rafmacalaba/data-use-mentions-extended huggingface dataset:
# DISORGANIZATION OR **SELF-** ORGANIZATION?1

## FRANCESCA RECANATINI AND RANDI RYTERMAN<sup>2</sup>

The transiton from plan to market provides a rare opportnity for insight into the endogenous development of economic institutions. Economic actvities during the Soviet regime were coordinated by a central authority. These coordintg mechanisrs were disrupted duing the transition period, leading to an increase in the transaction costs for firms. Blanchatc and Krerner (1997), among others, emphasize the negative impact of this "disorganion on output behavior at the beginning of transition. Though this argument is correct, we believe that this and simlar works stop short of a fuler characterization of transition by concentrating only on the disruptive dfects of the reforn process.

This paper begins where the former works end by exanmining one of the key institutons that have energed spontaneously in response to the challenges of transition: business associations. Its main contribution is to provide enpirical evidence that institutions that help coordinate production and trade spontaneously emnerge in an enviromment characterized bywidespread 'disorganization". Using a lagely unexplored, firm-level data set, ve document the emergence of business associations at the beginning of transition and provide evidence that these new coordinating institutions mitigated the intial output decline. Building on the growing literature on transaction costs and comnplexity, we interpret the emergence of these infornal institutions as the firns' rational response to coordinate activites in a decentralized economy.

Keywords: Coordination Problems, Transition, Alternative Institutions JEL classificatio **n** os.: D23, L10, 017, P21, D71 I We would like to thank Lee Benhamn, John Haiwager, Karia Hoff, Robert Klitgaard, Peter Murrell, John Shea and seminar participants at the World Bank the University of Maryland, SEA, the 1999 ISME Conference and the 3ri Conference on "Institutions and Transition" for their many insights and comments. We are also grateful to Bill Evans and Bob Schwab for sharing their comnputep **r** ogram, and _to_ Ouiner Blanchard and Michael KRemer for sharing their dama
the family background in the recent decades which counteracts the improvements in the mean eects. In all three countries, the inuence of father's education on the conditional variance is negative and substantial in magnitude in the 1980s, suggesting that the children born to educated parents gain in terms of a much lower conditional variance, in additional to a higher conditional mean. There are dramatic dierences in the earlier cohorts across countries: the estimate is negative in China, positive in Indonesia, and a zero eect in India for the 1950s

> 16The standard mean eects (see the IGRC estimates in the odd numbered columns of Table 3) show that the inuence of father's education is much higher for daughters in terms of the rst moment (expected years of schooling) in China. The gender advantages thus are opposite in terms of the mean vs. conditional variance eects. We discuss a simple summary measure of relative mobility that combines these two aspects in section 5 below.

12
%|52.17%|6.96%|15.03%|24.91%|18.73%|17.57%|0.80%|2.04%|0.74%|0.00%|2.59%|

1Power generating equipment corresponds with SITC Revision 4 code 71, which is comprised of steam boilers, steam turbines, internal combustion piston engines, various other non-electric engines and motors, rotating electric plants, and other power-generating machinery and parts thereof not elsewhere specified 2HIEuro is comprised of the following high income European nations: Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Slovenia, Spain, Sweden, and the United Kingdom. Source: Author's calculations based on WITS, UN COMTRADE database.

41. Simulations confirm that infrastructure investments in developing countries can significantly contribute to a global recovery and improve the trade balance in advanced economies. McKibbin, Stoeckel and Lu (2011) contrast the results of a permanent 1 percent increase of world GDP in government infrastructure investment versus an equivalent increase in current spending on goods and services in developing countries using a multicountry, multisector intertemporal general equilibrium model of the world economy (Figure 3).<sup>29</sup> They find that a rise in current spending leads to only a small increase in output that dissipates over time as a larger stock of government debt acts as a drag on overall economic activity. In contrast, the authors estimate that with a rise in infrastructure investment, GDP in developing countries rises by almost 7 percent over a ten-year period. World GDP rises by about 2 percent. Because spending on infrastructure raises private returns to capital in emerging countries, more capital flows into these economies to finance the expansion of the government as well as the private sector. As a result,

> 29 Simulations on infrastructure spending in this paper use the findings of a recent study that used
S. and Australia, and Sevilla-Sanz et al., 2010, using data from Spain); unearned income from transfers and property (Schultz, 1990); cash transfers (Hasan, 2010, examining a program targeted towards girls’ education in the Punjab province of Pakistan); as well as adult household members’ age, education, and marital status (see Fengdan et al., 2016, in a study from China).

