rafmacalaba/datause-encoder-data huggingface dataset:
2017). Finally, the complex linkages between credit access, temporary migration decisions, asset accumulation overseas and entrepreneurship after return have been modeled using a structural approach in the context of Bangladesh, which allows to simulate the effect of migration and credit market policy changes on entrepreneurship in the origin country (Bossavie, Gorlach, Ozden, and Wang 2021).<sup>18</sup> While temporary migration increases entrepreneurship after return, household entrepreneurial activities have been shown to decline during the migration episode in Bangladesh, due to the emigration of the most entrepreneurial household member (Mobarak, Sharif, and Shrestha 2020). Whereas most papers look at the impact of temporary migration on entrepreneurship for migrants themselves and their families, micro firms in areas connected to migration networks have been shown to experience higher levels of investment, sales, and profit in Mexico, suggesting an alleviation of capital constraints also for firms connected to migration networks (Woodruff and Zenteno 2007).

One policy relevant question is whether entrepreneurial activities started by temporary migrants back home are undertaken by choice, or instead, are a last resort option as return migrants are unable to find wage employment (the so-called “parking lot” hypothesis of entrepreneurship of Harris and Todaro (1970)). Available evidence so far lends more support to the former hypothesis. Return migrants to Bangladesh who become self-employed earn more than wage employees and are typically able to sustain these earnings in older age compared to wage employees (Bossavie, Gorlach, Ozden, and Wang 2021). Self-employment activities started by return migrants have also been shown to be more successful and to generate more jobs for the local economy in Albania (Piracha and Vadean 2010), Tunisia (Mah ́e 2019) and Bangladesh (Bossavie, Gorlach, Ozden, and Wang 2022). Entrepreneurial activities started by return migrants were also found to be more likely to survive in Egypt, even after controlling for selection into migration on non-observable characteristics (Marchetta 2012). The ”parking lot” hypothesis of entrepreneurship, however, appears to be valid in some contexts: self-employment after return has been shown to be a temporary choice before finding wage employment in the Kyrgyz Republic (Br ̈uck, Mah ́e, and Naud ́e 2018) and Senegal (Kveder and Flahaux 2013).

The ability of temporary migrants to start businesses and engage in entrepreneurship at home (after they return) depends on their migration experience. When migration is temporary, it is a component of the overall decision process of the worker where all stages are interlinked (Dustmann and G ̈orlach 2016; Bossavie, Gorlach, Ozden, and Wang 2021). In this context, employment outcomes after return are linked to the parameters and outcomes of the migration episode, such as migration costs, wages overseas and duration of stay.

> 18Also see Wahba (2014), Rapoport and Docquier (2006) and Naud ́e, Siegel, and Marchand (2017) for related surveys of literature on the topic of migration and entrepreneurship.

28
- 54 _

- Poyo, Jeffrey, 1988. "Deposit Mobilization and the Political Economy of Specialized Financial Institutions: The Case of the Dominican Republic-, The Ohio State University, Economics and Sociology Occasional Paper No. 1467.

- Rochin, Refugio and Jodee Solomon 1983. 'Cooperative Credit Programs for Small Farmers in Developing Countries: A Compendium of Recent Studies", University of California, Davis, Department of Agricultural Economics.

- Rochin, Refugio and ingrid Nyborg, 1988, 'Organizing Credit Cooperatives for Small farmers: Factors Conducive for Operational Success," University of California, Davis, Department of Agricultural Economics.

- Schaefer-Kehnert, W. and J3D. Von Pischke, 1986, "Agricultural Credit Policy in Developing Countries", Savings and Development, (1), pp. 5-29.

- Schaefer-Kehnert, Walter, 1983, "Success with Group Lending in Malawi," in Rural Financial Markets in Developing Countries, J.D. Von Pischke, Dale W. Adams, and Gordon Donald, eds., Baltimore: Johns Hopkins University Press.

- Sheu, Y.D., 1980, "Cooperative Agricultural Financing by Farmers Associations in Taiwan," Extension Bulletin #147, Taipei City, Taiwan (July).

- Srinivasan, A. and R. Meyer, 1987, "Policy Implications of Financial Intermediation Costs in Bangladesh", The Ohio State University, Economics and Sociology Occasional Paper No. 1389.

