# Post-disaster housing reconstruction the case of Nepal: Exploring the reasons for low take of government financing packages

Sujan Adhikari, Dileep K Adhikary

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Sujan Adhikari, Dileep K Adhikary. Post-disaster housing reconstruction the case of Nepal: Exploring the reasons for low take of government financing packages. Project Research and Management Associates. 2023, pp.17. hal-04280513

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# Post-disaster housing reconstruction the case of Nepal: Exploring the reasons for low take of government financing packages

Dileep K. Adhikary¹, Sujan R. Adhikari²

¹ Project Research and Management Associates, Nepal

² University of Western Ontario, Canada

## Abstract

Every locality is prone to natural disasters and Nepal experienced a devastating one in 2015 best known as Gorkha Earthquake. It required a massive reconstruction for which Government of Nepal floated financing packages to support private housing reconstruction. The actual take was, however, too low. The study explored the reasons for the low take using the data drawn from the survey of the earthquake affected households and the institutions involved in executing the financing packages. The study shows that there were short comings in the timing, making and the managing of the financial package itself, and the study provides a critical lesson to learn requiring advance preparedness, completeness of the financial package and providing oversight on the management of the financial package as a way to secure sought results.

**Keywords:** post-disaster, earthquake, reconstruction

## 1. Introduction

Natural disaster is a recurring phenomenon faced by every human settlement on the earth. What has been stated on the whole is that across its time cycle the planet earth has undergone five major extinctions leading to dying out of three-quarters of all species thriving in respective geological period. In the aftermath of extinction of the dinosaurs, humans emerged and have now settled across the globe. Around the globe limited level disasters are continuously happening here and there as a regular phenomenon. Apart from natural disasters man-made disasters have also become a matter of concern as human activity is causing climatic change, reshaping of the earth's landscape and have caused extinction of other species (Gaind, 2020).

<table><caption>Table1: Disaster type and loss in Nepal (1971-2020) (*Dead and missing Source DRR Portal, MoHA, Nepal)</caption><thead><tr><th rowspan="2">Type</th><th rowspan="2">Events</th><th colspan="3">Life (heads)</th><th colspan="6">Property</th><th rowspan="2">Loss (NPR million)</th></tr><tr><th>Human D+M *</th><th>Human Injuries</th><th>Livestock Lost</th><th>Farm (ha)</th><th>Ed. centre</th><th>Med. centre</th><th>Others Lost</th><th>Others Damage</th></tr></thead><tbody><tr><td>Avalanche</td><td>125</td><td>352</td><td>125</td><td>658</td><td>1.01</td><td>0</td><td>0</td><td>82</td><td>33</td><td>20.30</td></tr><tr><td>Boat mis.</td><td>153</td><td>796</td><td>158</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.32</td></tr><tr><td>Cold wave</td><td>743</td><td>943</td><td>87</td><td>732</td><td>26906.5</td><td>12</td><td>0</td><td>0</td><td>0</td><td>834.65</td></tr><tr><td>Drought</td><td>167</td><td>0</td><td>0</td><td>0</td><td>465901.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>531.70</td></tr><tr><td>Earthquake</td><td>316</td><td>9719</td><td>29361</td><td>516353</td><td>0</td><td>2974</td><td>6</td><td>905</td><td>63981</td><td>7060581.00</td></tr><tr><td>Epidemic</td><td>3596</td><td>16817</td><td>43076</td><td>7608</td><td>2000.94</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2.631</td></tr><tr><td>Famine</td><td>29</td><td>10</td><td>0</td><td>0</td><td>26136</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1.10</td></tr><tr><td>Fire</td><td>5552</td><td>1268</td><td>1274</td><td>21893</td><td>2192.49</td><td>72</td><td>8</td><td>75113</td><td>2369</td><td>25223.98</td></tr><tr><td>Flood</td><td>4233</td><td>5026</td><td>585</td><td>543214</td><td>275364.3</td><td>133</td><td>5</td><td>99427</td><td>123114</td><td>15747.62</td></tr><tr><td>Forest fire</td><td>285</td><td>96</td><td>62</td><td>423</td><td>46327.9</td><td>0</td><td>0</td><td>2152</td><td>51</td><td>1414.38</td></tr><tr><td>Frost</td><td>8</td><td>7</td><td>0</td><td>0</td><td>5005</td><td>0</td><td>0</td><td>3</td><td>0</td><td>457.20</td></tr><tr><td>Hailstorm</td><td>797</td><td>66</td><td>102</td><td>951</td><td>133481.9</td><td>16</td><td>1</td><td>218</td><td>4648</td><td>2732.85</td></tr><tr><td>Heat wave</td><td>60</td><td>53</td><td>20</td><td>250</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Landslide</td><td>3469</td><td>6024</td><td>1956</td><td>11073</td><td>22584.3</td><td>147</td><td>8</td><td>19347</td><td>34496</td><td>1437.59</td></tr><tr><td>All Others</td><td>3992</td><td>2850</td><td>5122</td><td>83210</td><td>148453.6</td><td>648</td><td>8</td><td>6236</td><td>14668</td><td>1401.24</td></tr><tr><td>Total</td><td>23525</td><td>44027</td><td>81928</td><td>1186365</td><td>1154355</td><td>3077</td><td>4</td><td>84239</td><td>523027</td><td>7110386.3</td></tr></tbody></table>
Natural disasters could be of either 'meteorological (atmospheric: cold, heat, windy/storm), climatological (land conditions affected by weather: droughts, famine, wildfires, avalanche), hydrological (water related: rains, floods, landslides), geophysical (earthquake, volcanic eruptions, tsunami) or biological (life affected by way of diseases, infestation, etc.) origins. Whereas man-made disasters could result from deliberate or negligent human actions such as accidents (explosion and blasts, leakage and bursts, fire), disruptions/disorders (civil unrest, power blackout, transport blockades, cyber terrorism), aggression (violence, armed incursion, war) and emergencies (medical such as chemical contamination, and environmental such as pollution) (Adhikari & Adhikary, 2019).

