rafmacalaba/datause-encoder-data huggingface dataset:
10 In order to get estimates of infrastructure capital stock in each country into the model to facilitate the analysis in this paper, we make the following assumptions. First, advanced countries have approximately equal rates of return in public infrastructure and private capital. Second, there is an obvious difference in the rates of return in public infrastructure and private capital in emerging countries; and we calculate this difference by comparing the infrastructure stock/output (GDP) ratio and assume the output elasticity of infrastructure capital is equal across all the regions in the model. This is supported by Calderon, Moral-Benito and Serven’s (2011) recent study. Third, we also assume that the private capital return is the same across regions. In this sense, the ‘capital return’ we refer here is more like an estimated scalar rather than a real estimated value. The aim to do so is to derive a reasonably comparable size of infrastructure stock so that we can accommodate the productivity shocks for each region in the model simulation. A more detailed illustration of our calculation method can be found in appendix 1.

Once we have an estimate of the infrastructure capital stock in each region we can apply the results from Calderon, Moral-Benito and Serven (2011) who find that for every 10 percent increase in the stock of infrastructure capital, productivity in private sector output rises by 0.8 percent. In the scenarios considered below the permanent changes in fiscal spending mean an accumulating infrastructure capital stock, which, even with the assumed depreciation of 5 percent per year, leads to a large change in the stock after a decade in many emerging economies.
- Guillaume, D., R. Zytek, and M. R. Farzin. 2011. "Iran–The Chronicles of the Subsidy Reform." IMF Working Paper WP/11/167, International Monetary Fund, Washington, DC.

IMF (International Monetary Fund). 2014. Subsidy Reform in the Middle East and North Africa: Recent Progress and Challenges Ahead. Middle East and Central Asia Department, IMF, Washington, DC.

IndexMundi. 2015. http://www.indexmundi.com.

Kojima, M. 2013. Reforming Fuel pricing in an Age of $100 Oil. The World Bank, Washington DC.

Verme, P. and Araar, A. 2016. The Quest for Subsidies Reforms in the Middle East and North Africa Region: A Microsimulation Approach to Policy Making, World Bank, Washington DC 31
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**Table 4:  Programmatic Parties and Project Ratings, Democracy Subsample**

||Base Model|Full Model|Base Model|Full Model|
|---|---|---|---|---|
||Inst. Dev. Im|pact Rating|Overall Out|come Rating|
|Programmatic|5.976<sup>*</sup>|8.688<sup>*</sup>|5.108<sup>**</sup>|9.367<sup>*</sup>|
|Parties|(0.020)|(0.015)|(0.007)|(0.012)|
|IEG Quality at||2.104<sup>***</sup>||3.498<sup>**</sup>|
|Entry||(0.000)||(0.001)|
|ICRG Bureaucratic||0.717||0.407<sup>**</sup>|
|Quality||(0.177)||(0.001)|
|Baseline controls|Yes|Yes|Yes|Yes|
|Region Dummies|Yes|Yes|Yes|Yes|
|Observations|161|147|164|150|
|Pseudo_R_<sup>2</sup>|0.254|0.339|0.135|0.354|

Logistics regressions, with the same specification/control variables as in Table 2. Odds ratios reported; p-values in parentheses + p < 0.10, * p < 0.05, ** p < .01, *** p < .001. GDP per capita and population are averaged over the project period; progression to secondary school is a country average; programmatic parties and ICRG bureaucratic quality are measured at the project start year

# **Unobserved country characteristics and the association of programmatic parties and successful public sector reform**

The results in Tables 2-4 are the first robust evidence of an association between the organizational characteristics of political parties and the success of personnel and financial management reforms of a country’s public administration. However, while we can control for unobserved regional fixed effects that might influence both the presence of programmatic parties and the success of public sector reform, data coverage makes it less easy to control for fixed unobserved country characteristics that might also yield a spurious correlation between parties and reform.

