rafmacalaba/datause-encoder-data huggingface dataset:
Finally, since only _relative_ political influence matters for lobbying success in our model, we have A 1 percent increase in head influence has an equivalent effect on water theft and land values as a 1 percent decrease in tail influence.

Before taking these propositions to the data, note that we have described a possible scenario in which an equitable allocation of water between head and tail is Pareto optimal and yet property rights are de facto assigned exclusively to the head. In a Coasean world with zero transactions costs, water would be transferred from head to tail in exchange for payment. While there are many reasons to believe that such water contracts between head and tail outlets of a channel are infeasible, it is worth considering their empirical implications. Of course, no actual theft (misallocation) would be observed in this situation and, in particular, _τ_<sup>_′_</sup> ( _θ_ ) = 0. Moreover, the value of land at the tail would be _s_ 0 _− t_ , where _t_ is the payment to the head, and the value of land at the head would be _s_ 0 + _t._ So, the price of land at the head would still carry a premium relative to the price of land at the tail, but _δ_<sup>_′_</sup> ( _θ_ ) = 0 _._

# **4 Data**

## **4.1 Survey of Irrigation Outlets in Punjab**

In 2016, the World Bank commissioned a survey of 4,294 outlets on 470 irrigation channels distributed across 24 of 49 administrative divisions of the Punjab Irrigation Department (see Figure 2). Only channels in the 32 divisions not covered by the post-2005 irrigation management reform were selected; see Appendix B for the precise selection criteria. Although, for convenience, we sometimes refer to the selected channels collectively as a “sample,” they actually comprise the full population of channels meeting our selection criteria (this point 14
> <sup>18</sup>

_interaction_ of candidate strongholds and reserved seats would be associated with lower MPLADS spending.

Fourth, candidates in reserved districts might be more dependent on political parties because they have less extensive social networks and smaller personal constituencies. Compared to candidates from non-reserved districts, they would be more likely to agree to exert effort on behalf of party objectives at the expense of MPLADS spending. This effect would be greatest when parties are strong: in constituencies that are both party strongholds and reserved seats.

In the next section of the paper we test the effects of voter attachment to parties and political affirmative action on MPLADS disbursements using cross-constituency data after 1999. We take advantage of the fact that, even after the dramatic rise in MPLADS disbursements following the increase in newspaper coverage, 30 percent of districts still had spent less than 75 percent of accumulated allocations by 2004, leaving at least $500,000 of their entitlement unspent. There was also much more variation across the 543 districts after 1999: the standard deviation of utilization rates of accumulated funds across districts increased from 9 percentage points before 1999 to 16 percentage points afterwards. In the state of West Bengal, MPs left 40 percent unspent; in Tamil Nadu, only 6 percent.

# **4. Cross-constituency variation in MPLADS spending:  Data and specifications**

The MPLADS spending data is available from the relevant central ministry responsible for overseeing its implementation. The first available data point is for cumulative spending incurred in each parliamentary district since the inception of the program in 1993 until March 31<sup>st</sup> , 2000 (the end of fiscal year 1999).<sup>17</sup> Three different cohorts of MPs had

> 17 The implementing ministry, the Ministry of Statistics and Program Implementation, informed us that annual data on spending during this period is not available because of lack of proper reporting
**Table 4: The** **<u>effect of closures on labor market variables, robustness for barriers</u>**

||(1)<br>Emp|(2)<br>Emp|(3)<br>Wage|(4)<br>Wage|(5)<br>W_days|(6)<br>W_days|(7)<br>W_hours|(8)<br>W_hours|
|---|---|---|---|---|---|---|---|---|
|_PB_<sup>_30km_</sup>|-0.0004**||-0.0030||-0.0027**||0.0027||
||(0.000)||(0.002)||(0.001)||(0.002)||
|_CP_<sup>_30km_</sup>||-0.0028***||-0.0517***||-0.0208***||0.0328***|
|||(0.001)||(0.010)||(0.006)||(0.008)|
|_REG_<sup>_30km_</sup>||-0.0001||0.0010||-0.0012||0.0001|
|||(0.000)||(0.002)||(0.001)||(0.002)|
|Obs.|96,318|96,318|85,532|85,532|93,865|93,865|88,845|88,845|
|R-sq. within|0.082|0.082|0.027|0.028|0.015|0.016|0.008|0.008|
|Nr. workers|45,584|45,584|42,652|42,652|44,560|44,560|43,838|43,838|