Earthquake has been the major cause of loss of property in Nepal as indicated in Table 1 taking into the account the period of 50 years from 1971 to 2020. Moving on from the great earthquake on January 15, 1935 of M 8.0 scale that caused a loss of about 8519 human loss and more than 200,000 buildings damaged or destroyed, Nepal recently faced a major earthquake in April 25, 2015 of M 7.8 scale (and an aftershock of M 7.3 scale on May 12, 2015) that caused a loss of 8,790 people and 498,852 buildings destroyed and 256,697 buildings partly damaged (Adhikari & Adhikary, 2019). In between, the earthquakes were accounted in 1,980 (M 6.5 causing 103 casualties 25,086 buildings damaged and 12,817 collapsed), 1,988 (M 6.5 causing 721 casualties and 66,382 buildings damaged), and 2011 (M 6.9 causing 5 casualties, 14,554 buildings damaged, and 6,435 buildings collapsed). The events across 1911-1991 and 1994-2016 are depicted in Table 2 (Chaulagain et al., 2018).

<table><caption>Table 2: Earthquake event at different time period</caption><thead><tr><th rowspan="2">Year</th><th colspan="4">1911 - 1991</th><th colspan="4">1994 - 2016</th></tr><tr><th>M5-M6</th><th>M6-M7</th><th>M7-M8</th><th>M>8</th><th>M5-M6</th><th>M6-M7</th><th>M7-M8</th><th>M>8</th></tr></thead><tbody><tr><th>Magnitude</th><td>M5-M6</td><td>M6-M7</td><td>M7-M8</td><td>M>8</td><td>M5-M6</td><td>M6-M7</td><td>M7-M8</td><td>M>8</td></tr><tr><th>Events</th><td>41</td><td>17</td><td>12</td><td>1</td><td>76</td><td>8</td><td>2</td><td>-</td></tr></tbody></table>

The extent of disaster caused by Gorkha Earthquake spread over 32 among 77 districts of Nepal displacing as many as 649,815 families among affected 886,456 families with 604,930 houses fully damaged and 288,856 houses partially damaged with 7 severely hit districts, 7 crisis hit districts, 5 districts hit with heavy losses, 6 hit districts, and slightly affected 7 districts as per Post Disaster Needs Assessment which put the estimates of the total value of disaster effects (damages and losses) at NPR 706 billion equivalent to US$ 7 billion with NPR 517 billion i.e. US $5.15 billion (76% of the total effects) accounting for the value of destroyed physical assets, and NPR 189 billion i.e. US $1.9 billion (24% of the total effects) reflected the losses and higher costs of production (Government of Nepal, 2015).

In the aftermath of Gorkha Earthquake Nepal established National Reconstruction Authority (NRA) to manage post disaster recovery and reconstruction. It prepared and published a Post-Disaster Recovery Framework (PDRF) 2016-2020. The key provisions of Rs 838 billion five-year plan included a) decentralized implementation, b) building back better, c) non-discrimination and transparency, d) relocation kept at a minimum. The Plan put the time frame for reconstruction of a) private houses within two years, b) all quake-hit academic institutions and health facilities within three years and c) other public buildings within five years.

Regarding the reconstruction of private houses, the Plan incorporated owners driven approach and financing supports were offered in 2017 as follows:

a. grant of $ 3000 (NPR 300,000) in three tranches through any of the listed financial intermediary with bank opening the dummy account in the name of house-owner of destroyed/damaged house.
- 1st tranche (NPR 50,000): Account activated after verification of know your customer & amount deposited.

- 2nd tranche (NPR 150,000): Amount released after completion and technical inspection and certification of plinth level.

- 3rd tranche (NPR 100,000): Amount released after completion and technical inspection and certification of the roof band.

b. concessional loan scheme of interest-free NPR 300,000 (from any of the listed financial intermediary channeled through the Nepal Rastra Bank)

- The provisions included interest grant of 5%, no additional charge by the financial intermediary, and the loan insured by Credit Guarantee Corporation. GoN to contribute the interest, and 75% of the insurance and guarantee fee on a specific account opened for the purpose at the Nepal Rastra Bank.