To identify the effects of parties based only on within-country variation in parties and reform success, we require many countries in the sample to exhibit multiple public sector reforms over a sufficiently long period. Of the 109 countries with public sector 35
|**Table A3: Distri**<br>|**butional c**<br>|**hanges in g**<br>|**lobal pove**<br>|**rty profiles**<br>|**with root**<br>|**N allocatio**<br>|**n rule**<br>|
|---|---|---|---|---|---|---|---|
|Region|Per<br>capita<br>poverty<br>rate (%)<br>at**$5.50**|Root N<br>poverty<br>rate (%)<br>at<br>**$12.00**|Change<br>in<br>poverty<br>(pp)|Millions<br>of per<br>capita<br>poor|Millions<br>of root<br>N poor|Change<br>in<br>millions<br>of poor|Absolute<br>deviations<br>in<br>millions<br>of poor|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Middle East &<br>North Africa|45.8|43.5|-2.3|151|143|-7.7|18|
|South Asia|78.5|77.3|-1.2|1379|1357|-21.0|33|
|Sub-Saharan<br>Africa|86.2|85.5|-0.8|901|893|-8.1|21|
|**World**|**49.9**|**49.9**|**0.0**|**2924**|**2924**|**0.0**|**132**|
|Other High<br>Income|1.3|1.7|0.4|13|18|4.5|5|
|Europe and<br>Central Asia|12.6|14.2|1.5|62|70|7.5|16|
|Latin America &<br>Caribbean|23.0|25.0|2.0|134|146|11.7|14|
|East Asia &<br>Pacific<br>Source:Authors’ calcul|44.4<br>ations from th|46.4<br>e Global Mon|2.0<br>itoring Datab|284<br>ase (GMD) an|297<br>d Luxembour|13.1<br>g Income Stu|25<br>dy (LIS)|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD) and Luxembourg Income Study (LIS) Notes: This table reports poverty estimates for the 2017 reference year. For countries with no surveys conducted</u> exactly in 2017, the poverty estimates are extrapolated from the latest survey if conducted before 2017, otherwise extrapolated or interpolated from the closest surveys before and after 2017. The extrapolation and interpolation rules applied are the same ones the World Bank applies when “lining-up” global poverty estimates for every year (see the - Poverty and Inequality Platform (PIP) Methodological Handbook via <u>https://worldbank.github.io/PIP Methodology/lineupestimates.html). The table is arranged in ascending order of change in poverty (column 4). These</u> results are based on the full sample of 162 countries covered in the paper.

47
-9-

producers, consumers and labor interests are all theoretical possibilities; in practice, producer interest groups are likely to be more politically effective.

Regulatory benefits and costs are frequently concentrated for producers and diffuse for consumers. (Labor is somewhere in between.) The costs involved in organizing and maintaining the functioning of an interest group can be large and typically increase with the number of members in the group. As a result, while it may be common for producers to find it worthwhile to form a politically active interest group, or even to undertake political activity individually, consumers would rarely find the expected benefits of interest group activity to exceed the expected costs.<sup>9</sup> As a consequence, producer interest groups may be more effective in pursuing political means to economic ends than are consumer interest groups.<sup>10</sup> As for staffing, former regulated producers may come to dominate the staffing of the regulatory body when regulators require, or are believed to require, very specialized knowledge concerning either the regulatory apparatus or the regulated industry. Often, the only individuals possessing this knowledge are themselves regulated producers, and this leads to the 9 Mancur Olson (1965) provides an excellent discussion of the determinants of group participation in the political process.

10 In the U.S., congressional committee membership is responsive to the particular concerns of the individual congresspersons. As a result, House and Senate members who have a strong interest in some regulatory activity are more likely to serve on the committee with oversight responsibility for that activity. If the senators and congresspersons are producer-oriented for the reasons outlined above, this will be reflected both in legislation aiad in the activities of the regulators who rely on their oversight committees for support and appropriations. (See Weingast, 1981, and Shepsle, 1978.)
# **2. MATHEMATICAL PROGRAMING MODEL FOR ESTIMATING INTERRGIONAL TRADE AND INTERINDUSTRYTRANSACTION FLOWS**

Our model builds upon earlier work by Wilson (1970) and Batten (1982) with two important departures. First, it explicitly incorporates interregional trade flow information into both its accounting framework and initial estimates. We find this greatly enhances the accuracy of estimation results. Second, a multi-regional input-output (MRIO) account is estimated first, then extended to an interregional input-output (IRIO) account, which substantially reduces data requirements and the "dimension explosion" problem in real world applications.

Consider a national economy consisting of N sectors that are distributed over M regions. The sectors use each other’s products as inputs for their own production, which is in turn used up either in further production or by final users. Each region exports some of its products to other regions and some to other nations. They also import products from other regions and nations to meet their intermediate and final demand. Assuming a predetermined location of production that defines the structure of the national economic system of regions, the deliveries of goods and services between regions are determined by imbalances between supply and demand inside the different regions.

