rafmacalaba/datause-encoder-data huggingface dataset:
y��� �α� �α�Corruption��� �α�Regulation��� � α�Corruption��� ∗Regulation��� �α�X��� �α�D� �α�D�� �α�D�� �α�D� �e��� _(1)_ where _y_ is labor productivity (henceforth, productivity), _X_ is control variables; _i_ , _j, s, c_ denote firm, country, industry and cell, respectively; D�, D��, D��, D� denote country, industry, sector and region dummies (fixed effects), respectively. We also include year trend in the model to control for the potential impact of global shocks in different years on productivity, given that data across 111 countries were collected throughout 2009-2017.

The results of the baseline model without any interaction terms are reported in Table 1. As observed, irrespective of the set of controls in place, there is a significant negative correlation between productivity and corruption. Specifically, a 1 percent increase in corruption is associated with around a 0.32 percent decrease in productivity (Table 1).

For the remaining variables, there is no statistically significant relationship between regulation and productivity, once the control variables are included (Table 1). Firm-size has a significant positive relationship with productivity, but this relationship becomes insignificant once we control for training provided to employees and the innovative capacity of the firm ( _Certificate_ ). Thus, one possibility is that the impact of firm-size on productivity travels mainly through its impact on innovation and skills acquired through training. As expected, productivity is significantly higher for exporting firms; firms with higher share of foreign ownership; better access to finance; higher innovative capacity; and higher training opportunities for their employees. As Table 1 shows, these findings hold irrespective of the set of controls in place. In all regressions, we also included per capita income, prevalence of crime, perception of firms on the impartiality of courts, manager’s experience, and gender. We have not reported the results for these variables 19
poverty line to determine the poverty status of each household in each simulation, which is aggregated across simulations to estimate poverty in each area. Estimates of mean squared error are obtained through a parametric bootstrapping approach, which is described in detail in Gonzalez-Manteiga, et al (2008) and Molina and Marhuenda (2015). We leverage the open nature of the code to make one modification to the EMDI package related to the use of weights, which allows for household size to be used as a weight when aggregating the simulated household welfare outcomes into area-level poverty estimates.<sup>16</sup> As a robustness check, we also utilized the second and third versions of an alternative software package, the Stata SAE package (Nguyen et al, 2018).<sup>17</sup> The main differences between the packages are summarized in Table 4, and discussed in Annex A. These differences in design and methodology across different packages, particularly the choice of a non-parametric versus parametric bootstrap method, affects the resulting estimates of poverty rates and their mean squared errors. Comparing simulated small area estimates against actual census data provide evidence on how these differences affect estimated poverty rates and mean squared error in two use-case scenarios.

_Table 4: Differences between Stata SAE and R SAE package_

||Stata ELL||Modified R EMDI|
|---|---|---|---|
||Version 2|Version 3||
|Heteroscedasticity<br>correction|Optional|Optional|Not available|
|Type of bootstrap|Traditional Non-Parametric<br>clustered bootstrap with<br>varyingsample composition.|Parametric bootstrap<br>with constant sample<br>composition.|Parametric bootstrap with<br>constant sample<br>composition.|
|Model fitting method|Henderson method 3 with<br>non-parametric bootstrap|Henderson method 3|Maximum likelihood<br>estimation|
|Empirical Best<br>estimation|Optional|Required|Required|
|Sample weights|Optional|Optional|Not available|
|Sample appended to<br>census for simulations|Not available|Not available with<br>sample weights|Required|

# E.  Normalizing transformation

Welfare typically follows a right-skewed distribution, and it is therefore standard practice to transform the welfare indicator before implementing small area estimation models based on simulated welfare. The most common transformation is to take the logarithm of welfare, following Elbers, Lanjouw, and 16 This modification involved taking a weighted mean instead of a simple mean across areas when aggregating households’ simulated poverty status for each area. Code is available upon request from the authors. 17 Version 2 was released in September 2019 and is available for download at: https://github.com/jpazvd/sae. Version 1 is available through the Stata SSC archive. Version two implemented a clustered bootstrap at the level of the enumeration area, using the traditional non-parametric bootstrap. Version 3 is a comprehensive update that implements a parametric bootstrap approach similar to the R EMDI package (Corral, et al, 2020).