_Robust standard errors (Huber-White method) clustered at individual level in parentheses; *significant at 10%; **significant at 5%; ***significant at 1%. All regressions include individual fixed effects, time effects and a set of controls including years of schooling, age, age squared, marital status, tenure, urban area / refugee camp residence dummies, a set of work place dummies and Hebron district-time effects._

**Table** **<u>5: The effect of closures on labor market variables, no basic controls</u>**

||(1)<br>Emp|(2)<br>Wage|(3)<br>W_days|(4)<br>W_hours|
|---|---|---|---|---|
|_CP_<sup>_30min_</sup>|-0.0044***|-0.0544***|-0.0185**|0.0342***|
||(0.001)|(0.014)|(0.008)|(0.010)|
|_REG_<sup>_30min_</sup>|-0.0005*|0.0028|-0.0011|0.0000|
||(0.000)|(0.003)|(0.002)|(0.003)|
|Basic controls|NO|NO|NO|NO|
|Individual effects|YES|YES|YES|YES|
|Time effects|YES|YES|YES|YES|
|Observations|96,318|85,532|93,865|88,845|
|R-squared|0.080|0.013|0.014|0.007|
|Number of individual|45,584|42,652|44,560|43,838|

_Robust standard errors (Huber-White method) clustered at individual level in parentheses; *significant at 10%; **significant at 5%; ***significant at 1%. All regressions include individual fixed effects, time effects and Hebron district-time effects._ 39
Figure 11. Under 5 Mortality — progress made during 1990-1998 and projections for 2010 and 2015 <!-- Start of picture text --> os<br>Pol<br>2 40<br>ey eee<br>= 30<br>ps<br>0<br>1990 1998 2010 2015<br><!-- End of picture text --> Source: ADB (2002a) A need for sub-national targets - Analysis shows a positive relationship between infant mortality and poverty: infants have a lower chance of survival in poor provinces. Reaching goals will depend crucially on making rapid progress among the poorest 25 percent (Wagstaff and Nga Nguyet Nguyen, 2002) and in the most disadvantaged areas. These regional disparities in mortality rates suggests that there is scope for setting more ambitious targets, in line with the MDGs, for these areas (especially the central highlands and the northern mountains) (Table 7).

Tahle 7: Progress and projections for Mortality Indicators for Disadvantaged Areas.

|IMR<br>|[1990 <br>|2005<br><br>|2010|<br><br>|2015 <br>|atestValue|
|---|---|---|---|---|---|
|Northern|<br>f|<br><br>|<br>t<br>t|<br>t||
|Uplands<br>||||21|44 (1998)<br>|
|CentralHighlands|||||64(1998)|
|<br>Percentofunderweightchildren (0-59months) <br>|P|<br>||P|o<br>|<br>ff<br>|
|NorthernUplands<br>||. |<br>|35<br><br>||25<br><br>|41 (1997)<br>|
|CentralHighlands|f--[|3<br>|5<br>30|[25<br>|420997|

# Source: ADB and WHO (2001).

The Government of Vietnam is highly committed to reducing infant and child mortality. Given the competing demands for scarce resources in the country, focus over the coming decade should emphasize a 25
## **Figure 2**

