rafmacalaba/datause-encoder-data huggingface dataset:
addressing them may require the use of panel data to control for individuals’ or firms’ fixed effects.

Exploring such concerns, Gelb et al. (2007) examine subjective data yielded by the core Enterprise Survey perceptions-question cited in Box 1. They conclude that while ‘perceptions of critical business climate constraints may not always correspond fully to “objective” reality’, firms ‘do not complain indiscriminately’ and response ‘patterns correlate reasonably well with several other country-level indicators related to the business climate’. Likewise, Aterido et al. (2007) underline that: ‘subjective rankings are highly correlated with objective measures in 16 of the 17 variables [and] also significantly correlated with external sources, including Doing Business indicators. Pierre and Scarpetta (2004) use 38 countries and confirm that countries with more restrictive labor regulations are associated with higher shares of firms reporting labor regulations as constraining’<sup>32</sup> However, even if objective and subjective measures are significantly correlated, it is important to remember that the latter remain prone to bias. For example, a study by Olken (2006) compares corruption perceptions among villagers in Indonesia with objective measures of corruption in road construction projects. It shows that although subjective and objective measures are positively correlated, there are also systematic individual-level biases in the latter. Similar issues are very likely to arise in firm-level surveys.

In spite of these problems, subjective indicators can still play a useful role in identifying important constraints through descriptive statistics. For instance, Carlin, Schaffer and Seabright (2006) have highlighted the ease with which a subjective ranking of constraints by firms allows a comparison of the importance of different constraints. This is not readily possible with objective indicators that measure various elements of the business climate in variable-specific units.<sup>33</sup> For instance, it is much easier to directly ask firms to rank the perceived severity of the constraint posed by the power supply relative to corruption, rather than trying to rank it based on two objective measures such as the number of power outages relative to the amount of bribes paid. Carlin et al. also argue that while over-optimism or pessimism may affect estimates of the absolute level of measured constraint severity, there is no reason to think that average _differences_ between constraint rankings are likely to be biased. Thus subjective data may be helpful to shed light on the relative importance of different constraints within economies. However, even if they can play an important complementary role, subjective indicators are probably less useful than objective ones in standard econometric analyses.

> 32 Aterido, R., M. Hallward-Driemeier and C. Pagés. 2007: 11-12. 33 Carlin, W., M.E. Schaffer and P. Seabright. 2006: 13.

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There is no apparent clear relationship between the level of entitlement rights and the level of development. Just by looking at the most recent data (2007), we cannot detect any clear pattern between the size of government and the level of GDP per capita (Figure 3).

**Figure 3.  Entitlement Rights vs. per Capita Income, 2007**

<!-- Start of picture text --> 12<br>11<br>10<br>9<br>8<br>7<br>6<br>5<br>4<br>2 3 4 5 6 7 8 9 10<br>ERs measured by the size of governemnet (inverted scale)<br>ln GDP per capita (PPP)<br><!-- End of picture text --> Source: Freedom House and World Bank.

Economic freedom and civil and political rights are not always related. The biggest cluster of countries enjoys both high levels of economic freedom and civil liberties, but there are several countries with high levels of either economic freedom or civil liberties and moderate levels of the other index (Figure 4).

**Figure 4. Economic Freedom vs. Civil and Political Rights, 2007.**

<!-- Start of picture text --> 0 2 4 6 8<br>CL<br>8<br>6<br>EFW<br>4<br>2<br><!-- End of picture text --> Where 8 = maximum level of rights, dots represent the number of countries. Source: Freedom House and Fraser Institute, 2007.

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Zambia’s road freight industry faces competition from other Southern African operators. Several foreign trucking companies operate extensively along Zambian main transport corridors. The importance of the foreign operators can be seen in Table 4 which gives the estimated<sup>21</sup> numbers of trucks operating on a continuous basis on the different routes and which are registered in other SADC countries. The market is therefore highly competitive, with Zambian trucking companies’ market share of up to 40%. It was even confirmed through monitoring at Chirundu border-post that most of the trucks operating on the international routes are owned by South African and Zimbabwean companies<sup>22</sup> , as much as 70-80% of the traffic through Chirundu.

