rafmacalaba/data-use-mentions-extended huggingface dataset:
As regards DCs, the literature is extremely scarce<sup>30</sup> . However, Wang (2006) found convincing evidence that unemployment had fostered start-ups in Taiwan, China over the period 1986-2001; in contrast, in the previously-cited work by Santarelli (2011), no significant impact of the unemployment rate on new firm formation in Vietnam was found.

# **<u>4.2.7 The role of ethnic minorities</u>**

While most of the empirical literature on entrepreneurship is biased towards evidence from the industrialized countries, a particular driver of new firm formation is instead mainly studied with regard to DCs<sup>31</sup> : the role played by ethnic minorities in generating above-theaverage rates of entry and better post-entry performance among newborn firms. The basic hypothesis is that alien minorities may have an entrepreneurial advantage based on their opportunity to exploit their minority community networks to overcome important hindrances to entrepreneurship (see Section 4.1), such as information asymmetries, high transaction costs, credit constraints and difficulties in accessing available inputs and technologies (see Kilby, 1983 and Biggs and Shah, 2006). In addition, from a sociological point of view, an ethnic minority, characterized by common traits such as language, culture and religion, generates trust, social cohesion and emulation, which are all factors that favor entrepreneurial behavior (see Greif, 1993; Hobday, 1995; Iyer and Schoar, 2010). Finally, a minority group may also be affected by a feeling of insecurity and frustration (in comparison with a dominant group), which encourages members to seek economic success and a better social status (see Elkan, 1988)<sup>32</sup> .

Empirical evidence is generally consistent with the hypotheses just discussed; for instance, Ramachandran and Shah (1999) - using firm level data from Kenya, Tanzania, Zambia and

> 30 This is unfortunate since, as discussed in the introduction, ‘defensive and necessity entrepreneurs’ appear
# **1. Introduction**

In the Poverty Reduction Strategy Paper produced by the government of Rwanda (2002), rural development represents one of the pillars for the fight against poverty. In this context, the development of non-farm employment opportunities is emphasized and the definition of specific policies is broadly envisaged. However, the identification of specific measures requires first a better understanding of the non-farm sector in rural Rwanda<sup>1</sup> and the constraints that currently undermine its expansion.

Non-farm sector development generally entails multiple benefits. For instance, the nonfarm sector may absorb a growing rural labor force that cannot be employed in the agricultural sector and it may slow down rural-urban migration. Similarly, non-farm sector expansion may enhance growth and promote a more equitable distribution of income (Lanjouw and Lanjouw, 2001) also by enabling more effective and beneficial income diversification opportunities for rural households (Reardon, 1997).<sup>2</sup> These broad considerations become even more vital in the context of a country such as Rwanda, characterized by a high population density, small and fragmented land holdings, and large discrepancies between land holdings across households. As Clay et al. (1990) note, poorer Rwandans with little access to land would benefit greatly from participating in the non-farm sector, if given appropriate training, capital, and credit. Despite the compelling rationale for furthering non-farm policies in developing countries, the empirical evidence has been mixed. In particular, the development of the non-agricultural sector is at times found to be associated with increased inequality, whereby richer households are better able to benefit from non-farm opportunities. Consequently, any policy recommendations in this area require a good understanding of the specific country context so as to ensure equitable access to non-farm opportunities.

To provide such context, this paper relies on the Rwandan Integrated Household Living Conditions Survey ( _Enquête Intégrale sur les Conditions de Vie des Ménages au Rwanda or EICV)_ conducted by the Statistics Department of the Ministry of Finance and Economic Planning between October 1999
**Table 7. Impact of Gas Price Increase on Gas Consuming Households**

|**Quintile**|**Amount**_(in drams per_|**Impact**_(% of household_|
|---|---|---|
||_month)_|_consumption)_|
|**Armenia**|||
|_Poorest_|1,680|2.4|
|_2_|2,743|2.8|
|_3_|3,047|3.1|
|_4_|3,600|3.0|
|_Richest_|4,274|2.4|
|_All_|3,221|2.8|
|**Yerevan**|||
|_Poorest_|1,782|2.5|
|_2_|2,868|2.6|
|_3_|2,945|2.7|
|_4_|3,515|2.4|
|_Richest_|4,079|1.9|
|_All_|3,148|2.4|
|**Other Urban**|||
|_Poorest_|1,590|2.4|
|_2_|2,576|2.9|
|_3_|2,985|3.3|
|_4_|3,450|3.3|
|_Richest_|4,317|2.8|
|_All_|3,164|3.0|
|**Rural**|||
|_Poorest_|1,624|2.4|
|_2_|2,653|2.7|
|_3_|3,078|3.1|
|_4_|4,252|3.5|
|_Richest_|4,388|2.6|
|_All_|3,384|2.9|

_Source:_ Author’s calculations based on 2008 ILCS Another illuminating approach to
; Lee _et al_ ., 2011). For instance, in a recent study, Ardagna and Lusardi (2010), dealing with GEM microdata from 37 countries including 8 DCs, showed that stringent entry regulation, soft contract enforcement rules and labor market rigidities play an important role in hindering entrepreneurship and in strengthening the adverse impact of risk aversion.

