rafmacalaba/datause-encoder-data huggingface dataset:
shown how different ‘ex ante’ characteristics may affect the post-entry performance of newborn firms (Section 4.2).

# **<u>4.1 Entrepreneurial learning, market failures and institutional constraints</u>**

From a theoretical point of view, Lucas (1978) was the first to put forward a theory of the size distribution of firms based on the relative endowment of entrepreneurial talents<sup>16</sup> . However, the first author to represent the post-entry evolution of newborn firms formally was Boyan Jovanovic in his celebrated contribution in _Econometrica_ (1982). Jovanovic proposed a Bayesian model of noisy selection, according to which efficient firms grow and survive, whereas inefficient ones decline and fail. In particular, in Jovanovic’s model of _passive learning,_ firms are initially endowed with unknown, time-invariant characteristics (i.e. _ex-ante_ efficiency parameters); _ex-post_ the prior distribution is updated as evidence comes in and some entrepreneurs discover that they are more efficient than others. Thus, in any period each firm has to decide its strategy: whether to exit, continue at the same size, grow in size, or reduce its productive capacity.

One can easily see that Jovanovic’s model is perfectly consistent with a world where founders are quite heterogeneous in terms of both general and specific characteristics, entry mistakes can easily occur, entry can be originated by a ‘try and see’ bet and early failures are rather common (see previous section; see also Lotti and Santarelli, 2004).

The same line of argument applies to more recent models of _active learning_ . While Hopenhayn (1992) first introduced innovation as an exogenous process, Ericson and Pakes (1995) assumed that all the decisions taken by firms were meant to maximize the expected discounted value of the future net cash flow, conditional on the current information set. In their model, a firm knows its own characteristics and those of its competitors, along with the future distribution of industry structure, conditional on the current structure. Therefore, Jovanovic's assumptions concerning small industry size and product homogeneity are

> 16 For a recent extension of Lucas’ model incorporating the possibility that entrepreneurial talents may be acquired by watching other entrepreneurs already active in the market, see Guiso and Schivardi (2005).

15
Over the past decade, new micro-level survey instruments (PETS, QSDS, SAS etc.), and dissemination tools like Citizen Report Cards, have proven to be powerful tools for identifying bottlenecks, inefficiencies and waste in service delivery, especially problems of resource leakage, delays, absenteeism, and inequities in the actual resource allocation. In the Ugandan education sector, for example, Reinikka and Svensson (2005, 2009) use a PETS approach to study the effects of a public information campaign aimed at empowering parents. They find a large reduction in resource leakage toward primary schools in response to the campaign. The introduction of a Citizen Report Card for Bangalore’s public agencies led to major improvements in public satisfaction and the agencies’ ratings. Similarly, corruption in the transactions between users and public officials declined markedly (Samuel, 2002). A randomized evaluation of a citizen report card intervention in the health sector in Uganda in 2005 concluded that the CRC led to significant improvements in the quality and quantity of primary health care provision and resulted in improved health outcomes in the communities (Björkman and Svensson, 2009).

This cumulative evidence provides grounds for hope that better information, particularly measurable and actionable indicators, will contribute to more accountability and transparency and ultimately to improved outcomes. The capacity to measure and compare key components of the main service delivery sectors over time and across countries should prove useful for various stakeholders. For citizens in particular, such information on public expenditure and service delivery performance would be especially valuable. As emphasized by CAFOD (2007), such information could help citizens: “ _keep an active eye on government’s progress and check whether policies are making a difference... help people give feedback to their government on the services they are providing... informed dialogue between government and citizens leads to more effective, fair and inclusive policies from which everyone benefits_ ...” (CAFOD, 2007, p.i) This concept paper is structured as follows. Section 2 presents the citizen’s perspective guiding the construction of the DSI Index and the main categories of indicators. It also presents the proposed indicators and the justification for their inclusion in the DSI index. Section 3 discusses the source of data to be used to construct the indicators. Section 4 presents the indicators’ aggregation process and country ranking mechanisms and section 5 briefly discuss how the data could be presented and the pilot survey. Section 6 concludes.
# Liquidity Constraints and Investment in Transition Economies: The Case of Bulgaria

