rafmacalaba/datause-encoder-data huggingface dataset:
Department for police stations and police housing, jails and narcotics stores etc. Each of these authorities possesses this land without any compulsion or obligation for optimal utilization of the land. Consequently data are not readily available to develop well informed policies and programs for monetizing excess land and adapting public use to current needs.

Similar studies should be replicated in other cities. Where cities do not have TPS, the maps and databases could be developed from other sources, such as the basic cadasters. (e.g., village maps, city survey maps and relevant extracts of record of rights). This will have to be followed by creating a mosaic of digitized vector maps corrected with reference to geocoded satellite images and then attribute data can be developed in GIS. To accomplish this, a team that is competent in interpreting cadasters and using analytical capabilities of GIS would be necessary. However such a study can be replicated whenever access to ownership records is available on a consistent basis. Such a requirement should be possible to meet in other cities.

This study fairly conclusively demonstrated that unlocking the value of public land could be a significant fiscal contribution to investment in urban infrastructure. Not only is excess public land highly valuable, but this study also shows that aggregate public land holdings have a substantial footprint— amounting to 32 percent of total buildable space even in a city such as Ahmedabad, which is known more for its vibrant private sector than its large government presence.

31
Another factor that may overstate equity capital is the presence of large amounts of accounts receivable (both from brokers and the insured and from reinsurers). Also, large investments in real estate, company shares and other assets that are illiquid and difficult to value fairly may also result in an overstatement of equity. On the basis of 2002 data, 5 companies have high levels of accounts receivable and investments in other illiquid assets (i.e. other than shares or real estate).

A newly solvency margin of 150 percent of required capital was introduced in 2003. This is based on a risk-based capital requirement on both assets and liabilities. Companies are required to comply with the new margin by the end of 2003. Preliminary calculations show that most companies satisfy the new margin.

20
Shetty, S. (1988), "Limited Liability, Wealth Differences and Tenancy Contracts in Agrarian Economies', _Jowrnal of Development Economics, 29_ (1), 1-22.

Tanner, C. (2002), "Law Making in an African Context The 1997 Mozambican Land Law", _FAO Legal Papers Online #26._ Tran, T. Q. (1998), "Economic Reforms and Their Impact on Agricultural Development in Vietnam", _ASEAN Economic Bulletin,_ 15(1),3046.

Udry, C. (1997), "Recent Advances in Empirical Microeconomic Research in Poor Countries: An Annotated Bibliography", _Journal of Economic Education, 28 (1),_ 58-75.

Wan, G. H. and E. Cheng (2001), "Effects of Land Fragmentation and Returns to Scale in the Chinese Farming Sector", _Applied Economics, 33_ (2), 183-194.

World Bank (2000), "Vietnam Rural Strategy, _The World Bank Rural Development and Natural Resources Sector Unit, East Asia and Pacific Region._ World Bank (2002), "Mexico- Land Policy A Decade after the Ejido Reforms", _The World Bank, Rural Development and Natural Resources Sector Unit._ Zeldes, S. P. (1989), "Consumption and Liquidity Constraints: An Empirical Investigation", _Journal of Political Economy, 97_ (2), 305-346.

Zimmerman, F. J. and M. R. Carter (1999), "A Dynamic Option Value for Institutional Change: Marketable Property Rights in the Sahel", _American Journal ofAgricultural Economics, 81_ (2), 467-478.
# **9 Conclusion**

In this paper, we propose a new framework to model institutions and institutional change based on Acemoglu and Robinson [2020] and the theory of norms by Kimbrough and Vostroknutov [2022a]. We show how moral agents, who care about following social norms and thus strive to cooperate with others, can form institutions that facilitate cooperation. Similarly to game-theoretic Industrial Organization, the framework allows to model informal as well as formal institutions as games played by moral agents. It is possible to conceptualize institutions as inclusive or extractive and model institutional change as a consequence of choice of moral agents among available institutions as time unfolds. We demonstrate how the framework can be used to understand the origins of informal institutions. With a series of examples of clientelistic networks, we show that our framework can be useful for understanding how and why such networks form and persist.

While in the paper we show how our framework is able to account for some interesting phenomena related to institutions in developing countries, these models are just mere illustrations of the full power of the framework that can be used to model any interactions among moral agents, thus giving rise to a wide variety of possible institutional settings. This opens new possibilities to model institutional change; devise new types of policies that take into account normative considerations of the agents; and understand informal and formal institutions in greater detail.