15
13 Figure 5: Domestic and global food prices

# (A) Prices of staple foods

<!-- Start of picture text --> Index, 1=2006 Domestic World<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>(C) Wheat prices<br>Index, 1=2006 Domestic World<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>2012<br>2010 2013<br>2006 2007 2008 2009 2010 2011 2013<br>2006 2007 2008 2009 2011 2012<br><!-- End of picture text -->

# (B) Rice prices

<!-- Start of picture text --> Index, 1=2006 Domestic World<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text -->

# (D) Maize prices

<!-- Start of picture text --> Index, 1=2006 Domestic World<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text -->

- (E) Domestic and global staple food prices during 2007-08 and 2010-11

- (F) Average increase in world and domestic price index, 2010-11

<!-- Start of picture text --> Index, 1=2006 Percent Domestic World<br>3.0 120<br>2.5 100<br>2.0 80<br>1.5 60<br>1.0 Domestic prices: 2007-08 40<br>World prices 2007-08<br>0.5 Domestic prices: 2010-11 20<br>World prices: 2010-11<br>0.0<br>0<br>Rice Maize Wheat<br>t-12 t-10 t-8 t-6 t-4 t-2 0 t+2 t+4 t+6 t+8 t+10 t+12<br><!-- End of picture text --> Sources: Ivanic and Martin (2014b), World Bank. A. Rice, wheat, maize, oil and sugar prices.

E. Event study based on monthly cross-country average domestic staples prices (average of wheat, rice and maize prices) and global staples prices (average of wheat, rice and maize) during 2007-08 and 2010-11. Period 0 represents the month of the peak of the world food price spike.

F. Average percent increase in the price index.
# **Contents**

|I.<br>Introduction .......................................................................................................................................... 4|
|---|
|II.<br>Political Economy Dynamics and Public Sector Reform: Lessons from the Literature ......................... 6|
|II.1 Political Economy Factors_Hindering_Public Sector Reforms .............................................................. 7|
|II.2 Political Economy Factors_Facilitating_Public Sector Reforms .......................................................... 10|
|II.3 Public Sector Reform Implementation and Sustainability ................................................................ 17|
|II.4 Summing up: PE factors favoring and hindering public sector reforms and their frequency across<br>countries ................................................................................................................................................. 21|
|III.<br>Findings from the World Bank Portfolio Review ............................................................................. 23|
|III.1 Public Sector Reform Efforts: Spread and Scope ............................................................................. 23|
|III.2 Frequency and Types of Political Economy Problems: Patterns Across Countries and Operations 26|
|III.3 Attention to Political Economy Drivers: Evolving Practices and Potential Pay-offs ........................ 33|
|IV.<br>Making Public Sector Reforms Work: Preliminary Conclusions ...................................................... 37|
|IV.1 Summary of Key Findings: Public Sector Reform Trajectories ......................................................... 38|
|IV.2 Implications for a Research Agenda and for External Support to Public Sector Reforms ............... 40|
|References .................................................................................................................................................. 43|

2
and 64 percent in March. In Kwajok on the other hand, response rates were both declining and consistently low. After a drop in February, response rates in Malakal increased from their initial level of 73 percent in December to 77 percent in March.