Nina Budina, Harry Garretsen and Eelke de Jong' ' First author: World Bank, Washington DC USA, second and third author: Department of Applied Economics, University of Nijmegen, The Netherlands. We would like to thank Elmer Sterken for the provision of the data-set and for useful comments on a previous version, we also would like to thank Robert-Paul Berben, Kyle Peters, the editor and three anonymous referees for their comments and suggestions.
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# **Table 2: Summary Statistics of variables used in analysis of EDS-RDC and local GDP data**

|Variable|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|
|**Outcomes:**|||||
|Wealth Index|4621.605|103646.1|-107521|345191|
|Dummy: Multi-dimensional poverty<br>indicator|0.816736|0.386912|0|1|
|Wealth deprivation score|0.7273733|0.280303|0|1.002|
|Health and educational deprivation score<br>|0.3539926|0.223059|0|1|
|**Variables of interest:**|||||
|Cost to market|19.83385|21.69498|0.7342197|124.2525|
|No. of fatalities around the nearest market<br>in thepast 5years|0.0438948|0.144152|0|0.9066222|
|No. of fatalities around household in the<br>past 5years|0.0171938|0.072611|0|0.8148879|
|Dummy=1 if conflict near household and<br>market is high|0.328478|0.469695|0|1|
|**Instruments:**|||||
|Time taken to reach market using natural<br>and historicalpath|23.55944|26.48469|0.8028989|152.413|
|Fractionalization index within 50 km radius<br>of market|0.6114013|0.207289|0|0.8391254|
|Fractionalization index within 50 km radius<br>of household|0.5741688|0.20693|0|0.8456575|
|Distance to east border from household<br>(km)|880.9615|486.3099|0.6008699|1715.954|
|Distance to east border from market(km)|882.179|486.5322|2.126719|1682.461|
|**Controls:**|||||
|Agricultural potential (factor of ln agri.|||||
|potential for cassava,maize andground nut)|-0.0096933|1.007303|-7.474744|1.851499|
|Dummy: HH agriculturallyinvolved|0.5426576|0.498214|0|1|
|Age of HH head|41.4731|13.03215|15|93|
|Dummy: Female HH head|0.188912|0.391468|0|1|
|No. of HH members|5.846463|2.960631|1|28|
|No. of female HH members aged 15-49 yrs|1.344828|0.855208|0|9|
|No. of female HH members aged 15-59 yrs|0.6578478|0.940663|0|13|
|No. of children in HH aged 0-5yrs|0.5815993|0.936135|0|10|
|Dummy=1 if type of residence is rural|0.5551427|0.496987|0|1|
|Dummy=1 if region is bas-congo|0.0954221|0.293819|0|1|
|Dummy=1 if region is bandundu|0.1068668|0.308967|0|1|
|Dummy=1 if region is equateur|0.1037455|0.304953|0|1|
|Dummy=1 if region is orientale|0.0771403|0.266834|0|1|
|Dummy=1 if region is nord-kivu|0.0324019|0.177078|0|1|
|Dummy=1 if region is maniema|0.0936385|0.291347|0|1|
|Dummy=1 if region is sud-kivu|0.0425089|0.201762|0|1|
|Dummy=1 if region is katanga|0.1086504|0.311223|0|1|
|Dummy=1 if region is kasaï oriental|0.1030024|0.303985|0|1|
|Dummy=1 if region is kasaï occident|0.0921522|0.289262|0|1|
|No. of observations: 6728|||||

31
D d= (Equation 1) Spooled _Spooled_ is the within-estimate standard deviation for the treatment and control groups combined. Where this is not directly reported in the studies we calculate it using the following equation derived from Borenstein et al. (2009): where _n1_ is the sample size for the treatment group, _n2_ is the sample size for the control group, and _SED_ is the standard error of the raw mean difference. For a complete derivation of Equation 2 please see the mathematical appendix in Appendix C.

