rafmacalaba/datause-encoder-data huggingface dataset:
Table 5c shows the results. We find very strong positive effects of formalizing on trust in the provincial government and trust in the municipal government, the two levels of government that business owners interact with during the registration process. The p-value on trust in the provincial government is 0.007, so remains significant even after controlling for multiple hypothesis testing over the 9 outcomes in the table. In contrast, we find no significant impact on trust in the national government, trust in the courts, or trust in the police, and formalizing does not make firms any more likely to be confident in the police and courts to resolve business disputes.

One interpretation of this increase in trust is that those firms that formalized had to deal with the DS and municipal governments in the registration process, and may have been surprised to find the process less burdensome and less subject to bribes than they had imagined. That is, the _process_ of formalizing might be the lever for attitude change, by demonstrating that these levels of government can be trusted. An alternative potential explanation is that the change in attitudes is a consequence of being formalized. That is, if firm owners no longer worry about the provincial and municipal governments shutting them down for lack of compliance, they may be more trusting of these levels of government.

We also see formalizing leads to changes in attitudes towards taxes. Firms that formalize are much more likely to agree that part of being a good citizen means paying taxes. However, they are no more likely to think the government spends its revenue on items important to these firm owners, and also significantly more likely to agree that the government charges businesses too much in terms of taxes. Recall that actual taxes are quite low for most businesses in this sample, so this perception contrasts with the reality facing most firms.

# **6. Discussion and External Validity**

Our baseline sample was a random sample of informal firms in the two largest cities in Sri Lanka. As with all microeconomic studies, there is a question of external validity. We present evidence here to suggest that our results from Sri Lanka are likely to be informative of constraints to formalizing firms in other countries.

First, we note that the pattern of informality with regard to firm size, and what firms say are the potential advantages and disadvantages of formalizing are similar to those we see in other countries for which data exists. Appendix Figure 1 compares municipal government and tax authority registration rates by firm size in Sri Lanka, Bangladesh and Mexico. In all three countries, the data are representative of firms in the largest urban areas.<sup>10</sup> Sri Lanka has tax registration rates which lie between those in Mexico

> 10 The Sri Lankan data come from the Sri Lankan Longitudinal Survey of Entrepreneurs, which draws a random sample of 2255 firms from household listings in 31 cities outside the northern province. In Mexico, the data are from the 2002 version of the National Microenterprise survey, conducted in urban areas with a sample drawn from a

20
**Figure 12: Two main challenges since returning home (%)**

<!-- Start of picture text --> 34 Challenge 1 Challenge 2<br>30<br>24 23<br>13 14<br>10 10 11<br>8<br>7<br>2 2 2 1 0<br><!-- End of picture text --> Source: Listening to Displaced People Survey, 2014.

Figure 13 shows regional differences of the challenges faced by returnees. Those who returned to Gao faced fewer challenges than those returning to Timbuktu and Kidal. In Kidal, the major challenge was the absence of infrastructure and drinking water, followed by unemployment. In Timbuktu, poverty and food security were at the forefront of returnees’ concerns. 31% of the returnees in Gao stated that they faced no challenges since returning home; 19% said they do not know and 18% state insecurity.

**Figure 13: Main challenge since returning home, by region (%)**

<!-- Start of picture text --> I don’t know 19 5 2<br>No difficulties 31 3 2<br>Other difficulties 1 19 2<br>Insecurity 18 4 2<br>Difficulties living...1 8<br>To start life from scratch 1 3<br>Loss of goods 4 1<br>Unemployment 3 13 22<br>No infrastructure 16 8 48<br>Poverty 7 45 14<br>0 10 20 30 40 50 60 70 80<br>Gao Timbuktu Kidal<br><!-- End of picture text --> Source: Listening to Displaced People Survey, 2014.

19
Walker, I. et.al. (1999), Reform efforts in the Honduran water sector. In Savedoff, W. and Spiller, P. (eds.), _Spilled Water: Institutional Commitment in the Provision of Water Services._ Washington DC: Inter-American Development Bank, pp. 35-88.

World Bank (2005), _India’s Water Economy: Bracing for a Turbulent Future_ . Washington DC. The World Bank.

World Bank (2010), _Deep Wells and Prudence: Towards Pragmatic Action for Addressing Groundwater Overexploitation in India_ . Washington DC: The World Bank.

