rafmacalaba/datause-encoder-data huggingface dataset:
10. We usually began by asking about the price of each product and the output in a busy month and in a slack month, together with a question about when the workshop was busiest. If the respondent was unsure, we changed tack and asked, for example, how many units of a product were produced from one unit of a material, and how often and how many units of the material were purchased in a busy month and a slack month.

11. The Hausman specification test does not reject the consistency of the random-effects model estimator for the regressions of keeping records, analyzing records, value added, and gross profit as shown at the bottom of the table in columns (ii), (iii), (v), and (vi), even though the test results were not obtained for the other two regressions.

# **References**

- Akoten, J.E. & Otsuka, K. (2007). From tailors to mini-manufacturers: The role of traders in

- the transformation of garment enterprises in Nairobi. _Journal of African Economies_ , 16 (4), 564-95.

- Altenburg T. & Meyer-Stamer, J. (1999). How to promote clusters: Policy experiences from Latin America. _World Development_ , 27 (9), 1693-1713.

- Bertrand, M. & Schoar, A.S. (2003). Managing with Style: The Effect of Managers on Firm Policies. _Quarterly Journal of Economics_ , 118 (4), 1169-1208.

- Bloom, N., Mahajan, A., McKenzie, D. & Roberts, J. (2010). Why Do Firms in Developing

- Countries Have Low Productivity? _American Economic Review: Papers & Proceedings_ , 100 (2), 619–23.

- Bloom, N. & Van Reenen, J. (2007). Measuring and explaining management practices across firms and countries. _Quarterly Journal of Economics_ , 122 (4), 1351-1408.

Bloom, N. & Van Reenen, J. (2010). Why do management practices differ across firms and 25
**Table 5 Gravity equation estimation results (baseline). Dependent variable is bilateral trade in levels.**

||Allgoods|HS> 27|HS>83|Diff. Goods|Homog.<br>Goods|
|---|---|---|---|---|---|
|GDP Importer|0.771***|0.844***|0.860***|0.792***|0.691***|
||[0.050]|[0.060]|[0.074]|[0.078]|[0.053]|
|GDP Exporter|0.788***|0.933***|0.977***|0.934***|0.596***|
||[0.061]|[0.068]|[0.078]|[0.093]|[0.063]|
|Tariff (RG Weighted)|-0.784|-2.807***|-3.132**|-0.936|-0.923|
||[0.488]|[0.921]|[1.597]|[1.015]|[0.691]|
|NTB (RG Weighted)|0.305|-1.045**|-2.034***|-0.069|1.046***|
||[0.462]|[0.434]|[0.663]|[0.220]|[0.365]|
|Imp. Transparency|**6.886*****|**8.901*****|**9.622*****|**8.371*****|**2.379**|
||[2.028]|[2.401]|[2.817]|[3.324]|[2.052]|
|Exp. Transparency|**4.842*****|**6.826*****|**7.258*****|**5.170****|**2.046**|
||[1.655]|[2.069]|[2.463]|[2.677]|[1.745]|
|Observations|29,376|21,114|4,284|76,500|50,694|

Robust standard errors adjusted for clustering by country-pair in brackets, * significant at 10%; ** significant at 5%; *** significant at 1%. Estimation method is Poisson QML with fixed effects by country pair and HS 2-digit sector.

**Table 6 Gravity equation estimation results (instrumental variables). Dependent variable is bilateral trade in levels.**

||Allgoods|HS> 27|HS>83|Diff. Goods|Homog.<br>Goods|
|---|---|---|---|---|---|
|GDP Importer|0.605***|0.596***|0.599***|0.577***|0.641***|
||[0.023]|[0.016]|[0.018]|[0.021]|[0.028]|
|GDP Exporter|0.660***|0.745***|0.789***|0.770***|0.557***|
||[0.020]|[0.017]|[0.016]|[0.770]|[0.026]|
|Tariff (RG Weighted)|-0.701|-1.421|-2.121|0.138|-0.875|
||[0.588]|[0.988]|[1.603]|[1.194]|[0.702]|
|NTB (RG Weighted)|0.414|-0.951**|-1.881**|0.076|1.057***|
||[0.469]|[0.439]|[0.805]|[0.023]|[0.367]|
|Imp. Transparency|**1.828*****|**1.864*****|**2.583*****|**3.889***|**1.987**|
||[0.302]|[0.373]|[0.401]|[2.533]|[2.049]|
|Exp. Transparency|**-0.406**|**-0.856*****|**-0.681*****|**3.071***|**1.939**|
||[0.260]|[0.239]|[0.199]|[2.113]|[1.749]|
|Observations|29376|21114|4284|76500|50694|

Robust standard errors adjusted for clustering by country-pair in brackets, * significant at 10%; ** significant at 5%; *** significant at 1%.