Turning to the independent variables, as originally coded, the 51 indicators for which we collected information capturing various design and implementation characteristics of the training programs took a number of forms. These consisted of dummy variables (e.g., the intervention provides textbooks alongside training = 0 or 1), categorical variables (e.g., the primary focus of the training was subject content [=1], pedagogy [=2], new technology [=3]), continuous variables (e.g., the proportion of training hours spent practicing with students), and string variables capturing open-ended perceptions (e.g., which program elements do you think were most effective?). In order to maximize the comparability of output from our regression analysis we convert all categorical and continuous variables into dummy variables. For categorical variables, this is straightforward. For example, we convert the original categorical variable for the location of the initial teacher training – which includes response options of schools, a central location, a training center, or online – into four dummy variables. In order to convert the continuous variables to a comparable scale, we create a dummy for each continuous variable which, for a given program, takes a value of 1 if the continuous variable is greater than the median value of this variable across all programs, and a value of 0 if it is less than or equal to the value of this variable across all 14
(World Bank, 1964:4-5). But even more importantly, the stabilization program could not address the underlying political structure that was distorting the allocation of resources and hampering a more rational plannmng process. Policies thevt appeared a complex and confusing patchwork from an economic perspective can be explained by Rhee's use of the instruments of economic :>Aicy--allocation of foreign exchange, bank credit, import licenses, and the distributio'.; of state-owned enterprises--to sustain and build a base of support.

As soon as the war ended, Rhee began to face more intense political competition. New opposition parties formed. Rhee ran unopposed in 1956 because of the sudden death of his major opponent. Nonetheless, he managed to capture only 55% of the popular vote, down from 72% in 1952, and his vice presidential candidate was defeated. Most observers of Korean politics note a shift in the style of leadership in a more autocratic and defensive direction during the late fifties (Palais, 1973; Henderson, 1968). "Hardliners" within the party and the administration gained at the expense of those favoring technocratic solutions and greater emphasis on economic development. One obvious way of mobilizing funds to finance elections an-' other party activities was to forge a closer alliance with portions of the private sector. A new group of businessmen arose in the post-World War II period through commerce or the acquisition of state-owned enterprises. As these firms diversified beyond their commercial activities lato manufacturing they sought government support. The grant of various privileges generated rents that could be recycled back to government officials, to the party and to individual legislators (Kim, 1964; Park, 1982; McNamara, 1990).

It is impossible to get an aggregate estimate of the size or composition of what might be called the rent-seeking sector. Evidence from a variety of sources, including evaluations of the aid program by the U.S., those scandals and incidents that became political issues during the 1950s and those that were later exposed during investigations launched by the military government suggest that 7
# **Introduction**

Firms do not all face the same probability of exiting. Several studies have used plant level data to show how size, age, sector and productivity can help predict a plant’s survival (Bernard and Jensen (2002); Bernard and Sjoholm(2003); Frazer (2005); Harding et al. (2004)). But these studies do not address the role of the broader business environment in which a plant operates and how it can interact with firm heterogeneity in explaining exit patterns. Using a new panel dataset of 4,800 firms with detailed measures of the business environment collected in a comparable way across 27 countries demonstrates the significance of including investment climate factors in explaining which firms exit. The results show that these factors not only affect the rate of exit, but also the composition of firms that exit.

The paper examines five areas of the investment climate: the efficiency of government services, access to finance, the extent of corruption or cronyism, the strength of property rights and the degree of competition. Many of these dimensions have been found to be important in explaining firm entry, investment and growth. (Acemoglu and Johnson (2004); Aterido et al. (2009); Beck et al. (2005); Dollar et al. (2005); Johnson et al. (2002); Klapper et al. (2004). This is the first paper to test for their importance in understanding firm exit rates.

The measures of the investment climate fall into two broad groups, those that affect transactions costs of doing business and those that affect the market disciplining mechanisms. Delays and red tape are examples of the former. Greater rule of law, more developed institutions – including financial institutions—and greater competition are examples of the latter. The paper looks both at their effects on overall rates of exit as well as their effects on the composition of firms that survive.

