rafmacalaba/datause-encoder-data huggingface dataset:
inputs by up to 21%. This effect remains unchanged when I condition on household characteristics proxying for overall household wealth such as education, household size and total land owned. The estimate for households below the median is indistinguishable from zero throughout.

What alternative channels might explain the relationship between debt relief and the observed decline in household investment and productivity? Duflo, Kremer and Robinson (2011) study the investment decision of farmers in Kenya and show that time-inconsistent preferences limit profitable investments. In principle, it is possible that the same mechanism is operational in the sample of farmers studied here. However, in order to explain the results reported in the preceding sections, debt relief would need to induce differences in the time-preferences between treatment and control households. In non-reported robustness checks I test the impact of debt relief on measures of time preferences and find no effect. Hence time-inconsistent preferences may constrain the investment decisions of households in the sample overall, but not differentially so among the treatment and control group. Another potential mechanism that might induce a reduction in investment expenditure after debt relief is the possibility that households perceive debt relief as a windfall and shift their expenditures away from investment and towards consumption (see e.g. a discussion in de Mel, McKenzie and Woodruff 2008).<sup>23</sup> Two observations make this mechanism unlikely to operate in the setting studied here. First, note that the program eliminated debt on the books, but did not involve cash payments. Additionally few households used debt relief to obtain a new loan so that the effect on household liquidity and the potential to increase consumption was limited. Second, when I examine the consumption expenditures of households after the program I find no difference between households in the treatment group. I also find no evidence of an impact of debt relief on savings, which suggests that, households lack sufficient liquidity to finance investments from precautionary savings prior to the program, and that debt relief does not lead households to increase savings ex-post.

> 23The authors find that micro-enterprise owners in Sri Lanka who received unconditional cash transfers invested 58% of the grant in their business but only 5% on household consumption.

26
# **Reference List**

Amani Elobeid & John Beghin, 2006. "Multilateral Trade and Agricultural Policy Reforms in Sugar Markets," Journal of Agricultural Economics, Wiley Blackwell, vol. 57(1), pages 2348, 03.

Ariga, Jayne, and Njukia. 2010. “Staple food prices in Kenya.” Prepared for the COMESA policy seminar on “Variation in staple food prices: Causes, consequence, and policy options”, Maputo, Mozambique, 25-26 January 2010 under the Comesa-MSU-IFPRI African Agricultural Marketing Project (AAMP).

<mark>Benson, T., Mugarura, S. and Wanda, K. (2008), Impacts in Uganda of rising global food prices: the role of diversified staples and limited price transmission. Agricultural Economics, 39: 513–524.</mark> Borrell, B., and D. Pearce. 1999. “Sugar: The Taste Test of Trade Liberalization.” Center for International Economics, Canberra and Sydney, Australia. September. 158 Global Agricultural Trade and Developing Countries Brian Chisanga, John Gathiaka, George Nguruse, Stellah Onyancha, and Thando Vilakaz. 2014. “Competition in the regional sugar sector: The case of Kenya, South Africa, Tanzania and Zambia”.

Cleveland, W. S. 1979. Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association 74: 829-836.

John Creedy & Robert Dixon, “The Relative Burden of Monopoly on Households with Different Incomes,” 65 Economica 285 (1998).

Deaton, A. 1989. “Rice Prices and Income Distribution in Thailand: A Non-Parametric Analysis,” Economic Journal 99 (395): 1–37.

Deaton, A. 1997. The Analysis of Household Surveys. A Microeconometric Approach to Development Policy. Baltimore, MD: John Hopkins University Press.

De Groote and Kimenju. 2012. “Consumer preferences for maize products in Urban Kenya.” Food and Nutrition Bulletin, vol. 33, no. 2, 2012, The United Nations University.

European Commission. 2013. “Evolution of the sugar imports in the European Union from LDC and ACP countries”. Brussels, 31.5.2013.

