rafmacalaba/datause-encoder-data huggingface dataset:
Yuan was diagnosed with serious uremia in March 1916, and died 3 months later that year. Yuan’s sudden and unexpected death left behind a vacuum of power that led to the fragmentation of China. No other politician could have matched his power to unite the various regional military forces. His former followers formed their own factions, and became rivals for power. Other local leaders who had obeyed Yuan’s orders out of fear of his military power no longer hid their separatist intentions. In 1917, after failing to form a federation government among these antagonist factions, the country became fragmented and fell into the control of numerous warlords.

Now political and military power was largely in the hands of the warlords, and the nominal central government had little control. The warlords made independent decisions over their internal and external affairs. They nominated and appointed local officials and no longer contributed their shares of taxes to the central government. Enjoying the benefits of local control, all warlords wanted more territory, and had to prepare for likely military conflicts on their borders. Consequently, they levied high requisitions on peasants, who lost protection from the central government, and induced “appalling man-made suffering” (Moore 1966, p. 187).

# **2.2 Famines in China**

Over the centuries, Chinese residents had frequently faced the threat of famines. Moreover, after decades of Malthusian growth and dramatic population expansion in the middle of the Qing China, the average land-mass per capita was too small to support a family even during the harvest year (Will, 1990). It left a typical rural household with little private savings and great odds of famine in the event of crop failure. During the 20th century, China, with a total population equaling twice that of Europe, had 46 times higher casualties as a result of famine.<sup>7</sup> Famine prevention and relief has been a key function of the Chinese bureaucratic system (Will, 1990). Mandated by the imperial court, local officials had to provide monthly reports on local weather and precipitation. The central government then issued forecasts on harvests and food shortage in all regions of China. Local officials were also required to keep necessary

> 7 From http://www.emdat.be/advanced_search/index.html, Centre for Research on the Epidemiology of Disasters (CRED).

8
**Table 4:  Summary Statistics on Governance Indicators**

||**Voice and**<br>**Accountability**|**Political**<br>**Stability**|**Government**<br>**Effectiveness**|**Regulatory**<br>**Quality**|**Rule of Law**|**Control of**<br>**Corruption**|**Overall**|
|---|---|---|---|---|---|---|---|
|**Number of Cou**|**ntries**|||||||
|1996|194|180|182|183|171|154|177|
|1998|199|189|194|194|194|194|194|
|2000|200|190|196|196|196|196|196|
|2002|201|190|202|197|197|197|197|
|2003|201|200|202|197|202|198|200|
|2004|208|207|208|204|210|206|207|
|2005|209|208|209|204|210|206|208|
|2006|209|209|212|206|211|207|209|
|**Median Number**|**of Sources Per**|**Country**||||||
|1996|4|4|3|4|6|4|4|
|1998|5|5|4|5|7|5|5|
|2000|5|5|5|6|8|6|6|
|2002|7|6|8|8|11|7|8|
|2003|8|6|8|8|11|8|8|
|2004|8|7|9|8|12|8|9|
|2005|9|7|9|8|12|9|9|
|2006|9|8|10|9|13|10|10|
|**Proportion of C**|**ountries with On**|**ly One Data S**|**ource**|||||
|1996|15|16|21|11|6|18|15|
|1998|11|7|10|10|9|10|10|
|2000|11|8|8|7|7|8|8|
|2002|10|7|5|7|7|8|7|
|2003|3|10|5|7|5|7|6|
|2004|6|6|8|7|9|8|8|
|2005|6|7|8|7|9|7|7|
|2006|6|6|9|8|8|8|7|
|**Average Standa**|**rd Error**|||||||
|1996|0.26|0.36|0.34|0.40|0.30|0.32|0.33|
|1998|0.27|0.30|0.19|0.41|0.22|0.24|0.27|
|2000|0.26|0.31|0.22|0.27|0.19|0.22|0.25|
|2002|0.22|0.29|0.23|0.25|0.19|0.21|0.23|
|2003|0.21|0.29|0.22|0.21|0.20|0.20|0.22|
|2004|0.21|0.28|0.23|0.21|0.19|0.20|0.22|
|2005|0.21|0.28|0.20|0.21|0.20|0.20|0.22|
|2006|0.20|0.27|0.22|0.22|0.20|0.20|0.22|

