rafmacalaba/datause-encoder-data huggingface dataset:
It is worth noting, however, that not all types of trade openness are equivalent. An increase in the differential between export and import growth rates (that is, exports growing faster than imports) reduces the probability of the worst outcome (cluster V). It also increases the probability of being in a higher growth and lower vulnerability (G) cluster and, even more, the probability of being in a lower growth and lower vulnerability (L) cluster. Thus, a country’s export orientation reduces the worst of all possible outcomes (cluster V). It does provide a metric to gauge a country’s choice of development path.

# **Figure 4. Probability and Shocks in 2-Year Average Estimation**

# **A One Standard Deviation Increase in:**

<!-- Start of picture text --> Trade Openness L GV H<br>Export-Import Growth Diff. V H GL<br>Financial Openness G L VH<br>FDI, Net, % GDP VH GL<br>Counter-Cyclical Fiscal Policies HGV L<br>Monetary Tightening VH L G<br>Exchange Rate Flexibility VL G H<br>Capital Account Liberalization G H VL<br>Initial Income, % of G7 GDP GH L V<br>Market Volatility, VIX HL G V<br>Oil Balance, % Exports & Imports V L H G<br>-.3 -.2 -.1 0 .1 .2 .3<br>Change in the Predicted Probability<br><!-- End of picture text --> Sources: IMF’s World Economic Outlook database and authors’ calculations. Notes: The horizontal axis measures the change in the predicted probability in each individual cluster following a change (a one standard deviation shock) in the indicator specified in the vertical axis. A positive value represents an increase in the probability of being in that cluster; e.g., greater exchange rate flexibility increases the probability of being in the higher growth and higher vulnerability (H) cluster. The opposite is the case for negative values; e.g., greater financial openness reduces the probability of being in the higher growth and lower vulnerability (G) cluster. The standard deviation shocks are derived from the data for the countries covered and that were observed during the period 2000-08. The charts are based on the 2-year average estimation.

As is the case with trade, financial openness also appears to increase vulnerability. However, it appears to do so in a similar magnitude in both the higher growth and higher vulnerability (H) cluster and the lower growth and higher vulnerability (V) cluster. Financial openness also appears to reduce quite significantly the probability of being in a higher growth and lower vulnerability (G) cluster, an effect that does not take place in trade openness. In other words, financial openness might deliver high growth, but might also lead to the worst of all possible outcomes (cluster V). Thus, financial openness appears to act primarily at the level of the vulnerability dimension. Interestingly, the type of financial openness also matters. If FDI plays an important role, then countries are more likely to end in either cluster L or cluster G, suggesting that FDI type financing sources have stronger stability features than other financing sources. While these results are in many ways in line with our priors, they still serve to 12
- Wilkie, D. S., Starkey, M., Abernethy, K., Nstame Effa, E., Telfer, P., & Godoy, R. (2005). Role of prices and wealth in consumer demand for bushmeat in Gabon, Central Africa. _Conservation Biology_ , _19_ (1), 268–274.

- Wilkie, D. S., Wieland, M., Boulet, H., Le Bel, S., van Vliet, N., Cornelis, D., BriacWarnon, V., Nasi, R., & Fa, J. E. (2016). Eating and conserving bushmeat in Africa. _African Journal of Ecology_ , _54_ , 402–414.

- Willis, J., Ingram, D. J., Abernethy, K., Kemalasari, D., Muchlish, U., Sampurna, Y., Midoko Iponga, D., & Coad, L. (2022). WILDMEAT interventions database: a new database of interventions addressing unsustainable wild meat hunting, consumption and trade. _African Journal of Ecology_ , _60_ , 205–211.

- Wright, J. H., Malekani, D., Funk, S. M., Ntshila, J., Mayet, L., Mwinyihali, R., Fa, J. E., & Wieland, M. (2022). Profiling the types of restaurants that sell wild meat in Central African cities. _African Journal of Ecology_ , _60_ (2), 197–204.

- Yocum, L. F., Vanegas, L., & Day, B. A. (2022). From the forest to the fork: Why we need to “reframe conservation”’ for conservation behavior change campaigns. _Applied Environmental Education & Communication_ , _21_ (1), 3–6.