Estimation method is Poisson QML with fixed effects by country pair and HS 2-digit sector. Importer and exporter transparency are instrumented by British colonization of the importer and exporter.

36
production of generic goods to avoid such investments. Such economies cannot rise on the valuechain of exports (Levchenko 2007, Amirapu 2021). Because of the relation with the ladder of value enhancement, foreign direct investment is positively correlated with the efficiency of legal institutions, which in turn is linked to higher economic growth (Sabir et al., 2019, Bénassy-Quéré et al., 2007).

Survey evidence from Senegal shows that firms are willing to pay higher legal fees to achieve faster delivery of justice, suggesting positive benefits of judicial reform (Kondylis and Stein, 2022). Several studies have shown a link between entrepreneurship rates and the efficiency of the judicial system, suggesting that an efficient judiciary promotes entrepreneurial activity (Ippoliti et al., 2015, Ardagna and Lusardi, 2009).

Active case management has been consistently highlighted as a necessary tool in the pursuit of court efficiency. The Indian judicial system is plagued by especially high disposition times across all levels. Gupta and Bolia (2020) use simple measures of judicial resources, namely, number of judges and staff members as inputs, and two outputs, number of civil and criminal cases disposed. The results identify specific courts that are efficient in disposing cases, especially courts that use active case management.

A study using data on disposition times in Italy shows that three policies can make a significant contribution to the efficiency of the system: active case management, break-ups of large courts of justice into smaller ones to exploit economies of scale and use of offsite technologies. These three measures reduce the disposition time by 30% (Peyrache and Zago 2020). Another study on Italy finds that the causal effects of business uncertainty from court delays on company turnover, hiring and dismissals are of significant negative direction, and of sizable magnitude (Bamieh et al, forthcoming). Other work using Italian court data studies the effects of court inefficiency on public work performance. Where courts are inefficient, proxied by disposition times, Coviello et al (2018) find that public works are delivered with longer delays; that delays increase for more valuable contracts; that contracts are more often awarded to larger suppliers; and that a higher share of the payment is postponed after delivery.

18
As mentioned in Section 5.1, my empirical estimates are best captured by a NK model with sticky wages and a share of hand-to-mouth households. In that NK model, the aggregate present-value transfer multiplier _M_<sup>_AggPV_</sup> _Tr_ at business cycle frequencies is simply proportional to the aggregate present-value purchase multiplier _M_<sup>_AggPV_</sup> _G_ , scaled by the fraction of transfers targeted at hand-to-mouth households _ωT_ such that _M_<sup>_AggPV_</sup> _Tr_ = _ωT M_<sup>_AggPV_</sup> _G_ .<sup>45</sup> Consistent with the ndings of NS, this type of model produces aggregate purchase multipliers _M_<sup>_AggPV_</sup> _G_ at business cycle frequencies that are small ( _≪_ 1 ) when monetary policy leans against the wind but are large ( _>_ 1 ) when monetary policy is more accommodating of ination (using a constant real interest rate here for simplicity).<sup>46</sup> My empirical estimates of one-o cross-region transfer multipliers are consistent with a modest share of hand-to-mouth households _ω_ , which also pins down the scaling factor for untargeted transfer multipliers ( _ωT_ = _ω_ ). Hence my NK model produces large aggregate transfer multipliers ( _M_<sup>_AggPV_</sup> _Tr >_ 1 ) at business-cycle frequencies when both monetary policy is accommodating and transfers are targeted at hand-to-mouth households ( _ωT ≫ ω_ ), modest multipliers _M_<sup>_AggPV_</sup> _Tr ≈_<sup>1</sup> _/_ 2 for untargeted transfers with constant real interest rates, and small transfer multipliers _M_<sup>_AggPV_</sup> _Tr <_<sup>1</sup> _/_ 2 otherwise (see Appendix 6.3 for details).

# 7. Conclusion

In this paper, I investigate the size of the cross-region transfer multiplier in the US. This is novel, as the cross-region transfer multiplier is conceptually dierent from the MPC and cross-region purchase multipliers estimated in the literature, and is relevant, as transfers are countercyclical and are the largest component of federal spending. I nd that cross-region transfers signicantly

> 45Aggregate transfers boost demand if they are spent as well as possibly reducing the labor supply of households receiving the transfer (see Giambattista and Pennings 2017). In this paper, my simplifying assumptions for sticky wage setting abstract from the second channel, leaving only the eects on demand.