_r r r r r_ Denote<sup>_x_</sup> _i_<sup>,</sup><sup>_y_</sup> _i_<sup>,</sup><sup>_v_</sup> _i_<sup>,</sup><sup>_e_</sup> _i_<sup>, and</sup> _mi_ as sector i’s gross output, final demand (excluding exports), value-added, exports, and imports in region r respectively, and denote<sup>_x_</sup> _i_<sup>,</sup><sup>_y_</sup> _i_<sup>,</sup><sup>_v_</sup> _i_<sup>,</sup><sup>_e_</sup> _i_<sup>,</sup> _sr_ and _mi_ as their respective national counterparts. Also denote<sup>_d_</sup> _i_ as delivery of sector i’s product from region r to region s,<sup>_z_•</sup> _ijr_<sup>and</sup><sup>_z_</sup> _ij_<sup>as intermediate transactions from sector i to sector j in</sup> region r and the national level respectively.<sup>3</sup> All variables are measured in annual values. In such a static national system of economic regions, the following accounting identities must hold at each given year for all i ∈ N and s, r ∈ M.

7
|ominal Wages<br>(5)<br>(6)<br>0.001<br>-0.002<br>(0.002)<br>(0.002)<br>82,247<br>0.862<br>0.862<br>82,228<br>0.0003<br>0.002<br>(0.00246)<br>(0.002)<br>0.862<br>0.862<br>82,251<br>82,232<br>0.003<br>-0.00637<br>(0.008)<br>(0.006)<br>82,247<br>82,228<br>**king Age Pop.)**<br>0.288***<br>0.328***<br>(0.023)<br>(0.025)<br>151.57<br>446.21<br>82,738<br>82,719<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>No<br>Yes<br>nd i) armed attacks by illegal armed groups<br>ent transfers to health, education, and other<br>00 GDP share in agriculture, services, and<br>are reported in parentheses. _∗∗∗_signifcant|
|---|
|N<br>(4)<br>0.001<br>(0.0016)<br>82,267<br>0.861<br>0.003<br>(0.00190)<br>0.861<br>82,271<br>0.002<br>(0.005)<br>82,267<br>**DP (% Wor**<br>0.353***<br>(0.013)<br>711.12<br>82,758<br>Yes<br>Yes<br>No<br>No<br>No<br>No<br>ear dummies a<br>entral governm<br>n 1995, viii) 20<br>t the frm level|
|al Employment<br>(2)<br>(3)<br>-0.016***<br>-0.001<br>(0.004)<br>(0.004)<br>82,676<br>0.932<br>0.932<br>82,657<br>-0.008*<br>0.0003<br>(0.00492)<br>(0.004)<br>0.932<br>0.932<br>82,680<br>82,661<br>-0.055***<br>-0.00394<br>(0.016)<br>(0.010)<br>82,676<br>82,657<br>**p. Variable: Share of I**<br>0.288***<br>0.328***<br>(0.023)<br>(0.025)<br>151.57<br>446.21<br>82,738<br>82,719<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>No<br>Yes<br>ntrols include interactions of y<br>lic expenditures in 1995, v) c<br>ber of tax collection offces i<br>5. Clustered standard errors a|
|**Dependent Variables (in logs)**<br>Tot<br>(1)<br>**Panel A. Reduced Form**<br>Predicted Infows<br>-0.010***<br>(0.0027)<br>Observations<br>82,696<br>R-squared<br>0.931<br>**Panel B. OLS**<br>Share of IDP (% Working Age Pop.)<br>-0.001<br>(0.00344)<br>Observations<br>0.931<br>R-squared<br>82,700<br>**Panel C. 2SLS**<br>Share of IDP (% Working Age Pop.)<br>-0.028***<br>(0.007)<br>Observations<br>82,696<br>**Panel D. First Stage**<br>**De**<br>Predicted Infows<br>0.353***<br>(0.013)<br>First Stage F-statistic<br>711.12<br>Observations<br>82,758<br>**Controls (for all panels)**<br>Year FE<br>Yes<br>Firm FE<br>Yes<br>Year_×_Department FE<br>No<br>Homicides Rates<br>No<br>Confict Controls<br>No<br>Additional Controls<br>No<br>_Notes:_ Each coeffcient corresponds to a separate regression. Additional co<br>in 1995, ii) fatal victims of attacks in 1995, iii) homicide rates 1995, iv) pub<br>expenditures in 1995, vi) number of fnancial institutions in 1995, vii) num<br>industry, ix) municipal tax income in 1995, and x) night light density in 199<br>at the 1%, _∗∗_signifcant at the 5%, _∗_signifcant at the 10%.|