# **Appendix: List of variables and symbols used in the model, with explanations**

E = total electricity supply, Ei = electricity supply to group i (= 1,2) α = share of total electricity supply that goes to illicit users (including losses) U = objective function (to be maximized by the public sector) Vi = utility of electricity consumption for group i (= 1,2).

h = shadow value of standing groundwater including pumping cost, per unit c = marginal electricity supply cost G = value of electricity consumption for illicit users β = fraction of farmers that belong to group 1, 1-β = fraction in group 2 (fractions in terms of electricity consumption) k = marginal value of electricity consumed by group 2, when rationed (k < c+h) B = effort by farmers to have the illicit electricity consumption share reduced, through influence activities directed at the utility.

H = effort by illicit electricity consumers to increase their illicit electricity consumption, through influence activities directed at the utility.

τ = parameter indicating the marginal value of electricity for farmers, in response to effort from utilities Z = effort by the power utility to affect farmers’ value of electricity (τ) Q = cost incurred by the power utility to reduce the share of illicit electricity consumption Φi = efficiency in electricity use of farmers per unit of electricity consumed, for farmers in group i (=1,2) Φa = average efficiency in extracting groundwater through pumping, average over all farmers Ci = cost to farmers of implementing private efficiency improvements for groundwater pumping 17
|P089244|2005|2007|6/28/2007|2 Closed|Tajikistan|
|---|---|---|---|---|---|
|P089244|2005|2008|1/2/2008|3 Closed|Tajikistan|
|P089244|2005|2008|6/13/2008|4 Closed|Tajikistan|
|P089244|2005|2009|12/30/2008|5 Closed|Tajikistan|
|P089244|2005|2010|12/28/2009|6 Closed|Tajikistan|
|P089244|2005|2011|12/23/2010|7 Closed|Tajikistan|
|P089244|2005|2011|6/27/2011|8 Closed|Tajikistan|
|P089244|2005|2012|6/26/2012|9 Closed|Tajikistan|
|P089244|2005|2013|6/26/2012|9 Closed|Tajikistan|
|P089244|2005|2014|7/7/2013|10 Closed|Tajikistan|
|P089244|2005|2014|4/12/2014|11 Closed|Tajikistan|
|P089244|2005|2015|12/29/2014|12 Closed|Tajikistan|
|P089254|2007|2007|10/5/2006|1 Closed|Senegal|
|P089254|2007|2007|6/15/2007|2 Closed|Senegal|
|P089254|2007|2008|12/27/2007|3 Closed|Senegal|
|P089254|2007|2008|6/27/2008|4 Closed|Senegal|
|P089254|2007|2009|12/30/2008|5 Closed|Senegal|
|P089254|2007|2009|6/26/2009|6 Closed|Senegal|
|P089254|2007|2010|12/29/2009|7 Closed|Senegal|
|P089254|2007|2010|6/29/2010|8 Closed|Senegal|
|P089254|2007|2011|3/27/2011|9 Closed|Senegal|
|P089254|2007|2012|10/19/2011|10 Closed|Senegal|
|P089254|2007|2012|3/17/2012|11 Closed|Senegal|
|P089254|2007|2013|7/4/2012|12 Closed|Senegal|
|P089259|2007|2007|11/26/2006|1 Closed|Yemen,Republic of|
|P089259|2007|2007|4/24/2007|2 Closed|Yemen,Republic of|
|P089259|2007|2008|11/25/2007|3 Closed|Yemen,Republic of|
|P089259|2007|2008|3/18/2008|4 Closed|Yemen,Republic of|
|P089259|2007|2009|8/1/2008|5 Closed|Yemen,Republic of|
|P089259|2007|2009|3/17/2009|6 Closed|Yemen,Republic of|
|P089259|2007|2010|10/20/2009|7 Closed|Yemen,Republic of|
|P089259|2007|2010|6/26/2010|8 Closed|Yemen,Republic of|
|P089259|2007|2011|1/7/2011|9 Closed|Yemen,Republic of|
|P089259|2007|2011|6/21/2011|10 Closed|Yemen,Republic of|
|P089259|2007|2012|12/31/2011|11 Closed|Yemen,Republic of|
|P089259|2007|2012|6/25/2012|12 Closed|Yemen,Republic of|
|P089259|2007|2013|2/22/2013|13 Closed|Yemen,Republic of|
|P089259|2007|2014|12/28/2013|14 Closed|Yemen,Republic of|
|P089259|2007|2014|6/20/2014|15 Closed|Yemen,Republic of|
|P089289|2006|2006||0 Closed|Cameroon|
|P089289|2006|2007|12/6/2006|5 Closed|Cameroon|
|P089289|2006|2007|6/29/2007|6 Closed|Cameroon|
|P089289|2006|2008|12/20/2007|7 Closed|Cameroon|
|P089289|2006|2008|6/1/2008|8 Closed|Cameroon|
|P089289|2006|2009|12/19/2008|9 Closed|Cameroon|
|P089289|2006|2009|5/29/2009|10 Closed|Cameroon|
|P089289|2006|2010|11/16/2009|11 Closed|Cameroon|
- 5 -

