rafmacalaba/datause-encoder-data huggingface dataset:
Cameron, C., J. Gelbach, and D. Miller. 2008. “Bootstrap-based Improvements for Inference with Clustered Errors.” _Review of Economics and Statistics_ 90(3): 414-27.

Carter, M., R. Laajaj, and D. Yang. 2014. "Subsidies and the Persistence of Technology Adoption: Field Experimental Evidence from Mozambique." _NBER Working Paper_ w20465.

Corral, C., X. Giné, A. Mahajan, and E. Seira. “A Multiple Intervention Approach to Improve Agricultural Yields.” In Preparation.

Dulfo, E., M. Kremer, and J. Robinson. 2008. “How high are rates of return to fertilizer? Evidence from field experiments in Kenya.” _American Economic Review: Papers and Proceedings_ 98 (2): 482-88.

Duflo, E., M. Kremer, and J. Robinson. 2011. “Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya.” _American Economic Review_ 101(6): 2350-90.

Feder, G., R. E. Just, and D. Zilberman. 1985. "Adoption of Agricultural Innovations in Developing Countries: a survey." _Economic Development and Cultural Change_ 33(2) 255-298.

Goyal, A., and J. Nash. 2017. _Reaping Richer Returns: Public Spending Priorities for African Agriculture Productivity Growth._ Africa Development Forum series. Washington, D.C.: World Bank.

Harou, A., N. Amuri, M. Madajewicz, C. Magomba, H. Michelson, K. Tschirhart, J. M. Semoka, and C. Palm. “When does information make a difference? A field experiment with farm-specific soil information in Tanzania..” In preparation.

Holden, S., and R. Lunduka. 2012. “Do fertilizer subsidies crowd out organic manures? The case of Malawi.” _Agricultural Economics_ 43 (3): 303-14.

Jack, B. K. 2013. "Private Information and the Allocation of Land Use Subsidies in Malawi." _American Economic Journal: Applied Economics_ 5(3): 113-35.

Jayne, T.S., D. Mather, N. Mason, and J. Ricker-Gilbert. 2013. “How do fertilizer subsidy programs affect total fertilizer use in sub-Saharan Africa? Crowding out, diversion and benefit/cost assessments,” _Agricultural Economics_ 44: 687-703.

Jayne, T.S., N. M. Mason, W. J. Burke, and J. Ariga. 2016. “Agricultural input subsidy programs in Africa: An assessment of recent evidence.” Michigan State University International Development Working Paper 145.

Jayne, T.S., and S. Rashid. 2013. “Input subsidy programs in sub-Saharan Africa: a synthesis of recent evidence,” _Agricultural Economics_ 44: 547-562.

Karlan, D., R. Osei, I. Osei-Akoto, and C. Udry. 2014. “Agricultural Decisions after Relaxing Credit and Risk Constraints.” _Quarterly Journal of Economics_ 129(2): 597-652.

30
Table A2 in the appendix describes the definitions and sources for the remaining data series. We use G7 GDP growth to measure global growth; using U.S. GDP instead made little difference for the results. Global inflation is measured by the log-difference of the U.S. GDP deflator, while the global short-term interest rate is given by the 3-month U.S. Treasury Bill rate. Finally, real commodity prices are constructed deflating the UNCTAD index for all commodities with the U.S. GDP deflator.

In accordance with the discussion in the preceding section, we use as conditioning variables **z** in (1’) a measure of trade openness (log imports plus exports as a fraction of GDP), the Chinn-Ito index of capital account openness, and an index of commodity intensity, to capture countries’ degree of productive specialization in commodities. Following Leamer (1984, 1995), we measure commodity intensity by net exports of commodities per worker.<sup>12</sup> The model consisting of (1) (or (1’)) and (2) is block-recursive. Hence we estimate the GDP growth equation (1) and the global block (2) separately. In each case, we use Akaike, Hannan-Quinn, and Schwarz information criteria to determine the optimal number of lags. Although the three criteria are asymptotically equivalent, in small samples the Akaike criterion tends to select the highest number of lags, and the Schwartz criterion the lowest. Noting that the number of lags selected by the three criteria is not too different, we chose to use the Hannan-Quinn criterion as a compromise between the least and most conservative criteria. Specifically, for the domestic growth equation the Schwartz criterion selects 1 lag of the endogenous variable and only the contemporaneous global variables, while the other two criteria select 1 lag for the endogenous

> 12 We compute aggregate net exports as the sum of net exports of Leamer’s six commodity clusters: (i) petroleum, (ii) raw materials, (iii) forestry, (iv) live animals, (v) tropical agriculture, and (vi) cereals. Table A3 lists the SITC codes for each cluster.