> 4 Evolving approaches to measuring time allocation and effort can also allow for a broader view on the links with asset ownership — Friedman et al. (2021), for example, collect physical activity tracking data from men and women in agriculture in two districts of Malawi, and find, among other results, that women tend to exert more physical effort among couples with larger gender inequalities in the number of plots owned.

3
countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database.

# **e. Are there any other kinds of hidden subsidies?**

Besides tariffs, there are also other less explicit mechanisms by which policy makers subsidize consumption. For instance, when policy makers overlook, tolerate, or even promote certain operational inefficiencies, they are in practice transferring resources from one sector of the economy to another, from the producer to the consumer, from future taxpayers to current customers, and so on.

Tolerance of nonpayment is an implicit tax on utilities (and/or a transfer to consumer). Tolerance of pilferage—one of the main causes of transmission and distribution losses—is an implicit subsidy to customers and an implicit burden on future taxpayers. Acceptance and promotion of over-employment represents an untargeted transfer of resources from the utility to the society. These inefficiencies can be empirically quantified and prove to be substantial relative to GDP (Briceño-Garmendia, Foster, and Smits 2008). For most countries, over-manning as well as collection inefficiencies amount to less than 0.2 percent of GDP, whereas transmission and distribution losses tend to be much larger in value amounting to around 0.2-0.6 percent of GDP (figure 24a). These operational inefficiencies also look very large in 34
Eagle, et al. (2012), Enns and Amuasi (2013), Chang et al. (2018) and Ihantamalala et al. (2018) among others, do not incorporate seasonality in incidence and focus on identifying potential sources and sinks based on travel patterns and annual malaria prevalence data. Yet Buckee, Tatem, and J. Metcalf (2017) point out that seasonal variation in biological factors related to climate and seasonal population movements are important for many infectious diseases and failing to account for seasonality could lead to misallocation of resources.

4
# **II. Methodology**

The quantitative analysis of survey data has the advantage of making generalizable statements about large populations, but it is disadvantaged by an unwieldy apparatus that tends to limit what it reveals to hypotheses that circulate within academic or policy circles. Survey modules are often borrowed wholesale from previous questionnaires originally designed to address a different set of issues, then propagated over the Internet. These are of limited use for studying understudied populations, such as slum dwellers, or novel topics, such as slum leadership. Many econometricians also tend to analyze data collected by others from unfamiliar settings. Informed by potentially dated secondary literatures, these run the risk of overlooking the current reality of the subjects under study. Innovations in the literature thus can be more the result of a speculative moment in an air-conditioned office rather than the active exploration and dismissal of alternative hypotheses in the field.

By administering specialized surveys, economists have attempted to overcome such shortcomings. This epistemological tradition dates at least as far back as Epstein (1962), followed by Bardhan and Rudra (1978), Bliss and Stern (1982) and many others. Our paper goes a step further by employing the “participatory econometrics” approach (Rao 1997, Rao 2002), integrating participatory appraisals, focus group discussions, in-depth interviews and participant observation with quantitative data from representative surveys. The survey instruments emerge from the qualitative investigation, drawing on insights from the field to define topics of interest and identify appropriate methods of measurement.

Participatory econometrics uses qualitative insights to generate hypotheses whose generalizability can be tested through a broader analysis of quantitative data. Qualitative insights allow for a greater emphasis on the study of process and contextual issues that complement the 4
the non‐compliers.<sup>3</sup> Our main survey approach was face‐to‐face interviewing using an extensive survey that asked about employment outcomes, took physical health measurements, asked about preferences, and played incentivized lab‐in‐the‐field games which we describe in detail later in the paper. Each principal applicant in this long survey was linked to a partner, defined as either a parent or sibling, who, according to the PAC rules, would not be eligible to move to New Zealand even if the principal applicant was a ballot winner. These partners were used for the household efficiency game described in Section 5.

We supplemented this long, in‐person, survey with a shorter telephone‐based survey designed to reach those who had on‐migrated from New Zealand or who were in less accessible parts of New Zealand. Since surveying often uncovers details for other people (through snowball effects) we also gave this short survey to some recently identified migrants in areas where the longer survey had already been fielded, as well as to additional households in Tonga.