- Tohtong, C., 1988, "Joint Liability Groups for Small Farmer Credit: The BAMC Experience in Thailand", Rural Development in Practice, (1), pp. 4-7.

- Turtiainen, Turto "Cooperative Credit and Savings in Developing Countries".

- Vogel, Robert C., and Paul Burkett, 1986, "Deposit Mobilization in Developing Countries: The Importance of Reciprocity in Lending," Journal of Develovins Areas (July), pp. 425-38.

- Vogel, Robert C., 1988, "The Role of Groups, Credit Unions and Other Cooperatives in Rural Lending," World Bank, Agriculture and Rural Dev. Dept. (August).

- Von Pischke, J.D., and John Rouse, 1983, "Selected Successful Experiences in Agricultural Credit and Rural Finance in Africa," Savings and Development (1), pp. 21-44.

- Von Pischke, J.D., D. Adams and G. Donald (eds.), 1983, "Rural Financial Markets in Developing Countries," Johns Hopkins University Press, Baltimore.
**Annex 1: Average monthly compensation of white collar civil servants, FY2008/09**

(including monetary and in-kind allowances, selected ministries)

|Position and Ministry|**# of**<br>**records**|**Range of**<br>**grades &**<br>**steps**|**Average**<br>**years of**<br>**service**|**Gross base sala**|**ry**|**Gross base sa**<br>**monetary**<br>**allowances**|**lary +**|**Net base sa**<br>**monetary**<br>**allowances**|**lary +**<br>|**Total net remuneratio**<br>**salary + monetary & (u**<br>**in-kind allowances**|**n: base**<br>**ntaxed)**|**Base salary as %**<br>**total remuneration**<br>*****|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||**Kip**|**USD**|**Kip**|**USD**|**Kip**|**USD**|**Kip**|**USD**||
|DG of department|||||||||||||
|Education|15|5-13 to 4-12|32|1,058,600|$125|1,150,300|$135|1,022,655|$120|1,622,655|$191|60%|
|Health|1|4-13|38|945,000|$111|1,065,000|$125|950,598|$111|1,550,598|$182|57%|
|Info. & Culture|1|4-15|40|999,000|$118|1,103,000|$130|981,416|$115|1,581,416|$186|59%|
|Labor|10|5-11 to 4-8|32|962,700|$113|1,058,000|$124|942,464|$111|1,542,464|$181|58%|
|DDG of department|||||||||||||
|Education|37|5-11 to 4-10|31|1,021,622|$120|1,106,351|$130|984,400|$116|1,484,400|$175|64%|
|Health|2|5-11 to 4-15|38|1,065,000|$125|1,141,000|$134|1,012,234|$125|1,512,234|$178|65%|
|Info. & Culture|3|5-8 to 4-10|31|886,000|$104|951,500|$112|846,611|$100|1,346,611|$158|61%|
|Labor|17|5-10 to 3-15|26|926,824|$109|1,016,882|$120|907,572|$107|1,407,572|$166|61%|
|Head of division|||||||||||||
|Education|69|5-11 to 4-6|27|948,609|$112|1,025,609|$121|914,176|$108|1,114,176|$131|77%|
|Health|3|4-15 to 4-7|32|909,000|$107|988,667|$116|881,321|$107|1,081,321|$127|76%|
|Info. & Culture|3|4-12 to 3-9|18|747,000|$88|807,000|$95|722,712|$85|922,712|$109|74%|
|Labor|21|5-11 to 3-11|23|863,143|$102|946,214|$111|846,401|$100|1,046,401|$123|75%|
|Deputyhead of division|||||||||||||
|Education|68|5-11 to 3-12|22|894,618|$105|964,662|$113|861,528|$101|1,011,528|$119|80%|
|Health|4|4-15 to 4-9|24|904,500|$106|964,750|$114|859,987|$106|1,009,987|$119|81%|
|Info. & Culture|3|4-5 to 3-7|14|636,000|$75|693,500|$82|624,458|$73|774,458|$91|75%|
|Labor|25|4-12 to 3-10|14|790,560|$93|849,840|$100|761,218|$90|911,218|$107|79%|
|Technical staff|||||||||||||
|Education|130|5-8 to 3-1|12|746,409|$88|781,094|$92|699,761|$82|749,761|$88|90%|
|Health|26|4-15 to 3-1|14|735,577|$87|774,654|$91|694,311|$87|744,311|$88|89%|
|Info. & Culture|11|4-3 to 3-1|6|607,636|$71|622,545|$73|559,884|$66|609,884|$72|91%|
|Labor|107|4-15 to 3-1|5|691,626|$81|701,776|$83|643,829|$76|693,829|$82|90%|

# **Notes:**

(1) For Ministries of Health and Labor, data are for one department only.