46 Multipliers can be substantially larger when monetary policy is more accommodating, for example when the Zero Lower Bound binds for an extended period. Párraga Rodríguez (2018) estimates aggregate transfer multipliers using Romer and Romer's (2016) series of Social Security increases as an instrument. She nds a large transfer multiplier of around unity after a year, which might be rationalized by the high persistence of Social Security increases and a high MPC.

38
|Community has garbage|||||||
|---|---|---|---|---|---|---|
|collection|0.081***|0.00|-0.113***|0.078***|0.003|-0.111***|
||(4.31)|(0.00)|(6.53)|(4.13)|(0.15)|(6.37)|
|Community has police|||||||
|station|-0.025|0.028*|0.01|-0.022|0.029*|0.009|
||(1.49)|(1.83)|(0.64)|(1.31)|(1.89)|(0.57)|
|Tirana|0.178***|-0.173***|-0.072**|0.181***|-0.183***|-0.080***|
||(5.79)|(6.33)|(2.55)|(5.85)|(6.63)|(2.82)|
|Coast urban region|0.105***|-0.112***|-0.055**|0.106***|-0.121***|-0.065**|
||(3.47)|(4.13)|(1.98)|(3.47)|(4.42)|(2.32)|
|Coast rural region|0.043*|0.015|-0.079***|0.043*|0.01|-0.085***|
||(1.91)|(0.75)|(3.84)|(1.91)|(0.53)|(4.16)|
|Central urban region|0.031|-0.120***|-0.062**|0.031|-0.130***|-0.071**|
||(0.99)|(4.35)|(2.16)|(0.99)|(4.67)|(2.47)|
|Central rural region|0.060***|-0.113***|0.110***|0.063***|-0.118***|0.104***|
||(3.16)|(6.63)|(6.24)|(3.29)|(6.86)|(5.87)|
|Mountain urban region|0.057|-0.150***|-0.086**|0.056|-0.161***|-0.097**|
||(1.26)|(3.70)|(2.05)|(1.22)|(3.95)|(2.30)|
|_Remittances and non-lab income:_|||||||
|Remitt. from current int.nal||||-0.004|-0.001|0.001|
|migr. (log, pc)||||(1.59)|(0.38)|(0.34)|
|Rem/gifts from relatives||||-0.004*|-0.002|0.003|
|abroad -last 12 months (log, pc)||||(1.84)|(1.13)|(1.52)|
|Rem/gifts from relatives in||||-0.004|0.005**|-0.001|
|Albania -last 12 months (log, pc)||||(1.64)|(2.02)|(0.58)|
|Other non-lab income (log, pc)||||0.00|-0.004*|-0.007***|
|||||(0.11)|(1.90)|(3.75)|
|Constant<br>0.430***<br>0.234***<br>0.202***|-0.244***|0.007|0.396***|-0.240***|0.012|0.399***|
|(40.24)<br>(26.38)<br>(19.19)|(3.85)|(0.12)|(6.79)|(3.78)|(0.22)|(6.83)|
|Observations<br>6592||6592|||6592||
|_Notes_Absolute value of z statistics in brackets|||||||
|* significant at 10%; ** significant at 5%; *** significant at 1%|||||||