Figure 1 details the sampling procedure by group. Altogether we have a sample of 282 principal applicants who received the long survey and played all the lab‐in‐the‐field games, comprising of 133 migrants (out of 307 in total), 6 non‐compliers (out of 60 in total), and 143 ballot losers (out of 4,329 in total). We have an additional 103 principal applicants who were given the short survey (61 migrants, 3 non‐compliers, and 39 ballot losers) for a total sample of 385 principal applicants. In the end, we were able to survey 64 percent of the principal applicants who had ever migrated to New Zealand (Figure 1). Since non‐ compliers were sampled at a lower rate, we use the administrative data to reweight the migrants and non‐complier samples to be representative of the ballot winners. We intentionally surveyed only
**Figure 10: Employment in exports (million), by broad sector and income category, 1995-2018**

<!-- Start of picture text --> High-income countries (as of 1995) Low- and middle-income countries (as of 1995)<br>100 350<br>90<br>300<br>80<br>70 250<br>60<br>200<br>50<br>40 150<br>30 100<br>20<br>50<br>10<br>0 0<br>Agriculture Mining Manufacturing Agriculture Mining Manufacturing<br>Business services Other services Business services Other services<br>China India<br>180 80<br>160 70<br>140<br>60<br>120<br>50<br>100<br>40<br>80<br>30<br>60<br>40 20<br>20 10<br>0 0<br>Agriculture Mining Manufacturing Agriculture Mining Manufacturing<br>Business services Other services Business services Other services<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> Source: Own computations. Data: OECD TiE data, covering 51 countries and 45 sectors for the period 1995-2018. Income categories as of 1995 are used to classify countries into high- and low- and middle-income countries (see Appendix 8).

Several underlying factors could
included in the follow‐up survey. The attrition was due to the enumerators’ inability to resurvey the baseline respondents within the short follow‐up survey period. (Since beliefs and behaviors are subjective, baseline respondents could not be replaced with other members of the household.) The control group for this pilot was also very small given the small sample size of the overall study. In addition, because control group households were sampled from T1 villages, there was high risk of spillover due to information diffusion from treatment to control households and presence and activity of norm entrepreneurs in the villages. As a result, comparisons between the treatment and control groups could not be made in our analysis.</mark> <mark>The endline data show relatively large changes in both behavior and attitudes in favor of latrine use, relative to the baseline. Given the small sample size, our study was underpowered to detect significant effects, but a simple difference in means test between baseline and endline outcomes shows large and statistically significant differences in both behavior and beliefs related to latrines across both treatment arms (see Appendix 2 for detailed results).</mark>

**<mark>Figure 1.</mark>**

15
in the microdata. We are then left with corporate profits and property income of the households.

Here, we note two features of WID National Account aggregates related to corporate profits and property income of households, summarized in Figure C3. First, at close to 18% in more recent periods, the share of corporate profits over NNI in Honduras is much higher than most countries in LAC, with the exception of Nicaragua, and also much higher than high-income countries such as Spain, France and the United Kingdom. In Latin America we see a very large variation, with shares below 5% for Mexico for example, and in the range of 5 - 15% for Peru and Costa Rica. At the same time, the share of property income of households, which is mostly slightly negative in Honduras, is much lower than in other countries in the region and in high-income countries, where it ranges from 5 - 20% in recent years. While we are unable to assert the precise method to arrive at these figures for Honduras, these differences are substantial.

Given the negative amounts of property income of households we observe, adjusting directly our measure of capital income in our microdata to these aggregates would mean income to match these amounts – we lack more subtracting negative granular measures on property income on national accounts, so we could not adjust income from interests and dividends separately, for example. Instead, and informed by these large gaps in NA measures between Honduras and other countries, we decide to sum distributed income with undistributed and the up property corporate profits, adjust total amount to national accounts. Therefore, when we adjust our microdata to national accounts we multiply the total amount of distributed capital income and undistributed corporate profits in the microdata by a yearly factor that matches the total to national account aggregates.<sup>4</sup>

> 4In panel (e) of Figure C3, we show that the sum of corporate profits and household property income for Honduras is less of an outlier when compared to their LAC
<!-- Start of picture text --> Share of poor population Average amount per poor population, monthly<br>100 40 39.3 384<br>70.6 ><br>=<br>£i< 50 5= 6<br>3s<br>a258 3<br>3 0.0 3 0.0<br>BaSw 8a 0<br>ze 2 44<br>&r<br>8os<br>oOo Sot -20.9 5E<br>5 -50 neo<br>a -49.3 2<br>g<br>< 30.0<br>-100 -40<br>———— ett.7<br>Net market Disposable Consumable Net market Disposable Consumable<br>@ Fiscal Impoverishment MH Fiscal Gains to the Poor ‘Total Fl “Total FGP<br>erelotes: Authors' estimates based on PECS 2016/2017; LFS 2017.<br>i2 ] Fiscal impoverishment:gains to the poor:PercentPercent ofofMeerapopulationi)i) pop o orr at postat pre -fiscal income and i i) madeexperience poorergains by thein fiscalincome systdu e m.to the fiscal system.<br>[3] Pre-fiscal income is excluded from the graph because no fiscal impoverishment or fiscal gains can occur, by definition.<br>[4] Final income is excluded from the graph because we don’t measure changes in poverty at Final income.<br>[5] Sum of absolute value of bars doesn't add to 100 because percentages are calculated over poor populations at different income concepts.<br><!-- End of picture text -->
The main conclusion of the paper is that young people across all three countries follow a similar trend over their early life cycle:<sup>3</sup> they leave school to spend a short time in the informal sector, move on to a formal position for longer spells, and finally become selfemployed. Thus, the informal sector appears to play the role of informal job training, as suggested by Hemmer and Mannel (1989), where youth have the flexibility to continue in the labor market or return to school, while a formal job implies entering labor force for good and self-employment is a sector to enter once skills and connections have been accumulated through several years of working.