**Table 4 Numbers and Origins of Trucks Operating along Main Zambian** **<u>Corridors</u>**

|**Road Route / Corridor**|**Zambia**|**South**<br>**Africa**|**Other**<br>**Countries**|
|---|---|---|---|
|**Zambia – Zimbabwe – South Africa, via**<br>**Chirundu**|500|900|300|
|**Zambia – Zimbabwe – via Chirundu**|100|0|150|
|**Zambia – Botswana – South Africa, Via**<br>**Kazungula**|<br>200|300|100|
|**Zambia – Tanzania, via Nakonde**|100|0|300|
|**Zambia – DRC via Kasumbulesa**<br>**(Zambian trucks and trucks in transit)**|300|100<sup>23</sup>|200<sup>24</sup>|
|**Zambia – Namibia, via Katimo Mulilo**|50|0|20|
|**Zambia – Malawi via Chipata**|50|0|30|
|**Zambia internally – Bulk Goods**|200|0|0|
|**Total number of currently licensed**<br>**Zambian Heavy Trucks**|**1300 -**<br>**1500**|||
|**Estimated total number of foreign**<br>**Heavy Trucks on Zambian routes at**<br>**any one time**||**1200**|**1100**|

> 21 The statistics provided by the Cross Border Agencies in Zambia and South Africa, and also the Zambian Revenue Authority, do not publish data on the number of trucks using the various road corridors. The only effective way to obtain this information would be to undertake a detailed border post and customs survey. Temporary permits do not indicate the numbers of trips carried out by each truck for the duration of the permit. Estimates are based on data from cross-border permits, customs records, previous observations of border-posts, and information provided by selected transport operators.

22 It is worth noting that most South African-owned trucking companies use mainly Zimbabwean drivers whereas Zambian-owned firms mainly employ Zambian drivers. 23 Most of trucks are in transit from South Africa and Tanzania.

24 Mainly trucks in transit from Zimbabwe.

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by the positive impact that improved institutions in the South via a PTA has on the total number of firms in the North offshoring to the South (the sum of FDI and foreign outsourcing).<sup>10</sup> Next, we consider the impact on FDI/outsourcing of the provisions in a PTA that improve the contractibility of headquarter services (��) such as protection of intellectual property rights or investment provisions. AH show that the share of firms that engage in FDI over the total number of active firms is decreasing in �� (Proposition 9(ii)). With better contracting of headquarter activities, under-investment in these services becomes relatively less important, so that a larger share of final good producers value more the incentives that they can provide to component suppliers in the South through outsourcing. The graphical intuition for this case is provided in Figure 3. As before, the dashed lines represent profits under a trade agreement, which in this case only contains provisions that affect (or affect disproportionally) the contractibility of headquarter services. The profitability of firms under vertical integration (��) increases less than the profitability under foreign outsourcing (��), leading to a decrease in FDI. As the profitability of firms engaging in domestic sourcing is not affected by the PTA, the figure shows that better contracting institutions for headquarter services in the South increase the number of firms in the North that offshore. This implies that the share of global sourcing through FDI versus outsourcing is unambiguously lower. Summing up, the AH model has two clear predictions on the relationship between deep trade agreements and firms’ global sourcing strategies:

- i. PTA provisions improving the contractibility of components (��) are associated with an increase in profitability under vertical integration relative to outsourcing, leading to an increase in the share of firms engaging in FDI;

- ii. PTA provisions improving the contractibility of headquarter services (��) are associated with an increase in profitability under outsourcing relative to vertical integration, leading to a decrease in the share of firms engaging in FDI.

> 10 This ambiguity has limited the ability of empirical studies to test the predictions of the AH model using trade data (see, Nunn and Trefler, 2012).

