rafmacalaba/datause-encoder-data huggingface dataset:
# **2.1 Multiple switching and never-switching: Common issues with MPL data**

Despite their widespread use, there are a number of issues with MPLs that many, if not most, researchers have encountered. One common issue is inconsistent choices, often termed multiple switching behavior (MSB). Take a (hypothetical) subject who chooses “no report” in row 2, but “report” in row 3 of the Allcott and Kessler (2019) MPL. This subject has the preferences ($5, no report) ≽ ($10, report) ≽ ($9, no report) and, by transitivity, could be inferred to prefer $5 to $9. The term “multiple switching” refers to the fact that consistent choices require that the subject switches between the two options at most once, from the MPL option with decreasing (relative) value to the option with increasing value.

In addition to MSB, many subjects exhibit never-switching behavior (NSB). NSB occurs when a subject chooses the same option through the entire price list. Since the researchers typically vary the value difference between the two options across the full support of a reasonable distribution, this behavior often appears implausible. For example, take a subject in Alphonce and Alfnes (2017) (see Figure 1) who chooses to buy con– ventional tomatoes at all prices up to TZS 1000 this would be implausible given that the market price at the time of the experiment ranged between TZS 300 and 400. In the MPL from Burchardi et al. (2021) (also shown in Figure 1), NSB would be an even clearer indicator of irrational choices, since valuations are elicited for a “card” that is directly redeemable for its face value of UGX 1400 in cash, and all subjects should be willing to buy the card for a price below 1400 but not for a price above; in fact, the authors use this as a rationality test when comparing different elicitation methods. In general, however, a problem for the measurement of subjective preferences is that the researcher cannot distinguish between NSB that is the expression of a very strong preference for one of the options, and NSB that is the result of an error.

A survey of 54 published risk elicitation studies employing MPL designs found that 17.1 percent of observations included inconsistencies in the form of MSB or subjects picking dominated options (Filippin and Crosetto, 2016; Crosetto and Filippin, 2016).<sup>10</sup> In WTP elicitation where the trade is realized later, subjects often ex post decline the agreed-upon price: Maffioli et al. (2020) report a high rate of reneging on MPL outcomes,

> 10A similar systematic review of the MPL for WTP literature is not available. Of the papers we surveyed, a subset of which are shown in Appendix B, MSB was rarely reported and was often suppressed by instrument design, as we discuss in greater detail in Section 2.3. NSB was more commonly reported, and ranged from 1-2 percent to 74 percent of subjects.

8
**Table 9. Projections for developing countries from 2010 to 2030 under reformist scenario and persistent informality parameter options, i.e. annual growth rates of μ = 1%,** **_b_ = 0.25%,** **_a_ = 0.25%, and** � **= ‐ 0.25%.**