35
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Finally, in order to illustrate the use of higher poverty measures, we provide time poverty gap and squared time poverty gaps for the adult population in table 4, using the time poverty line for the normalization. As for table 3, all values have been multiplied by 100. The key conclusions in terms of comparing urban and rural areas, as well as men and women, are the same with these measures as what was observed with the headcount index.

# **_4.3 Time poverty with consumption poverty constraint_**

When now turn to the new definitions of time poverty, which includes a constraint on consumption poverty (or the risk to become consumption poor if one reduces working time). In order to assess whether a currently non-poor household would become poor if an individual who works more than the time poverty line reduces his/her working hours, we need to have an estimate of the hourly earnings of each such individual. For many working individuals we observe wages, but for some we do not. The procedure we used consisted in estimating a wage regression (whose results are provided in Appendix Table A3) in order to impute hourly wages for those individuals working but without a wage (for example because they are self-employed). We also used alternative methods to value the time of working individuals based on the argument that only few individuals are actually paid a “wage” for one hour of their time. For this reason, we derived a value of time by dividing the household consumption by the total working time of all its members (a sort of “household consumption productivity” measure). We also used an alternative measure obtained by dividing the household consumption by the total number of hours spent by household members in employment (for a wage, in the informal labor market, or as contributing family members). Because the simulations results were very robust to alternative estimation methods we provide here only the results based on the actual wage data and the imputed wages as obtained from the regression analysis provided in appendix.

The estimates of time and consumption poverty among adults are reduced by slightly more than half, which is very substantial. With the time poverty threshold of 50 hours, 20.4 percent of all adult individuals are time and consumption poor. This headcount remains much higher for women (24.7 percent) than men (15.1 percent), and in rural areas (26.4 percent) as compared to urban areas (7.7 percent). As before, more women living in rural areas are time and consumption poor (31.5 percent) than women living in urban area (8.1 percent). For men, time and consumption poverty is now also higher in rural than in urban areas (19.5 vs. 7.4 percent), 17
# Table 5: Regressions

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|**..**Dependent Variable|_Raw_<br>_Materials_|_Raw_<br>_Materials_|_Raw_<br>_Materials_|_Upstream_<br>_Inventories_|_Upstream_<br>_Inventories_|_Upstream_<br>_Inventories_|_Raw as % of_<br>_Raw +_<br>_Upstream_|_Raw as % of_<br>_Raw +_<br>_Upstream_|
|Log real PPP GDP/capita|-0.0229<br>(0.0186)|0.0010<br>(0.0285)|-0.0304*<br>(0.0171)|-0.0328***<br>_(0..0950)_|-0.0193*<br>(0.0103)|-0.0320***<br>(0.0077)|0.0444*<br>(0.0227)|0.0523*<br>(0.0274)|
|Telephone mainlines per<br>person|<br>-0.2934***<br>(0.0948)||<br>-0.1968**<br>(0.0928)|<br>0.0950*<br>(0.0539)||<br>-0.0926<br>(0.0549)|-0.5417***<br>(0.1695)||
|<br>Infrastructure Quality||-0.0300***<br>(0.0086)|||0.0021<br>(0.0044)|||-0.0374***<br>(0.0076|
|Transfers & Subsidies/<br>GDP|0.7427***<br>(0.2226)|<br>0.4105**<br>(0.1947)|0.6453**<br>(0.3128)|0.2136*<br>(0.1202)|0.3098**<br>(0.1235)|0.6608***<br>(0.1238)|0.4385<br>(0.4809)|-0.3475<br>(0.4063)|
|Imports/GDP|<br>0.0290*<br>(0.0166)|<br>0.0372***<br>(0.0124)|<br>0.0449<br>(0.0296)||||-0.1765<br>(.1598)|0.1615<br>(0.1596|
|Exports/GDP||||-0.0157<br>(0.0108)|-0.0151<br>(0.0111)|0.0158<br>(0.0110)|<br>0.2721<br>(0.1798)|-0.0767<br>(0.1856)|
|Lending Interest rate<br>(real)|||-0.0317<br>(0.0368)|||-0.0442***<br>(0.0149)|||
|<br>GDP Growth|||-0.0113<br>(0.0073)|||-0.0038**<br>(0.0016)|||
|GDP Growth Standard<br>Deviation|||<br>0.0108<br>(0.0075)|||-0.0066**<br>(0.0019)|||
|(24 industry dummy<br>variables)|Included|Included|<br>Included<br>I__|Included|Included<br>**I_I**|Included|Included|Included<br>**I**|
|<br># of clusters (countries)|42|29|31|44|<br>30|32|41|29|
|<br>R-Squared|.2528|.2897|.2846|.3893|.4291|.4549|.3234|.35 18|
|<br># of Observations|2086|1627|1408|1962|1642|1271|1554|1307|

