76|0.10|4.3|2.3|
|Japan|8.4|3.7|0.31|0.39|0.08|1.2|7.0|
|Malawi|13.8|4.1|0.57|0.70|0.14|3.9|3.5|
|Mexico|12.5|4.9|0.61|0.74|0.13|2.9|4.3|
|Nepal|11.0|3.0|0.33|0.44|0.11|4.0|2.7|
|Paraguay|14.6|5.3|0.77|0.92|0.15|2.7|5.4|
|Philippines|10.9|3.5|0.38|0.49|0.11|3.8|2.9|
|Singapore|13.6|3.1|0.42|0.56|0.14|6.1|2.2|
|Spain|9.8|4.3|0.42|0.52|0.10|2.9|3.4|
|Uganda|35.5|2.5|0.89|1.24|0.36|6.3|5.6|
|UK|7.5|4.6|0.35|0.42|0.08|2.4|3.1|
|Venezuela|13.3|3.0|0.40|0.53|0.13|3.1|4.3|
|Vietnam|8.1|2.9|0.23|0.31|0.08|7.3|1.1|
|**World**<br>**mean **|**13.9**|**4.0**|**0.55**|**0.69**|**0.14**|**4.0**|**3.3**|

Source: Based on the estimated education term of equation (5) Notes: Col.2 from Psacharopoulos and Patrinos (2004), average of social rates of return, full method Col. 3 from World Bank indicators Col. 4 = (Col. 2) * (Col. 3)/100 Col. 5 re-estimation based on a 1 percentage point increase in education expenditure Col. 6 = (Col. 5 - Col. 4) Col. 7 from Maddison (2010) Col. 8 = (Col. 6 / Col. 7) * 100
|SOE type<br>Central|-0.018|0.024<sup>**</sup>|0.025<sup>***</sup>|-0.029|0.062<sup>**</sup>|0.013|-0.031<sup>**</sup>|-0.020<sup>*</sup>|-0.016|
|---|---|---|---|---|---|---|---|---|---|
||(0.012)|(0.010)|(0.005)|(0.025)|(0.023)|(0.030)|(0.014)|(0.010)|(0.010)|
|Local|<br>-0.004|<br>0.038<sup>***</sup>|<br>0.083<sup>***</sup>|<br>0.014|<br>0.068<sup>***</sup>|<br>0.144<sup>***</sup>|<br>-0.039<sup>***</sup>|<br>-0.002|<br>0.027<sup>***</sup>|
||(0.011)|(0.012)|(0.014)|(0.019)|(0.018)|(0.015)|(0.007)|(0.010)|(0.006)|
|In 2020|<br>-0.054<sup>***</sup>|<br>-0.014|<br>-0.043<sup>*</sup>|<br>-0.105<sup>***</sup>|<br>0.024|<br>-0.024|<br>-0.117<sup>***</sup>|<br>-0.081<sup>***</sup>|<br>-0.071<sup>***</sup>|
||(0.008)|(0.022)|(0.018)|(0.019)|(0.050)|(0.038)|(0.007)|(0.012)|(0.009)|
|SOE dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Size dummies|No|No|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Value of subsidies received|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations<sup>**†**</sup>|2,098,570|47,698|11,260|2,016,257|45,597|10,878|2,094,821|47,624|11,249|
|Within R-squared<sup>**†**</sup>|0.018|0.016|0.013|0.048|0.034|0.042|0.028|0.023|0.030|

Source: World Bank staff analysis using Romania MoF firm-level data from 2016 to 2020. The sample includes all firms. Growth is calculated as the difference between the values in year t and t-1 divided by the average of the values in year t and t-1. SOE variables are dummy variables taking the value one if national or subnational governments have ownership stake of 10-24.9%, 25-49.9%, or 50% or more, respectively, in the firm. The reference group is private-owned enterprises (POEs), defined as firms where national or subnational governments own 0-9.9%. All models include 2-digit NACE industry and year-fixed effects. Standard errors clustered at the industry level are in parentheses. ***, **, and * indicate significance at 1 percent, 5 percent, and 10 percent. The reference categories for age and size groups are Start-ups (0-2) and Small (10-49), respectively. The classification of the 4- digit NACE sectors into competitive, partially contestable, and natural monopoly sectors are based on the SOE sector taxonomy developed by Dall'Olio, Goodwin, Alonso, Patino Pena, & Sanchez-Navarro (2022).<sup>**†**</sup> indicates values are for the state ownership model using 10-100% stake. All models include a constant term.