Hoffmann, Mutiga, Harvey and Milgroom. 2013. “A Market for Lemons: Maize in Kenya.” Working Paper.

d’Hôtel, Le Cotty and Jayne. 2012. “Is a Public Regulation of Food Price Volatility Feasible in Africa? An ARCH Approach in Kenya.” Paper prepared for the 123rd EAAE Seminar Price Volatility and Farm Income Stabilisation: Modelling Outcomes and Assessing Market and Policy Based Responses. Dublin, February 23-24, 2012 33
of employment, i.e., agricultural employment and self-employment in non-agricultural activities.<sup>18</sup> To further examine impacts on employment outcomes, we plot the distribution of the women’s employment index and the distribution of salaried income for each treatment group in Figures 5 and 6. These figures clearly indicate a rightward shift in the distribution of women’s employment outcomes.

**Women’s decision-making autonomy, gender attitudes, and intrahousehold dynamics.** We do not find any statistically significant impact of the intervention on women’s decision-making in the household, sharing of domestic work, freedom of movement, or gender attitudes (Table 8). For women caring for children under two years of age, there is a marginally significant increase in the likelihood that the respondent’s partner sometimes takes care of children (Table 9). However, we find no significant changes in time use, with the exception of a 20 percent increase in time spent doing paid work over the past 24 hours, compared to a control group mean of 3.4 hours (Table 10).

**Women’s psychological well-being.** Turning to women’s psychological well-being, we find a small beneficial impact of the intervention, reducing the likelihood of experiencing depression and self-reported unhappiness (significant at the 5% level) for the full sample (Table 11). We find similar effects for women in households with children under two years of age. These results are consistent with work from Bossuroy et al. (2022), finding that a multi-faceted poverty alleviation program in Niger (neighboring Burkina Faso) improved mental health by 0.13 to 0.23 standard deviations, particularly when it included a psychosocial component.

**Women’s financial outcomes.** We use an OLS specification to estimate impacts on financial outcomes since we only collected this information in the endline survey. Almost 59% of women in the control group had saved any money during the past 12 months and 40% were able to pay FCFA 20,000 (approximately USD 34) in the case of an emergency (Table 12). We find a 13% percent increase in women’s likelihood of having saved in the last 12 months (significant at the 5% level) and a 25% increase in women’s reported capacity to mobilize financial resources in an emergency.

> 18We collected data on extensive margin labor market participation for all non-agricultural non-salaried activity. However, due to a mistake in the skip patterns in the programmed version of the survey questionnaire, we only collected information on hours worked in the last month and total monthly income for non-agricultural activity in a household enterprise, therefore excluding non-agricultural self-employment income from non-household enterprises. Thus, while our results in column 1 of Table 7 are unaffected, results in columns 2, 3, and 4 unfortunately exclude self-employment activities and could therefore understate any treatment effects.

18
# **<mark>Table 12: Borrowing</mark>**

<mark>Coefficients, standard errors in parentheses</mark>

||Pooled|Mean|Impact|byRoun|d(OLS)|Pooled|Impact|byRoun|d(SAR)|
|---|---|---|---|---|---|---|---|---|---|
||N|R3|R1|R2|R3|Impact|R1|R2|R3|
|**_Panel A: Reported by Women_**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||2,295|.256|.007|-.015|-.012|-.007|-.003|-.020|-.020|
|Any repaid loan in the last 12 months [yes=1]|||(.030)|(.030)|(.029)|(.018)|(.03)|(.030)|(.030)|
|Value of current outstanding loan [in Cedis]|2,261|259|-28.4|-56.1*|59.6*|-6.80|-29.3|-58.2*|55.0*|
||||(34.4)|(33.1)|(32.4)|(19.5)|(34.2)|(32.9)|(32.3)|
|**_Panel B: Reported by Men_**||||||||||
||1,775|.186|.033|.047|-.003|.026|.030|.040|.004|
|Any repaid loan in the last 12 months [yes=1]|||(.031)|(.030)|(.030)|(.018)|(.030)|(.030)|(.030)|
|Value of current outstanding loan [in Cedis]|1,755|310|52.8|19.7|106**|59.4**|51.6|19.3|109**|
||||(44.3)|(42.9)|(43.3)|(25.5)|(44.2)|(42.9)|(43.2)|

