rafmacalaba/datause-encoder-data huggingface dataset:
# **Annex 5. Hedonic Regression Tables**

|**Table 5. Hedon**|**ic Estimates for A**|**ctual Rent (depende**|**nt variable: log**|**actual rent)**|
|---|---|---|---|---|
|Variables|(1) Kigali,<br>Rwanda|(2) Abidjan,<br>Cote d’Ivoire|(3) Dar<br>es<br>Salaam,<br>Tanzania|<br>(4) Maputo,<br>Mozambique|
|Rooms|0.312***<br>(0.110)|0.096***<br>(0.010)|0.555***<br>(0.050)|-0.657***<br>(0.208)|
|Floor (dummy)|0.427***<br>(0.072)|0.004<br>(0.100)|0.595***<br>(0.053)|-1.539**<br>(0.780)|
|Overcrowded|0.178|-0.027|0.033|-0.435|
|(dummy)|(0.142)|(0.038)|(0.082)|(0.449)|
|Roof (dummy)|0.537**<br>(0.273)|0.447***<br>(0.048)|0.667***<br>(0.172)|1.383***<br>(0.372)|
|Walls (dummy)|0.114*|-0.053|0.227|0.714*|
||(0.069)|(0.088)|(0.176)|(0.410)|
|Electricity|0.820***|0.187***|-0.185|0.695***|
|(dummy)|(0.069)|(0.0670)|(0.125)|(0.250)|
|Water (dummy)|0.247***|0.193***|0.018|-0.850*|
||(0.061)|(0.0350)|(0.054)|(0.072)|
|Toilet (dummy)|0.175*<br>(0.099)|0.749***<br>(0.0377)|0.357***<br>(0.069)|0.097<br>(0.239)|
|Constant|10.08***|11.50***|10.57***|11.69***|
||(0.119)|(0.147)|(0.323)|(1.384)|
|R<sup>~~2~~</sup>|0.62|0.42|0.37|0.42|
|Observations|555|1,598|511|242|

Asterisks denote significance at the * 10%, **5%, and ***1% levels.

34
# 1. Introduction

<mark>Since the start of the conflict in Sudan in mid-April 2023, large numbers of civilians have been forced to flee. Hundreds of thousands of people have moved into neighboring countries, notably to the Central African Republic, Chad, the Arab Republic of Egypt, Ethiopia and South Sudan.</mark> In response to the crisis in Sudan, Chad is demonstrating tremendous solidarity and generosity. An impoverished country itself, Chad now hosts more than 1.3 million refugees (almost 8 percent of its population) including, as of February 2024, some 550,000 recent arrivals from Sudan with the number expected to increase further. Yet, doing so also forces the authorities to make tough choices in balancing its support towards refugees and its responsibilities to its own citizens. The international community’s response and support to Chad have been rapid and substantial, which has helped ease some of the immediate emergencies.

However, situations end up being protracted (Devictor and Do 2017), and after an initial period, international funding for the refugee response tends to decline. (To illustrate, this note finds that aid covers only 14 percent of the cost of subsistence of existing refugees from Sudan.) In the face of these empirical realities and with limited prospects for a voluntary and safe return of refugees to Sudan, the question arises, what might be a sustainable approach to hosting refugees, and how could the Government of Chad and the international community bring it about? Increasing the financial autonomy of refugees to minimize reliance on humanitarian assistance seems critical. Not only does such a policy enhance the dignity and autonomy of refugees, it also creates savings in the amount of assistance needed thus frees up resources to invest in host communities and to unlock the productive potential of refugees.