30
-20 -

own stated objectives - to transform ownership through a process that was fast and fair - the coupon scheme was successful. The fairly narrow objective of Czechoslovakia's privatization program - ownership transformation -wu driven by the conviction that 'ownership mattcrs". Thus any assessment of the coupon scheme's success hinges on whether one believes that simply transferring ownership from the state to the private sector will result In eventual productivity gains. This Is an issue that has been much debated and only recently has there has been some empirical evidence on the wefare consequences of privatization." In detailed case studies of twelve enterprises by Galal et al, productivlty increased in seven firms after privatization and remained constant in five. Domestic welfare, which includes benefits to consumers, govrnment, workers and competitors, improved in ten out of the twelve cases. All of these case studies which have found that ownership matters have been in market economies where privatization was part of an incremental, rather than a fundamental, effort to achieve efficiency gains. Therefore, any economic assessment for a transition economy must also consider the possible consequences for corporate governance. The conclusions that follow evaluate the scheme in greater detail In terms of speed, equity, and corporate governance.

# A. Sped

In terms of speed, the coupon scheme was successful. ITe inventory of all state enterprises for privatization took much of 1991 with a full list of 2744 medium and large scale enterprises selected for the first wave published in November 1991. Six months elapsed for the supply side of the coupon scheme to be prepared, privatizatdon projects to be selected', and a list of the 1491 enterprises included was published in May 1992. The endre bidding process took eight months - from May to December of 1992; Thus the cycle of project preparation, public information, and bidding for almost 1500 enterprises took a total of 14 months - an average of over 3 medium and large scale enterprises privatized per day. The economic gains from speed are more rapid
# **_1._** **_<u>Introduction</u>_**

Timely investment in infrastructure remains a key enabler of Brazilian economic growth (Ferreira and Gonçalves, 2005). In practice, however, the execution of public works—which are the backbone of infrastructure investment—is a protracted and delayprone endeavor in the country (Frischtak, 2008). Among the various stages involved in the public works execution chain, one of the most heavily regulated (through federal- and state-level laws) is the procurement of construction services. As a result, procurement tends to be seen as a unique area of opportunity to drive efficiencies in the infrastructure investment cycle through regulation reform. To what extent are procurement rules responsible for delays in the implementation of infrastructure projects in Brazil? Is it possible to improve the timeliness of Brazilian public investments by changing procurement rules? What are the effects of different procurement rules on the prices paid to contractors for the delivery of public works?

In order to shed some light on these issues, in this paper we assess the effects of a prominent reform to the Brazilian open competitive bidding procurement method for public works. Commonly referred to as _inversão das fases_ , or bid assessment stage inversion, the reform has recently been implemented in four Brazilian states (and is under consideration by Congress at the federal level). The main innovation brought about by _inversão das fases_ vis-à-vis the “conventional” Brazilian public works procurement method is that it allows bid price proposals to be opened and taken into consideration before conducting the bids’ technical evaluation. Such additional information disclosure, in principle, should reduce the amount of time spent on the technical evaluation of submitted bids, as well as the time spent in legal delays arising from the handling of complaints associated with the procurement process. Furthermore, the combination of price and technical information as inputs to the bid evaluation process may influence the prices paid by government agencies for the construction of works.

The main contribution of this paper is the evaluation of a public procurement reform utilizing rigorous empirical analysis. Specifically, we study the impact of _inversão das fases_ along three dimensions of procurement efficiency: (i) procurement process duration; (ii) likelihood of procurement process complaint resolution litigation; and (iii) prices paid to contractors.