25
Table 13: Trade, Legal Origins <u>and Property Rights, Robustness</u>

|Dep. Var.: log of average tra|de flow|||||||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||OLS|OLS|OLS|OLS|OLS|OLS|OLS|
|Different Legal Origin|-0.25|-0.17|-0.25|-0.26|-0.28|-0.16|-0.21|
||(-5.96)|(-4.03)|(-6.00)|(-6.29)|(-6.44)|(-3.77)|(-4.97)|
|Protection of Property<br>|0.31|0.31|0.28|0.18|0.26|0.13|0.17|
|Rights<br>|(21.43)|(21.73)|(18.89)|(11.61)|(17.08)|(8.02)|(10.04)|
|log distance|-1.20|-1.19|-1.16|-1.16|-1.18|-1.13|-1.10|
||(-48.76)|(-48.58)|(-46.33)|(-46.91)|(46.66)|(-45.08)|(-43.21)|
|log product of GDP|0.98|0.98|0.97|0.92|0.99|0.90|0.94|
||(105.61)|(105.17)|(102.82)|(94.34)|(103.19)|(91.33)|(94.96)|
|log product of land area|-0.10|-0.09|-0.11|-0.04|-0.11|-0.03|-0.06|
||(-12.20)|(-11.47)|(-12.80)|(-5.04)|(-12.05)|(-3.77)|(-6.93)|
|no. of landlocked|-0.54|-0.55|-0.54|-0.48|-0.50|-0.47|-0.48|
||(-15.70)|(-16.00)|(-15.73)|(-13.80)|(-14.05)|(-13.83)|(-13.28)|
|no. of islands|-0.18|-0.19|-0.19|-0.16|-0.19|-0.19|-0.22|
||(-4.15)|(-4.45)|(-4.63)|(-3.75)|(-4.17)|(-4.62)|(-5.37)|
|common border|1.04|1.01|1.02|1.07|1.03|1.00|0.73|
||(8.38)|(8.04)|(8.31)|(8.76)|(8.03)|(8.22)|(5.60)|
|common language|0.41|0.27|0.41|0.56|0.42|0.37|0.41|
||(7.72)|(4.97)|(7.91)|(10.60)|(7.49)|(7.05)|(8.10)|
|currency union|1.31|1.12|1.39|1.71|1.35|1.38|1.16|
||(5.46)|(4.53)|(5.77)|(7.13)|(5.61)|(5.70)|(3.15)|
|common colonizer||0.46||||0.70|0.52|
|||(5.07)||||(8.00)|(4.66)|
|colony-colonizer||1.10||||0.98|0.95|
|||(8.53)||||(8.02)|(8.31)|
|both in GATT/WTO|||-0.62|||-0.13|-0.29|
||||(-5.05)|||(-1.16)|(-1.84)|
|only one in GATT/WTO|||-0.63|||-0.30|-0.44|
||||(-5.11)|||(-2.59)|(-2.78)|
|generalized system of|||<br>0.42|||<br>0.39|<br>0.21|
|preferences (GSP)|||(11.69)|||(11.20)|(5.94)|
|regional trade agreement|||0.66|||0.58|1.73|
||||(5.34)|||(4.88)|(9.83)|
|lo of roduct of hone||||||||
|g p p<br>||||0.18||0.19|0.15|
|lines per 1000 people||||||||
|,||||(15.44)||(16.44)|(11.63)|
|log of product of road|||||0.04|||
|length per 1,000 people|||||<br>(391)|||
|average weighted tariff|||||.||-0.01|
||||||||(-7.44)|
|Observations|29772|29772|29772|29696|26129|29696|19488|
|R^2|0.72|0.72|0.72|0.72|0.72|0.73|0.76|
|% increase in trade due to<br>1s.d. institution|57%|58%|51%|30%|47%|22%|29%|

Notes: t-values in parentheses. Standard errors are calculated taking into account country-pair clusters. A constant, year and country dummies are not reported. The sum of bureaucratic quality ranges from 0 to 8 with mean 4.672 and standard deviation 1.667. The percent increase in trade due to 1s.d. institution (bureaucratic quality) is calculated as follows: exp(coef*sd)-1.

48
# **FIGURE 4: SUPPLY AND DEMAND FORCES**

<!-- Start of picture text -->
1.5<br>VNM<br>PHL<br>upward supply, CH complement downward supply, CH substitute<br>1 ROU<br>IND<br>CZE<br>.5 NZL ISR BEL BGRFRA TWN MEX MYS<br>ZAFGRC EGY PERPRTHUNNLD GBR ITAKORFIN<br>IDN DEU THA<br>JPN<br>HKG TUR SGP BRA POL<br>0<br>DNK<br>SWEIRL SVK CHE AUT<br>ESPCAN<br>COL<br>-.5<br>AUS<br>UKR SVN CHL BGD<br>ARG<br>-1<br>downward supply, CH complement upward supply, CH substitute<br>-1.5<br>-2 -1.5 -1 -.5 0 .5 1 1.5 2<br>β (US,1i)<br>1.5<br>TUR<br>upward supply, US complementVNM downward supply, US substituteTHA<br>1 ROU<br>IDN<br>PHL ZAF JPNKOR SGPCAN<br>ARG FIN<br>HUNSWE AUSGBR<br>PER AUT<br>.5<br>PRTNLD CHENZL<br>SVK<br>BGDFRA IRL SVN<br>DEU IND TWN BGRESP CHLHKG<br>0 CZE GRCDNK BEL<br>COL UKR MYSPOL ITA<br>BRA<br>-.5<br>MEX<br>-1 EGY<br>downward supply, US complement ISR upward supply, US substitute<br>-1.5<br>-5 -4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -.5 0 .5 1 1.5 2 2.5 3 3.5 4 4.5 5<br>β (CH,2i)<br>(RW,1i)<br>β<br>(RW,2i)<br>β<br><!-- End of picture text -->