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# **The Informal Sector, Firm Dynamics and Institutional Participation**

Alec R. Levenson Milken Institute for Job & Capital Formation 1250 Fourth Street, Second Floor Santa Monica, CA 90401 Telephone: (310) 998-2646 Fax: (310) 998-2626 E-mail: levenson@,mijcf.org William F. Maloney World Bank Telephone: (202)473-7300 E-mail: wmaloneygworldbank.org We would like to thank Agustin Ibarra Almada, Julie Anderson Schaffner, Ragui Assaad, Timothy Besley, Enrique Davila Capalleja, Gina Franco, Gabriel Fuentes, James Hines, Sanjay Jain, Christina Paxson, Elaine Reardon, Stefanie Schmidt, Simon Wilkie, Kristen Willard, Bernard Yeung, Cindy Zoghi and seminar participants at Princeton, USC, UC-Riverside, the Milken Institute, the 1996 Northeast Universities Development Consortium Conference, the 1996 Latin American and Caribbean Economic Association meetings, and the 1996 Western Economic Association Meetings for helpful comments. Gina Franco, Kristin McCullough and Cindy Zoghi provided top notch research support. Our thanks go to the Instituto Nacional de Estadistica, Geografia e Informatica (National Institute of Statistics, Geography and Information) and the Secretaria del Trabajo y Prevision Social (Secretariat of Labor and Social Welfare) for supplying the data. The usual disclaimers apply, with the exception that each of us blames the other for any obvious mistakes, taking full credit for the obscure ones.
# **2.5 Where and how costing is important to the stages of an MTEF**

Different types of MTEF place varying emphasis on the role of ‘top-down’ forecasting and ‘bottom-up’ costing within the medium term estimate. However, each category of MTEF, and the budget process to which it relates, implements a basic sequence of stages and informational outputs. These stages can be used to identify where and how costing practices and cost information are critical to an illustrative MTEF budget process.

Figure 2 below sets out a basic five stage process for an illustrative MTEF budget process. The stages correspond with one or both of either the top-down (finance ministry) or bottom-up (spending ministry) responsibilities. In stage 1, the finance ministry sets the strategic macroeconomic framework, including multiyear projections for revenues, expenditures and debt levels. In stage 2, the finance ministry prepares multiyear forecasts of spending on existing programs which may involve allocating ceilings at the sectoral or ministry level. In stage 3, spending ministries review their existing program spending and generate multiyear cost estimates of existing programs, new programs and enhanced existing programs, or new capital projects. Stage 4 is the finance ministry led annual budget process which assesses the relative priority of spending ministry program cost estimates in the context of indicative multiyear expenditure ceilings. In Stage 5 the annual budget is finalized in the context of the multiyear estimates, identifying variations to the baselines and adjusting the estimates over the multiyear period; this function is usually shared between the finance and spending ministries. Different types of cost information support the process at different stages – although arguably it informs all of them – and are of critical importance in Stages 2 and 3 combined, and Stage 4.

Figure 2. Basic stages of an illustrative MTEF budget process<sup>10</sup> <!-- Start of picture text --> Top-down<br><!-- End of picture text --> <!-- Start of picture text --> Bottom-up<br><!-- End of picture text --> <!-- Start of picture text --> Stage 1.<br>Macroeconomic<br>framework +<br>resource<br>availability<br><!-- End of picture text --> <!-- Start of picture text --> Stage 2.<br>Multiyear fiscal<br>envelop +<br>initial sectoral or<br>ministry ceilings<br><!-- End of picture text --> <!-- Start of picture text --> Stage 3.<br>Sector or ministry<br>review of<br>programs and<br>costings<br><!-- End of picture text --> <!-- Start of picture text --> Stage 4.<br>Assess program<br>proposals and<br>reconcile with<br>ceilings<br><!-- End of picture text --> <!-- Start of picture text --> Stage 5.<br>Finalize budget<br>and<br>adjust<br>multiyear<br>estimates<br><!-- End of picture text -->

> 10 Diagram adapted from Overseas Development Institute (2003) and Dorotinsky (2006).