**Data Availability:** All anonymized data are publicly available (Cisse et al., 2024).

**Code Availability:** All replication codes are publicly available (Cisse et al., 2024).

**Acknowledgments:** We thank Alexandra Jarotschkin, Megan Lang, John Loeser, James Sallee, and Juliet Wright for helpful comments, and Maximilian Auffhammer for Institutional Review Board assistance. Funding for the experiments provided by the Development Research Group of the World Bank. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank

40
**Figure 2: Confidence Sets for the Coefficient on Lagged Inequality (Horizontal Axis) and Twice‐Lagged Inequality (Vertical Axis) in the Levels Equation ‐‐ Halter, Oechshlin and Zweimüller (2014) Benchmark Specification**

<!-- Start of picture text --> Panel 1: Based on Wald Statistic Panel 2: Based on AR‐Statistic<br>WALD AR<br>95% Confidence set 95% Confidence set<br>-2 -1 0 1 2 -2 -1 0 1 2<br>beta: xlev2 beta: xlev2<br>Panel 3: Based on KJ Statistic Panel 4: Based on CLR Statistic<br>KJ CLR<br>95% Confidence set 95% Confidence set<br>-2 -1 0 1 2 -2 -1 0 1 2<br>beta: xlev2 beta: xlev2<br>Notes: This figure reports confidence sets for the coefficient on the lagged gini (horizontal axis) and twice lagged gini<br>(vertical axis). The dark‐shaded region corresponds to combinations of hypothesized values for these two parameters that<br>jointly are not rejected by the Wald, AR, KJ and CLR tests.<br>2 2<br>1 1<br>0 0<br>beta: xlev3 beta: xlev3<br>-1 -1<br>-2 -2<br>2 2<br>1 1<br>0 0<br>beta: xlev3 beta: xlev3<br>-1 -1<br>-2 -2<br><!-- End of picture text --> 29
**Table 2.2: Regional poverty lines and mean expenditures**

||**Region**||**Mean Per**<br>**Capita**<br>**Expenditures**<br>**(R$/month)**|**Lower**<br>**Poverty Line**<br>**(R$/month)**|**Mean**<br>**Welfare**<br>**Ratio**|
|---|---|---|---|---|---|
|1||Metro Belem|299.0|105|2.8|
|2|North|Urban|238.2|102|2.3|
|3||Rural|135.0|93|1.5|
|4||Metro Fortaleza|309.4|99|3.1|
|5||Metro Recife|331.3|104|3.2|
|6|Northeast|Metro Salvador|386.8|108|3.6|
|7||Urban|207.6|100|2.1|
|8||Rural|111.9|92|1.2|
|9||Metro Rio De Janeiro|547.7|107|5.1|
|10||Metro São Paulo|525.3|115|4.6|
|11|Southeast|Metro Belo Horizonte|429.1|103|4.2|
|12||Urban|381.3|109|3.5|
|13||Rural|207.0|97|2.1|
|14||Metro Curitiba|522.8|105|5.0|
|15|South|Metro Porto Alegre|485.0|111|4.4|
|16||Urban|368.3|99|3.7|
|17||Rural|236.9|90|2.6|
|18||Brasilia|596.2|109|5.5|
|19|Center|Goiania municipality|425.9|103|4.1|
|20|West|Urban|268.5|105|2.6|
|21||Rural|217.7|100|2.2|
||Total||335.9|103||

Source: Table 12 in World Bank (2007) and authors’ estimates using the 2002-03 POF.

While mean welfare ratios and poverty rates vary considerably both within and across regions, there are clear patterns. Within regions, mean welfare ratios are the lowest in rural areas, as one might expect, and highest in metropolitan areas (see Table 2.2). Likewise, the poverty rates are the highest in the rural areas and lowest in metropolitan areas (see Figure 2.1). Across regions, the Northeast and North exhibit the lowest welfare ratios while the Southeast, South, and Center West regions have the higher welfare ratios (Table 2.2.). The regional pattern of poverty is similar for metropolitan, urban, and rural areas, that is, the Northeast consistently has the highest poverty rates in each of the areas while the South has the lowest (see Figure 2.2). In the next section, we will investigate the factors behind these trends.

10
- Expenditures on transport and transport services during the recall periods

- Ownership of types of vehicles

- Travel mode, distance, time, and cost to different social services or facilities such as schools, health facilities, and workplace

# _Expenditures on transport and transport services_

The questions on expenditures ask whether the households purchased any vehicles or paid for transport-related goods or services during the following periods of time: i) 7days, ii) 1 month, iii) 3 months, iv) 6 months, and v) 1 year, and how much money they spent. Usually, the short-term time frame is used for transport expenditure on nondurables, such as public transport fares, fuels and lubricants, and maintenance and repairs, while the longer time frame is used for transport expenditure on durables, such as purchases of vehicles, spare parts, and accessories. As shown in the first column of Table 3, most countries in the sample collect the transport expenditure information using household survey instruments, however, the richness of the subcategories of expenditure on transport and transport services varies by country, for instance, some surveys do not capture the expenditure on vehicle purchases using the non-food expenditure modules.

