rafmacalaba/datause-encoder-data huggingface dataset:
- , and P. Morton, "How Sovereign debt has Worked." UC Davis

- Institute of Governmental Affairs Working Paper No. 45, August 1987.

- Also appeared in Jeffrev Sachs (ed). Dcveloping Country Debt and Economic Performance Vol.1, NBER, University of Chicago Press, 1989.

Maddala, C.. 19', Econometrics, MYcgraw-Hill, **New** York.

- .Manuelli, R. "A General Equilibriumr Model of Intternational **Credit** Markets" mimeo Northwestern University, June 1986.

- McDonald, D., "Debt Capacity and Developing Country Borrowing," **IMF Staff** Paper No. 29, December 1982, 29, 603-46.

- Obstfeld, M. "Capital Mobility in the W.orld Economy:Theory **and Measurement",** in K.Brunne- and A.H. Meltzer (eds.) Carnegie-Rochester **Conference Series** on Public Policv 24, Spring 1986.

- Ozler, S., "On the Relation between Reschedulings **and Bank Value"** Ibi American Economic Review, Vol:79, no 5, December 1989, **pplll**<sup>**7**</sup> **-1131.**

- "Evolution of Commercial Credit Terms: A Empirical Study of

- Commercial Bank Lending to Developing Countries," **forthcoming Journal** of Development Economics.

- , and C. Tabellini, "External Debt and Political **Instability"**

- UCLA Economics Dept. (manuscript in preperation).

- Ozler, S. and H. Huizinga, "Secondary Market Prices for **Developing** Country Debt: The Role of Creditor Country Governments", UCLA Economics Dept. mimeo, April 1990.

- Sachs J., "Theoretical Issues in International Borrowing," Princeton Studies in International Finance, No:54, July 1984.

- ______ and D. Cohen, "LDC Borrowing with Default Risk", Kredit und

- Kapital, Heft 8, 1985.

- Schawartz, A., "International Debts: What's Fact and What's **Fiction"** Presidential Address to the Western Economic Association, July 2, 1988, Economic Inquiry, Vol 27, no 1, January 1989.

- Winkler, M., Foreign Bonds: Ann Autopsy. Philadelphia: **Swain,** 1933.
- 7 -

do not have any outrage (Figure 6).<sup>3</sup> Our empirical model accounts for this firm-level heterogeneity in service quality within a country.

<!-- Start of picture text --> Figure 6. Duration of Electricity Outages per Day<br>(Poland) (Moldova)<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24<br>Hours Hours<br>Source: BEEPS.<br>Fraction Fraction<br><!-- End of picture text --> The following sections are organized as follows: Section II establishes our empirical model and describes our data uses. Section III provides the estimation results and some policy implications.

# **II. EMPIRICAL MODEL AND DATA**

Based on the traditional industrial organization literature (e.g., Nerlove, 1963; Christensen and Greene, 1976), the following cost function is estimated by a seemingly unrelated regression (SUR) technique:

> 3 These figures are unconditional. The conventional figures reported in BEEPS are the average number of hours needed to resume interrupted services on the condition that some interruptions happen. Thus, the observations without any service interruptions are usually excluded to compute the average duration of outages.
# **References**

- Ackerberg, D. A., Caves, K. & Frazer, G. (2015), ‘Identification properties of recent production function estimators’, _Econometrica_ **83** (6), 2411–2451.

- Aghion, P., Bergeaud, A., Boppart, T., Klenow, P. J. & Li, H. (2019), A theory of falling growth and rising rents, Technical report, National Bureau of Economic Research.

- Alam, A., Casere, Paloma, A. C. & Udompsaph, C. (2008), Unleashing prosperity: Productivity growth in eastern europe and the former soviet union, Staff Report 45627, World Bank.

- Bartelme, D. & Gorodnichenko, Y. (2015), Linkages and economic development, NBER Working Paper No. 21251, National Bureau of Economic Research.

- Biagi, F. (2013), ICT and productivity: A review of the literature, JRC No. 2013/09, JRC.

- Bond, S., Hashemi, A., Kaplan, G. & Zoch, P. (2020), Some unpleasant markup arithmetic: Production function elasticities and their estimation from production data, NBER Working Paper No. 27002, National Bureau of Economic Research.

- Brynjolfsson, E. & Hit, L. (2010), ‘Beyond computation: Information technology, organizational transformation and business performance’, _Journal of Economic Perspectives_ **4** , 23–48.