- The loan was also offered for the beneficiaries for a) Commercial Agriculture and Livestock/Poultry, b) Educated Youth Employment, c) Foreign Returnee Youth Project, d) Women Entrepreneurship, e) Dalit Community Business Development, f) Higher and technical or vocational education with loan amount varying from NPR 0.5 to 5.0 million.

c. Special loan offered through refinancing by the Nepal Rastra Bank. The designated financial intermediaries were authorized to provide NPR 1.5 million for housing reconstruction outside Kathmandu valley and NPR 2.5 million inside Kathmandu valley in four installments (minimum) with interest at 2% repayable in five to a maximum of 10 years. The pre-existing house loan customers could extend the tenure to 15 years provided the house hypothecated to the financial intermediaries was damaged by the quake.

By June 2017 (two years following the earthquake) more than 80 per cent of households eligible for financial support to rebuild their houses had received the first tranche of the grant, but only 13 per cent had started reconstruction with financial and technical support from the government and/or NGOs (HRRP, 2018). As of 2018 the state of housing reconstruction was reported (Asia Foundation, 2019) as follows:

- Three-quarters (75%) of households which reported housing damages now live in a rebuilt or repaired house, or another house not damaged by the earthquake.

- One fifth (21%) of households with housing damages have not yet started to repair or rebuild their damaged house – mostly those with partial housing damages and in urban areas.

- In urban areas, fewer have rebuilt or repaired their houses and more continue to live in partially damaged houses.

As of mid-July 2020, the overall situation stood at 8,11,758 beneficiaries signing the grant agreement and 753,052 (92.76%) beneficiaries taking up the reconstruction of their damaged houses with 5,93,293 houses reconstructed and 1,59,759 houses under construction. However, the loan financing schemes were lowly utilized as only 221 beneficiaries received the concessional loan and 1596 beneficiaries received the special loan. Earlier, Asia Foundation's report (2019) had revealed that the households mostly borrowed from cooperatives (25%), savings groups (19%), and relatives and neighbors (18% each) while those borrowing from the banks, money lenders or other sources were a few. The state of the situation, therefore, put a big question for the housing finance market. As such NRA felt the need for looking into the dynamics for the concessional and special financing schemes to stand out as attractive and viable option for housing reconstruction and commissioned a study in this regard on July 2021.
## 2. Review

A livelihood is a composite of the assets, capabilities and the activities that contribute to a living which would be sustainable provided it can cope the stresses and shocks, improvise the composites (Conway & Chambers, 1992) When a disaster occurs the destruction of the assets will put a pain on the livelihood as it stresses one's capability to recover. Naturally, the question emerges when and how one could reconstruct and recover the livelihood. Bilau et al. (2015) looked at the 'development of a general framework for the effective organisation and management of post-disaster housing reconstruction'. In a 2017 study they stated prioritizing reconstruction of permanent housing as it would lead to multiplying effects on 'social and economic recovery and the development of community resilience'. As numerous challenges arise during implementation, they suggested measures needed for better managing the reconstruction issues including financial management. Oliver-Smith (1990) discussed the social aspects for the success of both emergency shelters and permanent housing particularly in settlements permanently relocated or entirely rebuilt. Askar et al. (2020) propose an incremental housing strategy to take on the 'time-efficient housing construction approaches bridging over the two phases of temporary and permanent housing.

Karunasena & Rameezdeen (2010) point out that the beneficiaries would be more satisfied if the reconstruction follows the owner-driven approach. Jha et al. (2010) see this approach incorporating 'conditional financial assistance accompanied by regulations and technical support aimed at ensuring that houses are built back better'. Alternatively, donor-driven approach has been implemented in several disaster affected areas which faced such constraints as imposition of the buffer zone, non-availability of suitable land and supply limitations of the construction industry. Still the other approach is contractor-driven approach which have been found resulting in large numbers of homeowners unsatisfied with the quality of the materials used and the quality of construction. Anilkumar & Banerji (2020) examined the reconstruction of 2004 Indian Ocean Tsunami triggered destruction to housing and related infrastructures. They found that 'post-disaster reconstruction project success is attributed to critical dimensions of project management such as institutional mechanisms, reconstruction strategies, project implementation, and stakeholder management.

Fengler et al. (2008) suggested for managing post-disaster reconstruction finance in three key areas drawing lessons from Aceh, Sri Lanka, East Timor and Afghanistan: (i) establishment of special institutions to manage the reconstruction process; (ii) selection of public financial management systems with respect to the application of country systems, special fiduciary arrangements, or donor/NGO execution; and (iii) monitoring and evaluation systems. They went on to say that the main challenge in managing reconstruction finance consists of integrating 'the specific reconstruction needs and conditions (speed, flexibility, multiple actors) into regular country systems in order to meet the highest fiduciary standards'. They further stated that 'all funding does not necessarily need to be channeled through country systems if the regular budget cycle does not allow for a speedy and flexible implementation'.

Chang et al. (2011) studied resourcing bottlenecks of government, donor, and market driven reconstruction strategies. They found that the first (Chinese reconstruction following: the 2008 Wenchuan earthquake) was unable to meet the long-term reconstruction needs, the second compromised donors' efforts in post-tsunami (2004) resource procurement in Indonesia, and the third faced limitations (Australian reconstruction following 2009 bushfire) for want of facilitated solutions from the government and institutions. The 2016 blog of International City Managers' Association (ICMA, 2016) called for the local government to prioritize need, involve the public, build partnerships, secure financing, and support families for post disaster housing reconstruction and household recovery.