**Figure 1: Countries hosting refugees from Sudan Figure 2: Stock of refugees in Chad**

<!-- Start of picture text --> 1,600,000 800,000 748,610<br>1,400,000 700,000<br>1,200,000 600,000<br>1,000,000<br> 500,000<br>426,540<br>800,000<br> 400,000<br>600,000<br> 300,000<br>400,000<br> 200,000<br>200,000 134,500<br> 100,000<br>0 18,050 25,200<br>1,600<br> -<br>Sudan, Sudan, new Central Cameroon Nigeria Other<br>existing arrivals African Rep.<br>Ethiopia South Sudan CAR Chad Egypt<br>Apr-23 May-23 Jun-23 Jul-23 Aug-23 Sep-23 Oct-23 Nov-23 Dec-23 Jan-24<br><!-- End of picture text --> Source: UNHCR, February 2024.

2
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# V.pDeterminants of Poverty.

This section discusses the determinants of poverty. The central hypothesis is that lagging rural and agricultural development<sup>liesatthe root</sup> of Mexican poverty. Independently of the geographical distribution of the extremely-poor, this hypothesis is important in a behavioral sense:<sup>urban</sup> poverty is not only quantitatively less important, but is to a large extent a reflection of rural poverty, as migration is a key mechanism<sup>through which the</sup> rural poor attempt to reduce their income differences vis-a-vis the<sup>rest of</sup> the population. The poverty profiler of section IV showed that the extremelypoor, aside from being located mostly in the rural areas and having the lowest levels of education, derive most of their earnings from self employment<sup>and</sup> wage labor, presumably in agriculture and related activities. An obvious implication is that to study the determinants of poverty is to<sup>study the</sup> determinants of the returns to unskilled labor and land -the main assets owned by the poor<sup>46</sup> .

Given preferences, technology and the size and distribution of endowments, the returns to land and to unskilled labor depend on:<sup>(i)</sup> government policies broadly defined to include pricing, intra and intersectoral resource allocation, and (ii) the institutional environment in<sup>which</sup> agents make their decisions. Given its importance, most of this section is devoted to intra-rural policies, including a brief discussion<sup>of the</sup> institutional framework (sub-section V.1). However, I also briefly mention inter-sectoral (sub-section V.2) and macroeconomic policies (sub-section V.3).

Two caveats: one, discussing all these issues here involves a substantial risk of over-extending and thinning my arguments. I emphasize<sup>that my aim is</sup> only to raise some key points and discuss their bearing<sup>on poverty; there is</sup> 46Actually, some of the land exploited by the extremely-poor under the ejido tenure form has important restrictions attached<sup>to it, so it should be</sup> thought of as a very peculiar asset; see below.
# **SAVING**

Saving to cover future expenses—education, a wedding, a big purchase—or to provide against possible emergencies is a universal tendency. Globally, 36 percent of adults report having saved or set aside money in the past 12 months. Adults in high-income economies are the most likely to do so, followed by those in SubSaharan Africa and East Asia and the Pacific. In other regions only between 20 and 25 percent of adults report having saved in the past year (figure 2.1).

More interesting, there are marked differences in how people save. Many who save do so using an account at a formal financial institution. Many others, including some who have a formal account, turn to alternative methods of saving.

<!-- Start of picture text --> Formal and informal saving<br>Adults saving any money in the past year (%)<br>100<br>80<br>Adults who<br>saved<br>60<br>USING OTHER<br>METHODS ONLY<br>40<br>AT A FINANCIAL<br>20 INSTITUTION<br>0<br>MIDDLE EAST EUROPE & SOUTH LATIN AMERICA EAST ASIA SUB-SAHARAN HIGH-INCOME<br>& NORTH AFRICA CENTRAL ASIA ASIA & CARIBBEAN & PACIFIC AFRICA ECONOMIES<br>2.1 FIGURE<br><!-- End of picture text --> _Source:_ Demirguc-Kunt and Klapper 2012.

## **How does formal savings behavior vary around the world?**

Worldwide, about a fourth of adults report having saved at a bank, credit union, or microfinance institution in the past 12 months—though the share ranges from 45 percent of adults in high-income economies to less than 7 percent in Europe and Central Asia and the Middle East and North Africa (map 2.1). Sub-Saharan Africa has a larger share of adults who report having saved at a formal financial institution in the past 12 months than any other region except East Asia and the Pacific. This is in part because three of the countries with the largest shares of adults reporting formal saving in the region are also three of its most populous countries: Nigeria (with 24 percent of adults reporting formal saving), South Africa (22 percent), and Kenya (23 percent).

