rafmacalaba/datause-encoder-data huggingface dataset:
_Mexico: The 1997 Pension Reform_ _23_

**Table 10. Total Estimated Fiscal Cost of Pension Reform in 1997-2025 (percent of GDP)**

|A. Pensions paid to pre 1997 Retirees|0.39|.09|0.03|
|---|---|---|---|
|BI. MPG to Transition Workers Without<br>INFONAVIT)<br>(MPG to Transition Workers with<br>INFONAVIT)|0.21<br>0.05|0.70<br>0.38|0.80<br>0.53|
|B2. MPG to New Workers|0|0|0.16|
|C. Social Quota and other Govemment<br>Contributions|0.33|0.25|0.20|
|TOTAL**(without INFONAVIT)**|**0.93**|**1.04**|1.19|
|TOTAL (with INFONAVIT)|0.77|0.72|0.92|

_Source: SHCP._ On the methodology, two points should be noted: (i) the income distribution used in the model might underestimate the proportion of the population at the low-end of the distribution, and in turn, might underestimate fiscal costs. However, further analysis of this point provided by the SHCP, indicates that such underestimation would be no more than 10% of the fiscal cost; and (ii) fiscal costs might be sensitive to the evolution of minimum wages relative to the average wage. Under the projected scenarios the minimum wage decreases as a function of the average wage. Should they grow parallel under the projected scenarios the fiscal cost would be higher. However, preliminary calculations of this relation provided by the SHCP show that this cost would be at the most 5% of the total fiscal costs. Also, SHCP demonstrated that historically speaking the Mexican case illustrates that there is a poor correlation between the movement of the minimum wage and the average wage, and that minimum wages have shown a declining trend during the last 25 years.

Table 11 shows present value (PV) estimates of the flow projection of 1% of GDP during the next twenty years. The PV was calculated taking the flow of fiscal costs for the same period of time as the one used to calculate the PV of future pension deficits of 141.5% of GDP (Table 2) and discounting it with an average real discount rate of return of 3.5% (calculated as the difference between the nominal rate of return and the rate of inflation). Other financial assumptions were also consistent in both cases.
|Central African Backbone - APL2|Maria Isabel A. S. Neto|
|---|---|
|China: KunmingUrban Rail Project|Antoine Avedis Kunth|
|China: KunmingUrban Rail Project|Antoine Avedis Kunth|
|China: KunmingUrban Rail Project|Antoine Avedis Kunth|
|China: KunmingUrban Rail Project|Antoine Avedis Kunth|
|China: KunmingUrban Rail Project|Antoine Avedis Kunth|
|China: KunmingUrban Rail Project|Antoine Avedis Kunth|
|China: KunmingUrban Rail Project|Antoine Avedis Kunth|
|China: KunmingUrban Rail Project|Antoine Avedis Kunth|
|China: KunmingUrban Rail Project|Antoine Avedis Kunth|
|China: KunmingUrban Rail Project|Antoine Avedis Kunth|
|China: KunmingUrban Rail Project|Antoine Avedis Kunth|
|China: KunmingUrban Rail Project|Antoine Avedis Kunth|
|China: KunmingUrban Rail Project|Antoine Avedis Kunth|
|China: KunmingUrban Rail Project|Antoine Avedis Kunth|
|Global Partnershipfor Education Grant for Basic Education Project|Peter Darvas|
|Global Partnershipfor Education Grant for Basic Education Project|Peter Darvas|
|Global Partnershipfor Education Grant for Basic Education Project|Peter Darvas|
|Global Partnershipfor Education Grant for Basic Education Project|Peter Darvas|
|Global Partnershipfor Education Grant for Basic Education Project|Peter Darvas|
|Global Partnershipfor Education Grant for Basic Education Project|Peter Darvas|
|Global Partnershipfor Education Grant for Basic Education Project|Peter Darvas|
|Global Partnershipfor Education Grant for Basic Education Project|Peter Darvas|
|Global Partnershipfor Education Grant for Basic Education Project|Peter Darvas|
|Global Partnershipfor Education Grant for Basic Education Project|Peter Darvas|
|Global Partnershipfor Education Grant for Basic Education Project|Peter Darvas|
|Global Partnershipfor Education Grant for Basic Education Project|Peter Darvas|
|Fiscal,Social and Financial Sector Development PolicyLoan(DPL)|Sanja Madzarevic-Sujster|
|Fiscal,Social and Financial Sector Development PolicyLoan(DPL)|Sanja Madzarevic-Sujster|
|Third Development PolicyLoan|Kaspar Richter|
|Third Development PolicyLoan|Kaspar Richter|
|Third Development PolicyLoan|Kaspar Richter|
|Second Development PolicyLoan|Catalin Pauna|
|TAJIKISTAN PDPG 4|RoyS. Canagarajah|
|TAJIKISTAN PDPG 4|RoyS. Canagarajah|
|TAJIKISTAN PDPG 4|RoyS. Canagarajah|
|Georgia: Second Development PolicyOperation|Faruk Khan|
|VN-Public Investment Reform 1|Martin G. Rama|
|VN-Public Investment Reform 1|Martin G. Rama|
|Ethiopia-Transport Sector Project in Support of RSDP4|Stephen Muzira|
|Ethiopia-Transport Sector Project in Support of RSDP4|Stephen Muzira|
|Ethiopia-Transport Sector Project in Support of RSDP4|Stephen Muzira|
|Ethiopia-Transport Sector Project in Support of RSDP4|Stephen Muzira|
|Ethiopia-Transport Sector Project in Support of RSDP4|Stephen Muzira|
|Ethiopia-Transport Sector Project in Support of RSDP4|Stephen Muzira|
|Ethiopia-Transport Sector Project in Support of RSDP4|Stephen Muzira|
|Ethiopia-Transport Sector Project in Support of RSDP4|Stephen Muzira|
Policy Research Working Paper Series