12
# **Acknowledgments**

## <u>ddewalque@worldbank.org;</u> <u><mark>achukwuma@worldbank.org; nonoayivi@worldbank.org; mkoshkakaryan@worldbank.org</mark></u>

The evaluation was funded through the Health Results Innovation Trust Fund (HRITF) and the Strategic Impact Evaluation Fund (SIEF). The protocol was approved on January 11, 2019 by the Institutional Review Board of the Center of Medical Genetics and Primary Health Care in Armenia (02570094) and registered under the American Economic Association’s registry for randomized controlled trials on January 18, 2019 under <mark>AEARCTR-0003776.</mark> We acknowledge the helpful comments and suggestions received from Sylvie Bossoutrot, Volkan Cetinkaya, Tania Dmytraczenko, Gabriel Francis, Alaka Holla, and Gil Shapira. The team also greatly appreciates the engagement of the Ministry of Health, Health Project Implementation Unit, and participating primary care facilities for their invaluable contributions to the design and implementation of this study. Finally, we acknowledge the excellent fieldwork undertaken by Media Model, Armenia. All errors and omissions are the authors. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

2
**Table 3: First difference estimates of labor force participation in various activities**

||**Participation**<br>**in agr.**|**Participation**<br>**in non-agr.**|**Participation**<br>**in wage labor**|**Participation**<br>**in family**<br>**business**|
|---|---|---|---|---|
|Migrant household status (lagged)|0.066***|-0.004|-0.005|0.001|
||(0.021)|(0.029)|(0.011)|(0.028)|
|Household member has bad health<br>(lagged)|-0.044*|0.080**|0.008|0.064*|
||(0.026)|(0.034)|(0.015)|(0.033)|
|Age 16-20 in initial year|-0.016|-0.009|-0.001|-0.003|
||(0.083)|(0.083)|(0.042)|(0.079)|
|Age 21-25 in initial year|0.082|0.003|-0.026|0.033|
||(0.074)|(0.060)|(0.035)|(0.056)|
|Age 26-30 in initial year|0.176***|-0.008|-0.014|0.006|
||(0.058)|(0.050)|(0.024)|(0.050)|
|Age 31-35 in initial year|0.215***|-0.000|0.002|0.007|
||(0.054)|(0.051)|(0.027)|(0.048)|
|Age 36-40 in initial year|<br>0.235***|<br>0.045|<br>0.030|<br>0.017|
||(0.059)|(0.048)|(0.021)|(0.047)|
|Age 41-45 in initial year|0.222***|0.045|0.018|0.047|
||(0.054)|(0.046)|(0.017)|(0.045)|
|Age 46-50 in initial year|0.206***|0.023|-0.002|0.038|
||(0.050)|(0.049)|(0.016)|(0.048)|
|Age 51-55 in initial year|0.134**|-0.008|0.002|-0.007|
||(0.056)|(0.046)|(0.016)|(0.048)|
|Age 56-60 in initial year|0.060|0.098*|0.024|0.092*|
||(0.049)|(0.052)|(0.019)|(0.054)|
|Years of schooling in initial year|0.002|0.003|0.003**|0.000|
||(0.003)|(0.003)|(0.001)|(0.003)|
|Years of schooling squared in initial<br>year|-0.000*|-0.000|-0.000***|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Years of schooling of head in initial<br>year|-0.005*|-0.004|-0.001|-0.003|
||(0.002)|(0.003)|(0.002)|(0.003)|
|Age of household head in initial year|-0.004***|-0.002|-0.001|-0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Minority household|0.094**|-0.032|-0.027|-0.013|
||(0.042)|(0.040)|(0.032)|(0.033)|
|Household size in initial year|<br>-0.007**|<br>-0.004|<br>-0.001|<br>-0.003|
||(0.003)|(0.004)|(0.001)|(0.004)|
|Number of girls 0-6 in initial year|0.013|0.035|0.007|0.033|
||(0.022)|(0.031)|(0.015)|(0.032)|
|Number of boys 0-6 in initial year|0.016|0.039|-0.002|0.040*|
||(0.022)|(0.024)|(0.016)|(0.022)|