- Byrne, D. & Corrado, C. (2017), ‘ICT prices and ICT services: What do they tell us about productivity and technology?’, _Finance and Economics_ **2017/015** . Discussion Paper.

- Campbell, J., Goldfarb, A. & Tucker, C. (2015), ‘Privacy regulation and market structure’, _Journal of Economics & Management Strategy_ **24** (1), 47–73.

- Cette, G., Lopez, J. & Mairesse, J. (2017), ‘Upstream product market regulations, ICT, r&d and productivity’, _Review of Income and Wealth_ **63** , 68–89.

- De Loecker, J., Eeckhout, J. & Unger, G. (2020), ‘The rise of market power and the macroeconomic implications’, _The Quarterly Journal of Economics_ **135** (2), 561–644.

- De Ridder, M. (2019), ‘Market power and innovation in the intangible economy’. **URL:** _https://www.repository.cam.ac.uk/handle/1810/332313_

- Dedrick, J., Gurbaxani, V. & Kraemer, K. L. (2003), ‘Information technology and economic performance: A critical review of the empirical evidence’, _ACM Computing Surveys_ **35** (1), 1–28.

25
could be granted in these formerly restricted areas.<sup>43</sup> The effectiveness with which these provisions have been enforced can sometimes be inferred from local financial markets. For instance, an event study of stock prices of South Korean pharmaceutical firms (most of which focused on copycat production of drugs for which patents were not allowed in Korea) shows that these prices plummeted with the implementation of the 1987 reforms.<sup>44</sup> Finally, there are reforms for which effective implementation has been almost completely absent. In 1993, after extensive negotiations with the United States, China introduced a patent statute which seemed to offer substantial protection for patent holders. However, this law has not been effectively enforced.<sup>45</sup> Interestingly, our regression results tend to confirm the conventional wisdom regarding the IPR environment in China. When versions of equation (1) are estimated using only Chinese data, there is no measurable response whatsoever to the enactment of Chinese patent reform – precisely the result one would expect if effective enforcement were absent and widely recognized as such. On the other hand, estimates of equation (1) on data for countries such as Mexico, where the reforms are widely considered to have been both substantial and effectively enforced, tend to give large and statistically significant results.

> 43   International patent conventions prior to the TRIPs Agreement allowed an inventor up to 12 months to choose the countries in which she would seek patent protection and initiate the patent application process. This period typically dated from the application for patent protection in the first country chosen by the inventor.  After this 12 month period expired, the invention would be effectively regarded as part of the “public domain” in all countries in which the inventor had not explicitly sought protection. Recall that there is no global patent system.  Patent protection must be sought in each country.

> 44   See La Croix and Kawaura (1996).

> 45   Argentina’s 1996 pledge to strengthen the IPR environment, particularly for pharmaceutical products, has also been heavily criticized due to weak enforcement.

33
The probability function **(1)** allows us to estimate a propensity score for each IA. We then use four different methods to match the IMT IAs with non-IMT IAs: kernel density weighted, radius, nearest neighbor, and stratification method. Each method uses a slightly different approach to match the propensity scores of the two sets of IAs – details of these methods can be found in Abadie et al. (2003) and Imbens (2004).

Once we have matched IMT IAs with non-IMT IAs, we measure the impact of IMT on the irrigation system by examining two outcome indicators: maintenance and irrigation service fees collection. Thus, we arrive at four alternate measures of ATT, each of which estimate the impact of IMT on the IAs as the average difference in maintenance and service fee collection between the matched IMT IA and the non-IMT IAs.

We actually use two measures of maintenance indicators: (i) whether the IAs prepare a maintenance plan every year, (ii) whether canals in the IAs are reshaped/maintained more than twice a year or when needed. The effectiveness of irrigation service fee collection is measured by (iii) collection efficiency in the dry season of 2003 for each IA. Collection efficiency is defined as the ratio of actual collection to the target set by NIA.