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|WPS2530|The Role of Nongovernmental<br>Organizations in Primary Education:<br>A Study of Six NGOs in india|Shanti Jagannathan|January 2001|S. Kumar<br>87021|
|WPS2531|Growih Implosions, Debt Explosions,<br>and My Aunt Marilyn: Do Growth<br>Slowdowns Cause Public Debt Crises?|William Easterly|January 2001|K. Labrie<br>31001|
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|WPS2534_|indigenous Ethnicity and<br>Entrepreneurial Success in Africa:<br>Some Evidence from Ethiopia|Taye Mengistae|January 2001|R. Bonfield<br>31248|
|WPS2535|Trade Policy Reform in the East Asian<br>Transition Economies|Will Martin|January 2001|L. Tabada<br>36896|
|WPS2536|Malaysian Capital Controls|Ron Hood|January 2001|H. Abbey<br>80512|
|WPS2537|Regulatory Reform in Mexica's<br>Natural Gas Industry: Liberalization<br>In the Context of<br>a Daminant Upstream<br>Incumbent|Juan Rosellén<br>Jonathan Halpern<br>|January 2001|K. Nemes<br>85440,|
|WPS2538|Designing Natural Gas Distribution<br>Concessions in a Megacily: Tradeoffs <br>Between Scale Economies and<br>Information Disclosure in Mexico City|Juan Rosell6n<br> Jonathan Halpern|January 2007|K. Nemes<br>85440|
|WPS$2§39|Disorganization or Self-Organization?<br>The Emergence of Business<br>Associations in a Transition Economy|Francesca Recanatini<br>Randi Ryterman|January 2001|M. Jannsen<br>36320|
|WPS2540|Recapitalizing Banking Systems:<br>implications for Incentives and Fiscai<br>and Monetary Policy|Patrick Hanchan|February 2001|A. Yaptenco<br>31823|
|WPS2541|Household Schooling Decisions in<br>RuralPakistan|Yasayuki Sawada<br>MichaelLokshin|February 2001|P. Sader<br>33802|
transparent that the group of OECD countries. In this respect, EAP and AFR follow after ECA while only MNA fails to cross the threshold of 50% of public strategies.

When we turn to the percentage of strategies expressed in terms of benchmarks the OECD countries dominate in this respect the non-OECD countries, see also section 3.1. From the WB’s regions, ECA, followed by LAC shows the highest percentage of strategic benchmarks, a slightly lower one than the corresponding percentage for OECD countries. One could be curious whether the higher incidence of benchmarks in ECA and LAC could be attributed to a relatively higher analytical capacity for risk management in ECA and LAC in comparison with AFR, EAP and MNA.

# 3.3 Comparison across Levels of Indebtedness

The ...rst row of panels in Figure (3) shows the distributions of the percentage of strategies out of total observations across country groups with di¤erent levels of indebtedness. The second row of panels in Figure (3) shows distributions of the percentage of public strategies and benchmarks out of strategies across country groups with di¤erent levels of indebtedness. The distributions are constructed using two di¤erent bases. In both cases countries were ...rst ordered in ascending order in terms of their levels of indebtedness. The ...rst column of panels in Figure (3) uses the ranges of indebtedness to classify countries into groups. The second column of panels in Figure (3) divides the countries into groups of an equal size. The two slightly di¤erent approaches will help us get a better picture about the distribution of the characteristics of interest across levels of indebtedness.