# **Table 4: Attrition and Non-response**

|**Characteristic**|**N**<br>**1**|**Mean**<br>**2**|
|---|---|---|
|Overall response rate: Round 1(December) (%)|1007|68.8|
|Overall response rate: Round 2(January) (%)|1007|62.4|
|Overall response rate: Round 3(February) (%)|1007|55.2|
|Overall response rate: Round 4(March) (%)|1007|51.5|
|Total surveys completed(out of 4): None(%)|1007|16.9|
|Total surveys completed(out of 4): One(%)|1007|12.8|
|Total surveys completed(out of 4): Two(%)|1007|16.3|
|Total surveys completed(out of 4): Three(%)|1007|23.5|
|Total surveys completed(out of 4): Four(%)|1007|30.5|
|Attrition: Dropped out after round 1(%)|1007|7.2|
|Attrition: Dropped out after round 2(%)|1007|6.2|
|Attrition: Dropped out after round 3(%)<br>_1N = Observations with non-missing values_<br>_2N = Unweighted_|1007|10.4|

Participants can be categorized by response pattern: (1) “full-compliers,” who completed all four rounds of the survey, (2) “intermittent compliers,” who completed between one and three surveys intermittently over the four rounds, (3) “drop-outs,” who dropped out after one or more successfully completed surveys and (4) “non-compliers,” who did not complete a single survey. Approximately 17 percent of the sample did not complete a single survey, while 31 percent completed all four surveys between December and March. Approximately half of all households (52 percent) completed between one and three surveys over the data collection period. Of these households, close to half (24 percent of the sample) dropped out after at least one successfully completed interview, 7 percent after round 1, 6 percent after round 2, and 10 percent after round 3.

11
37

**Table 4.25: Decomposition of Change in Poverty (Pa, 1985-93) into Growth and Redistribution Components**

|**P,**Indicator|Total Change|Growth Component|Redistribution Component|Residual|
|---|---|---|---|---|
|P0|-0.089|-0.042|0.141|-0.188|
|P,|-0.010|-0.059|0.067|-0.018|
|P2|-0.004|-0.052|0.038|0.018|
|**_Rural_**_Decomposition_|||||
|P0|-0.133|-0.190|0.013|-0.044|
|P,|-0.037|-0.094|0.065|-0.008|
|P2|-0.006|-0.057|0.066|-0.003|
|**_Urban_**_(Excluding_**_Lag_**|**_os) Decomposition_**||||
|P0|-0.014|-0.077|0.099|-0.036|
|P,|0.043|-0.031|0.080|-0.006|
|P2|0.024|-0.015|0.058|-0.019|
|_Lagos_<br>_Urban Decomposit_|_ion_||||
|P0|0.036|0.008|0.043|-0.015|
|P,|0.027|0.003|0.023|0.001|
|P2|0.014|0.001|0.015|-0.002|
|Source:<br>NCS,<br>1985/86|and 1992/93.||||

Table 4.25 shows the estimates of the decomposition of changes in national, rural, urban excluding Lagos and Lagos urban, for per capita expenditures. The tables give the changes in percentage points, in the two periods of our study 1985/86 and 1992/93. For example, the national headcount started at 43.0 percent in 1985/86 and fell by 8.9 points to 34.1 percent in 1992/93. By components, distributionally neutral growth accounted for a decline of 4.2 points, while the distributional shifts accounted for an increase by 14.1 points; the residual effect contributes to decreasing poverty by 18.8 point. The growth component dominates for all measures and contributes more to poverty reduction. This trend is also true for all the regions except the Lagos Urban, where the redistribution effect contributes to decrease in the P0 and P, measures of poverty in Nigeria. However, the effect of the growth component in all the cases, mitigated the adverse effect of the redistribution effect.
# Policy Research Working Paper Series

