rafmacalaba/data-use-mentions-extended huggingface dataset:
We therefore suggest to alternatively consider the use of predicted growth rates in the persistence term. Based on equation (2), and suppressing the country subscript _c_ , we can define predicted growth as: ∆lny� 1 , (4) such that any changes in cc _x_ will be reflected in this predicted growth rate and hence in subsequent cc−1 1,cc kk kk,cc = θθ∆lny� + ββ ∆xx + ⋯+ ββ ∆xx _x_ persistence terms. Note that the presence of a lagged predicted growth rate in equation (4) requires some approximation (e.g., with actual growth) in the starting period.

# **6.3 What If the Model Does Not Predict Actual Growth Rates Well?**

In the above example for Bangladesh, our model explained actual growth in the 2005-2009 period very well: the residual of 0.27 percentage points (=4.35% - 4.08%) is less than 10% of the actual growth rate. More generally, discrepancies of 1/3 are not exceptional, and they can easily amount to 1/2 in some cases. Such large residuals may result from various sources that may be informative in its own and require country-specific expert knowledge and additional analysis and tools to detect the underlying reasons of such deviations.

One possibility is that the model is not adequate for the country under consideration. For example, small-island states, resource-abundant economies, or transition countries feature specific peculiarities that may not be adequately captured by the model – either because relevant model variables are missing, or because the growth correlation with existing model variables is different for those countries. In the former case, extending the model with those relevant variables can be considered. In the latter case, one can investigate parameter heterogeneity for country groups.

Another possibility is that national GDP numbers are inflated – due to GDP rebasing or creative national accounting – or otherwise mismeasured. Comparing GDP data to alternative proxies of economic activity (e.
research provides insight into firms’ mitigation efforts to respond to climate change, primarily in developed economies (see, for example, Kalantzis and Revoltella, 2019; Fleitera et al, 2012, Thollander et al, 2007). However, there is limited research in the developing countries’ context. Hence, while the existing literature investigates the determinants of climate-related investments in advanced economies located in the European Union and the United States, little is known about the factors influencing firms’ behavior in emerging economies, which adopt comparatively less green profiles. In addition, while some determinants of firms’ strategies of adapting to climate change and its policy-related impacts, such as their characteristics or green management practices, have been proposed (Fankhauser et al, 1999; Bleda and Shackley, 2008), they have not been investigated comprehensively. Yet, knowledge about these determinants is particularly important for policy makers to support favorable conditions for firms’ green investment strategies. Our work is closest to that by De Haas et al (2022), who show that both financial frictions and managerial constraints slow down firm investment in more energy efficient and less polluting technologies.

Based on the unique data from the joint European Bank for Reconstruction and Development (EBRD)European Investment Bank (EIB)-World Bank Group (WBG) Enterprise Surveys (ES), this paper empirically examines such determinants in a sample of almost 18,000 firms in 30 countries in Europe and Central Asia (ECA) and the Middle East and North Africa (MENA) regions. In this respect, the scope of firms’ green investment strategies (measured by the total number of mitigation measures) and measures that firms take to follow specific strategic directions of their business model (such as to protect the affected business) are analyzed. We consider the case of 10 climate-related measures, namely: heating and cooling improvements, type of on-site energy generation, machinery and equipment upgrades, energy 2
# **_Price controls on goods and services_**

Price controls are widely employed across advanced economies and EMDEs. They tend to be much more pervasive in EMDEs than in advanced economies, especially so for energy and food-related goods (Figure 1.A). The relatively high prevalence of other types of price controls in advanced economies reflects the greater prevalence of controls for services such as telecommunications. While price control regimes tend to be more restrictive in EMDEs when compared to advanced economies, the extent of controls varies greatly across economies (Figure 1.B).

> 2 Data on price controls on tradable goods combines the information on controlled prices from the World Trade Organization’s Trade Policies Reviews with 4-digit HS trade values from the World Bank’s World Integrated Trade Solutions database.

3
As the World Bank Enterprise Surveys (WBES) reveal, bribing and corruption occur frequently (though less commonly in the Western world), they are obstacles to doing business, and they carry a large cost for businesses. In Argentina, almost 9.5 percent of establishments in the economy participated reported bribing of a government official, almost 1 percent more than the average in Latin America and the Caribbean but below the worldwide average. In addition, bribes in Argentina were sizable, averaging 6.5 percent of the establishments’ annual sales.