Consider now the ...rst row of Figure (3) which shows the distribution of the percentage of countries with strategies. Starting from the left, we can observe that the relationship between the probability of a country having a strategy and its level of indebtedness is hump-shaped where countries with debt levels as a percentage of GDP higher than 100% have the smallest probability of having a strategy, i.e. smaller than countries with levels of indebtedness between 0�100%. However, countries with smallest levels of indebtedness between 0 � 50% have lower probability of having a 11
discussed in section 4.2.

# 3.5 The International Natural Rubber Agreement

The rubber agreement is the only ICA which still gives rise to active market intervention. The structure of the INRA is quite complicated - see Table 1. Intervention is defined in terms of a Daily Market Indicator Price (DMIP) which is an average of the Kuala Lumpur, London, New York and Singapore cash prices, all converted to be f.o.b. Malaysia in an average of Malaysian and Singapore C/kg (MS C/kg). The INRA denominates a floor and ceiling which will have been unchanged from the start of the first INRA in October 1980 until the third INRA comes into force in 1996. The effective floor and ceiling are, however, the lower and upper trigger action prices (the LTAP and the UTAP) where the International Natural Rubber Organization (INRO) BSM is required respectively to buy and sell. Within this range are the lower and upper intervention prices (the LIP and UIP) which determine the prices at which the BSM may respectively buy and sell. If the price is between the LIP and the UIP he is constrained to inactivity. The INRAs have been successful in the same terms as the first five ITAs in that the price has generally remained above both the INRA floor and the LTIP - see Figure **5.18** However, as in the ITA, the INRO was unable to contain the price beneath either the ceiling or the UTAP during the AIDS-inspired latex boom of 1987-88 when its buffer stock became exhausted. From 1989 until mid-1994 the rubber price was close to or within the lower intervention range.

The INRA makes provision for automatic revision of the reference price, and hence also the UTAP, UIP, LIP and LTAP which are defined symmetrically around this price, if the average DMIP is above the UIP or below the LIP over a six month period (also if buffer stock sales or purchases exceed a specified amount over a six month period). The reference price was

**18** Source: International Rubber Study Group, _Rubber Statistical Bulletin_ (IRSG, Wembley), various issues.

23
...ndings. There no major changes with the rest of the variables.

One typical objection made to regressions like R2, is that when they include several controls, they are still leaving aside unobserved heterogeneity in the form of time speci...c e¤ects and country speci...c e¤ects. To cope with that unobserved heterogeneity we next include year dummies and country dummies.<sup>27</sup> The estimated equation is: where zi is a variable that takes a unitary value for country i and a value of zero otherwise, and t denotes the value of calendar time.

The results of this ...fth regression, R5, are shown in Table 5. We see that the young dependency ratio, the urbanization ratio, the real interest rate and time and saving deposits ratio are no longer signi...cant, but the in‡ation rate is signi...cant and positive. The per capita gdp level is signi...cant at a 10% level. Other interesting features of the regression are that voluntary funded pension saving is no longer signi...cant while mandatory funded pension saving is still signi...cant and with the expected sign. However, the value of the estimated coe¢ cient of psr:m suggests a substantial substitution between mandatory funded pension saving and ordinary saving that was not captured in R2.

Another problem with regression R2 not addressed in R5 is that some of the controls that we have included might be simultaneously determined 27Our database for Panama, Japan, Canada, Korea and Philippines did not cover the whole universe of pension assets as it does for the rest of the countries. This is an unobserved heterogeneity and is captured by each country dummy.

34
in an area to behave in a certain way, independent of unobserved household heterogeneity, which would affect program participation. That is, household consumption may be completely independent of seasonal variations and still co-vary strongly with the lean season, simply because of common season-specific shocks. Resolving both sources of bias— unobserved household- and village-level heterogeneity and common seasonal effects— requires season-specific household panel data.