(2) US dollar figures are based on an exchange rate of 8,500 kip per dollar.

(3) Value of in-kind allowances for gasoline and mobile phone estimated based on regulations.

***** Calculated in gross terms for ease of comparison, since net base salary figures are not available and in-kind allowances are not taxed.

36
of disposable income is presented in Figure 8. It shows that total taxes experienced a continuous increase as a percentage of GNP since 1950, diminishing private disposable income. This trend was reversed in some periods (1978-1983) or remained stable (19861990) but, between 1983 and 1993 total taxes increased almost _5%_ of GNP explaining 71% of the decline in private disposable income observed during the same period. Although this evidence cannot be interpreted as a causal relationship between taxes and savings, existing empirical evidence suggests that tax increases is not the most efficient way of rising national savings given its large negative effect on private savings (see, among others, Corbo and Schmidt-Hebbel (1991)). The magnitude of this effect is an empirical matter that deserves a careful investigation in Colombia.

<!-- Start of picture text --> Figure 7: Components ofPrivate Disposable Income<br>4<br>3<br>ffi 0. 2<br>0-1<br>-2-<br>1951_ 1958 1965 1972 1979 1986 1993<br>--- Subsidies TR TR' x ONFP<br>Source: National Accounts and Authors Calculations<br>Figure 8: The Behavior of Taxes<br>-5<br>0.<br>-15<br>-20 +-t-<br>1951 1958 1965 1972 1979 1986 1993<br>- - - - Total Taxes- Direct Taxes ------- Indirect Taxes<br>Source: National Accounts<br><!-- End of picture text --> 11
# **Figure 11: Land use Slums vs Land cover Irregular Residential circa 2003**

<!-- Start of picture text --> Land use Slums vs. Land cover Irregular Residental<br>10<br>8<br>6<br>4<br>2<br>0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Distance from CBD (kilometers)<br>Land Use Residential (slum) Land Cover Residential (Irregular)<br>Land area in sq kms<br><!-- End of picture text -->

**Source: Author’s Calculations**

# **Kigali, Rwanda**

Home to roughly 1 million, Kigali, the capitol of Rwanda, is considerably smaller in population size than Dar es Salaam and Nairobi. Prior to 1994, Kigali’s landscape was relatively free of informal construction due to government regulations that restricted rural to urban migration.<sup>40</sup> But over the past two decades the city of Kigali has experienced rapid urbanization with population growth rates of 4% per annum (2002‐2012) a rate only slight below Dar es Salaam and Nairobi.<sup>4142</sup> By 2020 the population of Kigali is expected to double.<sup>43</sup> This increase in urban population has placed considerable pressure on Kigali’s housing stock, and resulted in the construction of informal settlements. To meet projected housing demands, Kigali would need to construct roughly 344,000 formal housing units by 2022, but currently less than 1,000 are constructed annually.<sup>44</sup> This high demand for housing has left much of the population living in substandard conditions. In 2012 roughly half of Kigali’s housing stock was considered by the government as “Low quality, overcrowded units which need to be replaced”. Rwanda’s Environment Management Authority echoed this concern, recently reporting that 83% of Kigali’s population was living in slum housing quality, with many neighborhoods built on steep or sandy slopes, and lack basic access to utilities and transport networks

> 40 Uwimbabazi, Penine and Ralph Lawrence. Compelling Factors of Urbanization and Rural‐Urban Migration in Rwanda. Rwanda Journal Volume 22, Series B, 2011: <u>Social Sciences.</u>

> 41 National Institute of Statistics of Rwanda. 2012. “2012 Population and Housing Census: Report on the provisional Results.” Published November 2012.

> 42 2015 "City Mayors: World's fastest growing urban areas". <u>Retrieved</u> August 2015.

> 43 Institute of Statistics, Rwanda 2012 (http://www.kigalicity.gov.rw/).

> 44 2012, City of Kigali. The Kigali Housing Market Brochure.