|**Country**|**Fo**|**rmal lab**|**or**|**Mod**|**ern Info**<br>**Labor **|**rmal**|**Ru**<br>**Inf**|**diment**<br>**ormal L**|**ary**<br>**abor **|
|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Albania|39%|67%|90%|10%|5%|5%|52%|28%|5%|
|Argentina|46%|67%|90%|36%|22%|5%|18%|11%|5%|
|Burundi|7%|10%|13%|7%|5%|5%|86%|85%|82%|
|Benin|6%|8%|8%|7%|5%|5%|87%|87%|87%|
|Bangladesh|5%|10%|27%|38%|51%|68%|57%|40%|5%|
|Bulgaria|75%|90%|90%|18%|5%|5%|8%|5%|5%|
|<br>Bolivia|13%|19%|33%|33%|28%|25%|54%|53%|42%|
|Brazil|58%|89%|90%|20%|5%|5%|23%|6%|5%|
|Botswana|17%|28%|42%|73%|67%|53%|10%|5%|5%|
|Central African||||||||||
|Republic|5%|4%|3%|8%|5%|5%|87%|91%|92%|
|Chile|56%|90%|90%|21%|5%|5%|23%|5%|5%|
|China|34%|82%|90%|19%|13%|5%|48%|5%|5%|
|Côte d'Ivoire|14%|8%|2%|10%|5%|5%|76%|87%|93%|
|Cameroon|17%|22%|22%|12%|5%|5%|71%|73%|73%|
|Congo, Rep.|12%|16%|22%|17%|11%|6%|71%|73%|72%|
|Colombia|30%|47%|90%|28%|20%|5%|43%|32%|5%|
|Costa Rica|57%|90%|90%|24%|5%|5%|19%|5%|5%|
|Dominican||||||||||
|Republic|30%|50%|87%|34%|31%|8%|37%|19%|5%|
|Ecuador|27%|41%|74%|31%|24%|13%|42%|36%|14%|
|Egypt, Arab Rep.|54%|84%|90%|26%|10%|5%|21%|5%|5%|
|Ghana|10%|15%|27%|16%|14%|12%|74%|72%|61%|
|Guinea|7%|8%|12%|5%|5%|5%|88%|87%|83%|
|Guatemala<br>Hong Kong SAR,|21%|29%|42%|38%|28%|17%|41%|43%|42%|
|<br>China|76%|90%|90%|17%|5%|5%|7%|5%|5%|
|Honduras|18%|26%|46%|32%|26%|21%|50%|48%|34%|
|Hungary|86%|90%|90%|8%|5%|5%|6%|5%|5%|
|Indonesia|14%|22%|48%|31%|31%|37%|55%|47%|15%|
|India|12%|19%|40%|11%|11%|7%|78%|71%|53%|
|Iran, Islamic Rep.|41%|62%|90%|23%|9%|5%|36%|29%|5%|
|Iraq|43%|63%|90%|35%|22%|5%|21%|15%|5%|
|<br>Israel|85%|90%|90%|8%|5%|5%|7%|5%|5%|
|Jamaica|21%|26%|37%|47%|32%|21%|32%|42%|42%|
|Jordan|52%|73%|90%|41%|22%|5%|7%|5%|5%|
|Kenya|19%|27%|43%|28%|22%|13%|53%|51%|44%|

52
WPS3806

# **Two Decades of Reform: The Changing Organization Dynamics of Chinese Industrial Firms**

## **Shahid Yusuf and Kaoru Nabeshima Development Economics Research Group The World Bank**

### **Abstract**

Since the early 1980s, China has begun gradually integrating with the global system. In doing so, the country has moved towards its own unique brand of market socialism, which recognizes private ownership, and is adopting market institutions and pursuing industrial change within the framework of an urban economic environment. The process of transition has now permeated every corner of Chinese life and no organization has been left untouched. Yet, industrial organization in China—especially in the state sector—has been slow to shed many of the distinctive structural characteristics of the old line Maoist era state enterprises. The main prong of the industrial strategy in support of urban change is ownership reform that transforms stateowned enterprises into corporate entities with majority state ownership or places them wholly in private hands. This strategy is bolstering the incentives for and the dynamism of the private sector. While the central government spearheads the ownership reform initiative, in the majority of cases the actual implementation is in the hands of municipal, county, and prefectural governments. This paper situates industrial change in China within the context of urban development and examines the interplay of broad reform strategy with local implementation, and its actual practice by the reformed firms.

**JEL Code** : P31; R11; O14; O31

**Key words** : China, urban economy, state-owned enterprise, reform

### World Bank Policy Research Working Paper 3806, January 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at_ _<u>http://econ.worldbank.org.</u>_ The paper was prepared for the project on “Urban China in Transition” sponsored by the Urban China Network, SUNY Albany and was presented at a conference in New Orleans, January 1516, 2005. We greatly appreciate the constructive feedback from conference participants. Authors would like to thank, in particular, John Logan and Susan Fainstein for their helpful suggestions and Shiqing Xie for providing excellent research assistance.
those that invest domestically, suggesting that when faced with investors that tend to seek higher returns, Chilean fund managers have incentives to invest at longer horizons. An important final piece of evidence regarding the importance of incentives is that the maturity structure of Chilean insurance companies (which face the same supply of domestic assets) is substantially more tilted toward the long term, with an average maturity of 10.32 years. The main difference between insurance companies and mutual and pension funds is that the former have long-term liabilities since investors are promised a defined benefit (de la Torre et al., 2007). Therefore, insurance companies benefit from matching the maturity structure of their assets and liabilities. Mutual and pension funds, on the other hand, are pure asset managers and seem to have incentives to invest short term, regardless of the investment horizons of their underlying investors. The evidence from insurance companies reinforces the conclusion that the maturity structure of asset managers is not driven by the supply side of instruments, but by the decision of asset managers to invest short term.