# **Correlations between our overall index and alternative indicators of aid quality and quantity**

<!-- Start of picture text --> Overall vs. Roodman Index Overall vs. Easterly_Pfutze<br>ASDB ASDB<br>WB WB<br>NLD DNKIRLIFAD IFAD IMF DNKNLDIRL<br>AUS NORGBRFIN SWE AFDBIADB FIN AUSSWENOR IADBAFDBGBR<br>JPN ITADEUAUTPOLTURUNTOTESPUSACHENZLBELFRACANEBRDHUNECLUXGLF NZLESPUNTOTEBRDEC DEULUXCANAUTUSAITA BEL FRACHEJPN<br>GRC GRC<br>KOR<br>CZE<br>PRT PRT<br>0 .2 .4 .6 .8 20 40 60 80<br>Roodman Index Easterly_Pfutze<br>Fitted values Overall Fitted values Overall<br>Roodman vs. Easterly_Pfutze Overall vs. Aid as % of GNI<br>IADB<br>AFDB<br>IFAD EC LUXDNKNLDIRL SWE ASDB WB IRL NLDDNK<br>EBRD FINCAN GBR AUS GBRFIN SWENOR<br>NZLESPUNTOTGRC USA BEL AUSFRACHEPRTNOR POL JPNITA NZLDEUCANFRAESPCHEBEL LUX<br>DEU AUTITA HUNTURUSA AUT<br>GRC<br>KOR<br>CZE<br>JPN<br>PRT<br>20 40 60 80 0 .2 .4 .6 .8<br>Easterly_Pfutze Aid as % of GNI<br>Fitted values Roodman Fitted values Overall<br>2 2<br>1 1<br>0 0<br>Overall<br>-1 -1<br>-2 -2<br>.8 2<br>.6 1<br>.4 0<br>Overall<br>.2 -1<br>0 -2<br><!-- End of picture text --> Source: Authors’ calculations, based in part on Roodman (2006 and 2009) and Easterly and Pfutze (2008)) 27
Table C1: Drivers of Total Debt Inflows Before and After the Global Financial Crisis, by Sector - Advanced Economies (Quarterly AHKS data, missing filled from Public Sources)

||Pr|e-GFC: 20|02q4-2007q|4||Post-GFC:|2008q1-20|14q4|
|---|---|---|---|---|---|---|---|---|
||(1)<br>Total|(2)<br>Public|(3)<br>Banks|(4)<br>Corp.|(5)<br>Total|(6)<br>Public|(7)<br>Banks|(8)<br>Corp.|
|log(VIX_t−_1)|-10.47<sup>_∗∗∗_</sup>|-0.405|-7.224<sup>_∗∗∗_</sup>|-2.843<sup>_∗∗_</sup>|1.879|2.081|-0.255|0.0529|
||(2.308)|(0.817)|(1.805)|(1.143)|(3.714)|(2.351)|(1.938)|(0.647)|
|GDP Growth_it−_1|0.00662|0.0293|0.00464|-0.0273|0.441<sup>_∗∗∗_</sup>|0.153<sup>_∗∗∗_</sup>|0.196<sup>_∗∗_</sup>|0.0919|
||(0.0741)|(0.0309)|(0.0576)|(0.0429)|(0.145)|(0.0462)|(0.0830)|(0.0478)|
|Observations|483|483|483|483|644|644|644|644|
|_R_<sup>2</sup>|0.042|0.001|0.025|0.039|0.030|0.008|0.011|0.013|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Sample is from 2002q4-2014q4, countries as listed in Appendix A.4. Total Debt is the sum of Portfolio Debt and Other Investment Debt inflow data, constructed by AHKS as described in Section 2. Public inflows are defined as the sum of General Government and Central Bank inflows. Dependent variables are expressed as a percentage of GDP. VIX is the implied volatility of S&P 500 index options. GDP growth is calculated as a year-on-year percentage growth. Errors are clustered at the country level. ** p _<_ 0.05, *** p _<_ 0.01
|% Planted with Other Cash Crops||||1.070***|0.941***|
|---|---|---|---|---|---|
|||||(0.180)|(0.188)|
|% Planted with Other||||1.639|1.523|
|||||(1.044)|(1.028)|
|% Planted with Pulses and Oils||||0.146***|0.149***|
|||||(0.046)|(0.044)|
|% Planted with Roots||||0.232***|0.303***|
|||||(0.050)|(0.048)|
|% Planted with Vegetables||||0.215|0.119|
|||||(0.132)|(0.125)|
|Intercropped||||0.409***|0.352***|
|||||(0.035)|(0.035)|
|Family Labor Days per Acre (log)|||||0.255***<br>(0.025)|
|Hired Labor Days per Acre (log)|||||0.116***<br>(0.027)|
|Reverse Dummy for Family Labor|||||0.992***<br>(0.124)|
|Reverse Dummy for Hired Labor|||||-0.022|
||||||(0.082)|
|Any Pesticides Applied|||||0.197**<br>(0.085)|
|Any Manure Applied|||||0.152**<br>(0.075)|
|Any Fertilizer Applied|||||-0.107|
||||||(0.171)|
|Improved Seeds|||||0.183***<br>(0.059)|
|Any Erosion Control or Water|||||0.094*|
|Harvesting Structure on Parcel|||||(0.048)|
|<br>Constant Term|4.345***<br>4.358***|4.075***|3.789***|3.404***|2.374***|
||(0.020)<br>(0.031)|(0.046)|(0.145)|(0.147)|(0.185)|
|Number of observations|6,999<br>6,999|6,999|6,999|6,999|6,999|
|note: *** p<0.01, ** p<0.05, * p<0.1||||||