Moreover, apart from the legal and institutional drawbacks characterising a developing country, a prominent role is also played by the wide diffusion of bribing, which may abort any chance of growth of a fragile new entrepreneurial activity<sup>18</sup> . For instance, Fisman and

> 17 A possible solution for credit-rationed potential entrepreneurs is to rely on networks and “social capital”, in order to identify financing subjects such as venture capitalists (see Batjarga and Liu, 2004). 18 Aterido, Hallward-Driemeier and Pagés (2009, p.10), using evidence from the World Bank Enterprise Surveys, show that 42% of firms declare they have paid bribes, with an average amount paid of 1.5% of sales.

17
Policy Research Working Paper 7871

# **Abstract**

The Cultural Trade Index aims to shed light on cultural trade and stimulate interest in how this little-known area can contribute to economic diversification, boost shared prosperity, and reduce extreme poverty. As the first index of its kind, the Cultural Trade Index would gather cultural trade data scattered across different sources, place them in one place, and show how countries are performing. The key objective is to help inform decision making to advance cultural trade for development. Since culture is not restricted to monetary gain, a Cultural Exchange Index could also be created to complement the Cultural Trade Index. The Cultural Exchange Index would rank countries according to their participation in international cultural exchanges. Comprehensive research efforts would be needed for these ideas to make meaningful contributions to cultural trade and development policy.

This paper is a product of the Office of the Chief Economist, Development Economics Vice Presidency. 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 author may be contacted at pkabanda@musikaba.net.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **S4. Vignettes: Protocol, administration, reason for use, and validation**

A vignette is a hypothetical case in which the interviewer acts as a “patient” and provides a very brief description of symptoms. The provider is informed that the “patient” will comply with the provider’s instructions, medications, and tests. The provider is then invited to proceed exactly as he or she would under normal circumstances, asking questions about the history of the illness and performing necessary examinations and tests. The “patient” provides standardized predetermined answers to the questions and examination/test procedures. The questions, examinations, and tests that a provider uses are compared to protocol checklist. Data are recorded on each of the items on the checklist the provider provided; the diagnosis given; and the treatment suggested. Four of the seven vignettes used in this study were originally developed for use in Tanzania, with the items drawn from the national protocol.<sup>4</sup> The list was subsequently expanded to include items required by the guidelines for the Integrated Management of Childhood Illnesses.<sup>5,6</sup> Vignettes were administered to a random sample of doctors, medical assistants, nurses, and nurse-midwives (i.e., any staff that provide outpatient or maternity services) who were present in the facility on the day of the first, announced visit. Absenteeism on that first visit, announced in advance, was 17.2 percent (for nurses and midwives) and 25.4 percent (for doctors).

The assessments were carried out by a team of two enumerators who were trained in the administration of vignettes. One acted as the patient and the other recorded outcomes. Normally, the recorder should have medical training: for example, in Togo the enumerators were medical students and in Niger, enumerators were medical workers with at least a university degree.

We use data from seven vignettes capturing conditions associated with the main causes of child and maternal mortality in
when constructing the instrumental variable, the parameter of the decay function was modified to give more or less weight to the proximity between the country of origin border point and the destination clusters. The IV regressions with the instruments constructed by dividing the number of refugees from country _O_ by the square or the square root of the distance between border points and the clusters also show similar elasticities between 0.08 and 0.14, for households within 50km and 100km of the closest refugee settlements.<sup>14</sup> Even when Kampala district is included in the analysis, the estimated coefficient remains in the same range across the decay functions of the instrument, for households within the 50km and 100km of the closest refugee settlements.<sup>15</sup> In our main results we do use the average distance to border crossing points for each cluster to construct the instrumental variables. Using the distance to the closest crossing point would not alter much the result, only pushing upward the estimated second-stage coefficient of interest.<sup>16</sup> The results therefore show that the presence of refugees has a significantly positive effect on household welfare. Living closer to refugee settlements is beneficial to the host populations.