The rest of the paper is organized as follows. Section 2 describes the data. Section 3 characterizes the maturity structure of Chilean and US institutional investors. Section 4 analyzes the factors that might determine the maturity composition of institutional investors. Section 5 concludes and highlights some policy implications.

# **2. Data**

The main data used in this study consist of asset-level holdings of institutional investors and come from different sources. The data on Chilean mutual funds and insurance companies come from the Superintendency of Securities and Insurance 8
Table 1: Number of enterprises by single digit ISIC industry‐Uganda Business Registry 2001 and 2010

||**Nu**|**mber of enterprise**|**s**<br>**Avg.**|**% sha**|**re of total**|
|---|---|---|---|---|---|
|**One Digit Industry (ISIC Rev. 3.1)**|**2001**|**2010**|<br>**Annual %**<br>**Growth**|**2001**|**2010**|
|**Traded sector:**||||||
|A - Agriculture, hunting and forestry|465|4,964|107.5|0.3|1.1|
|B - Fishing|241|3,147|134.0|0.1|0.7|
|C - Mining and quarrying|427|713|7.4|0.3|0.2|
|D - Manufacturing|12,011|31,814|18.3|7.3|6.9|
|_Traded sector total_|13,144|40,638|23.2|8.0|8.9|
|**Non-traded sector:**||||||
|**Other industry:**||||||
|E - Electricity, gas and water supply|29|100|27.2|0.0|0.0|
|F - Construction|253|665|18.1|0.2|0.1|
|**Services:**||||||
|G - Wholesale/retail trade; repair of motor vehicles, etc.|107,552|289,110|18.8|65.3|63.1|
|H - Hotels and restaurants|20,492|64,545|23.9|12.4|14.1|
|I - Transport, storage and communications|1,198|2,514|12.2|0.7|0.5|
|J - Financial intermediation|656|3,364|45.9|0.4|0.7|
|K - Real estate, renting and business activities|2,433|11,307|40.5|1.5|2.5|
|L - Public administration and defense; compulsory social security|134|11|-10.2|0.1|0.0|
|M - Education|3,370|2,702|-2.2|2.0|0.6|
|N - Health and social work|3,336|8,082|15.8|2.0|1.8|
|O - Other community, social and personal service activities|12,092|34,869|20.9|7.3|7.6|
|P - Activities//undifferentiated activities of private households||177|||0.0|
|Q- Extraterritorial organizations and bodies|50|8|-9.3|0.0|0.0|
|_Non-traded sector total_|151,595|417,454|19.5|92.0|91.1|
|**Grand total**|164739|458092|19.8|100.0|100.0|