Notes: The figure plots the tariff responses to the US-China tariffs, _β_<sup>�</sup> _iω_<sup>_n_=∑</sup> _ω_<sup>_λn_</sup> _Xiωβ_<sup>�</sup> _ziω_<sup>_n_using the taxonomy in Table 1.</sup> Panel A plots ( _β_<sup>�</sup> 1<sup>_US_</sup> _i_<sup>,</sup> _β_<sup>�</sup> 1<sup>_RW_</sup> _i_ ). Panel B plots ( _β_<sup>�</sup> 2<sup>_CH_</sup> _i_ , _β_<sup>�</sup> 2<sup>_RW_</sup> _i_ ). Countries noted in blue operate in the same quadrant in both figures. Countries in red operate along downward-sloping supplies in both figures. Countries in green operate along upward-sloping supplies in both figures.
ness, fatigue, or reduced consciousness. Before the doctors’ consultation, enumerators recorded patients’ basic demographic details, symptoms, and information on any prior treatment/diagnosis.<sup>22</sup> After the consultation was complete, the enumerator recorded details of all prescribed and purchased medications, blood tests taken, and costs paid to the clinic. All told, we completed 2,055 interviews.

**Home Follow-Up Survey.** We randomly selected 1,735 patients for a more detailed follow-up survey conducted in the patient’s home the day after the clinic visit, of whom 1,495 (86 percent) were successfully interviewed.<sup>23</sup> The home survey collected information on changes in the illness, any treatments or tests obtained after the clinic visit, and recorded whether patients were taking the medications that were purchased at the clinic. During this survey, 1,126 patients (75 percent of interviewed patients/65 percent of patients targeted for the home survey) gave separate consent to take an RDT and obtained a valid test result.<sup>24</sup> Appendix Table B5 shows that there are no significant differences in the probabilities of being selected for home survey, taking the home survey, or having a valid home-based RDT by treatment arm.

**Administrative Voucher Data.** Finally, we use administrative data on voucher redemption to quantify how informing patients about vouchers impacted redemption rates.

# **3.3 Background Characteristics and Randomization Ver-**

Table 2 presents summary statistics on the study population and verifies randomization balance. The first column of the table displays means and standard deviations of baseline characteristics in the Control group (patients

> 22We interviewed the patient whenever s/he was aged 15 or older and well enough to take the interview; otherwise a guardian answered the survey.

> 23From November 14-17 2016 we randomly selected up to six patients per clinic per study day to interview at home. Between November 18 and December 30 2016 (the end of the study) we randomly selected up to six patients per clinic per study day, and then oversampled at clinics where more than six patients were seen, until 60 patients per study day were sampled.

> 241,158 patients gave consent; 32 tests were inconclusive.

21
Once basic categories have been defined, and data gathered, a number of the most common summary cost measures can be derived. Moreover, it is possible to develop these cost measures for a variety of programs, allowing comparison3 across approaches. Common measures include the following types:

- Cost per capita population: often used by Primary Health Care programs.

- Cost per woman of reproductive age: often used by family planning programs. For Safe Motherhood programs, it is useful for an analysis of resource distribution, but it is not useful for cost-effectiveness analysis.

- Cost per birth/pregnancy: analysis should not be limited to this measure as it prevents comparability to more common per capita measures.

- Cost per service delivered: cost per woman receiving essential obstetric care, prenatal care, or other service. This is a practical cost measurement, useful for planning purposes. Its calculation can be important for deciding among alternative programs.

- Cost per outcome. This is the most significant cost in terms of maximizing the benefit of scarce resources. But unless a controlled experiment has been conducted, or multivariate analysis has been used to account for the contribution of other variables (e.g. income or education levels), we cannot say with any certainty that the outcomes achieved are attributable to the program. This is particularly true in the field of women's health, as many non-health sector interventions must have an impact.

Data should be collected at different units/levels of operation. Most importantly, and as stated above, costing must be seen as part of a wider evaluation process. Cost data needs to be related to information on effectiveness to be of most value. Many of the measures above can be used in cost-effectiveness analysis. By defining possible sets of activities that can achieve the desired goal, one can examine the lowest cost approach per pregnancy, service delivered, or per outcome.