# **5.2. The identifying assumption**

The causal identification of a positive impact of refugees on the hosts’ welfare rests on key identifying assumptions. First, the instrument is sufficiently relevant as shown by the relatively high values – above 14 – in the Kleibergen-Paap rk Wald F statistics (see Panel B of Table 3). Second, we assume

> 14 See Appendix 4, Tables A9 & A11; columns 8 & 12. The first stage results are reported in Tables A10 & A12 respectively.

> 15 See Appendix 4, Tables A13 & A15; columns 4, 8 & 12. The first stage results are reported in Tables A14 & A16 respectively.

> 16 See Appendix 4, Tables A17 & A19; column 4, 8 & 12). The first
the results in Table 13. We do not look at model 4 as we have seen that the results are quite robust across model specifications and this model requires a larger number of observations to meet the degrees of freedom requirements. Because we are dealing with time-series data, we test for the presence of serial correlation, and use the CochraneOrcutt procedure to correct for these effects when present.

Consistent with the analysis in section 3, we find decentralization of maintenance has the strongest impact on resource costs (see table 10). The coefficients for decentralization of maintenance are significant and negative (first order), then positive (second order) and negative (third order), implying that resource costs decrease with increasing levels of decentralized maintenance. The effect of decentralization on unit costs of construction is mostly linear, since the first order effect is the only significant variable of decentralization. Furthermore, we see that economies of scale are lost as these costs increase with increasing degrees of decentralization. The effect of decentralization on unit costs of administration in Korea is not very significant, although the overall effect is to reduce these costs, as was found earlier. However, total decentralization has a strong non-linear impact on resource costs. The overall effect is to reduce the total costs of providing roads (since the negative second order coefficient dominates).

**Table 10: Impact of Decentralization on Resource Costs--Case of Korea**

|Variable|Construction|Maintenance|Administration|Total Effect|
|---|---|---|---|---|
|year<br>|::<br>'**25**<br>year1|146...**.**..<br>(L_9)|1.093.4<br>(1.57)**|(2.92.|
|per capita GNP||||.....**..**|
post crisis ranges, despite showing some signs of compression.

Recent empirical research documents an increase in loan growth upon the introduction of NIRP in the euro area. Rostagno et al (2016) show that under NIRP, euro area banks have become more likely to turn extra reserves into lending or bond purchases, and that total lending to companies has gone up relative to a counterfactual scenario. The ECB’s July 2016 Bank Lending Survey also indicates that loan supply conditions for both enterprises and households continued to improve during the first half of 2016 (ECB 2016d). Across NIRP economies, credit growth has generally improved, albeit sluggishly in most cases.

> 22 Beyond profitability, European banks appear to have been able to enhance somewhat their shock-absorbing capacity in recent years. According to the latest stress test results on 51 large European banks, 43 maintained a common equity tier 1 ratio above 8 percent in a severe recession scenario, showing improved resilience from similar tests in 2014 (EBA 2016). Having said that, concerns remain about euro area banks because of a large stock of legacy assets and non-performing loans (World Bank 2016b).

> 23 Draghi (2016) emphasized that the ECB had _“no evidence that [negative interest rates] hampered the transmission of monetary policy, and when we look at bank profitability, we see that 2015, the first full year with negative interest rates has not affected, or has not caused negative interest income, net interest income going down. As a matter of fact, it went up.”_ He also noted that _“Now, of course we've got to be cautious here because we talk about the aggregate of the banking system in the euro area, and of course aggregates may actually conceal different realities.”_ Cœuré (2015) argued _“...whether bank profitability really falls when policy rates are lowered depends more generally on the slope of the yield curve, on banks' investment policies and on factors driving non-interest income.”_
Figure 5: _Composition of steering board and working groups_ <!-- Start of picture text --> Think tanks and research community<br>Trade associations or chambers of commerce Working Groups<br>Steering Board<br>Private sector<br>Public sector<br>0 10 20 30 40 50 60 70 80 90 100<br>Share of economies (%)<br><!-- End of picture text --> _Source: Reform Committee data Note: Figure based on 107 economies with a functioning business reform committee._ Among other stakeholders, think tanks and the research community are also engaged in the work of the steering boards and working groups, but in only a quarter of all reform committees globally. For example, engagement of business actors in the Malawi Public Private Dialogue Forum is through participation of chairpersons of business associations and managing directors of major corporations from the private sector. Business associations and professional societies that attend the Forum include the Society of Accountants, Manufacturers Association, Employers Association, Economics Association of Malawi, Indigenous Business Association, Road Transport Operators Associations, the National Association of Smallholder Farmers, Farmers Union, Tobacco Association, Tobacco Exporters Association, as well as Cotton Development Association (among others).