Notes: Standard errors are reported in parenthesis and are clustered at the individual level. *** denotes significance at 1%, ** at 5% and * at 10%. Standard errors are clustered by respondent and are shown in brackets. All outcome variables in Panel A (B) are measured conditional on the existence of a female (male) plot. Control variables include household structure, household size, number of females in the household, number of household members under five years of age, dummy variable for household head being male, age of household head, dummy for whether household head has ever been to school, three different dummies for the predominant chieftaincy institution in the community, number of sampled households situated around respondents’ house within a 200m radius (household density), distance between the house of the respondent and the main road demarcation.

34
|P065898|2004|2004|6/22/2004|1 Closed|Vietnam|
|---|---|---|---|---|---|
|P065898|2004|2005|6/14/2005|3 Closed|Vietnam|
|P065898|2004|2006|2/12/2006|4 Closed|Vietnam|
|P065898|2004|2007|10/5/2006|5 Closed|Vietnam|
|P065898|2004|2007|6/28/2007|6 Closed|Vietnam|
|P065898|2004|2008|5/2/2008|7 Closed|Vietnam|
|P065898|2004|2008|6/23/2008|8 Closed|Vietnam|
|P065898|2004|2009|6/24/2009|9 Closed|Vietnam|
|P065898|2004|2010|6/16/2010|10 Closed|Vietnam|
|P065898|2004|2011|6/29/2011|11 Closed|Vietnam|
|P065898|2004|2012|12/28/2011|12 Closed|Vietnam|
|P065898|2004|2013|10/22/2012|13 Closed|Vietnam|
|P065898|2004|2013|2/11/2013|14 Closed|Vietnam|
|P065902|2000|2000||0 Closed|Bolivia|
|P065902|2000|2001|6/1/2001|3 Closed|Bolivia|
|P065902|2000|2002|2/19/2002|5 Closed|Bolivia|
|P065902|2000|2003|5/28/2003|7 Closed|Bolivia|
|P065902|2000|2004|6/29/2004|11 Closed|Bolivia|
|P065902|2000|2005|1/3/2005|14 Closed|Bolivia|
|P065920|2005|2005||0 Closed|West Bank and Gaza|
|P065920|2005|2006|8/4/2005|1 Closed|West Bank and Gaza|
|P065920|2005|2006|12/30/2005|2 Closed|West Bank and Gaza|
|P065920|2005|2006|6/28/2006<br>|3 Closed|West Bank and Gaza|
|P065920|2005|2007|1/24/2007|4 Closed|West Bank and Gaza|
|P065920|2005|2007|3/30/2007|5 Closed|West Bank and Gaza|
|P065920|2005|2007|6/7/2007|6 Closed|West Bank and Gaza|
|P065920|2005|2008|10/11/2007|7 Closed|West Bank and Gaza|
|P065920|2005|2008|1/23/2008|8 Closed|West Bank and Gaza|
|P065920|2005|2008|6/29/2008|9 Closed|West Bank and Gaza|
|P065920|2005|2009|12/9/2008|10 Closed|West Bank and Gaza|
|P065920|2005|2009|6/25/2009|11 Closed|West Bank and Gaza|
|P065920|2005|2010|6/7/2010|12 Closed|West Bank and Gaza|
|P065920|2005|2011|5/9/2011|13 Closed|West Bank and Gaza|
|P065920|2005|2012|1/1/2012|14 Closed|West Bank and Gaza|
|P065920|2005|2020|2/24/2020|15 Closed|West Bank and Gaza|
|P065921|2004|2004|6/22/2004|3 Closed|West Bank and Gaza|
|P065921|2004|2005|3/24/2005|5 Closed|West Bank and Gaza|
|P065921|2004|2006|12/2/2005|6 Closed|West Bank and Gaza|
|P065921|2004|2006|6/27/2006|7 Closed|West Bank and Gaza|
|P065921|2004|2006|6/29/2006|8 Closed|West Bank and Gaza|
|P065921|2004|2007|12/21/2006|9 Closed|West Bank and Gaza|
|P065921|2004|2007|6/25/2007|10 Closed|West Bank and Gaza|
|P065921|2004|2008|9/17/2007|11 Closed|West Bank and Gaza|
|P065921|2004|2008|5/29/2008|12 Closed|West Bank and Gaza|
|P065921|2004|2009|5/29/2008|12 Closed|West Bank and Gaza|
|P065921|2004|2020|2/24/2020|13 Closed|West Bank and Gaza|
|P065927|2000|2000||0 Closed|Cameroon|
# **1. Background and Motivation**