36
Figure 11. Public transfers in Ecuador and Mexico: coverage and share of transfers in income, 2003, 2010, and 2012

- a. Ecuador

<!-- Start of picture text --> y<br>2003 2010 2012<br>20%<br>20%<br>17%<br>15%<br>10%<br>7% 7%<br>4% 4% 4%<br>2% 2% 2%<br>0% 1% 0% 0% 0%<br>60% 59%<br>50% 45% 42% 48% 44% 48%<br>40% 34% 35% 37%<br>30% 22% 25%<br>20% 18%<br>10% 5% 6% 8%<br>0%<br>Poorest Q 2 Q 3 Q 4 Richest Poorest Q 2 Q 3 Q 4 Richest Poorest Q 2 Q 3 Q 4 Richest<br>quintile quintile quintile quintile quintile quintile<br>2003 2010 2012<br><!-- End of picture text --> b. Mexico <!-- Start of picture text --> 2004 2010 2012<br>15% 15%<br>10% 9%<br>7%<br>6%<br>3% 3% 3%<br>1% 1% 1% 2% 1% 1% 1%<br>0%<br>40% 40%<br>40%<br>34%<br>31% 32%<br>30%<br>24% 23%<br>20%<br>20%<br>15%<br>14%<br>10% 9% 8% 7% 8%<br>5%<br>0%<br>Poorest Q 2 Q 3 Q 4 Richest Poorest Q 2 Q 3 Q 4 Richest Poorest Q 2 Q 3 Q 4 Richest<br>quintile quintile quintile quintile quintile quintile<br>2004 2010 2012<br><!-- End of picture text --> Source: Authors’ calculation from SEDLAC (CEDLAS and the World Bank).

The recent slowdown in the rate of expansion of public transfers seems to be related to the reversal in the decline of income inequality in Mexico after 2010, while the continuous expansion in Ecuador fed into the reduction of inequality between 2003 and 2012. Both the coverage and the size of public transfers as a share of total per capita income have increased in Ecuador between 2003 and 2012, while they both increased in Mexico between 2003 and 2010 and remained fairly constant between 2010 and 2012 (Figure 11). As a result, changes in public transfers explained a reduction of 0.3 annual Gini points in Ecuador in both periods, while they were responsible for a reduction of 0.18 annual Gini points in Mexico between 2003 and 2010 but for an increase of 0.06 Gini points between 2010 and 2012 (Figure 12).

19
<!-- Start of picture text --> 1<br>.8<br>.6<br>.4<br>.2<br>0<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>MALAWI_2010-11 UGANDA_2010-11<br>Agriculture Industry Services Unknown<br>HoursPart. indivPart. headNat'l acctDHS HoursPart. indivPart. headNat'l acctDHS HoursPart. indivPart. headNat'l acctDHS HoursPart. indivPart. headNat'l acctDHS<br>Share of sample<br><!-- End of picture text --> Figure 2: Comparison between different estimates of sector labor shares. The “Hours” measure is from variables generated using LSMS-ISA data. The “Part. indiv” measure is based on the primary occupation (most reported hours) of individuals in the dataset. The “Part. head” measure is based on the primary occupation of the household head. The “National account” measure is from the World Development Indicators database, and the “DHS” measure is based on DHS surveys, as described in the text.

22
# **1. Introduction**

Poor health at birth has large long-run effects on individual well-being. Poor health at birth adversely affects health in childhood and adulthood (Almond et al., 2005; Black et al., 2007, Alderman et al., 2006; Almond, 2006) and educational outcomes (Behrman and Rosenzweig 2004; Glewwe et al., 2001). Poor health at birth is also detrimental for intergenerational mobility because its impacts on health and educational outcomes lead to reduced earnings (Case and Paxson, 2008; Duflo, 2001), and low-income parents are more likely to have health-ill children (Case and Paxson, 2006). Therefore, identifying shocks that affect health at birth is of particular interest to researchers and policy makers. One intriguing hypothesis is that health outcomes at birth can be affected by extreme weather events, which are often cited as the most important risk factor faced by rural households (Giné, Townsend and Vickers, 2008).

This paper investigates the impact of exposure to extreme weather events while _in utero_ on birth outcomes in rural Colombia over the period 1999-2008. Weather-related shocks can affect child health in rural areas if they affect the consumption of food and other nutrition and health inputs. This occurs, for example, if the shocks affects households’ real income either through price changes or through profits from prolific or lean harvests (Hoddinott 2006; Conroy et al., 2011). Extreme weather events can also affect child health and behaviors by increasing stress levels of the pregnant mother in an unpredictable and unusual way (Shore et al., 1986, Maida, 1989, Dunkel-Schetter, 2011). Finally, weather events can have consequences on the health of pregnant mothers and their offspring by creating changing environments that lead to faster (or slower) spread of diseases (Aguilar and Vicarelli, 2011).