Motivated by this evidence, this paper aims to quantify the efficiency cost of rent-seeking activities in the form of bribes in Argentina. To this end, I introduce bribing into a general equilibrium model of firm dynamics as in Atkeson and Bursteinn (2010). In this model, firms are heterogeneous with respect to their levels of productivity and behave in an environment with imperfect competition.<sup>4</sup> Productivity in this model evolves stochastically, but the probability of increasing the level of productivity depends on research and development (R&D) expenditure. In in the spirit of Restuccia and Rogerson (2008) and Hsieh and Klenow (2009), firms also face idiosyncratic subsidies and taxes that affect their size, which generate static misallocation in the model. However, unlike the static subsidies and taxes presented in those papers, these subsidies and taxes also evolve stochastically, conditional on the rent-seeking efforts (bribes) that each firm chooses to undertake. As a result of these choices, some firms will invest in rent-seeking activities and others will invest in improving the quality of their production process (by improving physical productivity, management practices, or intangible capital).<sup>5</sup> By diverting talent and resources away from production and innovation activities, rent-seeking activities can have sizable negative effects on aggregate efficiency.

For the quantitative analysis, I calibrate an undistorted version of the model to match key features related to the size distribution of firms in the U.
channels of indirect reporting. The LIC-DSA coverage increases the debt stock that is reported in official statistics by an average of 5 percent of GDP; the degree of dispersion fluctuates between -15 and 30 percent.

**Figure 5: indirect-direct reporting debt stock gap (in percent of 2019 national GDP)**

<!-- Start of picture text --> 30.00<br> 25.00<br> 20.00<br> 15.00<br> 10.00<br> 5.00<br> -<br> (5.00)<br> (10.00)<br> (15.00)<br><!-- End of picture text --> Source: WB/IMF 2019 DSA and authorities’ websites. Notes: author’s elaboration.

This example shows that the degree of mismatch across different sources may be significant. Previous work has highlighted the role of variation in coverage and debt definitions as the main driver of gaps between different debt statistics. This paper studies these factors but extends the list of potential causes to also include recording errors (often amplified by decentralized and non-automated recording processes) and data manipulation by external agents to comply with standardization and comparability goals. “Hidden debt” is also touched upon, but analysis regarding this channel faces severe data limitations and is not addressed in this paper.

# _4.1. Sectoral and instrument coverage_

Public debt data is typically presented as aa matrix with instrument coverage on the horizontal axis and sector coverage on the vertical axis (Figure 6). This tool is useful for mapping a country’s progress, although it is difficult to rank the level of coverage solely based on this matrix as it implies a ranking along both axes that may not apply to all countries. For instance, if this matrix were used to assess coverage in LIDCs, no country would register instrument coverage beyond D1, as currency/deposits are not included in LIDC debt reports
The randomization was stratified at the subdistrict level – within each subdistrict, one branch was randomly allocated to treatment and one to the control group. All of the selected communities in treatment branches were treated in 2007 while the control communities were not treated until after the endline survey in 2011.

Table S1.4 reports baseline balance tests which compare the characteristics of ultrapoor households in treatment and control groups at baseline. For every variable, the table reports the mean and standard deviation in treatment and control communities, as well as the p-value on a test of equality of means (column 7) and the normalized difference of means (column 8). Overall, the samples are well balanced: only 2 out of 26 tests for the equality of the means are rejected; and all of the normalized differences are below the .25 threshold recommended by Imbens and Wooldrige (2009). Thus we can conclude that the randomization was successful and the control sample provides a valid counterfactual for the treatment group.

# **5 Empirical Analysis**

## **5.1 Estimation**

In order to estimate the effects of the TUP program, I pool observations from the two followup surveys and estimate an ANCOVA model. In particular, I estimate: where _yidt_ is the outcome of interest for household _i_ from subdistrict _d_ at survey wave _t_ with time periods referring to 2007 baseline (t=0), 2009 midline (t=1) and 2011 endline (t=2); _Ti_ is an indicator variable equal to 1 if household _i_ lived in a treatment branch and 0 otherwise; _yid_ 0 is the baseline level of the outcome variable for household _i_ ; and _δd_ are subdistrict (randomization strata) fixed effects. The parameter of interest is _λ_ , the difference between treatment and control observations. The standard errors are clustered at the BRAC branch office level (the unit of randomization) in all the regressions. Under the identifying assumption that the control branches represent a valid counterfactual for the treated branches in the absence
Uruguay Round set, without sensitive products, is used in the empirical analysis.

Several groups of developing countries listed in Table 2 are allowed smaller tariff reductions. Least Developed Countries (LDCs) are not required to make any reductions. Small and Vulnerable Economies (SVEs)<sup>2</sup> can make reductions 10 percent smaller in each band than other developing members, or may make an average-cut of 24 percent. Recently-acceded members (RAMs) are allowed to: make cuts reduced by 8 percentage points; make zero cuts in tariffs below 10 percent; to delay their reductions until a year

> 1 As conjectured by Falconer (2008) and shown by Jean, Laborde and Martin (2010a), the political costs, and hence the pressure for exceptions, may rise even more rapidly than the economic benefits. 2 Defined in general as countries with less than 0.1 percent of world trade, with some countries such as Congo, Côte d‘Ivoire and Nigeria treated on the same basis in agriculture. See Table 2 for the country list.