25
<!-- Start of picture text --> i Latin<br>East America Europe & Lower Upper<br>Water; Lesviho Benchotarktame °° KastandNoth Southen SS & Central”Middle Middle Middle World<br>_— " "Pacific Caribhea Asia" Income Income<br>Africa<br>nh<br>Access to Improved Water Sources (% ofpopulation) 76 63 64 a T2 73 90) af a a3 93 a<br>Access to Improved Sanitation (% of population) ci 4 ci TF & 60 TF 78 79 2 86 64<br><!-- End of picture text --> Note: Daia used isfrom the latest observation available between 1997 and 2603.

Source: A "Research" Datahase ofInfrastructure Economic Performance, Antonio Estache and Ana Goicoechea, World Bank Infrastructure Network, Office ofthe Vice President, June 2005,

|ICT|Lesotho|mas<br>owencome|SSA|Middle<br> ie <br>-<br>Africa|ee<br>1A|Fast<br>Asia <br>Pacific|Latin<br>America <br> «=&<br> Caribbea<br>1|Europe&<br>Central <br>Asia|||,<br> ule<br>ome|Lower<br>Middle<br>Income|=Upper<br>Middle<br>Income|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Teledensity (totaltelephone subscribers/1000peoplel|63|é4|oo|202|Ta|fa|43|54?|468|301|633|5al|
|Mainlines Teledensity(mainlines subscribers/1000 people)|16|2|al|129|35|76|192|aa4|195|126|261|ala|
|CellularTeledensity (cellular subscribers/1000 people)|47|7|7a|167|7|96|249|325|280|179|381|296|
|Cost of LocalPhone Call(US cents/3minutes)|Za|8|10|6|4|5|?|?|?|6|9|9|
|Cost ofPhone Callto theUS (U8 cente/3 minutes)|231|504|497|281|336|450|323|326|309|314|305|335|
|Cost ofCellularLocalCall(U8 cente/S offpeak minutes)|26|4|4a|ia|iF|42|it|4|49|44|54|49|
|Phone Faults (reported faults/100 mainlines)|75|f4|57|23|oF|39|24|34|25|33|18|37|
|UnmetDemand(%ofmaintelephonelinesinoperation)|73|4|30|26|24|13|17|8|12|20|4|21|

Note: Data used isfrom the latest observation available between 1997 and 2663, Source: A "Research" Database of Infrastructure Economic Performance, Antonio Esiache and Ana Goicoechea, World Bank Infrastructure Network, Office of<sup>theVicePresident, June2005.</sup>
# PRE Working Pager Series

||iwl|Autho,|D~~~~~~~aI|Contact<br>for oager|
|---|---|---|---|---|
|WPS402|The GATT as International Disciplin<br>Over Trade Restrictions: A Public<br>Choice Approach|e J. Michael Finger|March 1990|N. Artis<br>38010|
|WPS403|Innovative Agricuhural Extension<br>for Women: A Case Study of<br>Cameroon|S. Tjip Walker|June 1990|M. Villar<br>33752|
|WPS404|Chile's Labor Markets in an Era of<br>Adjustment|Luis A. Riveros|April 1990|R. Luz<br>34303|
|WPS405|Investments in Solid Waste<br>Management: Opportunities for<br>Environmental Improvement|Carl Bartone<br>Janis Bernstein<br>Frederick Wright|April 1990|S. Cumine<br>33735|
|WPS406|Township, Village, and Private<br>Industry in China's Economic<br>Reform|William Byrd<br>Alan Gelb|April 1990|K. Chen<br>38966|
|WPS407|Public Enterprise Reform:<br>AChallenge for the World Bank|Ahmed Galal|April 1990|G. Orraca-Tetteh<br>37646|
|WPS408|Methodological Issues in Evaluating<br>Debt-Reducing Deals|Stijn Claessens<br>Ishac Diwan|May 1990|S. King-Watson<br>33730|
|WPS409|Financial Policy and Corporate<br>Investment in Imperfect Capital<br>Markets: The Case of Korea|Mansoor Dailami|April 1990|M. Raggambi<br>37657|
|WPS410|The Cost of Capital and Investment<br>in Developing Countries|Alan Auerbach|April 1990|A. Bhalla<br>37699|
|WPS411|Institutional Dimensions of Poverty<br>Reduction|Lawrence F. Salmen|May 1990|E. Madrona<br>37496|
|WPS412|Exchange Rate Policy in Developing<br>Countries|W. Max Corden|May 1990|M. Corden<br>39175|
|WPS413|Supporting Safe MotherhooH:<br>A Review of Financial Trends<br>Full Report|L. M. Howard|May 1990|S. Ainsworth<br>31091|
|WPS414|Supporting Safe Motherhood:<br>A Review of Financial Trends<br>Summary|L. M. Howard|May 1990|S. Ainsworth<br>31091|
|WPS415|How Good (or Bad) are Country<br>Projections?|Norman Hicks<br>Michel Vaugeois|April 1990|M. Berg<br>31058|