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and housing markets and provide a unified framework with which to analyze and compare the effectiveness of different transportation policies to address the environment, traffic congestion, and social welfare.

Fourth, future research needs to devote more attention on policies aimed at addressing externalities associated with informal public transport and two-wheeler vehicles which are unique challenges in developing countries. Informal public transport and two-wheelers such as motorcycles are fundamental means of mobility especially for the poorest segments of the population in developing countries due to their lower costs and their ability to transfer commuters closer to their destinations regardless of the road quality (Collier et al, 2019; Shimazaki & Rahman, 1995; Yanez-Pagans et al, 2018).

Many of the policies discussed in the paper are suitable for mitigating emissions from twowheeler vehicles. Addressing negative externalities from informal transport operations is more challenging. Simply banning informal transport or replacing it with formal counterparts is unlikely to be successful given the complementarity characteristic of the informal modes of transport. It is important to note that informal vehicles are sometimes the only option commuters have due to limited resources and capacity constraints of the government. India is a good example of this. The Indian government provides bus- and rail-based public transport services in only 65 cities among 7,935 cities and towns. In the rest of the places, people’s mobility needs to non-walking distances are met through personal vehicles and informal transport (Kumar et al, 2016).

In order to improve the overall public transport services by maintaining and improving vehicles, working with informal providers and combining regulation with finance might benefit developing countries. Collier et al (2019) discuss cases in Lagos, Nigeria, and Ghana where the governments provided the finance or financial guarantees that allowed existing informal vehicle owners to form cooperatives and jointly invest in higher capacity buses. Including minibus taxi operators into negotiations for the new bus rapid transport system in Johannesburg, South Africa also positively contributed to engaging all actors in the new system.

# **References**

> Allcott H. 2013. The Welfare Effects of Misperceived Product Costs: Data and Calibrations from the Automobile Market. _Am. Econ. J. Econ. Policy_ . 5(3):30–66

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examples of these physical relocation programs.<sup>3</sup> Examples are present in high-income countries as well in programs such as ERASMUS and Teach for America.<sup>4</sup> This paper uses the context of the NYSC in Nigeria to ask: what is the impact of an individual’s temporary exposure to a different ethnic region on his or her actions and attitudes that are important for nation building? It focuses on ethnic exposure because it is the most salient dimension of diversity in this setting. In particular, it studies the impact on members of one of the dominant ethnic groups in Nigeria (the Yoruba) as they become exposed to other groups.<sup>5</sup> The empirical strategy exploits the randomized assignment of NYSC participants to one of the 37 states of the country (excluding the participant’s state of origin).<sup>6</sup> The difference in exposure arises because some participants are posted to a state where they are the ethnic majority (the “control group”) while others are posted to a state outside their ethnic region (the “treatment” group). Compliance with NYSC posting is not perfect; as a result, the analysis uses individuals’ treatment status given by their assigned state of service as an instrument for their actual treatment status and estimates local average treatment effects (LATE) for compliers. Since states in Nigeria differ on other dimensions besides ethnicity, the analysis controls for state characteristics such as distance, religion, urbanization and

> 3Malaysia’s service program seeks to “develop and enhance the spirit of patriotism amongst youths and encourage national integration and racial unity” (National Service Training Department Malaysia 2015). Similarly, one of the objectives of the National Service Scheme in Ghana is to “promote national unity and strengthen the bonds of common citizenship among Ghanaians” (National Service Scheme Ghana 2015). In Kenya, the National Youth Service seeks to promote among its participants, “values of discipline, democracy, citizenship and cooperation” (National Youth Service Kenya 2015).

4As part of its efforts to promote integration among member countries, the European Union enables over 250,000 university students to experience living in a different European country through its ERASMUS study-abroad and international work placement programs. In the United States, programs like Americorps and Teach for America place young people in national service assignments around the country, often in a location from their home communities.

> 5Yorubas are one of three dominant ethnic groups in Nigeria. Unlike the other two, Hausa and Igbo, which are majority Muslim and Christian, respectively, Yorubas are made up of both religious groups in similar proportions with a history of peaceful coexistence. As such, in moving to any state of the country, religion would not a primary dimension of exposure they would face as they are familiar with both religions.