# _Ownership of types of vehicles_

The questions on ownership of different types of vehicles are always present in the module of durable goods or household assets. The set of questions typically ask respondents whether their households own a list of vehicles (for example, car, motorcycle, minibus, bicycle, boat, animaldrawn cart), the number of vehicles they own, date of acquisition, purchase price, and estimated current resale price. However, the list of vehicles and the sophistication of questions vary by country, which means that some questionnaires do not collect sufficient information to estimate the annual use value<sup>5</sup> of vehicles, calculated based on the purchase and resell prices, and the date of acquisition. The standard LSMS household survey questionnaires of Albania, Malawi and Nepal even involve a question on whether the vehicle was purchased by the household or received as a gift, payment for services, or inheritance.

# _Travel to social services or facilities_

Many of the LSMS household questionnaires ask a group of questions regarding the accessibility to social services, including travel mode, time, distance, and cost to a long list of social facilities which varies widely by country. Typically, the list of social services includes school, health facilities (for example, hospital, private pharmacy, private doctor), telephone service, bus station, paved and dirt roads, bank branch and local shop. An example of the access to facilities module

> 5 The annual use value of transport durables = Stpt(rt −πt + δ) . Stpt is the current value of durables. rt −πt refers to the real rate of interest. δ is the depreciation rate of durables, which can be estimated using purchase 1 value, current resell value and age of durables using the formula <u>pt</u> T (Deaton and Zaidi, 2002). δ −π = 1 −�pt−T ~~�~~

8
The remaining expenditure covers overhead costs including (i) external auditing and verification of indicator reporting and payments at both the provincial and facility levels and for upgrading and maintaining the information system; (ii) training; (iii) national administration; and (iv) some one-time front-end fees. While over the whole period, overhead costs accounted for 16 percent of expenditures, by 2008 they accounted for only 11 percent.

**Table 4: Plan Nacer Expenditures (millions, current USD) 2005–08**

|**Description**|**2005**|**2006**|**2007**|**2008**|**Total**|**Percent**|
|---|---|---|---|---|---|---|
|Payments for services|1.89|13.03|24.68|28.40|67.11|63|
|Investment|3.40|8.72|7.83|2.63|22.63|21|
|Auditing and IT services|1.90|3.15|5.30|2.86|13.57|12|
|Training|0.15|0.26|0.31|0.18|0.90|1|
|Administration|0.20|0. 52|0.60|0.68|1.81|2|
|Front-end fees|0.68|—|—|—|0.68|1|
|Total|8.22|25.16|38.72|34.75|106.70|100|

_Source:_ Unidad de Financiamiento Internacional de Salud (UFIS), Ministerio de Salud Nacional. 2005-2008.

# **3. DATA**

In this section we describe the construction of the analysis sample and the dependent variables. We focus our analysis on seven of the nine provinces in the northern part of Argentina. The provinces included in the analysis are Catamarca, Corrientes, Formosa, Jujuy, Misiones, Santiago, and Tucumán. As described in the next section, we identify the causal effect of Plan Nacer on health outcomes by exploiting the geographic phasing in of the program over time within each province. We therefore excluded Salta because clinics were incorporated into Plan Nacer over a very short period of time, making our identification strategy inoperative. We also excluded Chaco because data were only available for a two-year period and did not cover the universe of births.

# **3.1. Sources**

In collaboration with the Ministry of Health, we constructed a unique database for analysis that combined information from the following sources:

- Sistema Informatico Perinatal (SIP) — individual birth and medical records for births at maternity hospitals.

13
_Management._

- Todo, Y, Nakajima, K, and Matous, P. 2015. "How Do Supply Chain Networks Affect the Resilience of Firms to Disasters? Evidence from The Great East Japan Earthquake." _Journal of Regional Science_ , 55(2): 209–229. https://doi.org/10.1111/jors.12119

- Tsuchiya, S, Tatano, H, and Okada, N. 2007. "Economic Loss Assessment due to Railroad and Highway Disruptions." _Economic Systems Research_ , 19(2): 147–162. https://doi.org/10.1080/09535310701328567

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44 more experienced personnel leave their venture capital subsidiaries to join partnerships in order to attain higher compensations. To prevent this lose of human resources, many of the bank-affiliated venture funds have changed their structure to partnerships, which allow for a more direct and higher participation in earnings (Venture Capital Journal, 1987).