**30**

MEASURING FINANCIAL INCLUSION
**Table 1: Descriptive Statistics on Firm Characteristics – By Type of Firm**

||**D**|**escriptive St**|**atistics Firms**||||
|---|---|---|---|---|---|---|
|**Firm type**||**_Ben Ali_**|**_Offshore_**|**_Onshore_**|**_Public_**|**_All_**|
|**_Economic Significance_**|||||||
|Number of firms|N|206|7074|20869|124|28273|
|% of all firms|%|0.73|25.02|73.81|0.44|100|
|% of overall imports|%|2.29|32.53|38.64|26.54|100|
|% of source-product-years with|%|7.22|36.62|81.16|12.40||
|at least one importer of this type|||||||
|**_Firm Characteristics_**|||||||
|_By firm-year_|||||||
||N|865|27926|82691|782|112264|
|Y|Mean|14.389|13.268|13.138|17.057|13.202|
||Sd|2.690|1.934|2.021|2.684|2.036|
|L|Mean|2.436|2.983|1.925|2.470|2.198|
||Sd|2.180|1.861|1.630|3.102|1.770|
|Age|Mean|9.279|7.607|12.755|30.197|11.562|
||Sd|10.776|6.746|11.250|18.585|10.731|
|Log total imports|Mean|12.690|12.421|11.391|14.011|11.676|
||Sd|2.444|2.410|2.092|3.438|2.245|
|#source countries|Mean|5.318|3.294|3.267|8.944|3.329|
||Sd|5.900|3.646|3.696|9.604|3.812|
|#products|Mean|25.383|13.127|9.256|46.219|10.600|
||SD|43.069|18.219|16.556|73.868|18.712|
|#source countries*products|Mean|36.091|17.139|12.687|80.004|14.444|
||SD|65.748|29.027|28.400|146.05|31.984|
|_By source-country and year_|||||||
||N|31219|478618|1049102|62563|1621502|
|Log imports per source-country|Mean|9.115|9.457|9.184|9.585|9.279|
||Sd|8.789|9.166|8.912|9.177|8.992|
|Log (1+Tariffs)|Mean|3.540|3.432|3.334|3.259|3.366|
||Sd|0.692|0.997|0.831|0.751|0.883|
|Log(1+NTBs)|Mean|0.060|0.024|0.037|0.058|0.035|
||Sd|0.309|0.202|0.255|0.293|0.243|

38
Although participation rates averaged around 60%, contamination was very low as a result of this process. Table 6 highlights this fact, where less than 1% of invited guests went to a different screening to the one they had been assigned to, strengthening the justification to use Equations 1 and 2 with our restricted sample to measure the ATET.

In the follow‐up survey we asked for self‐reported exposure, partly to confirm attendance, but also to understand whether people could remember the main activities and messages from the events – presented as a summary in Table 7. While people have no problem recalling the screening, they express some confusion about the details of the event. We find that 95% of people recall receiving an invitation and 96% of the people who were recorded through administrative records as attending the event confirmed that they had attended. When asked specifically about whether they saw the _Story of Gold_ , 90% in **Movie** and 93% in **Movie/MFB** acknowledged that they had done so, while 77% and 82% respectively could recall the main message of the movie without prompting. However, **C** and **MFB** groups also reported having seen the movie, although at significantly lower levels (59% and 58% respectively). Since the movie was tightly controlled, and not released to the public, this suggests a potential confusion between _The Story of Gold_ and the placebo movie screening – possibly confounded by the fact that neighboring business owners may have seen and mentioned something about the movie.