While the private sector is represented at the level of the steering board or working group of the reform committee in most economies, its role and level of engagement can be very different. For example, while the institutional structure of PEMUDAH in Malaysia allows the private sector to play an extremely proactive role, including equal decision-making power, the State Regulatory Service of Ukraine has no established working groups and private sector participation is extremely limited.

13
ship. The general finding is that corporate taxation has significant adverse effects on both investment and entrepreneurship.<sup>3</sup> One study that analyzes the Chilean economy is Hsieh and Parker (2007). The authors argue that the main cause of the investment boom in Chile in the last part of the 1980s and 1990s was due to a tax reform from 1984 through 1986 that cut the tax rate of retained profits from 50 percent to 10 percent. While these papers focus on investment and growth, our analysis is on the allocative effects of corporate tax rates.

The remainder of the paper is organized as follows. In Section 2, we describe the data used and document facts on the effective corporate tax rate distribution in Chile. Section 3 describes the theoretical framework used in our analysis and specifies the calibration of parameters. We perform our quantitative analysis in Section 4. In Section 5 we perform sensitivity analysis on the parameters chosen. Section 6 deals with caveats that may arise from our measurement of firm-specific effective tax rates. Last, we make concluding remarks in Section 7.

# **2 Description of the Data**

This section describes the data used in our paper and presents facts about the effective corporate tax rate distribution in Chile.

## **2.1 The Annual Census of the Chilean Manufacturing Sector: ENIA**

The data used are taken from the ENIA (Encuesta Nacional Industrial Anual), an annual census of the Chilean manufacturing sector. This data set is an unbalanced panel that covers all manufacturing plants with more than 10 employees and plants with fewer than 10 employees that belong to firms with multiple establishments. We use data for the period 1998-2007, as there were no reforms to the Chilean tax code in this time frame, except for pre-stipulated

> 3. See Djankov et al. (2010) and the references within.

6
and September-December. The PoI data has information on one’s employment status, time usage, and demographic characteristics like gender, education level, and marital status. The PoI data shows how much time a person spends on household activities, at work, and traveling. Reported time on travel is spent by a person traveling from one place to another for various purposes, including work-related activities. The CP survey does not ask specific questions about time spent commuting to work, searching for a job, or on leisure.

We use six waves of PoI from May-August 2020 to January-April 2022 (or from the 20th to the 25th wave). We also match the households in the PoI data to those in the CP data. Appendix Table D2 lists the study periods for the two sectional data sets. We restrict our sample to women (or households having women) aged between 15 and 65 at their in the data. Table D3 lists the variables used in the first appearance Appendix analysis and their definitions. Appendix Table D4 presents the summary statistics of our study sample. Panel A displays the household characteristics in December 2020. Differences between households in treated vs. control HRs regarding rural residence, number of people, and per-capita income and expenditures are relatively small. In panel B, we compare women in treated HRs to those in control HRs in May-August 2020. The distributions of age, marital status, and education are comparable for the two groups of women. Women in treated areas are less likely to participate in the labor market, but conditional on participation; they are more likely to be employed. They also tend to more time on household activities and work but less time on travel than women spend in control areas.

# **E.2 Delhi Primary Survey**

As explained above in footnote 28, we cannot include Delhi in our baseline analysis. To complement our inquiry, however, we use primary data collected in February 2020 by the Gesellschaft f ̈ur Internationale Zusammenarbeit (GIZ) India (Mahendru, 2022
**|28|74%|36|95%|
|**MDG 3.a Gender parity in secondary education**|16|42%|23|61%|
|**MDG 4.a Child mortality under five**|33|31%|48|46%|
|**MDG 5.a Maternal mortality**|20|21%|37|39%|
|**MDG 7.c Access to safe drinking water**|10|15%|32|48%|
|**MDG 7.c Access to sanitation**|6|6%|25|26%|
|Source: Authors’ calculations based on data fr<br>**Simple average**|om the World Develop<br>17|ment Indicators d<br>32%|atabase.<br>30|53%|

Source: Authors’ calculations based on data from the World Development Indicators database.

Indeed, many lagging countries are already within striking distance. From another perspective, table 3 provides the proportion of countries within 10 percent or 20 percent of getting on target. A third of off-target countries have, on average, a gap of 10 percent or less from being on target across the MDGs. Countries like Bangladesh (extreme poverty, hunger, and maternal mortality), Indonesia (hunger, child and maternal mortality, access to safe drinking water), and Mali (gender parity in primary education and access to safe drinking water) are in this category. More than half have a gap of 20 percent or less. Of the countries within 20 percent of target, the best results are for gender parity in primary education, primary education completion, gender parity in secondary education, and hunger. The worst results are for access to sanitation, extreme poverty, and maternal mortality, with access to safe drinking water and under-five mortality in the middle. Table 4 lists countries that are within