# 2. Financial Management Issues

# 2.1. Sources of Funds

The question is frequently asked about where do privately-owned venture capital firms (VCFs) get their funds from. Clearly, the essence of the operations embraced by VCFs requires a structure of financing sources that matches the characteristic performance of the asset portfolio. Typically, in the short term the VCF will see a number of the chosen investments collapse. Conversely, it will be necessary to wait for some five years before the "winners" can yield any significant revenues. Therefore those investing in VCFs must be able and willing to witness losses in the immediate future while waiting for the good investments to manifest themselves and prosper. Such behavior is most likely to be found in institutional investors operating in the longer term segment of the financial markets. In fact, pension funds are the largest suppliers of funds for VCFs in the U.S. and Canada. In the United Kingdom, they account for about a third of the total funds invested in independent venture capital firms6 in 1987. Foreign institutions have become the largest suppliers of venture capital with a share of 36 percent of total funds, which proves the international nature of the U.K. venture capital industry.

Data from other European countries show that: (i) in the Netherlands, pension funds are the leading source of capital (for independent funds), (ii) in West Germany--where pension funds are not structured as independent investment entities, independent venture capital companies receive most of their capital from banks and industrial corporations, (iii) in almost all other European countries, especially in France, banks are providers of venture capital through their own in-house subsidiaries as well as through investments in independent venture capital funds.

# 2.2 Portfolio Risk Management

The nature of the venture capital business involves a high degree of risk. Individual project risks are essentially very high. However, the risk of the overall portfolio can be minimized through adequate diversification. This can be accomplished through a wide allocation of investments into different economic sectors, regional areas, or companies that are in different developmental stages among other criteria of allocation. An effective diversification, in terms of minimization of risk, is most likely when the potential returns associated to the different investments are inversely 0 UK independent venture capital firms are defined as comprising private, public, and the Business Expansion Scheme venture capital firms.
Lambsdorff 2003; Mauro 1995, 1998; Wei 2000).<sup>2</sup> This impact can be heightened when corruption is unpredictable (Campos, Lien, and Pradhan 1999; Malesky and Samphantharak 2008). Finally, the structure of corruption, specifically if there is a single agency demanding rents or if multiple agents all have opportunities for corruption, affects both the level and the impact of corruption (Olken and Barron 2009).

Unfortunately, measuring corruption requires novel research design strategies for a number of reasons. First, firm managers may be generally reluctant to answer politically sensitive questions (Lensvelt-Mulders et al. 2005; Azfar and Murrell 2009). This reluctance can stem from social factors, where firm managers may be hesitant to express personal beliefs that are considered taboo.<sup>3</sup> Second, and more relevant for this paper, the political environment can affect responses to firm level surveys. Jensen, Li, and Rahman (2010) analyze cross-national firm-level _Productivity and Investment Climate Surveys_ (PICS) administered by the World Bank. They find evidence for nonresponse and false response on corruption questions in the PICS survey. Specifically, in countries with lower levels of Press Freedom and Political Freedom, a larger percentage of firms tend to provide false response to the corruption questions and a large number of responses fit patterns of false response. They find that in countries with lower levels of press and political freedom, firm-level survey data tends to underreport levels of corruption.

In this paper we take seriously the potential for nonresponse and false responses in the firm level surveys. In fact, we design a survey methodology to examine patterns of nonresponse and false response on survey questions on corruption. Our methodology

> 2 Corruption can also affect firm entry strategies (Henisz 2000; Rodriguez et al. 2005; Uhlenbruck et al. 2006) or alter other aspects of their operations (Kwok and Tadesse 2006; Luo 2006).

> 3 For example, see Berinsky (1999) for work on public opinion and school integration in the United States.

5
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

**ABBREVIATIONS AND ACRONYMS** AFPF Armed Forces Pension Fund CCK City Council of Kampala CMA Capital Market Authority CPG Commuted Pension Gratuity DB Defined Benefit DC Defined Contribution JE Job Evaluation MOD Ministry of Defense MOFPED Ministry of Finance, Planning and Economic Development MPS Ministry of Public Service NSSF National Social Security Fund PROST Pension Reform Options Simulation Toolkit PRP Pay Review Policy PSPF Public Service Pension Fund UBOS Uganda Bureau of Statistics UIC Uganda Insurance Commission