33
## **Appendix Figure A.3: Democracy; Anocracy; Autocracy**

# **Competitiveness of Political Participation**

|**Executive**<br>**Recruitment**|Repressed<br>(1)<br>Suppressed<br>(2)|Unregulated<br>(0)|Factional<br>(3)|Transitional<br>(4)|Competitive<br>(5)|
|---|---|---|---|---|---|
|(1)Ascription<br>(2) Ascription +<br>Designation||||||
|(3)Designation||||||
|(4)Self-Selection|_Autocracy_|||||
|(5) Transition from<br>Self-Select.|||Partial Autocr|acy||
|(6) Ascription +<br>Election|Partial Autocrac|**Non-**<br>|**Factional**<br>|**Non-**||
|(7) Transitional or<br>Restricted Elec.|y|**Factional**<br>**Partial**<br>**Democracy**|**Partial**<br>**Democracy**|**Factional**<br>**Partial**||
|(8) Competitive<br>Election||||**Democracy**|Democracy|

Note: Based on Executive Recruitment (EXREC) and Competitiveness of Political Participation (PARCOMP) variables in the Polity IV data set. Table is from Goldstone et al. 2005. Source: Goldstone et al. 2005. Full democracies make 22% of observation in the sample. Factional partial democracies make 11% of observations and non-factional partial democracies about 9 %. Partial autocracies are about 6% of the sample. Autocracies make about 48% of the observations in the sample. Transitional regimes and irregular transfers make a little more than 3% of the sample.

45
# **Table 3 Swiss Formula vs Full Liberalization. Real Income. Central case. Changes from baseline $m.**

||**Sw**|**iss Formula**||**Full**|**Liberalizatio**|**n**|
|---|---|---|---|---|---|---|
||_Trade_<br>_Weighted_<br>_Aggregator_<br>_(a)_|_Optimal_<br>_Aggregator_<br>_(b)_|_Multiplicative_<br>_Factor (b)/(a)_|_Trade_<br>_Weighted_<br>_Aggregator_<br>_(a)_|_Optimal_<br>_Aggregato_<br>_r (b)_|_Multiplicative_<br>_Factor (b)/(a)_|
|**World total**|**0.09**|**0.17**|**1.94**|**0.50**|**0.88**|**1.75**|
|**_Low & middle income ctries_**|**0.11**|**0.27**|**2.33**|**0.58**|**1.30**|**2.25**|
|Bangladesh|0.03|0.11|3.59|-1.44|-1.03|0.72|
|Brazil|0.13|0.30|2.28|1.39|3.18|2.28|
|Chile|0.00|-0.05|n.a.|0.30|0.06|n.a.|
|China|0.01|0.08|13.92|-0.06|0.17|sign rev.|
|Egypt|0.05|0.97|20.17|0.24|5.01|20.77|
|India|0.20|0.31|1.59|0.60|1.24|2.05|
|Indonesia|0.08|0.18|2.25|0.49|0.69|1.40|
|Pakistan|0.10|0.17|1.66|0.34|-0.34|sign rev.|
|Thailand|0.53|0.69|1.28|2.48|3.49|1.40|
|Mexico|0.06|0.10|1.73|-0.02|0.35|sign rev.|
|Nigeria|0.41|0.65|1.58|4.68|6.30|1.35|
|Turkey|0.19|0.26|1.34|1.03|2.21|2.14|
|Rest of Asia|0.49|0.94|1.91|3.40|4.68|1.38|
|Rest of LAC|0.06|0.07|1.26|0.38|0.44|1.16|
|Morocco & Tunisia|0.71|1.02|1.44|2.77|5.82|2.10|
|SACU|0.19|1.92|10.20|0.96|2.25|2.34|
|Rest of Sub Saharan Africa|0.06|0.02|0.42|-0.44|-0.55|1.26|
|Rest of the World|0.09|0.25|2.61|0.45|1.54|3.41|
|**_High income countries_**|**0.08**|**0.15**|**1.79**|**0.48**|**0.76**|**1.59**|
|Australia & New Zealand|0.03|0.04|1.19|0.46|0.59|1.29|
|Rest of Europe|0.10|0.28|2.72|1.53|3.56|2.33|
|EU 27|0.08|0.17|2.20|0.44|0.96|2.17|
|United States|0.01|0.03|2.25|0.10|0.13|1.36|
|Canada|0.04|0.10|2.74|0.05|0.24|4.44|
|Japan|0.13|0.14|1.08|0.94|0.94|1.00|
|Korea and Taiwan, China|0.78|1.32|1.69|2.99|3.95|1.32|
|H.Kong *& Singapore|0.07|0.07|0.93|1.12|1.28|1.15|