20
46 The discipline imposed by general equilibrium modeling also<sup>shows the narrow room for</sup> maneuvering when the three criteria of efficiency, welfare, and political feasibility<sup>are taken into</sup> account. In this setting, all actions have opportunity costs beyond<sup>those that would be revealed by a</sup> narrowly focused economic cost-benefit calculus. This exposes the potentially<sup>fatal flaws of narrowly</sup> designed adjustment programs, be they efficiency-focused<sup>or welfare-focused. By the same token,</sup> because the margin for maneuver is so small, unanticipated adverse<sup>shocks can be devastating for an</sup> ongoing adjustment program. Under those circumstances,<sup>foreign assistance loans are crucial for</sup> managing this tenuous balance, with foreign grants necessary when the size of<sup>the shock is very large.</sup> Nonetheless, despite these difficulties, the case studies in this symposium<sup>show that adjustment</sup> programs can be successful.

In the same vein, the research points out to a great diversity of impacts of adjustment programs. Sharply different distributional outcomes can occur with<sup>identical acjustment packages</sup> when institutional characteristics differ widely. Sharply different distributional<sup>outcomes can also</sup> emerge as a result of changes in the mix between current and capital expenditure<sup>cuts. This diversity</sup> suggests the need for careful package design - 'passe partout"<sup>adjustment programs will not do.</sup> Tailoring adjustment programs to take into account the economic<sup>and political environment is essential</sup> for equity and for the sustainability of the program itself. Moreover,<sup>the case studies in this</sup> symposium show that there is no inherent conflict between fiscal<sup>retrenchment, which implies a</sup> smaller state, and the balancing of the objectives of efficiency,<sup>welfare, and political feasibility, which</sup> implies an active state.

The debate on the impact of adjustment on the poor<sup>is far from being settled. Yet it is likely</sup> that, as our analysis of income distribution and poverty during<sup>adjustment expands and as reliable</sup> household-level data become available, our understanding<sup>will increase. We hope that the studies in</sup>
the risks of adverse selection, free riding and elite capture (Che et al., 2015; Xiao et al., 2017). Furthermore, there may be difficulties with achieving and maintaining compliance, fluidity of membership with some participants opting out, instability, and a preference for industry interests rather than public interests.

Moreover, the adopted facilitation policies can have limits. On the one side, they can require some logistic facilities that can be provided only at relatively high cost, barely affordable by SSCBTs. In fact, the evidence from elsewhere suggests that logistics related services such as transport, warehousing, and marketing can become important non-tariff barriers in trade facilitation (Madkour et al., 2020; Marti et al., 2014; Zaninovic et al., 2021). On the other hand, the prevalence of complex and cumbersome border procedures and other forms of institutional trade costs in Sub-Saharan African economies can inflate the costs of moving goods across borders. Given this, further trade facilitation actions will need to focus on the border documentation, time and real costs of trading across borders (Sakyi et al., 2017) and on providing some logistics services to small traders to develop and afford the cost of new modalities for conducting their operations.

# **3 Data and methodological approach.**

This section describes the data used in the paper and the estimation approach that was implemented.

## **3.1 Data and descriptive evidence.**

To assess the impact of adopting the CTA on SSCBT economic outcomes, we collected data in two provinces of the DRC, namely, North and South Kivu, from April to June 2022.<sup>3</sup> The two provinces include several markets and share four border crossing points with Rwanda. Thus, the sampling frame for the study is made up of active small crossborder traders. During the survey in the field, SSCBTs that were included in the sample were those operating within physical market structures and on roadsides.

To collect data, we first list all the markets and mains roads used by SSCBTs to sell their products. We exhaustively consider all these markets within Goma and Bukavu, and

> 3The sample includes both CTA’s adopters and non-adopters. Indeed, at the time of data collection, the Government of Democratic Republic of Congo had already eased some restrictions on the movement of people and goods. Indeed, on July 21, 2020, the government announced: (1): As of July 22, 2020, the resumption of commercial activities, stores, banks, restaurants, cafes, bars, and businesses, as well as the resumption of rallies, meetings, and celebrations, and public transportation. (2): The resumption of schools and universities was set for August 3. (3): From August 15, the reopening of churches and places of worship, the resumption of inter-provincial migration movements, the opening of ports, airports and borders. Thus, at the time of the collection, there were two groups of individuals, those who adopted and kept trading through the CTA and those who did not adopt and were still crossing the border for trade.