Emerging economies are urbanizing fast. The United Nations projects that the world’s urban population will increase by 2.6 billion people, up from 3.6 billion in 2011 to 6.3 billion in 2050. Almost all growth will take place in emerging economies, with Asia’s urban population increasing by 1.4 billion and Africa’s by 0.9 billion (United Nations, 2012). The rapid pace and magnitude of urbanization has stretched the capacity of countries to finance the large capital outlays needed to provide housing, infrastructure, and services as cities grow and urbanization picks up speed (World Bank 2013). In this paper, we focus on housing, as its consumption has direct bearing on living standards, and investment in housing is often the largest asset class held by families. For instance, the private wealth in housing for British households is 5.5 trillion— more than a third of the country’s total private wealth of 15 trillion (Collier and Venables 2013). Further, the construction of housing also contributes to job creation, especially in developing economies where the sector is typically more labor intensive than other sectors (Collier and Venables, 2013).

Our investigation focuses on answering two main questions. First, what has been the extent to which investment in housing has kept pace with demand from urbanization? Second, what are the impacts of differences in financing capabilities of countries on housing investment? In particular, what are the implications of relying solely on domestic savings to finance housing investments vis-à-vis the ability to leverage financial markets and foreign investments? We assemble a national accounts consistent data set of housing investment covering over 90 countries across the globe that explicitly accounts for investments by households, the government, and the private sector. These data cover structures used entirely or primarily as residences, including any associated structures, and permanent fixtures installed in residences. Investment figures include the costs of site clearance and preparation, and cover both formal and informal structures. Such a comprehensive and globally comparable data set on housing investment has never been compiled before.

This data set allows us to extend the literature on housing supply, which has previously focused on “single-family houses” and their corresponding prices for developed countries such as the United States (Proterba 1984, Topel and Rosen 1988). Despite the focus of the literature on single-family housing, these types of new ‘structures’ only represent a subset of total housing investment. In most developing countries, especially in Sub-Saharan Africa, formal housing 2
In our experiment we used two distinct samples of forest users from rural villages in Ethiopia and Nepal. Thus, our participants knew each other, had a long history of interdependencies, were part of the same forest user groups, had generally lived their whole lives as neighbors, and cooperated to greater or lesser degrees in daily life. In the experiment, though, they were matched anonymously, allowing us to compare naturally occurring groups with artificially created ad-hoc groups in a laboratory setting.

Overall, our results show a weak effect of nonbinding agreements on cooperation in the two locations. The main reason for this is that the cooperation level was relatively high even without an agreement and that relatively few participants changed their behavior when the agreement option was introduced. Nonetheless, we find that the willingness to enter an agreement varies between individuals and strongly correlates with their cooperativeness. The subjects who had optimistic beliefs and who contributed high amounts in the standard game were more likely to enter the agreement than those with more pessimistic beliefs and lower contributions in the standard game. This result points to the importance of institutions as a sorting mechanism of people’s degree of cooperativeness.

The subjects who voted in favor of the agreement also pledged and contributed high amounts if the group did not form an agreement. This indicates a high unconditional willingness to cooperate and is in stark contrast to the results from the student experiments (Dannenberg, 2015). A possible explanation for the observed unconditional willingness to cooperate and the relatively stable behavior irrespective of the institution in place is that the two countries where we conducted the experiments are characterized by a high degree of collectivism. This together with the common history and close social relationships may help explain why the subjects’ behavior is relatively stable.