Robust standard errors corrected for clustering at the country level are in parenthesis.

*Indicated significance at the 10%/o level; **Indicate **s** ignificant at the 5% level; ***Indicate **s** ignificant at the 1% level. Inventories greater than .5 have been dropped for these regressions.

Coefficients in regressions (1)-(6) represent the effect of an absolute change in the explanatory variable on inventory level expressed as fraction of a year. For example, if telephone mainlines per person increased from .5 to .6 in regression (1), inventories would fall by .02934 of a year or about 11 days. Coefficients in regression (7) and (8) represent the effect of an absolute change in the explanatory variable on the percentage of inventories held as raw materials. For example, if telephone mainlines increased from .5 to .6 in regression (7), 5.417% of inventories more are held as raw materials. In the U.S., the median industry<sup>holds</sup> 57% of inventories as raw materials so that the .I change in telephone mainlines leads to a 9% change in holdings.

19
future losses may be unavailable or scanty, and the reinsurer must depend much more on professional judgment and experience to evaluate the nature of an exposure.

Many insurance policies are written on an "occurrence" basis, rather than a "claims made" basis, particularly in the liability lines. With these kinds of underlying policies, claims may be filed years after a policy expires. Reinsurers are also subject to more reporting delays than primary carriers. The reason for this is that with excess of loss and stop loss contracts, if the primary carrier's reserves are set within its retention, no notification may be given to the reinsurer of claims that have been filed. But when the claims are ultimately paid, the retention may be exceeded. It is only at that time, which can be years later in some cases, that the reinsurer is notified.

Another characteristic of reinsurance risk is that it is susceptible to inflation impact. Because the reinsurance portion of coverage is often paid last, it is far more subject to the impact of inflation on repair costs, litigation, and wages than is the primary carrier's portion of coverage, which is paid much sooner. The impact of inflation on reinsurers may be exacerbated where the reinsurer has no capped retention limits. When inflation is severe, reinsurers will require the use of an inflation clause, designed to allocate the impact of inflation more fairly among the contracting parties.

Below we show US industry average loss development patterns for general liability and automobile liability business lines. Notice that in both cases, much more of the ultimate loss is paid sooner by primary carriers vs. reinsurers. This is particularly pronounced with general liability insurance. As the exhibit below reveals, after four development years reinsurers have paid only 39% of their total ultimate loss, whereas primary carriers have paid over two-thirds of theirs. Ten years after a loss has occurred, reinsurers will have paid only two-thirds of their total ultimate loss, whereas primary carriers will have paid over nine-tenths of theirs. Thus, the reinsurer is subject to more claims inflation and uncertainties devolving from changing economic and social/legal conditions than is the primary carrier. Moreover, there is a wide range of experience surrounding these industry averages, that varies by reinsurer and also by year.

<!-- Start of picture text --> Primary vs. Reinsurer Historical Loss Development:<br>100 General Liability (Excluding Environmental Liability)<br>90<br>as<br>-J<br>70 1<br>60<br>50<br>30<br>E10~~~~~~~~~~Ls DevlopentYea<br>* 0<br>0<br><!-- End of picture text -->

**_Sources: A.M. Best Company for primary carriers,_** _Reinsurance Institute_ **_of_** _America for reinsurers_

**In the case of automobile liability, the disparity** in payment patterns is similar to that in general liability **lines, but less pronounced. As the exhibit on the next page reveals,** after four development years reinsurers have paid **only 80% of their total ultimate loss, whereas primary carriers** have paid over 97 percent of theirs. Ten years after a

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