2
This approach has a number of important strengths, but is not without its weaknesses as well. A significant advantage of this methodology is in the simple and powerful policy implications it produces. The analysis identifies which socioeconomic groups are benefiting the most from various fiscal policies, and highlights how these policies impact the poor. Conceptually, however, the incidence analysis rests on strong operational assumptions: for instance, the approach assumes that publicly provided services are homogeneous across all consumers, yet quality may vary enormously. Incidence analysis also assumes that benefits received by individuals are equal to the costs of service provision; a perfect translation of taxes to consumers with no significant distortions arising from illegal behavior. Additionally, since data are more often available at the household rather than the individual level, additional assumptions must be made about the distribution of resources within the household. A common practice is to assume equitable intra-household allocations and to rely on per capita measures as the household‘s representative welfare measure. Finally, incidence analysis says nothing as to why the results are the way they are and rarely provides insights about how a certain program or policy influences behavior of beneficiaries (or nonbeneficiaries).

The precision of an incidence analysis depends on the quality and disaggregation of the available data. For example, an incidence analysis on health care in Zambia would ideally require that public healthcare expenditures be disaggregated at the provincial level by type of provision: hospitals (levels 1 to 3), clinics and health posts, and type of attention, i.e. inpatient versus outpatient. However, it is only possible to obtain data at the provincial level for combined expenditures on level 1 and 2 hospitals and combined expenditures on clinics and health posts. Additionally, only nationally aggregated expenditure data on level 3 hospitals are available. This limits the precision of the estimates.<sup>5</sup> In addition, households are assumed not to change their behavior after they receive (or fail to receive) benefits. All households are also assumed to value the transfers they receive and the contributions they make equally, regardless of quality differentials or household composition, circumstances, and preferences: one Zambian kwacha (ZMK) worth of primary education services is valued exactly the same as one kwacha of secondary education. Also, all expenditures are valued equally within each spending category, regardless of income level or expenditure subset. For example, a kwacha contributed by a poor household towards the primary

> 5 It is, however, hard to say by how much. To the extent that there are wide differentials across regions not explained by population or demographic factors (or even socioeconomic factors that can be controlled for with available information, such as level of income), the assumption of uniform allocation may be seriously misleading.

7
**Table 14. Determinants of Sixth Grade Spanish Test Score: PARE, 1994.**

||**Coefficient**|**t-value**|**Means**|**Std. Dev**|**Elasticity**|**Average**<br>**Spending**<br>**Elasticity**|
|---|---|---|---|---|---|---|
|Child's part Academic Record|-0.1400|-1.6850|70.8100|19.6400|-0.3470||
|Per student cost of PARE assistance|0.0055|2.0810|165.9700|364.3400|0.0320|0.0000185|
|Score in 4th Grade|0.2518|9.9160|22.6800|11.5000|0.2000||
|Family's Cultural Capital|0.1052|3.9550|53.5900|18.2300|0.1970||
|Teacher's Performance 5th Grade|0.0953|2.3990|51.8600|7.6100|0.1730||
|Teacher's Performance 6th Grade|0.1266|2.9340|42.8300|5.4200|0.1900||
|Director's Quality|0.0928|2.2020|52.2700|7.0500|0.1700||
|Supervision Quality|0.0472|2.7690|63.6900|18.3500|0.1050||
|Parenfts Participation|-0.0626|-2.8090|35.3400|12.2300|-0.0770||
|DUM:MYforRural|-8.8827|-9.0870|0.3000|0.4600|-0.3110||
|DUM:MY for Native|-12.4228|-8.8540|0.2600|0.4400|-0.4350||
|(Constant)|23.0799|4.7220|||||
|Adjusted R Squares||0.2565|||||
|F||67.2897|||||
|N=2114|||||||
|Dependent variable: 6th Grade Spanish test|score||||||
|Estimation method: two-stage least squares|||||||

Source: Own calculations.