An alternate parametric way of estimating the impact of IMT on the irrigation system is to use instrument variable method. For this we model the outcome indicators as a function of IA characteristics described above except the IMT peer effect indicator and include predicted IMT as one of the factors affecting the outcome indicators. The instrument variable, predicted IMT, is modeled as **(1)** above. This allows us to partly 19
**Table 3:  Parametric Hazard Regressions**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES||Totalsample||||Excl.|govt guarant|eed contracts||
|**Micro factors**||||||||||
|IFI involved|-0.234***|0.136|-0.0666|-0.0518|-0.558***|0.0422|-0.0664|-0.111|-0.114|
||(0.0453)|(0.103)|(0.109)|(0.111)|(0.187)|(0.126)|(0.127)|(0.146)|(0.152)|
|Priv. share>50%|0.115*<br>|||||||||
||(0.0620)|||||||||
|Priv. share>80% = x|<br>0.433***|0.523***|0.475***|0.481***|0.321***|0.494***|0.340***|0.388***|0.423***|
||(0.0457)|(0.0410)|(0.0423)|(0.0432)|(0.0731)|(0.0496)|(0.0518)|(0.0574)|(0.0571)|
|x * IFI involved||-0.437***|-0.301**|-0.295**|0.193|-0.383***|-0.298**|-0.330*|-0.269|
|||(0.114)|(0.120)|(0.122)|(0.207)|(0.140)|(0.139)|(0.169)|(0.165)|
|Competitive bidding|||||-0.0997|||||
||||||(0.0622)|||||
|OGM sector|0.0380|0.0319|-0.00218|0.0350|-0.171|0.125*|-0.0908|-0.122|-0.104|
||(0.0618)|(0.0618)|(0.0626)|(0.0633)|(0.128)|(0.0683)|(0.0663)|(0.0775)|(0.0714)|
|Energy sector|0.235***|0.235***|0.190***|0.211***|0.173***|-0.0248|0.113***|0.0941*|0.148***|
||(0.0321)|(0.0321)|(0.0329)|(0.0341)|(0.0580)|(0.0405)|(0.0397)|(0.0511)|(0.0451)|
|**Macro factors**||||||||||
|Democracy|-0.561***|-0.575***|-0.502***|-0.551***|-0.134|-0.762***|-0.223***|-0.241***|-0.466***|
||(0.0466)|(0.0466)|(0.0533)|(0.0543)|(0.0955)|(0.0715)|(0.0629)|(0.0668)|(0.0810)|
|Log income pc in 2000 = y|||4.136***|4.191***|2.761**|5.619***|4.573***|1.600|7.805***|
||||(0.614)|(0.635)|(1.210)|(0.855)|(0.813)|(1.073)|(1.191)|
|y^2|||-0.263***|-0.265***|-0.178**|-0.356***|-0.298***|-0.122*|-0.474***|
||||(0.0365)|(0.0378)|(0.0726)|(0.0510)|(0.0480)|(0.0649)|(0.0699)|
|Change in real pc GDP growth|||-3.482***|-3.369***|-1.154*|-4.749***|-1.077***|-1.753***|-0.999**|
||||(0.316)|(0.324)|(0.609)|(0.425)|(0.376)|(0.451)|(0.479)|
|Change in TOT|||-0.0750|-0.0690|-0.652***|0.212***||||
||||(0.0509)|(0.0520)|(0.154)|(0.0656)||||
|Right wing government (last year)||||||-0.393***<br>(0.0595)||||
|Increase in input cost (avg)|||||||12.46***|14.48***|11.50***|
||||||||(0.597)|(0.728)|(0.648)|
|Commodity windfall revenue (avg)|||||||-2.757***|-2.639***|-7.026***|
||||||||(0.249)|(0.303)|(0.943)|
|Constant|-4.135***|-4.102***|-20.34***|-20.68***|-14.19***|-28.40***|-23.58***|-11.38***|-37.25***|
||(0.0937)|(0.0855)|(2.545)|(2.633)|(4.968)|(3.539)|(3.423)|(4.416)|(5.000)|
|Observations|4,923|4,923|4,695|4,382|1,570|3,291|3,388|2,094|2,606|
|Regional fixed effect included|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|<br>Robustness checks||||||||Without LAC region|Without fuel exporters|
|Ln(α)|0.529***|0.530***|0.560***|0.575***|0.553***|0.951***|0.850***|0.855***|0.804***|
||(0.0120)|(0.0120)|(0.0123)|(0.0128)|(0.0201)|(0.0142)|(0.0146)|(0.0185)|(0.0166)|

*** p<0.01, ** p<0.05, * p<0.1; Standard errors in parentheses.

28
**Diagram 1: Risk Probability/Impact Rating Framework**

<!-- Start of picture text --> Descriptive<br>Probability Rating:<br>Low - Extreme<br>Rating Process Probability Index<br> 1 - 256<br>Supervisory Supervisory<br>Attention Stance<br>Index<br>Impact Index<br>Measurement<br>0.25 - 1000<br>Process<br>Descriptive Impact<br>Rating:<br>Low - Extreme<br>Source: APRA<br><!-- End of picture text --> The combined model is known as PAIRS/SOARS, referring to the Probability and Impact Rating System and the Supervisory Oversight and Response System.