We use the example of clientelistic networks to show how the framework can be applied to specific types of institutions. In principle, one direction for future research could be to create more specific and applied models of clientelistic networks or other types of institutions. However, we believe that the power of the framework lies in its ability to produce general conclusions about broader classes of institutions. For that, more theoretical research is needed that could explore the general game-theoretic properties of institutions with moral agents.

Another important direction for future research is to make the framework more practical; to test it with real or experimental/survey data; and to see how to calibrate the model’s parameters from surveys or specifically designed tasks. In the next step, policy implications can be considered and estimated, and cost-benefit analysis of impact could be performed. We believe that with a developed applied methodology, our framework can become an indispensable tool for studying institutions, conducting economic policy, and suggesting paths to economic prosperity.

# **References**

- D. Acemoglu. Politics and economics in weak and strong states. _Journal of monetary Economics_ , 52(7): 1199–1226, 2005.

- D. Acemoglu and J. A. Robinson. _Economic origins of dictatorship and democracy_ . Cambridge University Press, 2006.

- D. Acemoglu and J. A. Robinson. _The narrow corridor: States, societies, and the fate of liberty_ . Penguin, 2020.

35
**Figure 3.** Distance between the social contracts in MENA and OECD

# **4. Econometric analysis**

Our econometric analysis proceeds in three steps. First, since we are mainly interested in the Participation (P3) dimension of the SC, we perform some Granger-type causality tests to investigate its causal relationships with the Provision (P2) and Protection (P1) dimensions. Second, we run a Probit analysis that takes into account the fact that the data are fractional. Third, we test the robustness of our results by applying a propensity score matching analysis.

**_Causality_** To test whether both and cause , we use Dumitrescu and Hurlin's (2012) variant of the Granger (1969) causality test, which is one of the most widely used tests for its simplicity PP2 PP1 PP3 and its suitability for heterogenous panels. Prior to running this causality test, we check whether the indices need to be differenced in order to make them stationary. Table 5 shows the results of a Pesaran (2007) second generation panel unit root test that rejects the null hypothesis of non-stationarity of the three s in levels when both the MENA and OECD data are used. PP

|<br>**Table 5.**Panel unit root tests<sup>+</sup><br>H0<br>MENA<br><br>|OECD|
|---|---|
|P3nonstationary<br>−3.83<sup>∗∗∗</sup><br><br>|−3.53<sup>∗∗∗</sup>|
|P2nonstationary<br>−3.41<sup>∗∗∗</sup><br><br>|−4.89<sup>∗∗∗</sup>|
|P1nonstationary<br>−3.95<sup>∗∗∗</sup><br>|−4.62<sup>∗∗∗</sup>|
|+StandardizedZstatistics reported;*:p< 0.10,**:p< 0.05,***:p< 0.01.||

21
between the cash-flow coefficient for small and large firms given that this difference, though significant, is not very large.'<sup>6</sup>

# 5.2 **Sample split based on long-term and short-term debt**

For investment studies of Western economies the intuition behind such a sample-split is mostly that companies with a relatively large amount of debt are more likely (ex ante) to face liquidity constraints. In this sub-section we first look at a sample-split based on longterm debt and we subsequently split the sample using short-term debt.

From Tables 3 and 5 it could be discerned that there are notable differences among the firms in our sample with respect to the stock of long term debt. The sample is split based on the criterion whether a firm has any long-term debt on its balance sheet or not. As can be seen from Table 8 long term debt is predominantly held by larger firms and by firms with a negative(!) cash flow. Table 7 presents the estimation results.

Table 7 Estimation of the investment equation using long-term debt for sub-sampling

|Dependent variable<br>lB/K<br>Gross investment|Companies with positive<br>LT debt|Companies without LT debt|
|---|---|---|
|C<br>Constant term|0.225|0.234|
|T-statistic<br>Probability|(20.79)<br>(0.00)|(21.16)<br>(0.00)|
|ASAL/K First dif. of sales to fixed capital<br>T-statistic|0.022<br>(2.89)|0.012<br>(2.21)|
|Probability|(0.004)|(0.0275)|
|CF/K<br>Cash Flow to fixed capital|0.032|0.0356|
|T-statistic|(1.14)|(2.13)|
|Probability<br>|(0.252)|(0.0335)|
|AdjustedR<sup>2</sup>|0.0295|0.032|
|F-statistic|8.296|9.618|
|Prob.(F-statistic)|0.000|0.000|
|Sample size: 1993/95|481 companies<br>LTDEBT/K>0|522 companies<br>LTDEBT/K=0|

Numbers in brackets below the estimated coefficients are the t-ratios. Standard errors are robust White standard errors.