Recall of _Accion_ presence was much lower. We find significant increases in recall for **MFB** and **MFB/Movie** compared to **Movie** and **Control** as to be expected, but the proportions are still low. Only 16% of **MFB** attendees and 17% of **Movie/MFB** attendees recalled _Accion_ ’s presence at the event. We also asked a falsification question to assess the level to which respondents may have been adjusting their answers to respond positively to the interview. We find that only 1% of people responded positively to a question asking whether a certain MFB (Jaiz Bank), that is only based in Abuja, had visited them (an impossibility), and this is similar across treatment arms, suggesting that positive response bias does not seem to be a problem in our case. Since the interventions were monitored carefully and _Accion_ was indeed present at these events, this contrast between _Accion_ and _Story of Gold_ recall highlights the differential salience of each of the interventions.

# **_Financial Literacy_**

The quiz questions test basic numeracy and financial concepts. Since the movie screening aimed to influence emotions and perceptions rather than formal financial literacy, we expected these indicators to show balance across groups, which they do. In particular, the survey included 6 quiz‐like questions with 14
**Table 3 Job creation and job destruction: Moldova against selected countries**

||Moldova a) C|roatia a) B|ulgaria a) L|ithuania a)|Poland a)|France|
|---|---|---|---|---|---|---|
||2001|2001|2000|1998-99|1999|1984-91|
|Job creation|6.7|3.5|6.8|9.7|5.3|6.6|
|Job destruction|11.2|4.9|10.8|10.7|10.1|6.3|
|Employment growth|-4.5|-1.4|-4.1|-0.9|-4.8|0.3|
|Job turnover|17.8|8.4|17.6|20.4|15.4|12.9|
|Job reallocation|13.3|7.0|13.5|19.4|10.5|12.6|

a) Registered legal persons. Note: Continuing firms (i.e. firm openings and closures are excluded). Sources: Moldova: Department of Statistics and Sociology; author's calculations. Croatia: Central Bureau of Statistics; author's calculations. Bulgaria: Rutkowski (2002b) Lithuania: Rutkowski (2002a) Poland: Rutkowski (2001) OECD countries: OECD (1996) Furthermore, employment prospects faced by the unemployed in Moldova are poor as the overall pool of jobs is shrinking. As fewer jobs are being created than destroyed, the job losers find it difficult, if not impossible, to find a new job. Again , the comparison with Lithuania is instructive. While the job destruction rate in Moldova is similar to that in Lithuania, the job creation rate is significantly lower. So, the problem faced by Moldova is not as much high job destruction as insufficient job creation. As a result of this gap between job creation and job destruction, Moldovan jobseekers face worse chances of finding a job than their Lithuanian counterparts.

The focus on continuing firms (dictated by data availability) is likely to produce too rosy a picture of the labor market in Moldova. If one allows for the fact that firm openings (as well as closures) are probably less prevalent in Moldova than in other transition economies, then labor market in Moldova turns out to be even less dynamic. In particular, it lacks the significant dynamism springing form the birth of new firms and job opportunities that they create.

What does the pattern of high job destruction coupled with modest job creation tell us about labor market conditions in Moldova? High job destruction suggests that dismissal costs are not so high as to forestall the destruction of non-viable, low productivity jobs. This is in contrast to, for example Croatia, where extremely high costs 15
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- - <u>Panel D: Regulatory framework for deposit taking (0 4)</u>