26
are Sao Tome and Principe, the Comoros, Namibia, Djibouti, Senegal, and Ghana.

Combined, these estimates illustrate the magnitude of the challenges facing PNG. The fact that PNG faces significant monetary and non-monetary poverty challenges is not surprising (e.g., World Bank 2013; Pandey and Howes 2021; World Bank 2023; etc.), and PNG’s status as the poorest ranking country in the East Asia and Pacific region is also not surprising (e.g., World Bank 2023). However, the depth of the challenges across all the indicators and the fact that PNG has some of the highest share of deprivations anywhere in the world is surprising given the relatively large level of resource-related economic activity occurring in the country (e.g., World Bank 2023).

# **Section 4. The Change in Well-Being Following the End of the Resource Boom**

The new estimates of monetary poverty and access to essential services in 2016-2018 described above illustrate that the significant resource-related growth, the associated increase in resource revenue for the government, and substantial foreign assistance have resulted in little change in well-being since 2009. The lack of a change in any measure of well-being is consistent with there being little improvement in either the financial ability of households to afford goods and services and with there being no substantial improvement in the availability of essential services to be utilized.

Figure 6 reports the share of the population that was monetarily poor and deprived in each of the World Bank’s MPM indicators in 2009 and in 2016-2018, and further reports the 95 percent confidence intervals.<sup>24</sup> The similarity in the indicators across the two surveys is striking. The magnitude of the differences are very small for all indicators, with the average change in deprivation across the indicators equal to 0.05 percentage

> 24The indicators were made to be comparable across surveys, where the same categories that were defined as deprived in 2009 were used to define deprivation in 2016-2018. The 2009 estimates differ slightly from the figures reported in the World Bank’s Poverty and Inequality Platform (PIP) because the figures in the PIP impute missing observations based on the prevalence in the nearest geographically representative boundary and because some of the figures are in the process of being revised. In particular, after an evaluation of how access to clean drinking water was defined by experts in the field, one source was identified as mistakenly being identified as unimproved (rainwater) and one source was mistakenly being identified as improved (other sources of water). After these revisions, the access indicator is as is reported in Figure 6- 57.8 percent of the population lacked access to improved drinking water in 2009. As is mentioned above, poor access to improved drinking water in 2016-2018 is defined to be identical to the categorization in 2009.