_Source: Authors’ computation. LINKAGE Model._ 20
indicator of the concentration of urban population in the largest city, the primacy rate, does not suggest that the socialist regimes particularly favored the major (usually the capital) city. (See Table 1.1) Relative to other low and middle income countries, the primacy rate of countries in the region is not particularly high and the wide range of country values is largely in line with geographic size. Econometric analysis of a global country sample has revealed that urban concentration in general tends to rise then fall with per capita income and to decline with national scale, with increased openness to trade, and with political decentralization (or increased federalism) (Henderson, 2000). Based on this analysis of “optimum” levels of concentration at any given income level, it might be expected that prior to the transition, the ECA countries had a relatively high urban concentration; however, Henderson finds that at the time of transition the ECA countries in his sample were dramatically _less_ concentrated than their “expected” or “optimum” level.<sup>6</sup> Despite the presumable “pull” effect of highly centralized government favoring the capital city, socialist planning allocated industry in such a way that alternative urban areas grew more than a market economy would have permitted. However, secondary cities have suffered greatly during the transition from the decline of the noncompetitive state sector; and possibly lacking a strong natural economic and political base, these cities have been harder hit than the capital city, which can rely on government activities and growth of such competitive service sectors as the economy still sustains.

**Table 1.1. Urbanization rates and urban primacy rates by country of ECA region, 2001**

||**Urban Population**<br>(% of Total)|**Population in the Largest City**<br>(% of Urban Population)|
|---|---|---|
||2001|2001|
|**Region** (unweighted averages)|||
|**Balkans**|52|28|
|Albania|43|22|
|Bosnia and Herzegovina|43|31|
|Bulgaria|67|22|
|Croatia|58|42|
|Macedonia, FYR|59|36|
|Moldova|42|37|
|Romania|55|16|
|Serbia andMontenegro|52|30|
|**Caucasus**<br>|59<br>|51<br>|
|Armenia|67|55|
|Azerbaijan|52|47|
|Georgia|57|..|
|**Central Asia**<br>|40<br>|27|
|Kazakhstan|56|13|
|Kyrgyz Republic|34|43|
|Tajikistan|28|30|
|Turkmenistan|45|23|
|Uzbekistan|37|24|
|**EU Accession**|63|29|
|Czech Republic|75|16|
|Estonia|69|42|
|Hungary|65|28|
|Latvia|60|53|
|Lithuania|69|24|

> 5 _Henderson, Vernon. 2000. “How Urban Concentration Affects Economic Growth.” Policy Research Working Paper 2326. World Bank, Development Research Group, Washington, D.C._

> 6 _In another paper he finds Poland also relatively under-concentrated.  Uwe Deichmann and Vernon Henderson,_

_“Urban and Regional Dynamics in Poland,” Policy Research Working Paper 2457, World Bank Development Research Group, Washington, D.C. 2000._ 3
rates charged on loans at a lag. In all, however, our ex-post calculated spreads for private banks match up well with the headline indicators produced by Central Bank of Kenya, which are based on ex-ante interest rates charged by banks.

**Table 6.  Decomposition of Interest Spreads Over Time**

|**Panel A. All Banks**|2000|2001|2002|2003|2004|2005|2006|2007|
|---|---|---|---|---|---|---|---|---|
|Average Lending Rate|25.03|24.47|25.89|24.64|21.45|22.75|12.22|12.19|
|Average Deposit Rate|5.79|4.62|3.53|2.00|1.36|2.60|2.57|2.41|
|Spread|19.25|19.85|22.36|22.64|20.10|20.16|9.66|9.78|
|Overhead Costs|6.94|6.49|7.53|6.60|5.83|5.99|3.10|3.16|
|Loan-loss Provisions|4.28|2.66|3.43|2.68|2.02|1.68|1.64|0.85|
|Reserve Requirement|0.64|0.51|0.39|0.22|0.15|0.29|0.29|0.27|
|Taxes|2.21|3.05|3.30|3.94|3.63|3.66|1.39|1.65|
|Profit Margin|5.16|7.12|7.71|9.20|8.47|8.54|3.24|3.85|
|**Panel B. Private Banks**|2000|2001|2002|2003|2004|2005|2006|2007|
|Average Lending Rate|20.72|19.63|17.65|15.77|12.92|13.78|13.77|12.98|
|Average Deposit Rate|5.29|4.60|3.36|2.14|1.47|2.78|2.88|2.79|
|Spread|15.44|15.03|14.29|13.64|11.46|11.00|10.89|10.19|
|Overhead Costs|4.86|4.66|4.85|3.75|3.05|3.24|3.15|3.00|
|Loan-loss Provisions|3.01|1.89|1.76|2.77|1.93|1.32|1.08|0.68|
|Reserve Requirement|0.59|0.51|0.37|0.24|0.16|0.31|0.32|0.31|
|Taxes|2.09|2.39|2.19|2.06|1.90|1.84|1.90|1.86|
|Profit Margin|4.89|5.58|5.12|4.82|4.42|4.29|4.44|4.34|