||V|ersion 1(Ful samp|les)<br>Depvar: Portfolio at|Version 2<br>Risk(>30days)|(DroppingRural b|ank samples)|
|---|---|---|---|---|---|---|
||OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Regulatory framework for deposit-taking (0-4)|-0.0029|-0.0115***|-0.0121***|-0.0014|-0.0099***|-0.0119***|
||[0.701]|[0.001]|[0.009]|[0.842]|[0.002]|[0.004]|
|NBFI dummy * Regulatory framework for deposit-taking (0-4)|-0.0062|0.0004|0.0054|-0.005|-0.0001|0.0032|
||[0.589]|[0.936]|[0.417]|[0.669]|[0.988]|[0.608]|
|Combined group dummy *Regulatory framework for deposit-taking (0-4)|-0.0214*|0.0058|0.0016|-0.0068|0.0061|-0.0004|
||[0.075]|[0.260]|[0.812]|[0.668]|[0.325]|[0.968]|
|NBFI dummy|0.0221|-0.0011|-0.0078|0.0181|-0.0016|-0.0072|
||[0.328]|[0.898]|[0.467]|[0.450]|[0.845]|[0.475]|
|Combined group dummy|0.0641**|-0.0039|0.0085|0.0288|-0.0117|0.0045|
||[0.033]|[0.721]|[0.568]|[0.423]|[0.332]|[0.802]|
|Observations|627|627|598|572|572|546|
|Number of countries|39|39|39|39|39|39|
|R-squared|0.077|0.216|0.167|0.069|0.25|0.175|
|Adjusted R-squared|0.0479|0.192|0.14|0.0374|0.224|0.145|
|_Microscope index+ interaction term (P-value in brackets)_|||||||
|NBFI dummy|-0.00911|-0.0112***|-0.00672|-0.00642|-0.00995***|-0.00867|
||[0.374]|[0.007]|[0.326]|[0.510]|[0.010]|[0.172]|
|Combined group dummy|-0.0243**|-0.00572|-0.0105|-0.00818|-0.00382|-0.0123|
||[0.027]|[0.251]|[0.156]|[0.565]|[0.520]|[0.165]|

52
**<u>Table 7. Within-household Fixed effect Estimates of Land Tenure Impact on Land Investment</u>**

|||Land impro<br>|vement<br>||Access to I<br>|rrigation<br>|
|---|---|---|---|---|---|---|
||Improvement<br>past 8years|Family labor<br>used in 2008|Spending in<br>past 8years.|Days spent in<br>2008.|Private|Public|
|Barga land|-0.264***|-0.217***|-965.517***|-21.127***|-0.068***|-0.004|
||(10.08)|(9.52)|(5.01)|(11.49)|(4.36)|(0.46)|
|_lower bound_|[-0.098]***|[-0.068]***|||||
|Plot size (acre)|0.002|0.003|523.588|17.180***|0.003|0.001|
||(0.48)|(0.72)|(0.96)|(3.68)|(1.53)|(0.99)|
|Distance to|-0.005|-0.004|-70.953|0.490|-0.068***|0.011*|
|homestead (km)|(0.48)|(0.52)|(0.83)|(0.45)|(5.30)|(1.73)|
|No. of households|1,777|1,777|1,777|1,777|1,777|1,777|
|No. of obs. (plots)|9166|9166|9166|9101|9166|9166|
|R-squared|0.16|0.14|||0.05|0.01|

_Notes:_ All regressions include household level fixed effects. Col. 1, 2,5, 6 use a fixed effect linear probability model while col. 3 and 4 report results from fixed-effect tobit models. Plot and soil characteristics are included throughout. Robust t statistics in parentheses. *, **, and *** denote significance at 10%, 5%, and 1%. Figures in square brackets are lower bound estimates following Altonji, Elder and Taber (2005) 22
with 10 being the worst (most restrictive). The average of this indicator is the highest in transition countries (5.70), followed by the OECD sample (5.43) and Latin America (4.68). This synthetic indicator passes simple validation tests. For example, its correlation with a similar indicator of employment protection legislation developed by the OECD is 0.85, statistically significant at the 1 percent level.<sup>11</sup> In the sensitivity analysis, we also consider an EFW synthetic indicator of regulations in the product market. Regulations affecting markets for goods and services have a strong impact on the degree of competition and the pace and effectiveness of reallocation of resources, including labor. Thus, more restrictive regulations that stifle product market competition are also likely to influence job flows. The business regulation indicator is a simple average of five different indicators: price controls; administrative conditions and new business; time with government bureaucracy; starting a new business; and irregular payments. These five indicators are designed to identify the extent to which regulatory restraints and bureaucratic procedures limit competition and the operation of goods and services markets. Business regulation is measured on a scale from 0 to 10, with 10 being the most restrictive. This indicator is on average the highest in Latin America (4.21), followed by transition countries (3.32) and OECD countries (3.07).