The estimation results suggest that firms that have no long-term debt at all are liquidity constrained whereas firms with long-term debt are not. For Bulgarian firms it is the case t6 In order to investigate the sensitivity of the results for the cut-off value of NEMP=709 we ran regressions for the whole range of NEMPL. It turned out that the cut-off value of 700 has the best fit and still the largest difference between the 2 cash-flow coefficients. The mean of the sample (NEMPL=709) is close to this value, so this suggests that mean of NEMPL is an appropriate cut-off value.

18
Learning from Developing Country Power Market Experiences: The Case of Philippines Market intervention may be warranted in some situations, such as the sharp spot price spikes which feed through to final customers in the Philippines. However, such intervention should follow a clear and predefined process in terms of acceptable market outcomes and intervention measures. Following unacceptable market outcomes and the respective assessment, market adaptations should not only address the immediate problems (e.g. by imposing price caps on the spot price as the regulator did in 2013), but also aim at enhancing performance by encouraging competition (e.g. lowering entry barriers and encouraging competition in the contracts market).

# 7 References

- Abrenica, M. J. V. (2009). Detecting and measuring market power in the Philippine wholesale electricity market. _The Philippine Review of Economics_ , _XLVI_ (2), 5–46.

- Abrenica, M. J. V. (2014). _Designing a Competitive Electricity Market_ (Policy Notes) (Vol. 2014–2). Retrieved from http://icsc.ngo/sites/default/files/resources/0B2Q_bksekBT1SjVobE5DQmlHSXc.pdf

ADB. (2016). _Philippines: Electricity Market and Transmission Development Project_ .

- Adviento, P. C. (2015). _WESM Governance & Developments_ . Retrieved from http://www.upecon.org.ph/epdp/wp‐content/uploads/2016/07/4‐Presentation‐Material‐WESM‐ Governance‐Executive‐Course‐May.pdf

- BP. (2016). _BP Statistical Review of World Energy June 2016_ . Retrieved from https://www.bp.com/content/dam/bp/pdf/energy‐economics/statistical‐review‐2016/bp‐ statistical‐review‐of‐world‐energy‐2016‐full‐report.pdf

- DoE. (2016). _27 th Electric Power Industry Reform Act (EPIRA) Implementation Status Report_ . Retrieved from

- https://www.doe.gov.ph/sites/default/files/pdf/electric_power/power_industry_reforms/27th_ep ira_report _30_march_2016‐final.pdf

DoE. (2017). _Philippine Energy Plan 2016‐2030_ . Retrieved from https://www.doe.gov.ph/sites/default/files/pdf/pep/2016‐2030_pep.pdf DOE. (2014). _2014 Power Situation Report_ .

- EIA. (2015). Supply shortages lead to rolling power outages in the Philippines. _Today in Energy_ . Retrieved from https://www.eia.gov/todayinenergy/detail.php?id=20252

- Fe Villamejor‐Mendoza, M. (2008). Bringing Electricity Reform to the Philippines. _The Electricity Journal_ , _21_ (10), 42–58. https://doi.org/10.1016/j.tej.2008.11.003

Market Assessment Group. (2017). _Annual Market Assessment Report For the 2016 Billing Period_ .

- NGCP. (2015). _Transmission Development Plan 2014‐2015 Volume I, Consultation Draft_ . Retrieved from http://www.ngcp.ph/beta/cms/Attachment‐Uploads/TDP_2014‐2015_Vol_I ‐_Draft.pdf

- PEMC. (2014). _STUDY OF MITIGATING MEASURES FOR THE PHILIPPINE ELECTRICITY MARKET PHILIPPINE ELECTRICITY_ .

44
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# **Table 20: Impact on the Regions of Russia of WTO Accession and Simultaneous Energy Efficiency Standards to Reduce Carbon Emissions by 20 Percent, with decomposition of carbon emission effects. (Results are Percentage Change from Base Year.)**