(Shirley and Winston, 2004). Access to good port facilities is also essential for firms (Belderbos and Carree, 2002; Deichmann _et al_ ., 2005). There are only a few studies showing the impact of rail proximity. Banerjee et al. (2012) show the positive effect of rail infrastructure on local economies in China.

The current paper analyzes the modal choice made by firms in Tanzania. As discussed above, Africa has important rail infrastructure, of which the quality has been deteriorating in many countries. More and more shippers prefer using truck transportation, as the road network is improved in the region. However, certain companies are still taking advantage of rail transport. In the literature, there is little evidence on freight modal choice in Africa. This paper aims at casting light on the demand for rail freight in the region. With firm-level data collected from about 500 firms in Tanzania, the traditional discrete choice models are estimated.

The remaining sections are organized as follows: Section II provides an overview of recent developments in transport infrastructure in Tanzania. Section III develops our empirical models and describes data. Section IV presents main estimation results and discusses policy implications. Then Section V concludes.

# **II. RECENT DEVELOPMENTS IN TRANSPORT INFRASTRUCTURE**

Tanzania is a large country with a land area of 885,800 km<sup>2</sup> , where more than 55 million people live. Intercity connectivity is critical for the economy. While Dar es Salaam is the primary city, with an estimated population of 5.4 million, other secondary cities are also growing, such as Mwanza, Arusha, Dodoma, Morogoro and Mbeya. Each city is estimated to have a population close to 500,000. A number of firms are located in secondary cities and other small towns. According to the Central Register of Establishments, about 45,000 enterprises were formally registered by 2010 ( **Figure 3** ).
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20
<!-- Start of picture text --> Price, Price,<br>Value ofMarginal Product of Value of<br>Emissions<br>Emissions MarginalProduct of<br>1<br>Ir1/<br>!I¢Fa I1f<br>;/ ‘ f<br>1 / ! f<br>ive | / ve<br>I!, 1 /<br>'1,’ ff !I<br>1, f 1<br>1/fH<br>Kon11 f ,, /f 11:<br>\ ! a / / ‘<br>v\\ ‘wy.1 / / '<br>‘ F i / ' *<br>1' \ .. ifa7 anne 1 Py<br>\ ‘ an f '<br>P,Ipeemeemem1' hnNiefot‘ai ny/f Aenea11 P,<br>P,a1 i oo ‘ipsD, '<br>1i ’ of SNEXPEA, '<br>1s iW { Emissions<br>Go oOo, J B E 4H O,* a*<br>—_—_—_> <——_<br>poor country rich country<br><!-- End of picture text -->
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18
|**ONET skills**<br>**taxonomy**|**O*NET definition**<br>**"How important is ..."**|**SDS Question**<br>**"How often does your job involve...**|**Notes**|**SDS skills taxonomy**|**SDS Skills**<br>**typology**|
|---|---|---|---|---|---|
|**Systems**<br>**Analysis**|Determining how a system<br>should work and how changes<br>in conditions, operations, and<br>the environment will affect<br>outcomes.||Highly related to<br>problem-solving<br>questions. Not<br>included in<br>PIAAC or STEP.|||
|**Systems**<br>**Evaluation**|Identifying measures or<br>indicators of system<br>performance and the actions<br>needed to improve or correct<br>performance, relative to the<br>goals ofthe system.||Highly related to<br>problem-solving<br>questions. Not<br>included in<br>PIAAC or STEP.|||
|**RESOURCE**<br>**MANAGEMENT**||||||
|**Time**<br>**Management**|Managing one's own time and<br>the time of others.|developing specific goals and plans to<br>prioritize, organize, and accomplish your<br>work? (rather than following fixed<br>procedures or instructions)|SDS question is<br>related to<br>worker's time<br>only. Managing<br>the time of other<br>is included<br>under people<br>management<br>question.|Time Management|Cognitive|
|**Management of**<br>**Financial**<br>**Resources**|Determining how money will<br>be spent to get the work done,<br>and accounting for these<br>expenditures.|determining how money will be spent to<br>get the work done?||Management of Financial<br>Resources|Cognitive|
|**Management of**<br>**Material**<br>**Resources**|Obtaining and seeing to the<br>appropriate use of equipment,<br>facilities, and materials needed<br>to do certain work.|managing material resources such as<br>equipment, facilities, and materials<br>needed to get the work done?||Management of Material<br>Resources|Cognitive|
|**Management of**<br>**Personnel**<br>**Resources**|Motivating, developing, and<br>directing people as they work,<br>identifying the best people for<br>the job.|planning the activities of others and/or<br>managing people?||Management of Personnel<br>Resources|Socioemoti<br>onal|
|||As part of your job, how many employees<br>do you supervise?|SDS added this<br>question to<br>understand level<br>of complexity.|Management of Personnel<br>Resources|Socioemoti<br>onal|