||**Title**|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS883|Malaria: The Impact of Treated Bed-<br>Nets on Childhood Mortality in the<br>Gambia|Pedro L. Alonso<br>Allan G. Hill<br>Patricia H. David<br>Greg Fegan<br>Joanna R. M. Armstrong<br>Andreas Francisco<br>K. Cham<br>Brian M. Greenwood|April 1992|0. Nadora<br>31091|
|WPS884|Intercommodity Price Transmittal:<br>Analysis of Food Markets in Ghana|Harold Alderman|April 1992|C. Spooner<br>30464|
|WPS885|The Impact of EC-92 on Developing<br>Countries' Trade: A Dissenting View|A. J. Hughes Hallett|April 1992|A. Kitson-Walters<br>33712|
|WPS886|Factors That Affect Short-Term<br>Commercial Bank Lending to<br>Developing Countries|Sudarshan Gooptu<br>Maria Soledad Martinez Pe|April 1992<br>ria|L. Dionne<br>31426|
|WPS887|Power Sharing and Pollution Control:<br>Coordinating Policies Among Levels<br>of Government|William Jack|April 1992|P. Pender<br>37851|
|WPS888|Transformation of Agriculture in<br>Central Eastern Europe and the Form<br>USSR: Major Policy Issues and<br>Perspectives|Csaba Csaki<br>er|April 1992|C. Spooner<br>30464|
|WPS889|On-th-Job Improvements in Teacher<br>Competence: Policy Options and<br>Their Effects on Teaching and<br>Learning in Thailand|Stcphen W. Raudenbush<br>Suwanna Eamsukkawat<br>lkechuku Di-lbor<br>Mohamed Kamali<br>Wimol Taoklam|Ap.il 1992|C. Cristobal<br>33640|
|WPS890|Population, Health, and Nutrition:<br>Fiscal 1991 Sector Review|Denise Vaillancourt<br>Janet Nassi-n<br>Stacye Brown|April 1992|0. Nadora<br>31091|
|WPS891|Public Institutions and Private<br>Transactions: The Legal and<br>Regulatory Environment for Business<br>Transactions in Brazil and Chile|Andrew Stone<br>Brian Levy<br>Ricardo Paredes|April 1992|G. Orraca-Tetteh<br>37646|
|WPS892|Evaluating the Asset-Based Minimum<br>Tax on Corporations: An Option-<br>Pricing Approach|Antonio Estache<br>Sweder van Wijnbergen|April 1992|A. Estache<br>81442|
|WPS893|The Evolving Legal Framework for<br>Private Sector Activity in Slovenia|Cheryl W. Gray<br>Franjo D. Stiblar|April 1992|CECSE<br>37188|
**Table 3: Household and on-site model estimates of visitation to Lake Sevan**

|**_Variable_**|**_HH: NB_**|**_HH: ZINB_**|**_On-site: TRNB_**|**_On-site: TRNBES_**|
|---|---|---|---|---|
|_Mean model_|||||
|Travel costs|-0.0256***<br>|-0.0153***<br>|-0.0521***<br>|-0.0519***<br>|
||(-5.41)|(-3.46)|(-3.37)|(-4.79)|
|Income|0.00035***<br>(7.54)|0.00015***<br>(3.63)|0.000040<br>(0.60)|0.000013<br>(0.32)|
|Age|-0.0233***|0.0035|-0.0313***|-0.0263***|
||(-6.36)|(0.78)|(-3.45)|(-4.58)|
|Household size|0.1219***|0.0974***|0.2969***|0.2711***|
||(4.02)|(2.64)|(3.57)|(5.26)|
|Education|-0.0094|-0.0686***|-0.0912*|-0.0926***|
||(-0.43)|(-2.66)|(-1.66)|(-2.79)|
|Constant|-0.0392|0.2174|-10.7080|-15.4955|
||(-0.11)|(0.56)|(-0.33)|(-0.12)|
|_Logit inflation model_|||||
|Travel costs||0.0109<br>(0.91)|||
|Income||-0.0012***<br>(-4.77)|||
|Age||0.0903***<br>(8.47)|||
|Household size||0.0313<br>(0.43)|||
|Education||-0.2768***|||
|||(-4.80)|||
|Yerevan city||0.8631***<br>(2.68)|||
|Constant||-1.5611*<br>(-1.83)|||
|α|5.8005|3.7079|13.2317|17.0166|
|Log-likelihood|-3,334.71|-3,249.60|-656.48|-679.79|
|<br>LR test (α=0) ~ χ<sup>2</sup>(d.f.)|6,469.23 (1)|3,271.69 (1)|846.11 (1)|799.49 (1)|
|Vuong test ~ N (0,1)|-|4.86|-|-|
|Number of observations|3,358|3,358|389|389|
|Non-zero observations|842|842|389|389|
|Zero observations|2,516|2,516|||

t-statistics in parentheses; * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