||**Overall**||**St. **||**North-**||||||**Far **|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**average M**|**oscow**|**Peters.**|**Tumen**|**west**|**North**|**Central**|**South**|**Urals**|**Siberia**|**East**|
|**Aggregate welfare**||||||||||||
|Welfare (EVas % ofconsumption)|0.6|0.3|3.3|-3.2|4.2|0.7|1.5|1.0|-0.6|0.9|1.8|
|Welfare (EVas % ofGDP)|0.3|0.2|1.5|-0.7|2.0|0.3|0.7|0.5|-0.3|0.4|0.8|
|**Carbon Dioxide Emissions and Deco**|**mposition**|||||||||||
|CO2price (ruble per tonofCO2)||0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|CO2emissions, decomposedinto:|-19.7|-21.6|-18.9|-20.7|-13.9|-17.5|-18.9|-20.9|-21.7|-16.3|-16.0|
|Outputeffect(% ofCO2)|-3.0|-2.1|-1.1|-4.2|-0.8|-2.6|-2.9|-2.5|-4.4|-2.7|-1.3|
|Compositioneffect(% ofCO2)|-6.5|-7.1|-3.9|-12.7|-3.3|-5.3|-4.1|-6.1|-6.7|-4.8|-4.1|
|Technique effect(% ofCO2)|-10.6|-12.9|-14.0|-3.0|-9.9|-9.9|-12.3|-12.7|-11.3|-9.2|-10.7|
|**Non-Carbon Dioxide Emissions**||||||||||||
|Sulphur Dioxide|-1.3|-15.5|9.2|-16.6|-4.1|-1.6|-12.0|-11.7|-2.0|0.5|-0.4|
|NitrogenOxide|-16.4|-22.2|-17.0|-10.0|-19.9|-17.5|-25.2|-20.2|-15.7|-13.5|-15.4|
|Hydrocarbons|-6.4|-14.8|-10.2|-3.4|-7.5|-4.3|-9.0|-7.1|-9.6|-5.6|5.6|
|ParticulateMatter|-12.0|-22.1|-18.1|-6.2|-24.6|-14.4|-24.7|-22.8|-12.6|-8.8|-9.5|
|Volatile Organic Components|-5.8|-19.1|-11.9|-1.8|-2.0|-4.0|-14.2|-5.4|-8.6|-14.6|-7.5|
|Carbon Monoxide|-1.2|2.9|3.1|-1.9|2.4|-1.1|2.6|-1.9|-2.1|0.1|0.0|
|**Aggregate trade**||||||||||||
|Regional terms of trade (% change)|0.9|24.5|25.4|20.1|25.5|22.7|23.9|23.7|22.6|22.7|24.6|
|Regionalexports (% change)|-6.2|-4.4|-6.6|-5.7|-5.5|-6.1|-6.4|-5.3|-7.5|-7.3|-5.9|
|Realexchangerate (% change)|1.6|1.8|2.4|0.4|2.2|1.6|2.1|1.7|1.1|1.0|2.1|
|Internationalexports (% change)|6.3|4.6|9.9|6.6|5.1|5.4|6.5|8.5|7.3|3.1|6.8|
|**Returnto primary factors (% change)**||||||||||||
|Unskilledlabor|3.1|4.2|6.2|1.6|5.4|3.9|3.7|3.4|1.7|2.5|4.9|
|Skilledlabor|2.5|3.0|6.0|-1.7|6.1|3.9|3.8|3.5|1.2|2.8|5.1|
|Nationalcapital|-0.8|-0.5|0.0|-1.9|-0.1|-0.7|-0.2|-0.6|-1.2|-1.3|-0.3|
|Regional mobile capital|1.5|1.1|3.9|3.2|3.7|2.1|1.3|1.6|0.4|1.2|2.6|
|Crude oil resources|-3.3|||-3.9|-1.3|-1.3||-1.0|-2.8|-3.7|-0.2|
|Naturalgasresources|-33.1|||-35.2|-19.9|-12.4||-9.4|-11.8|-18.9|-11.1|
|Coal resources|5.0|||||7.2|7.9|6.6|4.6|4.2|8.4|
|Specific capital indomesticfirms||-32.9|-25.6|-49.1|-24.6|-26.6|-20.6|-28.1|-20.7|-21.5|-29.3|
|Specific capital in multinational firms||55.1|43.9|216.2|73.3|122.9|109.5|122.1|135.0|155.3|111.5|
|**Factor adjustments**||||||||||||
|Unskilledlabor(% changing sectors)|2.9|2.8|3.2|2.6|3.4|2.9|2.8|2.4|2.9|3.3|3.1|
|Skilledlabor(% changing sector)|3.5|3.4|4.0|3.4|4.0|3.3|3.7|2.9|3.6|3.8|4.0|
|**Source: Authors' estimates**||||||||||||

63
iii This is not our preferred specification because the distribution of doctors versus nurses and midwives may well be endogenous to local factors.

iv This is because the coefficients from Speybroeck et al.’s log-linear regressions are elasticities, such that the estimated coefficient b on the log of health care worker density can be interpreted as a 1% increase in health care worker density, ceteris paribus, leading to a b% change in the mortality rate.<sup><u>18</u></sup> v Robinson and Wharrad and Farahani et al. both report coefficients for doctors only, thus we do not use these as robustness checks as they are not strictly comparable to our model.<sup><u>15,16,19</u></sup> 14
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conditional on observed (predetermined) covariates _X i_ . Matching aligns the distribution of observables between the comparison group and participants.