14
1.2 cross-country evidence on revenue recovery 11

Table 1: Recovery of Taxes in Low-Income Countries, 1982-2006

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||FE|IV|Diff. GMM|System|GMM|
|Lagged Total Tax Revenue|.694***|.665***|.658***|.830***|.758***|
||(.034)|(.041)|(.115)|(.128)|(.082)|
|Trade Tax Revenue|-.045|**-.249****|**-.789***|**-.457*****|**-.320****|
||(.069)|(.103)|(.442)|(.155)|(.126)|
|Share of Imports in GDP|.036**|.044***|.078***|.066*|.066***|
||(.014)|(.016)|(.030)|(.037)|(.019)|
|Natural Resources Exports Per Capita|-.070|-.067|-.061|.023||
||(.080)|(.073)|(.108)|(.504)||
|Oil and Gas Rent Per Capita|||||.010<br>(.083)|
|Share of Agriculture in GDP|-.041*|-.046**|-.120***|-.044|-.049*|
||(.023)|(.020)|(.040)|(.511)|(.026)|
|Share of Aid in GDP|-.010|-.003|-.001|-.027|-.020|
||(.009)|(.010)|(.022)|(.132)|(.014)|
|Log of Infation|.017|.046|-.165|.035|.080|
||(.125)|(.114)|(.160)|(.733)|(.117)|
|Log of Per Capita GDP|-.371|-.071|1.705|-.822|-.545|
||(.630)|(.609)|(2.699)|(15.637)|(.771)|
|VAT|.026*|.027**|.051***|.027|.006|
||(.013)|(.013)|(.019)|(.135)|(.019)|
|_Long term replacement_(ω)|0.148|0.74***|2.31|2.69|1.32***|
||(0.225)|(0.241)|(1.43)|(2.62)|(0.638)|
|Serial correlation (1st order)|||-3.24***|-3.05***|-3.22***|
|Serial correlation(2nd order)|||0.44|0.77|0.61|
|No. of observations|645|643|567|645|672|
|Adj. R-sq.|.87|.86||||
|Time dummies|Yes|Yes|Yes|Yes|Yes|
|No. of countries|35|35|35|35|35|
|No. of instruments|35|35|35|38|38|

Note 1: robust standard errors in parenthesis Note 2: statistical significance indicated as * for p<0.1, ** for p<0.05, and *** for p<0.01 Note <u>3: coefficient of the lagged dependent variable in an OLS model (not shown) is 0.89</u>
the center. As a result, the center has had to resort to various ad hoc instruments to influe= revenue remittance from the localities, and these instruments have led to perverse reactions frct the localities. On the expenditure side, the center has failed to achieve corresponding reductions expenditure when revenue collection has been decentralized. The center's flexibility in ustri expenditure policy has been seriously undermined by the lack of centrally-controlled **f^ian.iL** resources and the heavy burden of "capital constructions." Between 1978 and 1992, the ratio government revenue to GNP dropped from 31 percent to 17 percent. Increasing deficits became **t** problem, and the lack of funds for infrastructure investment exacerbated bottlenecks in - economy.

Due to the lack of fiscal resources and policy instruments, the central government i- found itself in an increasingly difficult position to achieve the goals of macroeconom;.. stabilization, regional equalization, and public goods provision. In early 1994, the cent govermment initiated reform of the tax assignment system in an attempt to address these difficultes Under the new system, the center will recentralize the administration and collection of central a: shared-taxes and will obtain a larger share of fiscal resources as a result of the new revenue-shar=i formula. The VAT is shared 75-25 (centre-local) and all extra central revenues above the 1 Q- levels is then shared 70-30. The central government expected to improve significantly its abilim% use tax and expenditure policies in macroeconomic management as a result of these sterNevertheless, the new system fails to address a number of flaws in the old system: (1) the divisi.cvof tax bases according to ownership will continue to motivate the center to reclaim enterpr.se ownership whenever necessary; (2) the division of expenditure responsibility is not yet clear.. defined; (3) the new system impedes local autonomy as the localities are not allowed to determ'L' the bases and/or rates for local taxes; and (4) the design of intergovernmental transfers is not ful;. settled yet. More recently, in 1994 and 1995, the central government also imposed administratre restrictions on investments by provincial and local governments and their enterprises (see **Mi** 1995, and World Bank, 1994 for further details) to deal with inflationary pressures. M1. recently, the introduction of the State Council Document No.29 in 1996 and other measures **i:** 1997 to consolidate budgetary management over extra-budgetary funds, has sharply restricted the authority of local governments especially rural local governments to impose fees and levies to finance own expenditures (see World Bank, 1998).