To do so, we utilize a new dataset describing public works procurement processes carried out by the largest (and majority state-owned) water and sewage utilities in two Brazilian states—SABESP, of the state of São Paulo, and COPASA, of Minas Gerais— for the years 2007 and 2009. Since 2008 marks the year in which the state of São Paulo reformed the procurement of public works through _inversão das fases,_ we are able to evaluate the reform’s impact at SABESP utilizing a difference-in-differences methodology relative to COPASA in Minas Gerais, a state where no such reform took place.

Page | 2
Table 1. Assessment Results by Issue/Bias and Field

|||Econom|ics (n = 54)|Medicin|e (n = 54)|P-value of Ch|i-square Test|
|---|---|---|---|---|---|---|---|
|Bias|Issue|Number reporting|Number at low risk|Number reporting|Number at low risk|Reporting|Risk of bias|
|||adequately|of bias|adequately|of bias|||
|Selection|Randomization|34|29|52|52|<.001|<.001|
|Selection|Flow of participants|20|20|40|40|<.001|<.001|
|Selection|Baseline demographics|48|46|53|52|.051|.046|
|Attrition|Flow of participants|17|17|51|51|<.001|<.001|
|Attrition|Intent-to-treat|35|33|47|47|.007|.002|
|Performance|Data collection|43|43|51|51|.022|.022|
|Performance|Participant behavior|45|44|42|42|.466|.633|
|Detection|-|37|35|53|51|<.001|<.001|
|Reporting|Protocol/analysis plan|0|0|50|50|<.001|<.001|
|Reporting|Outcomes|0|0|49|49|<.001|<.001|
|Reporting|Interpretation of results|12|12|19|19|<.001|<.001|
|Sample size|-|34|34|51|51|<.001|<.001|
|Aggregated to bias level||||||||
|Selection||9|12|39|40|<.001|<.001|
|Attrition||15|16|46|46|<.001|<.001|
|Performance||38|38|41|41|.515|.515|
|Detection||37|35|53|51|<.001|<.001|
|Reporting||0|0|17|17|<.001|<.001|
|Sample size||1|1|52|52|<.001|<.001|

Source: Authors’ RCT assessment data.

48
<!-- Start of picture text --> 2.7<br>2.4 X(t)<br>2.1<br>1.8<br>1.5<br>1.2<br>0.9<br>0.6<br>0.3<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>The test statistic is scaled by the 5% critical value<br>2.8<br>R1(t)<br>2.4<br>2.0<br>1.6<br>1.2<br>0.8<br>0.4<br>0.0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>H(0)|H(4) H(1)|H(4) H(2)|H(4) H(3)|H(4)<br><!-- End of picture text -->

Figure 2: Recursively estimated trace test based on 75 initial observations In an alternative partial model, we used as a second exogenous variable Thai bank lending with the underlying reason that Thai bank lending growth could qualify as the variable that might explain the structural shift during the crisis period and measure the jump from one equilibrium pre-crisis to the second equilibrium post-crisis. In other words, bank lending could be thought as a proxy for overinvestment in the pre-crisis period, capturing the importance of the investment boom and bust argument by Warr and Vines (2003). This reasoning is plausible since most Thai investment relied on bank-based funding rather than capital markets. But this model is not stable as well.<sup>19</sup> As discussed before, Thailand su¤ered a serious current account de...cit in the pre-crisis period which was often seen as a contributing factor for the worsening economy before the Asian Crisis. We also attempted to include a current account variable as an exogenous variable into the model in various combinations with the exchange rate and banking lending but no stable and well-speci...ed VAR model could be obtained. Finally, partial models with a PPP relation as one of the exogenous variables were not successful as well.

# 5.2 Model 2: Broken linear trend model

Johansen et al (2000) analyze the cointegrated VAR model subject to structural breaks in the trend. The break points are known, and the trend is linear but piecewise. In general, a cointegrated VAR model for each subsample is estimated in which the parameters are the same for all subsamples except the deterministic trend component which could potentially be di¤erent.