> 6Nigeria has 36 states and a Federal Capital Territory. Seven of the states have a Yoruba majority.

3
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affected experienced a decrease in consumption of similar magnitude and mainly correspond to poor households since the incidence of poverty in Mogadishu was 74% in 2017-18. Households in the top 20% of the consumption distribution are likely to have savings or other sources of income, allowing them to smooth the shock from a terrorist attack and preventing them from reducing their consumption levels.

Figure 5: Spatial variation of the impact on consumption in Mogadishu <!-- Start of picture text --> 0.2<br>0.1<br>0.0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>-0.6<br>-0.7<br>1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0<br>Distance (Km) from the incident<br>Estimated impact on log of core consumption<br><!-- End of picture text --> Source: Authors’ calculations based on data from the SHFS and ACLED. Note: The vertical lines depict 95% confidence intervals.

Finally, we also relax the spatial criterion for the definition of treated households and classify as exposed or treated those households located from 1 to 6 kilometers away from the incidents.<sup>29</sup> Figure 5 presents the estimated coefficients for the effect on core consumption, considering as exposed those households located within the radius of each cutoff point.<sup>30</sup> The impact on consumption is similar for households located between 1 and 4 kilometers from the incident. After this threshold, the estimates become insignificant. The results suggest the immediate negative effect is clustered within a 4 kilometer radius from the attack. Households located within this radius suffer a decrease in consumption of similar magnitude. Conversely, those households located more than 4 kilometers away from an incident seem to be far enough, such that their consumption levels are not directly

> 29 We only consider households between 1 and 6 kilometers since there are no Wave 2 households interviewed beyond a radius of 6 kilometers from where the attacks took place.

> 30 The cutoff points of the different spatial criteria used to identify exposed households correspond to geodetic distances from incidents.

20
we find the TUP significantly impacted women’s phone ownership at midline (women were 5.4 percentage points more likely to own a mobile phone for herself, p-value = 0.074, see Bedoya et al. (2019)), we find no impacts on _household_ mobile phone ownership which alleviates concerns of potential selection bias from differential phone ownership. The difference in attrition rates across treatment and control groups is not statistically significant (Table A5 in the Online Appendix). Attrition at endline was predominantly driven by the constraint that households needed to have a working phone to be surveyed.<sup>18</sup> When conditioning on whether households had a working phone, the response rate was 94%.

## **4.4 Data Analysis**

We estimate impacts comparing outcomes across treatment and control groups at endline with the following specification: where, _Yi_ is the outcome of interest for household (or individual) _i_ at endline, _Ti_ is a dummy variable equal to 1 if household _i_ is assigned to receive treatment and 0 otherwise, and _β_ is the intention-to-treat estimate of the TUP. Since randomization is stratified by community, we follow Bruhn & McKenzie (2008) and include _Vj_ –a dummy variable equal to one if household _i_ comes from PRA group _j_ . For individual-level outcomes, we cluster the standard errors at the household level. Similarly, for outcomes collected in more than one survey round, we pool the data and cluster standard errors at the household level. Online Appendix Table A6 shows that the main results remain qualitatively unchanged when we re-estimate the regressions controlling for baseline characteristics.

# **5 Results**

As Bedoya et al. (2019) show, the TUP produced large and significant impacts across multiple dimensions of well-being two years after the asset transfer in 2018. Consumption increased by 30%, treatment households were 20 percentage points less likely to fall under the national poverty line (compared to 82% of the control group), women’s psychological well-being and empowerment have also improved significantly and the intervention was able to reduce gender gaps in labor participation and psychological well-being in one of the most conservative and challenging environments for women. The TUP also improved socioeconomic indicators for other household members, including primary men’s labor market participation and children’s school enrollment.

> 18Seventeen households were excluded from analysis upon closer inspection of the data. These households reported 0 consumption, which called into question the overall reliability of these select surveys.

17