IS
Pillar I, by definition, provides a basic benefit that is 25% of the local average wage or 23% of the final wage of the average member of the retiring cohort. Under the real wage growth and interest rate conditions assumed in this paper, Pillar II will yield a 32% replacement rate of final year's salary for an individual who retires after 45 years of contributions, has an expected retirement duration of 19 years, and chooses a price-indexed annuity (see Table 5). Thus, under these conditions, the expected pension for the average worker from the two pillars together, in the long run, is 60% of the economy-wide average wage or 55% of final year salary, the same as the reformed replacement rate in the PAYG system analyzed in Part II.' But the variance of the benefit in the multi-pillar system differs from that of the defined benefit in the PAYG system in at least two important respects. First, the provisicn of a basic benefit that depends on local average wage rather than the worker's own wage means that the replacement rate from the first pillar varies among workers from, say, 46% for a low income worker who earns half the average wage, to 11% for a high income worker who earns twice the average wage, and this means that the total replacement rate varies from 78% to 43t. Thus, a worker does not know ahead of time what his own replacement rate will be. This variance in the replacement rate increases uncertainty in one respect, but it simultaneously reduces the risk of poverty for low wage workers. A basic benefit of this sort could be build into the defined benefit formula, but often it is not and the non-transparency of the formula makes it difficult to calculate the true distributional effects.

Second, the interest rate plays an important role in the funded individual accounts that it did not play in the PAYG system--if it falls so too will the replacement rate and if it does not exceed the growth rate of wages the system is unlikely to produce an acceptable pension benefit. For example, if the financial market reform were delayed so that the interest rate after 2010 was 1% instead of 4%, the replacement rate for the average worker from the defined

> ' The replacement rate would be less for a worker who enters the system before 2010 because during 1996-2010 the interest rate is assumed to be less than the wage growth rate. For instance, a worker who enters in 1995 and retires in 2040 only gets a 53% replacement rate and therefore a lower rate of return. If the lower interest rate continues for longer, so too would the lower replacement rate. This is an example of the fact that both the wage growth rate and the interest rate determine the pension in a defined contribution account, and the worker is therefore subject to interest rate risk.

22
9 and fertility decline lagged well behind Java, although rates had also begun to drop outside Java by the early 1990s.<sup>13</sup> Inter-regional migration has also been a significant factor affecting different rates of population and labour force growth. This occurred especially during the resources boom period and the peak years of the transmigration program - from the mid 1970s to the mid 1980s. The flows mainly from Java to Lampung in south Sumatra in the 1970s gave way to a more diversified pattern towards the end of the decade. New areas of Sumatra and more land abundant regions of Kalimantan and Sulawesi became major destination areas of the transmigration program (Hugo, Hull, Hull and Jones, 1987: 179-185).14 The resource rich provinces and relatively small, underdeveloped provinces in Sumatra recorded particularly high rates of net inmigration. 15 Given some of the above-mentioned differences in economic performance, there was remarkable uniformity in unemployment rates in 1990 (Table 5). Outside Maluku and Irian Jaya where double digit rates were recorded, these ranged between five and nine per cent in most other provinces. Nevertheless, urban unemployment has tended to be higher in the resource rich provinces, in contrast to low rates in Java. 16 It seems likely that high wages in resource-based industries had an impact on interregional rates of migration - especially from Java and Sulawesi - to the resource rich provinces and contributed to higher rates of unemployment _The Changing Structure of Employment._ What was the impact of economic change and labour force growth on employment? Table 5 shows the change in the shares of employment in major sectors in 1971 and 1990, expressed as an index of growth in 13 See Hugo, Hull, Hull and Jones (1987) and Central Bureau of Statistics (1994).

14 The peak period of government assisted transmigration was in the Third Five Year Plan period (1979-84) when it is estimated that slightly over 350,000 households or around 1.5 million individuals were resettled outside Java. In this brief five year period more households were shifted outside Java than in any other period in Indonesia's history. The share of transmigrants going to all provinces of Kalimantan, Central and Southeast Sulawesi and Irian Jaya also rose steeply at this time.

15 Of all provinces, East Kalimantan recorded the most rapid increase in net-migration relative to the size of its population in 1971, mainly related to employment opportunities created by resource booms (first timber and later oil and gas), and supported by movement of households through the transmigration program.

16 Urban unemployment rates remained high throughout this period, although they did not increase significantly more rapidly than in other provinces. Data for 1976-77 and 1991/92 indicate that unemployment rates were highest in resource rich provinces such as East Kalimantan, Riau and Maluku in both periods.