Tests for misspeci...cation indicate the strong presence of autocorrelation in the exchange rate. Also, 1-step and breakpoint Chow tests in ...gure 3 provide evidence that the exchange rate faces a structural break in the broken linear trend model. The problem with this model is that we endogenize the exchange rate so the model su¤ers from the noise and volatility of that variable around the crisis period.

> 19 Instead of nominal banking lending which is a stock variable we also used the in‡ation adjusted banking lending growth rate as an exogenous variable but the full sample model remained unstable. 17
- 10 -

|**Table**|**3. Summary statis**|**tics**<br>|||||
|---|---|---|---|---|---|---|
|Variable|Abbreviation|Obs|Mean|Std. Dev.|Min|Max|
|Predicted principal component score|_pca_|154|0.00|1.69|-1.86|7.78|
|Length of roads (km)|_length_|154|7.4|5.1|0.2|34.0|
|Winning bid amount (NRs million)|_bid_|113|7.4|8.1|0.3|37.6|
|Number of bidders|_num_|154|6.0|3.9|1.0|16.0|
|Cost overrun rate (percent)|_costover_|139|4.0|6.4|-14.8|21.7|
|Project delay rate (percent)|_delay_|138|58.6|119.8|-19.3|571.4|
|Number of days required to award a contract|_govteff_|154|64.2|47.6|1.0|190.0|
|Number of security incidents during the three<br>months prior to each tender|_securitybefore_|113|10.8|10.0|0|38|
|Line distance between project and firm origin<br>districts|_dist_|113|93.8|143.9|0|477.47|
|Dummy for the same dominant ethnicity<br>between project and firm origin districts|D(_ethnicity_)|113|0.6|0.5|0|1|
|Dummy variable for postqualification of bids|D(_postqualify_)|154|0.9|0.3|0|1|
|Number of bidders purchasing bidding|_bdnum_||||||
|documents||119|22.4|17.8|4.0|107.0|
|Bid preparation period (days)|_bidtime_|119|26.0|9.2|6.0|52.0|
|Number of security incidents during the project<br>implementation|<br>_securityduring_|139|35.8|48.7|0|280.0|
|Precipitation during the project implementation<br>(mm)|<br>_rainduring_|139|1743.4|1793.1|0|8774.2|
|Difference between the winning bid and the<br>second lowest bid (NRs million)|_lowball_|139|2.7|-29.6|6.29043|0.0|
|Memorandum items:|||||||
|Engineering cost estimate (NRs million)|_cost_|154|8.6|0.2|39.4|0.0|
|Number of lanes|_lane_|154|1.0|0.2|1.0|2.0|
|Thickness of road surface (mm)|_thickness_|154|6.6|14.5|0.0|150.0|
|Gravel (m3)|_gravel_|154|2034.9|2505.4|0.0|18600.0|
|Bitumen (kg)|_bitumen_|154|3407.5|11977.7|0.0|79029.6|
|Earthworks (m3)|_earth_|154|7607.2|11659.7|0.0|93403.0|
|Brickworks (m3)|_brick_|154|62.8|161.3|0.0|1204.1|
|Gabion (m3)|_gabion_|154|239.0|743.9|0.0|8400.0|
|Excavation (m3)|_excavation_|154|2464.4|4851.7|0.0|29266.6|
|Cement concrete(m3)|_cement_|154|36.9|72.4|0.0|507.8|

# **IV. METHODOLOGY**

The basic analytical framework follows Benamghar and Iimi (2011), but this paper focuses more on examining the impacts of changing the size of contracts. One of the empirical issues that need to be addressed is how to measure the size of contracts. Unlike simple government
The statistical results follow more or less the lines that were reported in the case of the single equation models. That is, similar signs and sizes of the coefficients of the various equations in the system. However, several of the important coefficients were not significant in the system equation compared with the single equation estimates. These findings hold also for the simultaneously estimated equation system reported in Table 13. We will discuss the meaning of the results in the following section.