The study carried by Nadler (2005) on the most common models of housing finance in the US, Germany, and Denmark shows that the borrower's savings ability out of disposable income and
savings willingness thereof must be high enough to cover the repayments of the housing loan over and above the capacity to provide for the portion of financing not shared by the bank finance. Pati (2015) study shows that the housing finance system was relatively better in China compared to India in benefitting the urban poor for effectively implementing “cheap rented housing finance policy”. Daly et al. (2017) conducted ethnographic fieldwork in five urban settlements over a period of 18 months. The finding indicates delay in urban reconstruction for 'lack of a clear and well-supported policy, limited governance capacity and neglect of municipal and ward-level officials, financial restrictions caused by the funding cap per family, and the absence of a framework to support local community-driven rebuilding initiatives'.

Regarding the state of the situation in Nepal Poudel et al. (2020) state of Nepal Rastra Bank providing fresh directives to the participating banks and financial institutions (BFIs) in 2018 to ease concessional loans upon receiving multiple complaints from the earthquake victims over the BFIs reluctance to process the loan applications. The alternative was such that the households had to borrow from an informal lender: neighbors, family members, or local business that have been charging up to 43% (The Guardian, 2018) and Housing Recovery and Reconstruction Platform Nepal (HRRP) estimates, the average homeowner is paying 23% interest on loans.

Sharma et al. (2021) study found that different target groups perceived information differently. Though the government officials either agreed (78%) or strongly agreed (22%) on receiving timely information, local representatives were found to disagree (22%) on having received information on time while another 30% remained neutral. The study found a significant correlation between the reconstruction rate (time to completion) and the communication and information shared and received at the community level.

Shrestha et al. (2021) upon finding that the problem of land and access to finance are structural problems called on the Government of Nepal (GoN) to integrate reconstruction activities with a long-term urban renewal strategy. They further stated that Nepal could draw lessons from international best practices such as reconstruction of the 2010 Canterbury and 2011 Christchurch Earthquakes and 1995 Great Hanshin Awaji and 2011 Tohoku Earthquakes and 2001 Bhuj Earthquake.

### 3. Methodology

The key questions relating to low take of concessional and special loan schemes primarily surround on (a) how the beneficiaries were outreached and whether it enabled them to approach the bank and get access to bank finance under the government floated financing schemes, (b) how the participating financial intermediaries outreached the beneficiaries and managed the financing process, and (c) how the government handled the issues cropping on these very specifics. The study frame was articulated as Figure 1. The earthquake affected households (the beneficiaries of government grants and loan for housing reconstruction) and the banks (participating in the government funding scheme to help the beneficiaries) constituted the primary data base, interactions with the local municipality (for information) and the Nepal Rastra Bank (for providing funds to the participating banks) were equally vital. Accordingly, the study was planned as follows:

a) Study area: 14 urban municipalities from each of the 14 most affected (severely hit and crisis-hit) districts,

b) Institutional interaction with Nepal Rastra Bank, selected municipalities, and local NRA team,

c) Primary survey of beneficiaries in the selected municipalities.
<figure><img src="image_2.png" /><figcaption>Figure 1: Study framework</figcaption></figure>

As to the primary survey of the beneficiaries a multistage random sampling technique was applied. In the first stage, 14 urban municipalities located at the district center of the 14 most affected districts were purposively selected (Table 3). As one (Rasuwa) district did not have urban municipality, a replacement was applied in respect of heritage Shankarapur municipality from Kathmandu district.

<table><caption>Table 3: Name of sample survey affected district</caption><thead><tr><td>S.N.</td><td>District</td><td>Municipality/Rural municipality</td><td>No. of beneficiaries</td></tr></thead><tbody><tr><td>1</td><td>Bhaktapur</td><td>Bhaktapur municipality</td><td>8,149</td></tr><tr><td>2</td><td>Dhading</td><td>Nilkantha municipality</td><td>14,078</td></tr><tr><td>3</td><td>Dolakha</td><td>Bhimeshwor municipality</td><td>12,540</td></tr><tr><td>4</td><td>Gorkha</td><td>Gorkha municipality</td><td>10,823</td></tr><tr><td>5</td><td>Kathmandu</td><td>Kathmandu metropolitan city</td><td>7,671</td></tr><tr><td>6</td><td>Kathmandu</td><td>Shankarapur municipality</td><td>6,356</td></tr><tr><td>7</td><td>Kavrepalanchowk</td><td>Dhulikhel municipality</td><td>6,870</td></tr><tr><td>8</td><td>Lalitpur</td><td>Lalitpur metropolitan city</td><td>7,871</td></tr><tr><td>9</td><td>Makwanpur</td><td>Hetauda sub-metropolitan city</td><td>8,479</td></tr><tr><td>10</td><td>Nuwakot</td><td>Bidur municipality</td><td>13,514</td></tr><tr><td>11</td><td>Okhaldhunga</td><td>Siddhicharan municipality</td><td>3,737</td></tr><tr><td>12</td><td>Ramechhap</td><td>Manthali municipality</td><td>12,824</td></tr><tr><td>13</td><td>Sindhuli</td><td>Kamalamai municipality</td><td>10,843</td></tr><tr><td>14</td><td>Sindhupalchowk</td><td>Chautara-sagachowkgadi municipality</td><td>14,475</td></tr><tr><td></td><td></td><td></td><td>138,230</td></tr></tbody></table>