19
16

**Figure 11. MDG 5: Financing Gap**

<!-- Start of picture text -->
-10 -0.4<br>2008 2009 2010 2011 2012 2013 2014 2015<br>-13.79<br>-15 -16.60 -0.5<br>-0.50 -20.12<br>-20 -0.56 -22.52 -0.6<br>-25 -26.53<br>-0.65<br>-0.69 -30.23 -0.7<br>-30<br>-33.58<br>-0.75 -0.8<br>-35 -0.79<br>-0.82 -0.89<br>-40 Millions of US$ -39.13 -0.9<br>% of non-oil GDP (secondary axis)<br>-45 -1.0<br>  Source:  Authors’ calculations.<br><!-- End of picture text -->

**Table 8. MDG 5: Cost, Budget and Financing Gap (2008-2015)**

<!-- Start of picture text --> US$ 163.26 m<br>Total Cost<br>MRO 45,094.16 m<br>Budget US$ 38.86 m<br>Allocation MRO 10,753.74 m<br>US$ 124.4 m<br>Financing MRO 34,340.4 m<br>gap 0.71% of non-oil GDP<br>US$ 7.56 p.c.<br><!-- End of picture text --> <!-- Start of picture text --> Source: Authors’ calculations.<br><!-- End of picture text -->

# **MDG 6: Combat HIV/AIDS, Malaria and Other Diseases**

_Target 7 and 8: Have halted by 2015 - and begin to reverse - the spread of HIV/AIDS, tuberculosis, malaria and other diseases._

**_A. Current Situation:_ low levels of HIV/AIDS and tuberculosis, but high malaria prevalence.** Mauritania is characterized by a low prevalence of HIV/AIDS, estimated at 1.9 percent in 2003. Around 80 percent of the population in Mauritania is exposed to malaria. The prevalence rate for malaria was in the order of 10 percent on 2000, with 250.000 new cases per year. In the seven southern _wilayas_ , Malaria accounts for a significant proportion of deaths and is responsible for 60 percent of hospitalization. In 2005, the prevalence of tuberculosis was estimated at 0.44 percent of the population, but it detection is feeble (less than 58 percent of cases are reported).

**_B. Trends and Likelihood:_ a medium probability of achieving MDG6.** The HIV/AIDS goal is attainable. However, between 1990 and 2003 HIV prevalence increased from 0.5 to 1.9 percent (World Bank 2005), hence it needs to be monitored closely. Since 2006, the treatment of malaria has improved, but between 1990 and 2000, the prevalence rate has only marginally decreased from 12.7 to 10 percent. There are little data on tuberculosis trends. On the basis of current information, Mauritania has a medium probability of reversing the spread of these diseases by 2015.

**Figure 12. MDG 6: Current Situation and Trends**

<!-- Start of picture text -->
1.75 9.00<br>1.6 Prevalence rate of HIV/AIDS pop. 15-49 ages in %<br>1.4 Prevalence rate of Tuberculosis in %<br>Prevalence rate of Malaria in % (secondary axis)<br>1.40 8.4 1.3 8.00<br>1.2<br>7.9 1.1<br>1.0<br>1.05 7.4 0.9 7.00<br>7.0 0.8<br>0.7<br>6.6 0.7<br>0.70 6.2 6.00<br>5.8<br>5.1<br>0.35 0.42 0.40 0.38 0.36 0.34 0.32 0.30 0.285.5 0.26 0.244.8 5.00<br>0.00 4.00<br>2006 2008 2010 2012 2014<br>     Source:  Authors’ calculations.<br><!-- End of picture text -->
# **Table 6**