**Table 13: Results of a simultaneously-estimated OLS equation system of decentralization process-performance**

||Equat|ion 1|Equ|ation 2|
|---|---|---|---|---|
||**Wuasinvlv**|**PrblmsAfter**|**Wuasinvlv**|**ImprvRespons**|
|Constant|-0.033<br>(-0.19)|1.945***<br>(2.89)|0.019<br>(0.15)|-0.913<br>(-1.06)|
|%BgtSpnt|-0.09<br>(-1.20)|0.404<br>(1.24)|-0.100<br>(-1.42)|2.293***<br>(2.71)|
|%BgtSrcs|-0.219***<br>(-2.70)|-0.616*<br>(-1.58)|-0.234***<br>(-2.94)||
|ExistUsrGrp|-0.001<br>(-0.02)|0.415**<br>(2.14)|||
|FormsDisput1|0.076***<br>(2.64)||0.081***<br>(2.91)||
|GovrBdy|-0.024<br>(-1.27)|-0.087<br>(-1.06)|-0.025<br>(-1.34)||
|MainObj|-0.046<br>(-0.95)|0.316*<br>(1.65)|-0.038<br>(-0.81)|1.286**<br>(2.34)|
|PltclCst|||0.058*<br>(1.75)|-0.895**<br>(-2.20)|
|PrblmsBfr|0.014<br>(0.84)|0.489***<br>(6.61)|||
|SectrComposi|-0.018<br>(-0.44)||-0.01<br>(-0.27)||
|ShareSw|0.383***<br>(4.16)||0.360***<br>(4.15)|1.304<br>(0.90)|
|Wuasinvlv||-0.427<br>(-0.58)||0.902<br>(0.39)|
|YrsDecentralization|0.04<br>(0.99)|0.029<br>(1.53)|0.005<br>(1.26)|0.024<br>(0.49)|
|System F-test|8.75|***|2.|81***|
|System Adjusted-R<sup>2</sup>|0.4|29|0|.167|
|Note: *** p<0.01; **|p<0.05; * p<0.10.||||

35
What does all of this imply for indicator selection, namely for monitoring nutritional outcomes or nutritional targeting? Clearly, from the nutritional relevance point of view we established that dietary quality may be the most effective lever, and food consumption score and dietary diversity score the most discriminating indicators, but what about the considerations of time and cost effectiveness? A careful comparison of those two dimensions for this exact set of food security indicators is perhaps something that has not been done. But if the length of the questionnaire that one needs to field to obtain the necessary information to come up with each of these indicators is a guide, one can again crudely rank these indicators also in terms of their time and cost effectiveness. See Table 12.

**Table 12: Comprehensive Ranking of Food Security Indicators**

|**Indicator**|**Nutritional**<br>**relevance rank**|**Time and cost**<br>**effectiveness rank**|
|---|---|---|
|Per capita calories|8|5|
|Starchy staple ratio (SSR)|6|4|
|Starchystaple expenditure ratio(SSEXR)|7|4|
|Food consumption score(FCS)|1|2|
|Household dietary diversity score (HDDS)|2|1|
|Mother’s dietary diversity score (MDDS)|2|1|
|Child’s dietary diversity score (CDDS)|2|1|
|Household Food Insecurity Access Scale<br>(HFIAS)|3|3|
|Per capita expenditure|4|4|
|Share of food in total expenditure|5|4|

The household dietary diversity score can be constructed simply on the basis of a series of 9-12 yes/no questions and is perhaps the simplest, cheapest, and quickest indicator. The food consumption score similarly requires only a limited number of questions of the interviewee, but the responses need to be weighted a certain way to come up with the aggregate score. The HFIAS, based on a series of nested yes/no questions, is computationally still simple but captures multiple notions and dimensions of food security. The other food security indicators on the list are computationally more demanding and require a significantly richer set of information that generally comes from elaborate household surveys. The calculation of caloric intake is the most “costly” indicator in this manner of classification because in addition to detailed information on the quantity of various kinds of food items consumed, it also requires accurate food conversion tables.