The total size of beneficiaries in the selected 14 municipalities stood at 138,230. In consideration of 95% level of confidence and a 5% margin of error, the sample size was determined as 1,059 using the formula:

$$n = N * \left[ \frac{Z^2 * p * (1-p)}{e^2} \right] / \left[ N - 1 + \frac{Z^2 * p * (1-p)}{e^2} \right]$$

where, N = Population size, Z = Critical value of the normal distribution at the required confidence level, p = Sample proportion, e = Margin of error
In the second stage, two wards from each municipality were selected, one with the highest number of beneficiaries and the other with the greater number of mixed communities. The required number of respondents in each of them were selected and surveyed by applying a systematic sampling technique using a questionnaire on a mobile platform. The questionnaire created in Excel format was directly uploaded/deployed on the server provided by KoBo (KoBo.com).

The responses to the questionnaire from 1057 beneficiaries (out of 1059) generated from the survey was analysed taking on the beneficiaries' access to finance as an outcome variable and input variables were evaluated as supporting or hindering factors to the outcome variable. A modeling approach was used to assess the relationship between households' access to loans and their determinants. While households' access to loans constituted an outcome variable the contributing variables included the age, sex, education and occupation of household head, monthly household income, household size, household asset (farm size), distance to the financial institutions and past loan of the household. The relationship between the variables was assessed by applying the logit or probit model in terms of whether the error term has a standard logistic or normal distribution. The residuals were not found to be normally distributed (p-value for Shapiro-Wilk W test is 0.00000>) using the Kernel density estimate. Therefore, logit model was used to analyze the factors influencing households' access to loans using the maximum likelihood method, as per the likelihood function given by (Alberini, 1995).

Logit model was used as follows to analyze the relationship between variables. It was estimated by using statistical package STATA 13.

$$Y = \alpha + \beta X + u$$

where, Y = Respondent who decides to borrow (Y = 1 for the respondent who decided to borrow, 0 for the respondent who decided not to borrow), X = Vector of explanatory variables, which includes the age of household head, sex of household head, collateral requirement, education of household head, household income, household size, information on sources of credit and distance to be covered to take the loan. Here, α = Constant, β = Vector of coefficients of explanatory variable and u = Error term.

The key drawings from the beneficiaries' survey were checked for the degree of support to it from the inputs derived off the institutional survey conducted through interviewing the officials from local NRA offices, municipalities, and financial intermediaries in these municipalities using a checklist of questionnaires specific to respective institution. Further, it was checked with the qualitative inputs drawn from focus group discussions (FGDs) conducted with the key stakeholders in each of these municipalities.

## 4. Findings

PDNA (2015) set a target of completing housing reconstruction in five years by 2020. It did not happen, and the assessment of the situation revealed completion of private housing in four municipalities (Bidur, Chautara, Manthali, and Nilkantha) while in 10 out of 14 surveyed municipalities the reconstruction remained to be completed as the percentage of completed reconstruction stood as follows: Bhaktapur (21%), Bhimeswor (86%), Dhulikhel (39%), Gorkha (73%), Hetauda (46%), Kamalamai (78%), Kathmandu (33%), Lalitpur (27%), Shankarapur (37%), and Siddhicharan (20%).

The 1057 respondents with 51% of them making their livelihood in agriculture reported a household size of 5.38 and an average monthly income of US$ 313 whose houses with an average area of 1239.2 sq. ft. was destroyed by Gorkha Earthquake (82 partially damaged and 975 completely damaged). 142 of them (13.4%) have not initiated reconstruction while 491 undertook
reconstruction in damaged location (46.5%) and 424 in new location (40.1%) of which reconstruction of 867 housing units were completed. The financing of the reconstruction was made in a combo of various sources: Grant (US$ 2285): 944 households (89.31%) ; Own sources: 596 households (56.39%) and Loan: 817 households (77.29%)

<table><caption>Table 4: Sources of loan</caption><thead><tr><th>SN</th><th>Financiers</th><th>Households</th><th>% of 1057 households</th><th>US$</th><th>% of outsource finance</th></tr></thead><tbody><tr><td>1</td><td>Cooperatives</td><td>295</td><td>27.9</td><td>927086.75</td><td>26.0</td></tr><tr><td>2</td><td>Savings groups</td><td>111</td><td>10.5</td><td>226248.78</td><td>6.4</td></tr><tr><td>3</td><td>Commercial Banks</td><td>84</td><td>7.95</td><td>1165524.01</td><td>32.7</td></tr><tr><td>4</td><td>Employees Provident Fund</td><td>16</td><td>1.51</td><td>96738.24</td><td>2.7</td></tr><tr><td>5</td><td>Citizen Investment Trust</td><td>4</td><td>0.38</td><td>21328.12</td><td>0.6</td></tr><tr><td>6</td><td>Friends and relatives</td><td>266</td><td>25.16</td><td>1115155.74</td><td>31.3</td></tr><tr><td>7</td><td>Money lenders</td><td>41</td><td>3.88</td><td>11425.78</td><td>0.3</td></tr><tr><td></td><td>Total</td><td></td><td></td><td>3563318.68</td><td>100</td></tr></tbody></table>

As the grant takers stand at 944 households out of 1057 respondents it implies that 113 households as yet have not taken the grant. And some of those taking the grant are behind as the reconstruction (completed and partially completed) so far totals 915 households. As to the own source funds put to reconstruction by 596 households, 573 used past savings and 23 sold their assets. With respect to loan, the households secured it from different sources are shown in Table 4. The total cost incurred for reconstructing 915 houses so far stands at US$ 8847359.22 with loan contributing to 40.3 % and rest coming from own sources 36.1% and grant 23.6%.