An assessment of _capital support_ or _financial strength_ is retained in the rating of defined benefit funds (and the trustee companies of public offer defined contribution funds). In this case financial strength is measured by the extent to which the fund’s assets cover its short-term and actuarially estimated long-term pension obligations, by its earnings performance and by its access to additional capital (for instance, from associated employers) if needed. It is analogous to assessing capital adequacy in the case of banks and insurers.

The method of assessing riskiness and impact, PAIRS, is essentially a structured framework within which supervisors make assessments and reach judgments about the risk areas that are important for each fund and whether they are well managed or not.

For all APRA-regulated entities, the PAIRS assessment of _gross inherent risk_ considers:

- counterparty default risk: risk of losses from failure of a counterparty to meet its obligations

- balance sheet and market risk: risk of losses due to movements in interest rates and other market prices

- insurance risk: insurance underwriting risk, or the risk that insurance cover will not be available as expected when needed

17
Figure 7. Colima, Veracruz, Guanajuato, and Chiapas show the highest negative net migration balances _Net internal migration balance by states_ <!-- Start of picture text --> 200000<br>Men Women<br>100000<br>0<br>-100000<br>-200000<br>-300000<br>Source: Own estimates based on Population and Housing Census 2020. Abbreviation codes in Annex 1.<br>Figure 8. There is a high correlation between net internal migration flows and labor market outcomes<br>16000<br>14000 COA<br>BCS<br>12000 SON<br>COL NL<br>10000 BC<br>80006000 VER TABMEX SLPMICHZACCAMDGOSINTAMNAYCHHGROCDMXTLAPUEMORAGCJAL HGOYUC QROOQRO<br>GTO OAX<br>4000 CHS<br>2000<br>0<br>-250,000 -200,000 -150,000 -100,000 -50,000 0 50,000 100,000 150,000 200,000<br>Net internal migration flow (immigration minus emigration)<br>emigration)<br>(immigration minus<br>Net internal migrant flow NL BC QRO QROO YUC HGO COA JAL BCS AGC MOR PUE TLA CDMX GRO CHH NAY SON TAM SIN DGO CAM ZAC MICH SLP OAX MEX TAB CHS GTO VER COL<br>income<br>(Mexican pesos)<br>Average monthly labor<br><!-- End of picture text --> Figure 8. There is a high correlation between net internal migration flows and labor market outcomes Source: Own estimates based on Population and Housing Census 2020. Abbreviation codes in Annex 1.

# 4.3. Reasons for migration

## **Reasons for migration statistics are essential to distinguish between labor migrants and individuals**

**moving for other reasons and to understand the push and pull factors driving migration decisions.** Depending on the prevailing reasons, specific policy options can be formulated to increase the welfare of immigrants and those who stayed in the origin or host state.

**Economic-related reasons and family reunification appear to be the main reasons reported for interstate migration in Mexico.** Of the total population 15 years or older who lived in Mexico in 2015 and changed their residence to another state, 39.6 percent reported “Economic reasons (i.e., job search or job offer)” as the main reason for interstate migration. “Family reunification or getting married” explains around 37.7 percent, while “Study” represents 6.1 percent and “Crime insecurity and violence” accounts for nearly 4.5 percent of the internal migration among this population group.

12
# _Question 25: Government efficiency in delivering services._

Question 25 asked how efficient in general the government is in delivering services now and how efficient it was 10 years ago (5 years ago in the case of the transition economies). This is a summary question for the preceding 3 questions.

<!-- Start of picture text --> 25. How would you generally rate 90<br>the efficiency of government in<br>delivering services? 80<br>70<br>10 years<br>Now a-go 6<br>(1) very efficient [ [*<br>(2) efficient 0<br>(3) mostly efficient L1 LI 40t<br>(4) mostly inefficient O C*<br>(5) inefficient 0 0 30<br>(6) very inefficient E02 11<br>20<br>10<br>World LDC DC SSEA MNA CEE LAC SSA CIS<br>| 10 (5) yars ago<br><!-- End of picture text -->

Businessmen in all regions, except CIS and SSA thought that government became more efficient compared to 10 years (5 years for transitional economies) ago. The greatest progress was reported by Asian businessmen. 10 years ago the share of businesses who thought that government was inefficient in service provision was about 60 percent. It was even higher in Africa and in MNA region than at present. The situation is worst in LAC, both now and 10 years ago. Despite improvements more than 70 percent of surveyed businessmen in LAC said that government service provision is inefficient (compared to more than 80 percent a decade ago).