- 3 -
results show a positive redistribution effect for each year since the Gini coefficient falls after tax. The pattern of the distribution of the equalizing effect across the vertical, horizontal and reranking components is similar over the three years. The results also indicate a fairly high level of horizontal inequity. Using 1991 as base year, the authors Table 2.4. The Redistributive Effect of Income Tax in Korea

|Indicator|1991|1996|2000|
|---|---|---|---|
|Gini before tax|0.34718|0.33682|0.40077|
|Gini after tax|0.32455|0.31877|0.37899|
|RE|0.0222631|0.018041|0.021782|
|Kakwani|0.41158|0.40158|0.42643|
|Average Tax Rate|0.065178|0.066025|0.069124|
|V|0.028697|0.028389|0.028033|
|H|0.00523|0.008924|0.005491|
|R|0.000836|0.001424|0.000760|
|V(%)|126.8|157.35|128.70|
|H(%)|23.11|49.46|25.20|
|R(%)|3.7|7.89|3.49|

Source: Hyun and Lim (2005) make a normalized comparison of these components over time. They find that the redistributive effect was highest in 1991 and lowest in 1996. They explain the 1996 outcome by observing that it is also the year with the highest level of horizontal inequity. In general, they conclude that there is room for improving the redistributive power of income tax in Korea by abolishing most allowances and exemptions.

To confirm that the AJL methodology applies equally well to indirect taxes, we present in table 2.5 the results of an evaluation of the redistributive effect of the pre-2000 indirect tax system in Australia by Creedy (2001). To account for demand responses, the author derives demand elasticities and welfare changes on the basis of a linear expenditure system (LES) using data on the socioeconomic groups listed in table 2.5. The 14 expenditure components include those listed in table 2.2 and in footnote 14. However, the author explains that the computation of the three components of redistribution presented in table 2.5 does not account for demand changes in order to 16
# **Table 6.1. OLS results**

_Dependent variable: Total number of land privatizations per 100,000 residents_

||Model<br>6.1.1|Model<br>6.1.2|Model<br>6.1.3|Model<br>6.1.4|Model<br>6.1.5|Model<br>6.1.6|Model<br>6.1.7|
|---|---|---|---|---|---|---|---|
|Constant|0.081<br>(0.701)|-1.979<br>(8.064)|-2.896<br>(8.067)|-4.159<br>(8.114)|5.740***<br>(0.784)|5.910***<br>(0.798)|-1.348<br>(4.353)|
|Number of sales<br>though tender per<br>100,000 of regional<br>population|5.271***<br>(1.471)|4.489***<br>(1.486)|4.772***<br>(1.497)|4.542***<br>(1.506)|4.661***<br>(1.482)|4.921***<br>(1.498)|4.581***<br>(1.495)|
|Ln Regional GDP per<br>capita|0.417<br>(0.725)|0.703<br>(0.722)|0.804<br>(0.724)|0.398<br>(0.797)||||
|Distance from the<br>regional capital to<br>Moscow (in 1000<br>kilometers)||-0.393**<br>(0.184)|-0.408**<br>(0.184)|-0.521**<br>(0.206)|-0.352*<br>(0.181)|-0.362**<br>(0.181)|-0.530**<br>(0.205)|
|Federal okrug capital<br>dummy|||-2.130<br>(1.682)|-1.742<br>(1.708)||-1.871<br>(1.676)|-1.521<br>(1.670)|
|Ln of regional<br>territory in km sq||||0.545<br>(0.455)|||0.689*<br>(406)|
|Adj. R2|0.117|0.154|0.160|0.165|0.156|0.158|0.177|
|N|85|85|85|85|86|86|86|
|Mean of the<br>dependent variable|5.80|5.80|5.80|5.80|5.73|5.73|5.73|

*, **, *** Significance level of 10%, 5%, and 1%, respectively

# **7. Analysis of Reasons Companies Use Unofficial Payments**

_Hypothesis 5: Excessively complicated, longer and costly procedures are associated with more frequent unofficial payments._ The dependent variable for this hypothesis was constructed as a ratio of the number of specific steps in which unofficial payments were recorded to the total number of the steps to complete a specific procedure ( Ε ). The variables of interest were (i) different measures of procedure complexity and length, such as the official number of steps in each of the procedures or the number of documents necessary to collect in order to complete the procedure; and (ii) measures of a procedure’s official cost. Other standard control variables included in the model were regional and procedure dummies.

When testing this hypothesis, we concentrated only on the first three procedures and did not include procedure 8 in the estimates. This decision is made based on the nature of procedure 8, which is a secondary procedure dealing with a land plot when it is already owned/leased by a private entity. The current hypothesis focuses on the primary procedures – transfer of ownership or lease rights from a government entity to a private entity.

For all practical purposes we are going to test three simple “sub”-hypotheses:

1. _More complex procedures are more prone to corruption._

25