Source: Own calculations, based on CBK data The effects of the government’s program for the development of the banking sector are reflected in both the decline in spreads and their determinants. The decomposition for privatelyowned banks (both domestic and foreign) in Panel B shows a steady decline in the interest rates charged on loans and a substantial reduction in the interest rate paid for deposits. The decline in lending rates is consistent with greater competition, while the drop in deposit rates likely reflects 13
# **TABLE 5: WEIBULL MODELS WITH ALL SCENARIOS**

|**Variable**|<br>**AL**<br>**(Int**|**PANEL 1:**<br>**L PERSON**<br>**erval Base**|**S**<br>**d)**|<br>**A**<br>**(Interval**<br>**Respons**<br>**Non-**|**PANEL 2:**<br>**LL PERSON**<br>**Based for Z**<br>**es & Contin**<br>**Zero Respo**|**S**<br>**ero WTP**<br>**uous for**<br>**nses)**|**ONL**<br>**High S**<br>**(In**|**PANEL 3:**<br>**Y Persons**<br>**chool dip**<br>**Above**<br>**terval Bas**|<br>**with**<br>**loma &**<br> <br>**ed)**|<br>**ONL**<br>**Under**<br>**(In**|**PANEL 4:**<br>**Y Persons**<br>**graduate**<br>**& Above**<br>**terval Bas**|<br>**with**<br>**Degree**<br> <br>**ed)**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Coeff**|<sup>**Standard**</sup><br>**Error**|<br>**P-**<br>**Value**|**Coeff**|**Standard**<br>**Error**|**P-Value**|**Coeff **|<sup>**Standard**</sup><br>**Error**|<br>**P-**<br>**Value**|**Coeff **|<sup>**Standard**</sup><br>**Error**|<br>**P-**<br>**Value**|
|Intercept|0.53|0.51|0.30|0.58|0.49|0.24|-0.35|0.68|0.61|-1.77|0.90|0.05|
|Log of risk reduction|0.55|0.09|<.0001|0.54|0.08|<.0001|0.80|0.08|<.0001|0.89|0.11|<.0001|
|Age|0.03|0.02|0.18|0.03|0.02|0.20|0.05|0.03|0.16|0.13|0.05|0.01|
|Age squared|-0.0002|0.0003|0.46|0.00|0.00|0.53|-0.0004|0.0004|0.33|-0.0016|0.0006|0.01|
|Low income dummy|-0.37|0.16|0.02|-0.37|0.16|0.02|-0.31|0.19|0.11|-0.79|0.27|0.00|
|Middle income dummy|-0.19|0.15|0.22|-0.18|0.15|0.23|-0.05|0.16|0.76|-0.27|0.18|0.14|
|Primary wage earner *<br>household size|-0.04|0.01|0.01|-0.03|0.01|0.02|-0.04|0.02|0.09|-0.03|0.03|0.39|
|High school diploma|-0.51|0.30|0.09|-0.52|0.29|0.07|||||||
|Has had an accident (or knows<br>someone who did)|0.09|0.09|0.33|0.10|0.08|0.25|0.14|0.12|0.24|-0.04|0.15|0.81|
|Whether travels as part of the<br>job|0.32|0.09|0.00|0.32|0.08|0.00|0.20|0.13|0.12|0.04|0.17|0.81|
|Commute time (minutes)|0.01|0.0015|<.0001|0.01|0.00|<.0001|0.01|0.0022|<.0001|0.01|0.0028|<.0001|
|Risk reduction*high school|0.24|0.12|0.04|0.24|0.11|0.04|||||||
|Whether drives a two-wheeler|0.85|0.08|<.0001|0.80|0.08|<.0001|0.80|0.12|<.0001|1.16|0.15|<.0001|
|Scale||2.28|||2.27|||2.35|||2.29||
|Weibull Shape||0.44|||0.44|||0.42|||0.44||
|<br>Log Likelihood||-8296.37|||-5940.56|||-4163.04|||-2404.86||
|Number of Observations||3600|||3600|||1740|||1005||

27
- 9 -

where _x_ ' _j_  _j_ is the mean profit level at location (district) _j_ , and _ε ij_ is an idiosyncratic error including firm-specific unobservables; _x_ consists of location-specific observables, including a measurement of agglomeration economies and infrastructure variables.