33
ensuring a greater ownership of the agreed reform program, improving coordination within the public sector, and providing the private sector a clear view of policy intentions.

Our work contributes to the literature by constructing a new database of financial sector strategies, based on published documents across 150 low-, middle-, and high-income countries from all regions of the world. Using the assessment criteria proposed in Maimbo and Melecky (2014), we evaluate the quality of the strategies in our database. That is, we assess whether the strategy adequately covers the objectives of financial development and systemic risk management, whether the stated objectives are accompanied by an implementation plan, and to what extent the strategy addresses the potential trade-off between financial development and systemic risk management. Although determining how strategies were formulated is not a major goal of this paper, we provide some insight into the role of different actors in originating and formulating strategies. The origins of strategies are diverse, but good strategies often reflect the participation of major institutions involved in financial sector policy in one coordinated, consultative process.

To estimate the effect of financial sector strategies on financial sector outcomes, we control for several macroeconomic, social, and institutional factors commonly found in the literature (see Barajas et al. 2013; De la Torre, Feyen, and Ize 2013; De la Torre, Gozzi, and Schmukler 2007; Feyen, Lester, and Rocha 2011; and Yartey 2008). The set of macroeconomic indicators considered includes gross domestic product (GDP) per capita, GDP growth, the money market rate, inflation, and the change in exchange rate. To measure institutional development, we include the governance indicator developed by Kaufmann, Kraay, and Mastruzzi (2010) and the financial openness index of Chinn and Ito (2006). Socioeconomic indicators considered are the age dependency ratio and population density. A financial sector strategy is the highest-level policy document for the financial sector. Therefore, we do not include financial indicators as 3
where the independent variables are sex (female), age (continuous), education (dummies for secondary and tertiary education), married (only formally married) and household size (number of household members). These are the variables we thought relevant for individuals when they compare their own income with the income of others. Evidently, this is a normative choice and these variables may change across countries. We opted for this particular set of variables because we expected this set to be rather standard across countries and because we wanted to use the same set of variables for the five countries considered so as to compare the performance of the indices across countries. Income satisfaction is measured on a scale from one to five as follows: 1) not at all satisfied; 2) somewhat dissatisfied; 3) neutral; 4) somewhat satisfied; 5) completely satisfied.<sup>5</sup> The sample considered is restricted to individuals with positive incomes who replied to the question on income satisfaction. In table 5, we also introduced additional labor market variables (employed and self-employed status) among the explanatory factors of the first-step equation. Location variables such as regional or urban-rural location would be normally important to add. People compare themselves with other people around them. However, the database we have does not have these location variables for all countries and years and we finally preferred to keep all countries/years available.

Our objective is to test whether the individual scores generated by the deprivation indices proposed are able to proxy well individual deprivation feelings as reported by respondents. As we use an income satisfaction variable, we expect to find a negative relation between individual income satisfaction and individual deprivation. In order to assess the correlation between these two variables, we use an ordered logit regression model where the dependent variable is income satisfaction and the independent variable

> 5 Note that questions and answers for the different countries were slightly different in the original surveys. The five steps scale indicated in the text is the one reconstructed by the CHER consortium.

13