5
ratio, head employed for wage dummy, any HH member owns non-ag enterprise dummy, any HH member has primary education dummy, agricultural asset index; Manager - female dummy, age, primary education dummy; Plot - intercropped dummy, precipitation of wettest month, soil fertility index, cash crop dummy; Zone fixed effects. Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 56
analysis, are all cities with population greater than 500,000,<sup>5</sup> as well as the two most populous cities in each country (data permitting). This creates a total of 1,000 cities and includes 34 cities with reported population less than 50,000. In our regional database, we use all cities above 100,000 inhabitants within the BRI countries under consideration, which leads a total of 1,964 cities in 71 economies. The network is solved for each origin‐destination pair, with routing determined by shortest time path, adjusting for preference parameters.<sup>6</sup> The use of a network analysis to quantify shipment times and the impact of new and improved transport infrastructure has benefits and drawbacks. On the plus side, a network analysis can be a useful tool to support modeling of international trade flows. It can enhance the understanding of the impact of changes in infrastructure or other factors on an interconnected system allowing to extract information from spatial data. Specifically, a network analysis allows to obtain realistic measures for the time it takes to go from one location to another taking into account the overall quality and quantity of infrastructure, physical

> 4 See Annex 2 for more data on modal shares in the world and in the BRI countries. Air shipment represents a small fraction in terms of trade volume, but a larger one in terms of value since it concerns goods with higher value on average.

> 5 Population sources are https://www.citypopulation.de/world/Agglomerations.html and <u>http://data.un.org/Data.aspx?d=POP&f=tableCode%3A240</u>

> 6 To “attach” cities to elements of the transportation network (a rail segment or a port), a fuzzy tolerance of 10km was used. As a result, any city located more than 10km away from any network element is left as unconnected and de facto
specification of the type for the learning outcome of student _i_ in subject _b_ , stream _k_ , school _s_ , and round _r_ . We define _j_ = _j_ ( _bksr_ ) as an identifier for the teacher assigned to that subjectstream-school-round. The variable _Ij_ is an indicator for whether the teacher is an incumbent, and the index _q_ = _q_ ( _j_ ) denotes the qualification type of teacher _j_ if that teacher is a recruit (and is undefined if the teacher is an incumbent, so that _Tqd_<sup>_A_is</sup> always zero for incumbents). Drawing on the pseudo-panel of student outcomes, the variable ̄ _zks,r−_ 1 denotes the vector of average outcomes in the once-lagged assessment among students placed in that stream, and its coefficient, _ρbr_ , is subject- and round-

> 28Here we follow Dal B ́o et al. (2013) who measure the risk preferences and Big Five personality traits of applicants for civil service jobs in Mexico, and Callen et al. (2018) who study the relevance of Big Five personality traits for the performance of health workers in Pakistan.

24
, but only of the sub-population that was actually treated because of random assignment. This is a well-documented qualification of the IV method. Second, our analysis has focused on the outcome indicators only but there are important process related questions that this impact evaluation does not yet answer. For example, while we do not find any statistically significant impact on social capital and female empowerment, project documents including socioeconomic surveys of assisted households at baseline and currently suggest that women have greater control over their earnings and make more diversified investments than men. Seventyfour percent of all PAF members are women. Future evaluation should carefully explore these relationships.

12
of a gradual impact of funding costs and cyclical movements in output on the demand for excess reserves.

Figure 1 displays the evolution of some of the variables included in the regression equation during the period 1992-98. The first panel shows the evolution of the ratio of excess liquid assets (obtained as the difference between actual and required liquid assets) to bank deposits.<sup>1 0</sup> The figure shows that excess liquid assets started to increase significantly well before the crisis (as early as late 1996), rising quite dramatically in the immediate aftermath of the currency and financial turmoil that began with the devaluation of the baht in July 1997. At the same time, the cyclical component of output turned sharply negative. The figure also shows that the money market rate fluctuated considerably during the period 1992-98--exceeding at times the discount rate and falling well below it at other times. Finally, both the ratio of credit to the private sector to deposits, and the ratio of currency in circulation to deposits, increased at first and subsequently fell in the aftermath of the crisis.