43
spatial price adjustment for poverty analysis, it may be useful to exclude items that are not relevant to low-income households.<sup>12</sup> A few but growing number of countries have either officially reported or published as experiments subnational PPPs calculated based on the CPI price data. Those examples include the United States (Aten 2017), the United Kingdom (Office for National Statistics 2018), Germany (Weinand and von Auer 2019), and Italy (Biggeri and Tiziana 2014; Laureti and Rao 2018). An example from a developing country is Dikhanov, Palanyandy, and Capilit (2011) for the Philippines.

Finally, when price data (based on price surveys) are not available, unit values are often used as a proxy for price. As shown in Figure 1, about 40 percent of African countries currently rely on unit values for spatial price adjustment for their official poverty measurement. Unit values have advantages in several aspects, including their availability and geographic coverage. Nevertheless, the limited information about product quality and non-food goods and services is clearly a serious drawback for spatial price adjustment. An example is a substitution bias due to unobserved quality differences in unit values. Since Deaton (1986) established the use of unit values several decades ago, various adjustment methods have been devised to address concerns about the reliability of unit values (Gibson, n.d.). Recent research, however, has emphasized limitations in the use of unit values as a proxy for price data (Gibson and Kim 2019; McKelvey 2011). Thus, it would be better to avoid resorting to unit values for spatial price adjustment unless other price data are not available. In this paper, we focus on market price surveys and the CPI price data.

# **2.2. How should non-food items (particularly housing) be treated in measuring spatial differentials in the cost of living?**

_Treatment of non-food prices_ As people become richer, they tend to spend a lesser portion of their budget on food expenditures. This empirical association between income and food budget share is known as Engel’s law. A similar pattern is observed even at a cross-country comparison, as the average food budget share is higher in low- and middle-income countries (Figure 2). In many African countries, people typically spend more than half of their budget on food expenditures. However, this does not justify the construction of a spatial deflator for poverty measurement solely on food prices. As economies develop, non-tradable non-food items tend to account for a large portion of price dispersion across regions (for example, the Balassa-Samuelson effect), highlighting the importance of measuring those prices.

> 12 For example, Rwanda uses CPI price data to calculate a COLI for poverty measurement (National Institute of Statistics of Rwanda 2016). In the calculation of the COLI, it uses only items that are included in the basket used for poverty line construction. Another interesting study is Dikhanov et al. (2017), which calculates the PPP by focusing on items relevant for low-income households in Africa.

8
11 ―Center for Health Care Econ, U York‖ (1 publication) was not retained for cleaning and aggregation, while the appropriately Anglicized ―Centre for Health Econ, U York‖ (81 publications) was. Imposing this rule resulted in us dropping 12,500 of our 13,500 author addresses. This would be a calamity were it not for the fact that publications are highly unequally distributed across author addresses. The 980 records we retained accounted for around 21,500 (60 percent) of our 37,000 ―publications‖. We think it unlikely that our ―rule of five‖ will have had much—if any—effect on the ranking of institutions, at least at the top of the distribution: it seems likely that (say) Harvard and Yale will be just as likely to have lots of odd spellings for their various departments and research institutes; and in any case, almost 85 percent of the dropped author address records had only one publication to their name.

# **Citation data**

Like Medline, and in contrast to SSCI and SCOPUS, EconLit has no citation data. For the purpose of this study, therefore, each publication was looked up in Google Scholar<sup>16</sup> and the number of citations recorded (the numbers in the paper reflect citations as of June/July 2011). Google Scholar has some attractions over SSCI and SCOPUS as a source of citation data. It captures a very broad set of publications, including books, book chapters, established journals, new journals, open-access journals, technical reports, and even dissertations. This means that we end up finding citation data for a large fraction (85%) of our EconLit records. It also means that the citation data capture scholarly influence across a wide range of publication types, not just a narrow range of academic journals.

As a source of citation data, Google Scholar does, however, have its disadvantages. Titles are also sometimes incorrect, incomplete, ambiguous, or appear in multiple versions. The tool we used to ―harvest‖ citation data went through multiple versions before we could be sure it could cope with mispunctuation and misspellings. Even then we found mistakes, and had to correct manually or drop records that were flagged as outliers and/or which erroneously appeared highly ranked in the tables below. One defect of Google Scholar we have not corrected is the scattering of citations across multiple documents. Some publications appear with somewhat different titles in draft form or in working paper form, and will appear as different publications in Google 16 For details of Google Scholar coverage, see http://scholar.google.com/intl/en/scholar/inclusion.html.