InM has pre-intervention baseline information for the target and non-target households from the PRIME villages only. However, the data has limited information in terms of both explanatory variables and outcomes. Table 9 presents the summary statistics of seasonal outcomes in 2005 and 2008 with the pre- and post-intervention data. In the baseline data, we have only subjective measures of food deprivation by season as outcomes.<sup>23</sup> In Table 9, we see that household welfare measured in terms of food deprivation improves between 2005 and 2008. For example, the rate of occasional starvation during the lean ( _monga_ ) season declined from 50.1 percent in 2005 to 45.2 percent in 2008, while the rate of occasional starvation during the non- _monga_ season also decreased from 9.3 percent in 2005 to 2.3 percent in 2008. Year-round starvation similarly declined over this period. <u>Fixed-effects or double difference method</u> As we have more than one year of observation after the program intervention for the same season, we use a fixed-effects (FE) method to estimate the program effect. With panel data available, we rewrite the outcome equation (5), as follows: where _t_ = year and

> 23 However, we earlier found that the qualitative measures of deprivation are good approximations of quantitative measures of deprivation.

34
**table C. Exogenous variables for third child arrival in Turkey**

|||**no 3rd child**<br>**arrival**|**3rd child**<br>**arrival**|**Significance**<br>**of**<br>**difference**|
|---|---|---|---|---|
|**Stability on the labour market**|_Stable full‐time employment_|0,110|0,017|**|
||_Stable part‐time employment_|0,013|0,017||
||_Stable full‐time self employment_|0,107|0,052||
||_Stable part‐time self employment_|0,052|0,052||
||_Stable unemployment_|0,006|/||
||_Stable retirement_|0,005|/||
||_Stable student_|0,000|/||
||_Stable inactivity_|0,667|0,835|***|
||_Stable military service_|0,000|/||
||_Change in activity status within the observed_<br>_three‐monthsperiod)_|0,040|0,026||
|**Partner information**|_Partner in stable employment_|0,813|0,791||
||_Partner not in stable employment_|0,126|0,157||
||_No partner_|0,061|0,052||
||_Partner and married_|0,939|0,948||
||_Partner but not married_|0,000|/||
|**Both in stable employment**||0,227|0,113||
|**Household wage income**|_Zero household wage income_|0,079|0,070||
||_Low household wage income_|0,304|0,409|*|
||_Medium household wage income_|0,314|0,296||
||_High household wage income_|0,303|0,226||
|**Educational attainment**|_Low education (illiterate, prim.not completed)_|0,708|0,843||
||_Medium education (primary and secondary)_|0,239|0,139||
||_High education(tertiary)_|0,053|0,017||
|**Age**|_15‐24_|0,079|0,243|***|
||_25‐34_|0,422|0,617|***|
||_35‐45_|0,500|0,139|***|
|**Age of second child**|_0_|0,090|0,096||
||_1‐2_|0,161|0,278|***|
||_3‐6_|0,234|0,409|***|
||_7+_|0,514|0,217|***|
|**Age difference first‐second child**||4,318|3,087|*|
|**First two children have the same sex**||0,458|0,557|*|
|*p<0,05,**p<0,01,***p<0,001|||||
|Data Base: EU-SILC LT 2006-2011, w omen age|d 15-45 w ith 2 children||||

30
# **1 Introduction**

While agreeing on the importance of bureaucracy for a country’s prosperity, scholars disagree on how the quality of bureaucracy affects the economy (Besley et al. 2021). According to the Weberian view, bureaucrats are mission-driven professionals obeying rules and delivering expert public services and goods (Weber 1922). Their selection is based on mission, expertise, and competence, and their career tracks are based on merits. There is support for this view: better bureaucratic quality at the country level is associated with a higher GDP level, and its improvement is associated with higher GDP growth (Evans and Rauch 1999; Besley et al. 2021). According to the public choice school view, in contrast, bureaucrats are rent seekers that use public offices for private benefits, such as collecting bribes for delivering services, or using the public sector jobs for patronage (Tullock 1967; Stigler 1971; Peltzman 1976). Given the amount of resources under bureaucrat allocation, as well as their impact in determining the regulatory framework, it is important to shed light on the validity of these opposite views. To do this, we must understand how bureaucrats are selected and incentivized, how bureaucratic performance is measured, and how bureaucrats respond to bureaucratic performance criteria.

In this paper, we examine how the incentives of Chinese local bureaucrats shape their behavior in reporting GDP growth, commonly viewed as their most important performance measure (Li and Zhou 2005). This context highlights the three key elements behind understanding bureaucracy: incentives, selection, and measurement (Besley et al. 2021). In particular, how does the upper government design bureaucrats’ career tracks based on their GDP growth performance? How do they report their GDP performance and what are their incentives? Is the reported performance reliable? Does the institutional context matter for bureaucrat behavior in performance reporting?