The EFW indicator of law and order is measured on a scale of 0 to 10, with 10 being the worst. The average of this indicator is highest in Latin America (4.95), followed by transition countries (1.76) and the OECD sample (0.47). Appendix A contains more detailed definitions of the variables used in our analysis.

# **5.4 The Baseline Specification**

In our empirical investigation, we start with a baseline specification in which we only include the U.S. job flow benchmark and the country dummies (equation (2). We then test for differences in the estimated coefficient of the U.S. job flow benchmark across the three regions for which we have data (OECD countries, Latin America and transition economies). Further, we allow the coefficient of the U.S. job flow variable to vary by firm size class.

> 11We check the robustness of our results by using an alternative measure of employment protection legislation, the OECD EPL index. Since this measure is not available for Latin America and transition countries in the early 1990s, we augmented it in two ways. First, for transition countries we used data on EPL collected by Haltiwanger et al. [2003]. Second, for Latin America we imputed EPL by regressing a measure of hiring and firing practices from the Fraser Institute on EPL for transition and OECD countries and then using the estimated coefficient to calculate EPL. EPL is measured on a scale from 0 to 4, with 4 being the worst (most restrictive). It is on average the strictest in OECD (2.35) and the least strict in Latin America (1.73).

31
Demirgüç-Kunt, A., Karacaovali, B. and Laeven, L. (2005), Deposit insurance around the world: A comprehensive database, _World Bank Policy Research Working Paper No. 3628_ Demirguc-Kunt, A., Kane, E. J. and Laeven, L., (2008), _Deposit Insurance around the World: Issues of Design and Implementation_ , vol. 1, 1 ed., The MIT Press.

Djankov, S., McLiesh, C. and Shleifer, A., (2007), Private credit in 129 countries, _Journal of Financial Economics_ , Vol. 84(2), pp. 299-329, May Duan, N. (1983), Smearing Estimate: A Nonparametric Retransformation Method, _Journal of the American Statistical Association_ , Vol. 78, No. 383, pp. 605-610, September Duflos, E. & Glisovic-Mézières, J. (2008), National Microfinance Strategies, _CGAP Policy Brief_ . Dupas, P. and Robinson, J. (2009), Savings Constraints and Micro-enterprise Development: Evidence from a Field Experiment in Kenya, _NBER Working Paper #14693_ .

European Commission (2008) Financial Services Provision and Prevention of Financial Exclusion, European Commission, Directorate-General for Employment, Social Affairs and Equal Opportunities Inclusion, Social Policy Aspects of Migration, Streamlining of Social Policies, Brussels.

European Association of Cooperative Banks (EACB), Key Statistics, 2005.

FinScope (2006), _FinScope Uganda_ [www.finscope.co.za/uganda.html] FinScope (2008), _FinScope Malawi_ [www.finscope.co.za/documents/2009/Brochure_Malawi08.pdf ] FinScope (2008), _FinScope Rwanda_ .

[www.finscope.co.za/documents/2008/Brochure_Rwanda08.pdf ] FinScope (2009), _FinScope Pakistan_

[www.finscope.co.za/documents/2009/Brochure_Pakistan.pdf ] Guiso, L., Sapienza, P. and L. Zingales (2004), Does Local Financial Development Matter? _Quarterly Journal of Economic_ , Vol. 119(3), pp. 929-969, August Karlan, D. and Zinman, J. (2009), Expanding Credit Access: Using randomized supply decisions to estimate the impacts, _Review of Financial Studies_ , forthcoming.

Karlan, D. and Zinman, J. (2009), Expanding Micro-enterprise Credit Access: Using Randomized Supply Decisions to Estimate the Impacts in Manila, Mimeo, Dartmouth College.

38