Colombia is one of the countries with the highest incidence of extreme weather events, which have also increased considerably in the last three decades. According to the Global Climate Risk Index, in 2010 Colombia was the third most affected country by the impacts of weather-related loss events (storms, floods, heat waves, etc.), just behind Pakistan and Guatemala (Germanwatch, 2012). Moreover, the number of disaster events registered in the decade of the 2000s in Colombia has increased more than 60 percent with respect to the decade of the 1970s (World Bank, 2012). Colombia is particularly exposed to rainfall and temperature shocks. The exposure level distribution in the country indicates that 28 percent of the territory has high flooding potential (World Bank, 2
**Figure 7.  Evolution of overlapping deprivations over time (Percentage of urban and rural households experiencing zero, one, two, or all three deprivations from 2000 -11)**

<!-- Start of picture text --> 100% 1% 2% 7% 1%<br>90% 12% 19% 12% 18% 19% 11% 19%<br>28% 28%<br>80% 33% 33% 34%<br>70%<br>44%<br>60% 45% 41% 49% 36%<br>37% 50% 34% 46%<br>50%<br>42% 44%<br>40% 72%<br>30%<br>35% 25% 32% 35%<br>20% 27% 27% 41% 32% 39%<br>22% 18%<br>10%<br>9% 10% 13% 10% 17% 10%<br>0% 3% 4% 4%<br>2000 2011 2000 2011 2000 2011 2000 2011 2000 2011 2000 2011<br>Poverty, Poverty, Health Poverty, Poverty, Poverty, Health Poverty,<br>Education & Qual, &Educ Information, & Education & Qual, &Educ Information, &<br>Sanitation Sanitation Sanitation Sanitation<br>Urban Rural<br>Three Two One None<br><!-- End of picture text --> Figure 7 shows additional information that is not shown on the Venn diagrams presented in earlier sections. The figure also includes information on the dynamics of the proportion of households that are not deprived in any dimension. It shows that the percentage of households experiencing none of the selected deprivations has increased substantially over time, and is about one-fifth of the rural population in 2011 for rural areas and about one-third in urban areas. This means that roughly 4 out of 5 households in rural areas and 2 out of 3 households in urban areas are deprived for any set of three dimensions considered. In addition, there is an indication of narrowing in the rural-urban gap in deprivations because the increase (in percentage points) of non-deprived is, in general, higher in rural than urban areas. For example, in two of the three cases presented in Figure 7, the proportion of non-deprived increased by 17 percentage points 22
|**Table 7: Resul**<br>**Stage**|**ts from Second Experiment**<br>**Result**|**Aggregate**||**Jobs(10)**|||**high prestige jobs(**|**5)**|**lowp**|**restige jobs(5)**||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||**Statistics from**<br>**15 firms**|Candidates<br>(2002)|WLP<br>Candidates<br>(957)|Non WLP<br>candidates<br>(1045)|Candidates<br>(2002)|WLP Candidates<br>(957)|Non WLP<br>candidates|Candidates(2002)|WLP<br>Candidates<br>(957)|Non WLP<br>candidates|
||Matched||1413|790|623|1094|633|461|708|408|300|
|Treatment|Unmatched(to these tenjobs)||589|167|422|908|324|584|1294|549|745|
||Matches|3213|2269|1358|911|1406|849|557|863|509|354|
||Average number of times a|||||||||||
|matches|candidate was matched<br>Number of candidates who<br>were surveyed byus|1335|1.61<br>890|1.72<br>493|1.46<br>397|1.29<br>562|1.34<br>327|1.21<br>235|1.22<br>324|1.25<br>166|1.18<br>162|
||Number of applications|||||||||||
|applications|according to follow-up surveys<br>Number of applications|366|298|168|130|**196**|107|89|102|61|41|
||accordingto firms surveys|94|76|51|25|53|19|34|23|17|6|
|Interviews|Number<br>of<br>interviews<br>scheduled|26|25|12|13|14|4|10|11|8|3|
||Number of interviews that took<br>place|22|21|9|12|13|4|9|8|5|3|
|Job offers|Job offers|0|0|0|0|0|0|0|0|0|0|
|Salary offered|<sup>average monthly salary offered</sup><br>|||349|||329|||379||
|<br>Note: WLP den|JD<br>otes candidates say they are willin|g to take low pre|stige jobs|||||||||