This leaves the problem of selection bias due to unobservables. To help reduce this bias we exploit a subsample of panel households interviewed in both the baseline (October 2001) and after the program (October 2002) to obtain a double difference (sometimes called “difference-indifference”) impact estimator. This eliminates any time-invariant additive selection bias due to unobserved heterogeneity between participants and the applicants not yet receiving the program. Matching in combination with double-difference has been found to be effective in eliminating selection bias, to the extent that it is due to time-invariant omitted effects that might matter to participation (see, for example, Heckman et al. 1997). The panel sample will also allow us to look at how impact varies according to differences in baseline characteristics.

We have data for October 2002 on _N_ participants, indexed _i_ =1,.., _N_ and _C_ comparators, _j_ =1,.. _C_ in the region of common support, i.e., the set { _i_ : _f_<sup>ˆ</sup> ( _Pi_ | _D_ = )1 > 0) ∩ _f_<sup>ˆ</sup> ( _Pi_ | _D_ = 0) > 0)} where _f_<sup>ˆ</sup> is the empirical density of the scores. The smaller panel sample contains _n_ and _c_ individuals in the matched treatment and comparison groups. Let _Yitk_ be the outcome of interest for individual _i_ at time _t_ in state _k_ . There are two possible states for the outcome: _k_ =1 in the presence of the program, and _k_ =0 in its absence and there are two possible dates _t_ =0 (October 2001) and _t_ =1 (one year later, the date at which program participation is observed). The evaluation problem of estimating the impact of any program stems from the impossibility of observing simultaneously both states for the same individual. Since nobody participates at the

0 1 baseline, we continue to use _Di_ to denote _Jefes_ participation at _t_ =1. Note that _Yi_ 0 = _Yi_ 0 for all _i_ . Our matched single difference estimate of the mean impact is: 16
countries such as Indonesia, Morocco, Thailand and Tunisia. Cameroon’s per capita GNP in 1988 dollars was estimated at US $1,010 (World Bank 1990). These positive achievements in economic growth were generally attributed to fiscal prudence and political stability. The World Development Report of 1988 did praise Cameroon along with Indonesia for managing cautiously the windfall from the 1979-1981 oil boom<sup>2</sup> .

The fact that Cameroon did enjoy high and sustained economic growth throughout 1965-1985 has been abundantly documented (Bradford et al. 1994, World Bank 1995). However, little is known about trends in inequality and poverty during those “good” times for lack of data. Based on the 1983 Household Expenditure Survey, the World Bank (1995) found evidence of high levels of inequality in the distribution of income and rural poverty. The same report discusses factors indicating that the situation may not have been much better in years prior to the 1983 survey. While acknowledging that many urban residents did benefit from this growth episode, the report points to the following factors as contributing to high rural poverty: (1) an incentive structure that favored capital-intensive methods of production over labor-intensive ones; (2) an urban bias in the selection of public investment; and (3) the lack of human capital development in the rural areas.

In 1985, the economy was hit by a collapse of world prices of the country’s major export commodities, namely oil, cocoa and coffee. This was further complicated by a 40 percent appreciation of the CFA franc between 1985 and 1988, and gains in competitiveness by Nigeria since 1985. The export price index fell by 65 percent for oil, 24 percent for cocoa, 11 percent for coffee and 20 percent for rubber (Bradford et al. 1994). Faced with this difficult international environment, the government adopted initially a strategy of internal adjustment<sup>3</sup> between 1985 and 1993. This entailed cutting back on public spending (mainly investment spending) and building up arrears. This policy choice was in part dictated by the fact that, as a member of the franc zone, Cameroon did not have the option of adjusting the nominal exchange rate to deal with the terms of trade shocks. Early 1989, Cameroon entered a structural adjustment supported

> 2 It is reported that Cameroon saved up to 75 percent of the oil revenues abroad, and after the boom, ensured that expenditure grew slower than revenues in order to avoid deficits (World Bank 1988). 3 This point in time also marks the abandonment of five-year plans for socioeconomic management.  The last one was the 5<sup>th</sup> Five Year Development Plan covering the 1982-1986 period.

2