Besides shedding light on bureaucrat behavior, understanding GDP measurement is important on its own. As the key indicator of economic activity, GDP is at the core of macroeconomic analysis and policy making. The veracity of GDP data is vital to international and interregional comparisons as well as policy decision-making. It is believed that the quality of GDP figures in developing countries is lower mainly for technical reasons such as lower statistical capacity (Johnson et al. 2013; Hu and Yao 2019). However, perhaps due to the lack of full accountability of the government, GDP figures in developing countries are also more susceptible to manipulation.

The Chinese context is particularly relevant for understanding bureaucratic performance for several reasons. _First_ , China has had millenniums of history of centralized states, as well as relatively strong state capacity. Though there might be some accountability in China (Besley and Kudamatsu 2
35 Table 2 (con'd)

||_______________|-___________|___Structur**a**l<br>Equ tion|s|| Reduced Form|
|---|---|---|---|---|---|
|Variables|Births|Deaths|Fertility<br>Intention+|Contraceptive<br>Use|<br>Contraceptive<br>Use|
|Livingchildren|||0.48(7.73)|___||
|Doctor_<5 kn_||||0.19 (0.82)|0.22 (0.80)|
|Clinic 2 <5km||||0.13 (0.85)|-0408(0.43)|
|Clinic 3 < 5 km||||0.32 (2.31)00|0.19 (1.02)|
|Hospital**<**5 km||||0.10 (0.53)|0.22 (1.61)|
|Pharmacie<br>**<**5km||||0.55 (1.09)|0.56 (1.09)|
|Doctor < 10km #||||0.43 (0.93)|0.45 (0.77)|
|Clinic 2 < 10 km#||||-0.38 (0.87)|0.51 (0.98)|
|Clinic 3 < 10 km#||||-0.26 (0.89)|-0.35 (1.29)|
|Hospitat < 10km #||||420 (0.49)|0.05(0.14)|
|Pharmacyc10 km #||||0.36 (1.37)|0.33(1.11)|
|Fertility Intentions||||1.41(3.29)-|_|
|Husband absent||||-0.55 (4.80)-|-0.48 (3.89).|
|r supplies||||0.34(1.54)|0 .7(0.89)|
|r freq sup|||_|-.38 (1.16)|-0.53(1.66)|
|Constant|6.19 (34.13)|0.93 (10.53)|0.36 (1.16)|-6.02 (4.50)..|-0.40(0.83)|
|N|2860|2860|2860|2860|3064|
|_2 _<br>_<br>_|45|15|||1|
|OLS corrected for|Threshold||1.08 (17.14)..|||
|unobserved||||||
|' significa<br>significa<br>sgifir;ant|nt at 10%<br> <br>nt at 5%<br>at 1%|# Rural areas|+ 3 if wants no mo<br>2 if wants to wa<br>1 if wants anoth|re chiidren<br>nt two years<br>er children||
- “In your country, hidden import barriers (that is, barriers other than published tariffs and quotas) are an important problem or not an important problem?”

- “In your industry, how commonly would you estimate that firms make undocumented extra payments or bribes connected with the import and export permits?”

The first question aligns well with the subject of interest here and the answer serves as a proxy to gauge the degree of transparency in the application of non-tariff measures. The second question goes in a similar direction, but focuses more on NTBs related to red-tape and corruption.

We have collected the answers to these two questions for 19 available<sup>9</sup> APEC economies and rescaled the results from 0 (hidden import barriers/extra payments or bribes are not a problem) to 1 (hidden import barriers/extra payments or bribes are a problem). The results are presented in the first and second columns of Figure 2. In order to allow a comparison of APEC economies with other economies we also report the average performance of economies classified by the World Bank as low-income, lower- and upper-middle income, as well as high income.

**Figure 2: Hidden Trade Barriers and Irregular Payments (Coef. of Var.) in the case of APEC**

<!-- Start of picture text -->
1.00<br>0.90<br>0.80<br>0.70<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>Hidden Trade Barriers Coef. of Var. Irreg. Paym.<br>AUS BRN CAN CHL CHN HKG IDN JPN KOR MEX MYS NZL PER PHL PNG RUS SGP THA TWN USA VNM LowMiddle High<br><!-- End of picture text -->

According to the GCR data, Hong Kong China, New Zealand, and Singapore take the lead in this comparison. Most of the middle-income economies in APEC do better than

> 9 Brunei Darussalam and Papua New Guinea were not covered in the GCR 2004.

12