# **Covariance between overall financial inclusion and stability conditioning on multivariate country characteristics**

The results of the analysis of a proxy for covariance between overall financial stability and financial inclusion conditioning on multiple country characteristics. The dependent variable is constructed using equation (6): in Column 1 and 2 the covariance is between overall financial stability and overall financial inclusion; in Column 3 and 4, the covariance is between overall financial stability and financial inclusion for firms; in Column 5 and 6, the covariance is between overall financial stability and financial inclusion for individuals. Column 1, 3 and 5 present the estimates obtained using equation (5) and including the variables significant in the univariate analysis (Table 5). Column 2, 4 and 6 display the estimates obtained using equation (5) and maximizing the number of countries included in the estimation. Standard errors are corrected for heteroscedasticity.

|**VARIABLES**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
|Number of years firm operated without formal registration|-0.006||-0.004||-0.006||
||(0.005)||(0.004)||(0.006)||
|Chinn and Ito fin openness|-0.009**|-0.003|-0.006*|0.001|-0.009**|-0.002|
||(0.004)|(0.004)|(0.004)|(0.005)|(0.004)|(0.004)|
|Fiscal freedom|0.014*|0.017**|0.007|0.005|0.017*|0.020***|
||(0.008)|(0.007)|(0.007)|(0.008)|(0.009)|(0.007)|
|Investment freedom|0.005|-0.010|0.011|0.003|0.001|-0.020*|
||(0.007)|(0.010)|(0.009)|(0.010)|(0.008)|(0.012)|
|Firms formally registered when they started operations in the country (%)|-0.013*||-0.007||-0.014||
||(0.007)||(0.005)||(0.009)||
|Population density|0.046|0.012*|0.009|-0.006|0.068|0.025***|
||(0.031)|(0.007)|(0.023)|(0.007)|(0.046)|(0.009)|
|School enrollment, secondary (% gross)|0.018***||0.014**||0.015*||
||(0.006)||(0.007)||(0.008)||
|Domestic credit to private sector (% of GDP)|-0.005|-0.012|0.001|0.004|-0.007|-0.018*|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.012)|(0.010)|
|Depth of credit information index|0.002|0.009**|-0.004|-0.001|0.009*|0.015***|
||(0.004)|(0.004)|(0.005)|(0.004)|(0.004)|(0.004)|
|Rule of Law: Estimate|-0.007|0.000|-0.004|-0.001|-0.017|-0.007|
||(0.009)|(0.007)|(0.010)|(0.007)|(0.011)|(0.009)|
|Constant|-0.008|-0.012|-0.005|-0.005|-0.008|-0.012|
||(0.007)|(0.008)|(0.005)|(0.009)|(0.008)|(0.008)|
|Observations|99|144|99|144|96|141|
|R-squared|0.204|0.184|0.113|0.010|0.215|0.302|

28
- 1.7 -

pretasted structured questionnaire In the three ecobgical zones, coastal savannah, forest and northern savannah. Survey results were entered on computer floppy disks using a special data entry program which checked for consistencies In responses. Seven hundred questlonnaires were found to be useable for the analysis. The breakdown of respondents by region Is presented in Table l.B.1 There were 174 farm households in the North and 526 In the South. About 45 percent of the respondents In the South Table 113.1: Dlstrbution of **Survey** Households by Region

|**Region**|**Number**of<br>**Househids**|Cocoa<br>**Farmers**|
|---|---|---|
|**Ashand**<br>**Brong Ahafo**<br>**Central**|143<br>40<br>85|91<br>27<br>37|
|**Eas_m**<br>**G-eairAccra**<br>**NofWiem**|**81**<br>17<br>10|42|
|Upper**Eat**|32||
|<br>Upper**Wet**<br>**Vol**<br>|32<br>**84**<br>|5<br>|
|**Wstasm**<br>|**78**<br>|35|
|North|174||
|South|526|237|
|T O T A L|70|2S7|

respondents In the South cultivated cocoa. The age distribution of household members of the survey Is presented In Table l.B.2. When the distributlon Is compared with the national distribution (last two lines of Table i.B.2) as estinated by the Statistical Service, we see a sknilar pattern and so we can therefore Infer that our sample Is a normal one. The active labor force (age 15 to 64) represents 49.2 percent of all household members In the sample. Nationally the proportion of the population In the active lak'or force is about 49.5 percent (mid-year 1986 esthuate).