The commercial banks were designated financial intermediaries to dole out concessional loan (NPR US$ 2285) and special loan (US$ 11425.78 outside Kathmandu valley and US$ 19042.96 inside Kathmandu valley). However, of 84 households taking loan from these banks there were only 23 takers of concessional loan and 4 takers of special loan which is indicative of the fact that the banks promoted their own regular schemes instead of the financial packages of the government that carried no interest on concessional loan and low interest on special loan to the households affected by the earthquake. The question naturally arises why there has been very limited application of government offered financial packages. This requires examining of both sides (demand and supply) perspectives.

## Demand side

On the households front those not initiating the construction indicated the following reasons as given in Table 5:

<table><caption>Table 5: Response of Householder not initiating the construction</caption><thead><tr><th>Not initiating Reason</th><th>Households</th><th>Loan criteria</th></tr></thead><tbody><tr><td>Not getting grant</td><td>95</td><td>as it is available for only those having land title</td></tr><tr><td>Lack of additional fund</td><td>90</td><td>could not secure enough to meet total cost</td></tr><tr><td>Land not useable</td><td>45</td><td>alternative land not availed/could not afford</td></tr><tr><td>Has house elsewhere</td><td>17</td><td>package not available to one having 2<sup>nd</sup> house</td></tr></tbody></table>

It was further known from the survey that 317 households (30%) were not interested to take loan and 353 households (33.8%) were expecting grants from other sources. And those willing to take loan had varying reasons to offer as given in Table 6.
<table><caption>Table 6: Reason for taking loan</caption><thead><tr><td>Reason</td><td>Households</td><td>Percentage</td></tr></thead><tbody><tr><td>Family members not supportive</td><td>139</td><td>13.1</td></tr><tr><td>Faced with meeting daily needs</td><td>150</td><td>14.2</td></tr><tr><td>Inability to repay loan</td><td>230</td><td>21.8</td></tr><tr><td>Lack of collateral</td><td>145</td><td>13.7</td></tr></tbody></table>

In total 138 (13.9%) households did apply for the loan and only 90 (8.51%) of those making application did take loan, while 919 (86.1%) households did not apply for loan from the government designated financial intermediaries. On further questioning the households made the following responses as given in Table 7.

<table><caption>Table 7: Reason for not taking loan</caption><thead><tr><td>Reason</td><td>Households</td><td>Percentage</td></tr></thead><tbody><tr><td>No plan to apply</td><td>304</td><td>28.8</td></tr><tr><td>Lack of information</td><td>650</td><td>61.5</td></tr><tr><td>Lack of experts' opinion</td><td>256</td><td>24.2</td></tr><tr><td>Necessity of frequent visits</td><td>393</td><td>37.2</td></tr><tr><td>Could not agree to terms</td><td>105</td><td>9.9</td></tr><tr><td>Difficulty in accessing banks</td><td>147</td><td>13.9</td></tr><tr><td>Difficulty in completing documents</td><td>193</td><td>18.3</td></tr><tr><td>Unable to prepare documents</td><td>147</td><td>13.9</td></tr></tbody></table>

These responses could be categorized broadly into factors like (a) communication failures (lack of information and lack of experts' opinion) and (b) process bottlenecks (necessity of frequent visits, could not agree to terms, difficulties in accessing banks by virtue of their off-locations, and not so easy documentation needs). Besides, there is one more additional factor of beneficiaries' non-eligibility which relates to profile complexities in the eyes of bankers that could be any or all of the following: gender discrimination (males are more demanding in dealing with the banks while females are not leading to gender discrimination in complying to loan requests), household size, income level and existing indebtedness (which are part of the composite of household economy that affect the creditworthiness. Further, the disaster vulnerability of the location of reconstruction was also put to examination. The mean and variance inflation factor (VIF) shown in Table 8 indicate that there is no multi-collinearity problem among explanatory variables for the VIF values are less than 10.

<table><caption>Table 8: Statistical analysis of variables</caption><thead><tr><td>Variable</td><td>Mean</td><td>VIF</td><td>Coefficient</td><td>Standard Error</td><td>Marginal Effect</td></tr></thead><tbody><tr><td>Gender of household head</td><td></td><td></td><td>1.54</td><td>0.71</td><td></td></tr><tr><td>Monthly household income</td><td>US$ 331</td><td>1.03</td><td>0.38</td><td>0.44</td><td>0.09</td></tr><tr><td>Household size</td><td>5</td><td>1.04</td><td>-0.18*</td><td>0.097</td><td>-0.04</td></tr><tr><td>Process bottlenecks</td><td>0.32</td><td>1.04</td><td>-0.77*</td><td>0.39</td><td>-0.18</td></tr><tr><td>Disaster vulnerability</td><td>0.07</td><td>1.02</td><td>-1.47**</td><td>0.76</td><td>-0.35</td></tr><tr><td>Existing indebtedness</td><td>US$ 640</td><td>1.02</td><td>-1.20*</td><td>7.29</td><td>-2.65</td></tr><tr><td></td><td></td><td></td><td></td><td>Mean VIF</td><td>1.03</td></tr></tbody></table>