7
E. Columns indicate median inflation rates and inflation volatility in country-year pairs, with a central bank independence and transparency index in the top quartile of the sample. Bars denote medians for country-year pairs in the bottom quartile. F. Pass-through is defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change estimated from FAVAR models for 29 advanced economies and 26 EMDEs over 1998-2017. A positive pass-through means that a currency depreciation is associated with higher inflation. Bars show the interquartile range and markers show the cross-country median. The central bank independence index is computed by Dincer and Eichengreen (2014). Low and high central bank independence are defined as below and above the sample average.
carried out between 2014 and 2019 or later; recent price data are collected between 2018 and 2019 or later; recent census data are collected between 2009 and 2019 or later.

Once we have established that data has been collected recently, we assess whether the data is publicly accessible. For each data category, we classify microdata accessibility into 4 groups as follows:

1. **No coverage:** if no representative microdata was recently collected.

2. **No openness:** if representative microdata was recently collected but the data or a link to the data is not available on the website.

3. **Satisfactory openness:** if representative microdata was recently collected and the data (or link) is available on website but is restricted i.e., users need to submit a request and/or register to be granted access to the data.

4. **Excellent openness:** if representative microdata was recently collected and the data (or link) is publicly available on website in machine readable format for immediate download.

We differentiate between “satisfactory” and “excellent” openness because microdata openness is examined from the perspective of the data user. From this perspective “excellent openness” is ideal because there is no wait time for data users to access available data. However, “satisfactory openness” is acceptable because it is okay for data guardians to require registration, authorization, and clearance before releasing data to prevent unauthorized access. The best scenario being where following satisfactory registration, access to the data is granted automatically.

3.3 Implementation exercise of microdata classification in MENA The exercise of visiting the websites of the NSOs<sup>13</sup> and international organizations<sup>14</sup> to examine microdata accessibility was designed to be cost effective and easy to apply to countries in MENA and beyond. <mark>To prevent bias and ensure accuracy and replicability, the exercise is implemented in a 3-step process by a</mark> team comprised of 3 core researchers with language competencies in English, Arabic and French – major languages in the MENA region.

**<mark>Step 1:</mark>** <mark>Each of the three researchers in the research team independently visits the websites to classify m</mark> icrodata accessibility for all data categories into one of the 4 groups discussed in section 3.2.

**Step 2:** Researchers <mark>meet to discuss their independent findings from step 1 and reconcile any differences.</mark> When a researcher finds a representative microdata for the categories covered on a public facing website that other researchers do not, the reconciliation process involves providing

> 13 See Annex table A1 for list of NSOs in MENA and their websites.

> 14 See Annex table A2 for list of the websites of international microdata repositories.

8
as rainfed/irrigated/post-flooding cropland (the first indicator above). As a result, our calculations are likely to underestimate the impacts on agricultural extent.

# _8) GLWD-3_

A global gridded dataset of wetland areas of approximately 1 km resolution, developed by the World Wildlife Fund in partnership with Center for Environmental Systems Research, University of Kassel, Germany. In the dataset wetlands are differentiated by type, but for the purposes of this analysis all wetlands are considered equal. Data are available for download at: http://www.worldwildlife.org/science/data/item1877.html.

# _9) GRUMP urban area_

A global gridded dataset of urban extents compiled by the CIESIN GRUMP project from built-up areas polygons (DCW) and the NOAA-NGDC Nighttime Lights dataset derived from satellite imagery. Nighttime Lights is a dataset of visible light detected by the DMSP-OLS system. It is known to somewhat exaggerate the extent of lit areas due to spatial resolution and other characteristics of the sensor. A revised version of the GRUMP alpha urban extents dataset was provided by ftp upon request, but is not currently publicly available.

# _10) City Polygons from Urban Risk Index_

A subset of urban extent polygons from the GRUMP urban extents layer was linked to the urban population growth dataset compiled by Henderson 2002. Decision rules describing point-to-polygon linking are described in detail in the dataset documentation. In general, a polygon was assigned to a city based on the city affiliation of the center of the polygon. In cases of merging urban extents, thiessen polygons were created to divide urban extents from one another.

A summary of the various datasets used in this analysis is presented in Table 1.

11