37
# **Dual Credit Markets and Household Access to Finance: Evidence from a Representative Chinese Household Survey**<sup>**1**</sup>

Robert Cull World Bank Li Gan Texas A&M University and SWUFE Nan Gao SWUFE Lixin Colin Xu World Bank

**JEL codes** : G00, O17, O53, P34.

**Key words** : access to finance, costs of finance, household finance, political connections, financial knowledge, dual credit markets.

> * We have benefited from comments of Daniel Berkowitz, Jing Cai, Bert Hoffman, Xiaoning Long, Claudia Ruiz, Thierry Tressel, and other participants of the Chinese Economist Society meeting at University of Michigan, and a seminar at Xiamen University. The views are the authors and do not necessarily reflect those of the World Bank, its executive directors, or the countries they represent.

Corresponding author: L. Colin Xu, World Bank, MC 3‐307, 1818 H Street, N.W. Washington, DC 20433; lxu1@worldbank.org.
**Table 3: Impact of changes in wedges on aggregate output**

||**iil**||**Chan**<br>**Wed**<br>|**ge in outpu**<br>**ges change**|**t (%)**<br>**d in**|
|---|---|---|---|---|---|
|**Country**|**Inta**<br>**wedges**<br>**from**|**Wedges**<br>**replaced**<br>**from**|**Industry**<br>**&**<br>**services**|**Industry**<br>**only**|**Services**<br>**only**|
||(1)|(2)|(3)|(4)|(5)|
||**Panel**<br>|**A: Longest**<br>|**possible peri**<br>|**od at countr**<br>|**y level**<br>|
|CRI|2017|1980|-0.21|-0.09|-0.07|
|GTM|2017|2001|0.71|0.12|0.48|
|HND|2017|1991|0.85|-0.02|0.72|
|NIC|2012|2003|0.27|0.44|-0.12|
|PAN|2017|1970|1.03|-0.02|0.25|
|SLV|2017|1990|0.44|-0.43|0.45|
|Average|||.|.|.|
||**Pa**|**nel B: Longe**|**r uniform pe**|**riod (1991-2**|**017)**|
|CRI|2017|1991|0.04|0.03|0.00|
|GTM|2017|1991|.|.|.|
|HND|2017|1991|0.85|-0.02|0.72|
|NIC|2017|1991|.|.|.|
|PAN|2017|1991|0.95|-0.35|0.25|
|SLV|2017|1991|0.28|-0.26|0.32|
|Average|||0.53|-0.15|0.32|
||**Pa**|**nel C: Shorte**|**r uniform pe**|**riod (2003-2 **|**012)**|
|CRI|2012|2003|0.06|0.08|-0.05|
|GTM|2012|2003|0.64|0.15|0.43|
|HND|2012|2003|-0.48|-0.03|-0.46|
|NIC|2012|2003|0.27|0.44|-0.12|
|PAN|2012|2003|0.87|-1.97|0.45|
|SLV|2012|2003|-0.08|0.01|-0.09|
|Average<br>|||0.21<br>|-0.22<br>|0.03<br>|
|<br>Columns (3)-(<br>(column (1)) a|5) report the<br>re replaced by|output change<br>those from yea|ΔYpa1(in %) w<br>rt0(column (2|hen wedges in<br>)).|the yeart1|

The setup also allows me to quantify the efficiency implications of changes in the industrial and services wedges individually. Column (4) reports the output change when I alter the former, leaving the latter unchanged at the final year levels. Unlike the combined impact in column (3), the change in industrial wedges implies an output contraction in Honduras, Panama, and El Salvador. Hence, the shift in industrial wedges over time has contributed to output expansion. The individual component 21