Source: Field Survey, 2021

<footnote>**p<0.05, *p<0.10</footnote>
Apart from lack of information restricting the beneficiaries to seek bank financing, the above analyses (Table 8) indicate that upon making a move to secure bank finance the beneficiaries faced process bottlenecks (administrative awkwardness in the table) and though sex and income level did not support the beneficiaries' non-eligibility to access bank financing, but the household size and the existing indebtedness on the one hand and reconstruction plan in location prone to disaster on the other do support the non-eligibility. As such not so fully informed about the specific financial packages, complexities in loan process and non-eligibility raised by the large size of household, existing state of indebtedness and locational susceptibility of disaster have all put a limitation on accessing bank finance in general and the use of specific financial package. As the NRA assessment clearly states (a) an increase in household size by one person reduces the probability of access to a loan by 4.02%, (b) an increase in outstanding loans by Re 1 has the possibility of reducing access to loans by 0.24%, and (c) the households that were likely to suffer from the disaster had the possibility of 35.33% less access to loans than those who were not likely to suffer. On the process bottlenecks the NRA assessment did reveal that the 'households who experienced administrative difficulties had the possibility of 17.66% less access to loans compared to those who did not experience such difficulties.

## Supply side

On the bankers front they were not overwhelmed by the government floated financial package despite agreeing to be a participating institution to provide loans to the beneficiaries under these schemes.

The participating banks were not adequately sensitized by the NRA to make an aggressive outreach at place of disasters. The banks did not go to the beneficiaries and the beneficiaries failed to know that the banks were making the government packaged funds available to them and with short process of loan approval. Apart from the banks the information on reconstruction financing to the beneficiaries at the place of disaster affected areas NRA could have setup a mechanism of communication through its project offices or through the ward offices of the respective municipalities as the ward offices constitute the beneficiaries 'first point of contact' to get assisted even as a source of information. There was not even a display board providing such information at the ward office about the financing package and the mechanism of accessing them from the banks. Ward offices were just found equally ignorant on this subject.

Though the reconstruction framework followed the beneficiaries driven approach to build back better conditionality the package did not address the issues specific to devoid of land title, existing state of households' indebtedness and loan process simplification for reconstruction financing which made the banks follow their regular procedure of processing and approving loan application (which is rather a complex as well as too tight in the eyes of the beneficiaries). It is difficult for the households even to provide a proof of income for agriculture being the main source as transactions are unbilled and untaxed. The regular eligible short listing by the banks for loan approval puts an off limit to the beneficiaries who do not have a minimum 8 feet wide road access, who have a multiple ownership to the land or having the quake damaged house in a socially owned property without a land title. Nepal's situation compares well with what Kuma (2015) found in Nigeria where the house owners had to rely on personal savings and borrowings from local cooperatives.

Further, the banks lagged encouragement to promote government sponsored financial packages by the fact that the announcement failed to be adequately provisioned in government budget which made the doled out (concessional and special) loans left to delays in getting reimbursement from the central bank. The central bank (Nepal Rastra Bank) even changed the special loan financing over one year from 2% interest plus refinance to 2% interest plus the cost of funds subsidy. Besides, the package condition further added to loan processing difficulties that if a beneficiary has a house
in other places in Nepal in his/her name and in the name of other family members, as the verification
of which is obviously difficult. The plan was a non-starter for the banks as it failed to be all
beneficiaries inclusive for all these plus it did not include the loan repayment guarantee by way of
government pledge or through insurance modality.

**5. Discussion**

At the outset the financing packages were not meant to be a full funding package and was thrusted
as NRA agenda for propelling reconstruction. The financing packages were limited to being a one
of the sources to meet the cost of reconstruction in compliance to government set national building
code. The financial grant of US$ 2285 barely meets the reconstruction of house with 2 small rooms.
The concessional loan of US$ 2285 could help the poor household to opt for a small house lesser
than the size of quake destroyed house. But failure to disseminate the availability of the concessional
loan at the outset and failure to ease out the banking process with reasonable modality for the banks
to execute the package did not lead to make the financing scheme as a major recourse to housing
reconstruction even for the middle income level households though the provision of special loan of
US$ 11425/ 19042 subject to locations in and outside the Kathmandu valley could cover the major
if not the total financing needs. For the households in higher income category this provision was
just too little.

The packages were announced belatedly as two years had passed following the earthquake and by
then majority of the affected households had taken recourse to reconstruction whatever the way it
was possible. When it came it invited the banks to be the frontal face in providing loans under the
schemes on the security of land and house on reconstruction without considering the complexities
of processing and approving loans that were being followed in them as repayment was always the
major concern for them. Yet still the financing packages could have assumed a greater stake in
funding reconstruction, but it just got limited to a small percentage of the amount of loans taken up
by the beneficiaries to meet the cost of reconstruction. It could not be for lack of appropriately
communicating with the beneficiaries, for not being pitched outside a regular loan screening
conditionality, and for not owning and providing backup funds by the government. All these
requirements were not in place only speaks of government incompetence that it was not a
comprehensive plan of action as against the reconstruction imperatives.