The next Section presents a literature review of the hypotheses to be explored in the paper. In Section 3, we describe the methodology and the data. Section 4 presents analysis and results. Section 5 concludes by drawing together the findings from specific transitions to sketch the school-to-work transitions process over the early life cycle.

# **2. Literature Review**

Across the world, young people’s (completed) employment tenure is of a shorter duration than that of adults. The OECD literature suggests that there is frequent job turnover among younger workers who engage in a search process of “shopping around” temporary jobs until they find a career path to follow (Quintini, 2006; 2007).<sup>4</sup> This phenomenon is not exclusive of developed economies. Balán, Browning and Jelin (1973) observe that young Mexican workers engage in a search process where they try out various possible life choices. In Chile, Fajnzylber and Reyes (2005) find greater job instability for young people than for adults, partly because a high percentage of their employment contracts were short-term. Chacaltana (2005) uses a Peruvian panel from 1998 to 2001 to show that 60 percent of young workers changed their work status while only 40 percent of adults did.

> 3 Strictly speaking, the available data do not allow us
. (Lafuente 2009). The following describe common land valuation techniques given access to relevant data on transactions and land attributes.

# <mark>2.1 SALES COMPARISON APPROACH</mark>

The sales comparison approach relies on market data to analyze information on comparable properties to arrive at an estimated value. This approach is based on the assumption that consumers are willing to pay no more than they would for another property with similar characteristics (Gwartney and Arizona 2001). The reliability of the approach requires considerable amounts of data, particularly transaction information on similar properties to the one being assessed. This method is widely used where large pools of data are available, and is relatively straightforward to analyze, particularly with statistical software. However, there are a number of weaknesses to this approach, including:

- Sales data may not accurately reflect the transaction’s market value, thus

- appraisers must understand the underlying factors of each comparable sale and adjust accordingly, and

- Not all properties have enough market comparables (Arizona 2001: 3.3).

1.1. Valuing undeveloped land reveals another drawback of this method.  In urban areas, where there is a scarcity of undeveloped land, land sales are rare resulting in little data on these transactions. While transaction data on built-up parcels may be available, decoupling land and built-up portions of a site are difficult.  This form of analysis must often be paired with another to arrive at a more accurate valuation (German et al.).   As with any of these land valuation

> 10 For full text of WertV, see: http://www.werttax.de/downloads/wertv.pdf

5
stability of the financial system has the largest effect on diversification of saving instruments at 6 percentage points or a third of the sample mean.

<!-- Start of picture text --> Trust<br>BG HR CZ HU PL RO AL BA MK RS<br>stability of financial system deposit safety<br>domestically owned banks foreign owned banks<br>.8<br>.6<br>.4<br>of respondents<br>.2<br>0<br><!-- End of picture text -->

**Figure 5: Trust in financial institutions**

This figure presents the percentage of respondents who say they trust in (i) the stability of the financial system, (ii) deposit safety, (iii) domestically owned banks, and (iv) foreign owned banksSource: OeNB Euro Survey, 2012-2013. Note: All percentages are weighted by sampling weights.

We analyze these findings further by including the indicators of trust separately. Figure 6 shows the average marginal effect for each trust indicator on the respective saving instrument. The left panel shows that trust in banks (whether foreign or domestic) has the strongest impact on whether respondents hold any savings. The decision to save formally is influenced by trust in domestic banks and trust in deposit safety. By contrast, lack of trust in deposit safety increases the probability of saving in cash only. Turning to the choice of formal saving instrument, the right panel illustrates that trust in the stability of the financial system has the strongest impact on the decision to hold non-bank formal savings and to diversify savings. We also investigate whether trust has a differential impact for B40 versus T60 households (results not shown). We find that this is not the case.

22