# _(ii) Visitation sensitivity_

The sensitivity of trip demand for the household ZINB and tourist TRNBES models to changes in the parameter values is summarized in Table 4. Beginning with the household survey and under the _binary participation equation_ , estimated coefficients 13
**Table 20 A: Annual value added (in million LCU) in presumed comparative advantage industries by age group of enterprises weighted estimates of totals  at 2000 prices , UBI 2002 and UBI 2009‐**

||**_A. All comp _**|**_arative advantage_**|**_industries_**||**_E. Comme_**|**_rcialfarming_**|||
|---|---|---|---|---|---|---|---|---|
||||% share|in sector|||% shar|e in sector|
|Age Group|Annual valu|e added|total||Annual va|lue added|total||
|(years)|2002|2009|2002|2009|2002|2009|2002|2009|
|< 5|64,235|1,703,575|30.1|17.3|23,064|243,546|11.4|11.4|
|5 to 10|64,750|2,326,414|30.3|23.7|10,691|327,455|19.6|15.3|
|More than 10|84,376|5,791,994|39.5|59.0|20,770|1,567,851|38.1|73.3|
|Total|213,361|9,821,983|100.0|100.0|54,525|2,138,852|100.0|100.0|
||**_B. Compara_**|**_tive advantagep_**|**_rimary indust_**|**_ries_**|**_F. Food a_**|**_nd beverages i_**|**_ndustry_**||
||||% share|in sector|||||
|Age Group|Annual valu|e added|total||Annual va|lue added|% share|in sector total|
|(years)|2002|2009|2002|2009|2002|2009|2002|2009|
|< 5|25,104|363,313|42.5|15.8|25,462|310,718|20.9|7.1|
|5 to 10|11,454|327,455|19.4|14.2|43,730|1,746,088|35.9|40.2|
|More than 10|22,454|1,610,412|38.0|70.0|52,786|2,291,475|43.3|52.7|
|Total|59,013|2,301,179|100.0|100.0|121,978|4,348,281|100.0|100.0|
||**_C. Compara_**|**_tive advantage m_**|**_anufacturing_**|**_industries_**|**G. The gar**<br>**industry**|**ments**|||
||||% shar|e in sector|||||
|Age Group|Annual valu|e added|total||Annual va|lue added|% share|in sector total|
|(years)|2002|2009|2002|2009|2002|2009|2002|2009|
|< 5|36,875|1,333,328|25.5|21.7|8,673|861,239|67.7|87.4|
|5 to 10|50,582|1,976,504|34.9|32.2|1,672|57,873|13.1|5.9|
|More than 10|57,288|2,829,166|39.6|46.1|2,463|66,573|19.2|6.8|
|Total|144,746|6,138,998|100.0|100.0|12,808|985,686|100.0|100.0|
||**_D. Compara_**|**_tive advantage se_**|**_rvices_**||**H. Land t**|**ransport servic**|**es**||
||||% share|in sector|||||
|Age Group|Annual valu|e added|total||Number|of workers|% share|in sector total|
|(years)|2001|2009|2002|2009|2002|2009|2002|2009|
|< 5|2,255|6,934|23.5|0.5|2,161|6,612|23.0|2.8|
|5 to 10|2,714|22,456|28.3|1.6|2,607|10,354|27.7|4.4|
|More than 10|4,634|1,352,415|48.3|97.9|4,632|218,371|49.3|92.8|
|Total|9,603|1,381,805|100.0|100.0|9,400|235,336|100.0|100.0|

57
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# References

Bulatao, Rodolfo AX and Eduard Bos. 1989. _Projecting Mortality for All Countnes._ World Bank Policy, Resaarch, and External Affairs Working Paper 337. Washington, D.C.