Table 1 presents the summary of the estimation results of the autoregression using ordinary least squares based on monthly data and using the entire sample ending in September 1998. The table reports the sum of the coefficients and their standard errors of the lags of the regressors included in the estimation since individual coefficients in the autoregression are difficult to interpret directly. Conceptually, the sum of the coefficients are obtained by estimating the dynamic model and then setting L _=_ 1. Thus, the sum of the coefficients reported in the table correspond to the long-run effects once they - have been appropriately scaled by: 1 al(1). In other words, Table 1 shows 10As noted in Sirivedhin (1998), prudential regulations in Thailand around the time of the crisis required commercial banks to hold liquid assets (averaged over a fortnight) of not less than seven percent of their deposit base; eligible assests included at least two percent non-interest-bearing deposits
Policy Research Working Paper 5700

# **Abstract**

Pension funds have been expected to invest in a wide range of securities and provide liquidity to domestic capital markets since they are the most sophisticated investors, with plenty of resources to gather private information and manage portfolios professionally. However, by analyzing unique, monthly asset-level data from the pioneer case of Chile, this paper shows that pension funds tend to herd. This is consistent with pension funds copying each other in their investment strategies as a way to extract information, boost returns, and reduce risk. The authors compute measures of herding across asset classes (equities, government bonds, and private sector bonds) and at different pension fund industry levels. The results show that pension funds herd more in assets for which they have less market information and when risk increases. Moreover, herding is more prevalent across funds that narrowly compete with each other, that is, when comparing funds of the same type across pension fund administrators. There is much less herding within pension fund administrators and across pension fund administrators as a whole. This herding pattern is consistent with incentives for managers to be close to industry benchmarks, which might be driven by both market forces and regulation.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sschmukle@worldbank.org.

_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
attendance rates of the non-RSE group.

Our data do not show any evidence that the RSE has fostered the development of nonagricultural businesses among the households in our sample in Tonga. None of the households surveyed mentioned investment in a business as a main use of the money earned in the RSE, and we do not observe any individuals in RSE households starting a new business over the two year period of our surveys. In Vanuatu we only had 5 households in the round 4 survey say the most important use of the money earned through the RSE was starting a business or supporting an existing business. Given the low population densities and small local markets, it is not clear what the scope for such business start-ups actually is, but over the first two years of the RSE policy, there does not appear to be much evidence of it fostering self-employment.

# **5. Community-level impacts**

19
# **Figure 14. Backward and Forward Integration to Manufacturing GVCs by Sources and Destinations of Imports and Exports of Intermediate Inputs: China (1995 vs. 2015)**

<!-- Start of picture text --> 100.0<br>90.0<br>80.0<br>70.0 73.5<br>60.0 61.2<br>54.3<br>50.0<br>40.0<br>30.0<br>20.0<br>14.4<br>10.0<br>0.0 0.7 1.5 0.2 0.6<br>-10.0 West East Asia Benchmark NRR oil NRR Non- Resource Sub India USA Germany<br>Europe Exporters Oil Poor Saharan<br>Exporters Africa<br>Backward Integration (2015) Forward Integration (2015)<br>Δ GVC (2015/1995) Δ Backward Integration (2015/1995)<br>Δ Forward Integration (2015/1995) GVC (2015)<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.

**Figure 15. Percentage Rates of Backward and Forward Integration to Manufacturing GVCs by Sources and Destination of Imports and Exports of Intermediate Inputs: Western Europe (1995 vs. 2015)**

<!-- Start of picture text --> 100.00<br>90.00<br>87.1<br>80.00<br>70.00<br>60.00<br>50.00<br>40.00<br>33.7<br>30.00<br>25.3<br>20.00<br>10.00 8.0<br>0.00 1.9 1.0 0.5 0
their non-enrolled daughters going to school (30%) than to disapprove of schooling for non-enrolled sons (7.5%).

Field interviews conducted in five villages randomly selected, by region, from the PRHS-II sample also highlight the importance of mobility constraints for girls school enrollment, as the following quotes illustrate:<sup>5</sup> "...I took my daughters out of school because it was too far to walk and I feel that things are not safe there...” (woman, Southern Punjab) “When sons go to schools that are far away we don’t get worried, but for our daughter we get worried.” (woman, Southern Punjab).

“The problem is, when children are so small to attend primary school, we cannot expect them to walk all the way to a school in another settlement or another village. But when they grow a bit more mature, 8-9 yrs, we cannot send girls alone to walk this distance. There are tall sugarcane fields and all the way is deserted except young boys, who use the same route for going to school. This is very dangerous! The boys tease — young girls anyone could push a little child, girl or boy in the fields, one would not even know!" (woman, Southern Punjab)

# 3.2 Caste and Stigma

Caste has received scant attention in Pakistan, from any disciplinary perspective. Indeed, the PRHS-II is the first large-scale survey, as far as we are aware, to collect systematic data on caste affiliation in rural Pakistan. While caste groupings in Pakistan cannot be precisely mapped into the much more extensively studied social hierarchy of neighboring India, there are significant similarities between the two due to their long shared history. In particular, caste identity is embedded within occupational differences, which are associated with status and notions of purity and pollution. Various exclusionary norms follow from these hierarchies and are exercised in relations of mutual assistance, in social networks, and in