One of the main conclusions of this report is that the shorter, easier to collect, cheaper indicators such as the food consumption score and the dietary diversity score are also the best performers in terms of nutritional relevance. Food consumption score, in particular, has an additional desirable feature: its 32
Table 3: The impact of PAL on taking ENLACE tests

|_Outcome =_|(1)<br>Took at<br>least one<br>ENLACE|(2)<br>Took at<br>least one<br>ENLACE|(3)<br>Took at<br>least one<br>ENLACE|(4)<br>Took<br>ENLACE|(5)<br>Took<br>ENLACE|(6)<br>Took<br>ENLACE|
|---|---|---|---|---|---|---|
|In-kind|0.066*|0.009|0.002|0.033|0.000|-0.002|
||(0.039)|(0.019)|(0.017)|(0.021)|(0.011)|(0.009)|
|Cash|0.044|-0.001|-0.014|0.027|-0.003|-0.009|
||(0.047)|(0.027)|(0.025)|(0.027)|(0.016)|(0.014)|
|Z-score height for age|||0.022***|||0.015***|
||||(0.005)|||(0.002)|
|Closest school is a general school|||0.029|||0.021|
||||(0.025)|||(0.014)|
|Male|||0.000|||-0.007|
||||(0.014)|||(0.006)|
|Age|||0.100***|||0.073***|
||||(0.004)|||(0.002)|
|ln(Monthly per capita total expenditure)|||-0.014|||-0.004|
||||(0.012)|||(0.005)|
|Age of household head|||-0.001***<br>(0.001)|||-0.001***<br>(0.000)|
|Years of education of household head|||0.011***|||0.006***|
||||(0.002)|||(0.001)|
|Household head is married|||0.071***|||0.022**|
||||(0.025)|||(0.011)|
|Running water in the home|||0.004|||0.009|
||||(0.017)|||(0.008)|
|State fixed effects|NO|YES|YES|NO|YES|YES|
|Year fixed effects|NO|NO|NO|NO|YES|YES|
|Outcome mean in control group|0.65|0.65|0.65|0.27|0.27|0.27|
|Observations|5,444|5,444|3,817|38,108|38,108|26,719|
|**_Effect size:_ In-kind - Cash**|0.02|0.01|0.02|0.01|0.00|0.01|
|**_H 0:_ In-kind = Cash (p-value)**|0.50|0.69|0.51|0.75|0.83|0.64|

Notes: *** p<0.01, ** p<0.05, * p<0.1 (1) Standard errors are clustered at the village level.

(2) The outcome in columns 1-3 are indicators for whether a student was observed to take any ENLACE test between 2007 and 2013, and regressions include one observation per child. The outcome in columns 4-6 vary by year, and regressions include one observation for each child in every year from 2007 to 2013.

32
# **Figure 6:  Milk production in Bihar, Jharkhand and West Bengal, 2002 - 2017 (‘000 tonnes)**

<!-- Start of picture text --> 10000<br>9000<br>8000<br>7000<br>6000<br>Bihar<br>5000<br>West Bengal<br>4000<br>Jharkhand<br>3000<br>2000<br>1000<br>0<br>2002 2007 2012 2017<br><!-- End of picture text --> Source: India National Dairy Development Board (2018) Fourth, it is entirely possible that some spatial clustering is due to differences in the administration of public health and nutrition policy. These may include differences in the staffing and provision of clinical facilities and medical and nutritional outreach programs, the extension of such programs to rural areas, and the administrative efficiency of operations.

In summary, many valuable insights about the sources of spatial variation in mother-child health outcomes may come from further inquiry into the roles of food market conditions, cultural preferences, nutrition education and the administration of health/nutrition. The next phase of the research will focus on exploration of these issues, with possible inclusion of a survey that will target outlier (red and light yellow) districts for both wasting and anemia that have been identified by the regression, tabulation and mapping exercises in this paper.

# **7.  Summary and Conclusions**

The empirical literature on determinants of child wasting and maternal anemia is plentiful for South Asia. In this paper, we have used recent DHS data for Bangladesh and the neighboring Indian 26