- Coale, Ansley J., and Paul Demeniy, with Barbara Vaughan. 1983. _Regional Model Life Tables ana Stable Populations._ 2nd ed. New York: Academic Press.

- Coale, Ansley J. and Guarig Guo 1991. "The use of new model life tables at very low mortality in population -rojictions." _Population Bulletin of the United Nations,_ No. 30:1-22.

- Eurostat. 1987. _Denmographic Statistics._ Luxembourg: Statistical Office of the European Communities.

- Jamison, Ellen. 1989. _World Popuiation Profile: 1989._ United States Department of Commerce, Bureau of the Census.

- Monnier, Alain. 1988. "La conjoncture aemographique: L'Europe et les pays developpds d'outremer." _Population_ 43(4-5):893-904.

- United Nations. Department of International Economic and Social Affairs. Various years. _Population and Vital Statistics Report._ New York: United Nations.

- -----. 1983. _Manv al X. Indirect Techniques for Demographic Estintation._ New York: United Nations. -----. 1989. _World Population Prospects 1988._ New York: United Nations.

- -----. 1989. _Guide to the Estimation of Child Mortality._ New York: United Nations. 1991. ------ _World Population Prospects 1990._ New York: United Nations.

9
**Table 4: Description of charges relating to registration of land transactions**

|**Type of charge**||**Rate**||**Details**|
|---|---|---|---|---|
|Stamp duty||4.2%||Prescribed in Schedule IA of the<br>Indian Stamp Act, 1899 -<br>frequently revised. Present rate<br>fixed since 1985.|
|Stamp duty surcharge|Value<br>Upto Rs.<br>2,000<br>Rs. 2,001-<br>R.s. 5,000<br>Rs. 5,001-<br>Rs. 10,000<br>Rs. 10,001-<br>Rs.25,000<br>Above<br>|Rural<br>6.2%<br>7.7%<br>8.7%<br>9.7%<br>|Urban<br>7.2%<br>9.2%<br>11.2%<br>12.7%<br>|Surcharge levied for sale, gift,<br>settlement, mortgage and lease<br>transactions vide Additional<br>Stamp Duty Act, 1986|
||Rs. 25,000|10.7%|14.7%||
|Registration fee||2%||Payable under the Indian<br>Registration Act, 1908|
|Mutation fee|_______________|Rs. 8<br>_________________|____|For transfer of Form No. 3 from<br>the sub-registrar's office to the<br>tehsil office for initiating<br>mutation. Also the fee paid in<br>inheritance cases to initiate<br>mutation at the tehsil.|
|Writing of the sale deed||Rs. 40-50||Paid to private, licensed scribes<br>_(mohoris)_|
|Demarcation fee, if part of a<br>plot||Rs. 4 per plot||Penalty for not purchasing whole<br>plot. Paid to the tehsil office for<br>it to undertake demarcation of the<br>new plot|
|Search fee|______________|Rs. 17<br>____________||To check last 12 years' records -<br>mostly in urban areas|
|Endorsement fee||Rs. 5|||
|Incidental fee<br>||Rs. 2||As witness fee if paying money<br>|
|Consenting fee, ifjoint<br>property<br>|R<br>l|s. 40 per person||To get permission of joint holders<br>of the property|

Note: Figures in percentage are as percent of the value of the transaction. Source: Inspector-General of Registration, Board of Revenue, Cuttack, 1998.

reflect the true volume or value of land transactions owing to the widespread practice of under-valuing property in order to reduce stamp duty and registration fees payable (Box 8)22. The latter are fixed as a proportion of the value of property being bought or sold, and usually amount to 17 percent of the registered value of the land transferred. Even taking under-valuation into account, this represents a highly significant transaction cost. Issues relating to rationalisation of the stamp duty to prevent evasion are discussed in Box 8.

> 22 The RI, Begunia circle was not sure of the incentive to undervalue property since using the land as collateral a land purchaser is able to obtain bank loans for 10 times the face value of the land shown in the sale deed. He estimated that increasing access to formal credit is the primary motivation for around a quarter of all land purchases in the region.

30