18
Our laboratory-based measure of the social capital impact of LEAP could not be clearer— there was none at the community level. True, there were some issues with low-powered tests in one or two of the measures, especially the trust measure among poor subjects, but the average effect across all four measures of pro-social preferences points to an unmistakable conclusion: the program had no effect on these measures. Before closing we should mention that we estimated the impact of the program just on SHG members using the treatment as an instrument. Of course, these specifications require the stark assumption that the treatment only works through SHG membership. These results are available in the appendix in Table A2. In any case, the instrumental-variables estimated effects of the program on SHG members were considerably larger than on the community as a whole. Estimates of altruism, propensity to contribute to public goods and trust were about onethird larger and estimates of trustworthiness were about one-fifth larger for SHG members than for non-members. Unfortunately these estimates were highly imprecise so these effects were not statistically significant.

# **_4.3.2 Network effects_**

We now turn to the effects of the program on social capital as measured by our network survey. We are interested in whether the program caused greater network density. Did the increased participation in SHGs lead to a greater participation in other sorts of social relationship as well? Our measure of connectedness of members of the community is dichotomous, equal to one if a subject reports a given relationship with two or more other subjects from the experimental session.<sup>16</sup> We think this measure best captures the type of social transformation that LEAP was attempting to achieve. Our measure characterizes a society as having more social capital when a large number of villagers are connected by social links even if just by a few links in contrast to measures in which a society would be characterized as well endowed with social capital when a small number of villagers

> 16 Our network measure is based on the connections between household members that participated in the experimental session. However this group was chosen randomly from the village, and is thus a reasonable proxy of the complete village network.

22
droughts considerably increase PM2 _._ 5 concentrations around combustion power plants. At average FHD levels, we find an increase of 0.83 μg/m<sup>3</sup> , which increases PM2 _._ 5 concentrations from 15.76 in the absence of hydrological droughts to 16.59 μg/m<sup>3</sup> (i.e., roughly a 5.3 percent increase). With World Health Organization guidelines categorizing PM2 _._ 5 concentration greater than 5 μg/m<sup>3</sup> as harmful to human health (WHO, 2021), the excess PM2 _._ 5 created by hydrological droughts will likely lead to a considerable health burden for the region.

The second set of results highlights that the most likely mechanism for the documented increase in PM2 _._ 5 is the shift in electricity generation from hydropower to combustion power plants. Specifically, we show a clear dose-response gradient with PM2 _._ 5 monotonically increasing with FHD. We also provide evidence from two placebo exercises that rule out wildfires and dust storms as plausible drivers of our results. Specifically, we test and fail to find evidence of FHD increasing PM2 _._ 5 around clean power plants (solar, wind, and nuclear) or around combustion power plants in the years before they become operational. We also rule out changes in electricity demand or other meteorological factors driving the results by controlling for these factors in our specification. In our main specification, we assume that these effects are additive and linear. Still, our result holds even after relaxing these assumptions and using the post-double selection method of Belloni et al. (2014) to test far more flexible specifications. The pattern of heterogeneous effects with respect to plant size and fuel type further bolsters the case for the shift in generation to combustion power plants as the primary mechanism. Specifically, we find that the effects on excess PM2 _._ 5 are largest for smaller power plants and those that use oil or biomass and more muted among larger power plants and those that use coal. These results are consistent with the idea that smaller power plants are more able to respond during a drought as they tend to be air-cooled, while larger power plants tend to be water-cooled. Another possible reason the effect is more muted among larger power plants (mainly coal) is their limited flexibility (i.e., they provide baseload generation).

The third set of results quantifies the number of lives lost building on our estimates of drought-induced excess PM2 _._ 5, demographic data on the population exposed, and well-established concentration-response functions from the literature that documents the adverse health effects of PM2 _._ 5 (e.g., Deryugina et al. (2019); Liu et al. (2019)). Our preferred counterfactual calculation shows that the region experiences as many as 10,000 premature deaths per year. The monetized losses from the lives lost using conservative estimates of the value of a statistical life indicate an associated cost in the order of $12 billion (constant 2019 USD) per year.

3