The inability to ensure the funds (including loan) for meeting the reconstruction costs as well as to
secure the offsite (in case the existing location is non-usable or susceptible to further vulnerability)
location was simply not a matter of delay, the delay in construction was already affecting the
housing reconstruction for the rise in costs that necessitates additional resources over and above to
the earlier years following the earthquake. Further it was compounding the problems of unequal
housing recovery among the earthquake affected families. The finding of this study does corroborate
with the findings of the following studies:

a. As reported by Rawal et al. (2021) the market survey conducted over the last 5 years indicated a significant increase in construction cost: daily wages of unskilled labor had increased by two-thirds i.e., from $6 per day in 2015 to $10 per day in 2019 and the cost of building materials such as bricks, cement, steel, had gone up by 20 to 30%. Cost overrun in any ways would put pressure on the resource. As stated by Chang et al. (2010) 'inefficiencies in dealing with resource shortages in the aftermath of a catastrophe can trigger economic and environmental impacts on the affected areas, the effective resource management for post-disaster reconstruction lies in the appropriateness of the responses and improvements to address resourcing challenges. The success of resourcing depends on multi-stakeholder collaboration and the development of polices, plans, and tools to allow market flexibility, donor management, and government intervention'.
b. Given that the disasters have led to 'unequal housing recovery for different economic groups, in large part, because of the difficulty of obtaining funding for low-income groups' and yet the 'current recovery models simplify the financing process for homeowners to rebuild after earthquakes' but, 'cannot fully capture disparities in the recovery outcomes of economic groups'; Alisjahbana, et al. (2022) developed an agent-based financing model for post-earthquake housing recovery. The study conducted 'in the economically diverse city of San Jose, California, following a hypothetical 7.0 Mw earthquake' found inequalities and through evaluation of several strategies to reduce these disparities they derived 'that a combination of income-targeted funding and redistribution of construction crews can reduce inequalities in regional housing recovery'.

c. Johnson & Lizarralde (2012) have noted that Asian Coalition for Housing Rights after the South Asian Tsunami encouraged groups of people to form communities, to access financing for housing and land as a group. In Turkey, after the 1999 earthquakes, only families who were homeowners before the disaster were included in the government's permanent reconstruction programmes and thus tenants or squatters were not included, in Japan and in the United States after recent disasters, homeowners' insurance frequently played a large role in paying for the costs of reconstruction. However, the lack of financing or patchy financing may leave the poorest sectors of the society still without adequate post-disaster housing assistance.

d. As grant assistances do not meet the full cost of reconstruction households would have to take loan over and above the grant and their own resources. A house is affordable if it costs no more than 30 percent of the income of the household occupant (Andrews, 1998), the cost over and above that by any reason would excessively increase borrower's debt levels and result in an increase in problematic loans. From the lenders' point of view, issuing a loan depends primarily on the borrower's ability to pay back their debts, which is highly influenced by the borrower's financial fragility. A greater household debt burden would mean higher probability of credit default. Assessing the determinants of credit risk from a broad perspective, Canepa & Khaled (2018) and found that housing affordability and household fragility significantly affect the risk of banks' loan portfolios.

## 6. Conclusion

Obviously the intention of the government announced financing packages was in consideration of getting reconstruction completed within the five years schedule drawn for the purpose. However, many critical steps were missed that included a) informing the public at the spot, b) supporting the financiers to outreach the affected households and provide a backup to create a specific reconstruction window, and c) inducting insurance mechanism for repayment. This study does highlight the weaknesses of the scheme given its failing to be a pace setter. In essence, the usability of the scheme's rests on timely availability and appropriateness of the package that ease financing to both the households (including low-income groups) and the financiers concern for repayment. The schemes in this regard should best incorporate provisions such as suggested (Mahat, et al., 2021).

* Access of loans to landholders on a land valuation basis where road access is not reached.

* Providing subsidized loans to repay the high-interest loan taken from micro-finance and cooperatives.

* Provision of third-party land as guaranty for a subsidy house loan from the bank.

* The long period of loan repayment time and economic status improvement project for poor households.
* Mobilization of "D" class financial institutions (microfinance companies) for facilitating the subsidized loan to vulnerable families on saving groups guaranty as that of guarantee by Welfare Fund for the armies.

* Augmentation of the grant amount for affected vulnerable families who have not built their houses.

* Provision of additional support to maintain the standard prescribed for the heritage site and historical places for reconstruction.

* Increase in subsidy loan amount for city areas.

The key uptake of this study is that the mere intention of the government to support reconstruction is not enough. It requires a policy setting and integrated programmatic action to be followed on wholesome basis. The key composites of these should include a) opening of the information desk at the damaged site with schemes of reconstruction support available (ward offices of local government in particular) and how to access that, b) partnering with the local financial cooperative and the participating banks with respect to funding modality (the sharing framework of them and that of government support package) and repayment aligning with insurance mechanism, and c) reconstruction technical framework with specific infrastructure development order and in addition the options for alternative sites in case the exiting location is not worth undergoing that and in case the house owner is not still bestowed with land title.

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