Following Berry (1994), the set of firm-specific unobservables is defined by _Aj_   _i_ |  _ij_   _ik_ for  _k_  _j_  . Then the share of firms choosing to be located in district _j_ can be written implicitly as _s j_   _Aj f_ (  ; _x_ , <sup>)</sup> _d_  . When _ε_ is assumed to be independently and identically distributed, with the extreme-value distribution exp(  exp(  )) , the log of the probability of a firm choosing location _j_ is:

with the mean profit of the outside option normalized to zero; _s_ 0 is the share of firms that take an outside option, which is assumed to be employment in the informal sector in our context. In other words, people who are working in the informal sector can choose to formalize their businesses in a particular district. They do so if expected profits are positive. Otherwise, they continue to stay in the informal sector or unemployed. Recent labor force statistics were used to calculate the number of informal businesses that choose the outside option, and it is assumed that the average size of informal businesses is five employees.

Transport connectivity can be measured differently. In this analysis, six variables are examined: rural accessibility ( _RAI_ ) in district _j_ , road density ( _RDDEN_ ), share of roads in good condition ( _GDRD_ ), transport costs to a nearby large city ( _TCMK_ ), transport costs to the largest port (Freeport of Monrovia, _TCPT_ ), and market access index ( _MAI_ ) at location _j_ . The first variable is a global indicator of rural accessibility at the district level, as discussed above. _RDDEN_ and _GDRD_ are traditional transport indicators. It has long been argued whether they can measure actual connectivity accurately.
collected disaggregated cost data via WFP’s accounting ledgers and interviews, including staff costs. To improve the reporting on cost-effectiveness, the World Bank Strategic Impact Evaluation Fund’s 2019 guidelines recommend relying on administrative data (e.g., budgets and spending reports) as much as possible to use costs information that is “disaggregated, intervention-specific, and captured in real-time over the course of an intervention.” The document also discusses the need to collect non-financial information that is not typically quantified in accounting ledgers. Beneficiaries’ travel time to obtain transfers (as in Aker et al. 2011) is an example of non-financial data that can be systematically different across transfer modalities.<sup>23</sup>

# **4. Comparative Performance of Transfer Modalities**

Decisions about the types of transfers (e.g., cash, voucher, vs. food) and the delivery mechanisms (e.g., mobile money vs. cash-in-envelope, or lump sum vs. multiple transfers) involve multiple factors. First, the condition of market functioning needs to be assessed. As is the case in many humanitarian settings, missing markets can undermine the effectiveness of cash transfers if there are limited opportunities to use the cash transfer. Second, the existing physical and financial infrastructure needs to be taken into account because different modalities require delivery systems. The system may vary from staff traveling to communities distributing manual cash or goods, to payment service providers such as post offices disbursing cash over the counter, to electronic mechanisms such as transfers by mobile phone or directly into bank accounts. In particular, conflict and rapid-onset natural disasters can disrupt payment systems, damage infrastructure, and displace people and businesses. Delivering cash digitally may not be the most effective delivery method if the cost of cashing it out is high for villagers in remote areas with poor road conditions. Third, the selection of food items matters. If the in-kind food items are _inframarginal_ (the consumption quantity even without transfer is greater than the quantity provided), in-kind will have little to no distortion effects. On the other hand, if there is a household surplus of some food items, then in-kind transfers can be distortionary in the sense that it might induce

> 23 A good example of collecting costing data is the World Bank’s <u>Partnership for Economic Inclusion’s Costing Dashboard, which reports and compares costs of economic inclusion policies. This type of tool could be used as a</u> framework to collect costing data for cash and in-kind programs in humanitarian settings. Hendren and Sprung-Keyser (2020) also developed a unifying framework to compare the effectiveness of different policies and interventions, which relies mainly on understanding the long-term impact of policies as well as knowing the implementation cost.

29