The collection of this data will require planning to determine carefully both the types of data required and the most appropriate sources. Cost data are needed on the health care system itself, non-health sector specific inputs (e.g. administration), and service users (including demand assessment). Data collection can be guided by the determinants listed in the Columbia University model and the implementation needs indicated by the MotherCare approach. Several different collection strategies can be used:

- Routine data/records. These can come from health facilities, central or subnational offices, or from other sources, such as price lists for equipment and

12
**Table 3.2. Unconditional correlation between different hazard measures by livelihood zone**

|||**Ma**|**ize**|||**Highl**|**and**||**R**|**oots and**|**lowland**|**s**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Total||||Total||||Total||||
||<br>seasonal<br>rainfall<br>|Soil<br>moistu|re<sup>SPEI</sup>|NDVI|<br>seasonal<br>rainfall|Soil<br>moistur|e <sup>SPEI </sup>|<sup>NDVI</sup>|<br>seasonal<br>rainfall|<br>Soil<br>moisture|<sup>SPEI</sup>|NDVI|
|Soil moisture|0.43||||0.49||||0.00||||
|SPEI|0.47|0.26|||0.44|0.28|||0.30|0.12|||
|NDVI|0.48|0.63|<br>0.32||0.32|0.51|0.18||0.22|0.43|0.07||
|WRSI|0.21|0.13|<br>0.12|0.16|0.22|0.15|0.18|0.11|0.15|-0.11|0.01|0.02|
||||**Pastoral**|||||**Ag**|**ropastora**|**l**|||
||Total seas<br>rainfal|onal<br>l|Soil<br>moisture|SPEI|<br>NDVI|To|tal seas<br>rainfall|onal<br> <br>|Soil<br>moisture|SPEI|NDVI||
|Soil moisture|0.46||||||0.32||||||
|SPEI|0.45||0.23||||0.51||0.39||||
|NDVI|0.57||0.54|0.28|||0.33||0.60|0.31|||
|WRSI|0.29||0.09|0.24|<br>0.26||0.26||-0.11|0.17|0.10||

_Source:_ World Bank. _Note:_ NDVI = Normalized Difference Vegetation Index; SPEI = Standardized Precipitation Evapotranspiration Index; WRSI = Water Requirement Satisfaction Index.

# 3.2. Hazard simulations data

For the analysis, we asked Verisk’s AIR Worldwide (AIR) to produce 10,000-year drought-related stochastic catalogs of synthetic simulations for Malawi at a spatial resolution of 0.05° and dekadal temporal resolution. Stochastic catalogs are designed to provide a view of hazard that goes beyond the data available in the historical record. AIR used historical data on rainfall to produce three such catalogs: for precipitation, soil moisture, and vegetation index.

To generate the stochastic catalogs, AIR used data similar to that used by the World Bank team in generating the historical hazard time series. CHIRPS data were used for precipitation; the Soil Water Index at a depth of 40 cm from the Copernicus Global Land Service was used for soil moisture; and the NDVI for vegetation cover was from MODIS. The resulting catalogs were validated by comparing the synthetic time series with the historical data in Malawi. The validation results show that the catalogs preserve the intrinsic properties in terms of spatial and temporal correlations, produce a realistic view of hazard, and are consistent with historical observations while allowing for the creation of new extremes.

For the purposes of this paper, the approach was tested using the stochastic catalog for soil moisture in Malawi. Further details on the methodology used to generate the 10,000-year catalog and the validation conducted are in appendix C.

11
|**Table 6: Impact on Formalization**<br>_Dependent variables:_|(1)|(2)|(3)|(4)<br>Showed a document<br>**or**declared formality|(5)<br>Showed a<br>document or|(6)<br> <br>Declared<br>formality or|
|---|---|---|---|---|---|---|
||||Showed a|and found in GUFE|found in|found in|
||GUFE data|Declared|document|data|GUFE data|GUFE data|
|Group 1|0.091***|0.041*|0.049**|0.063***|0.100***|0.079***|
||(0.021)|(0.024)|(0.020)|(0.022)|(0.025)|(0.027)|
|Group 2|0.130***|0.112***|0.092***|0.114***|0.149***|0.147***|
|Group 3|(0.013)<br>0.158***|(0.015)<br>0.122***|(0.013)<br>0.097***|(0.014)<br>0.126***|(0.016)<br>0.167***|(0.017)<br>0.163***|
||(0.012)|(0.013)|(0.011)|(0.012)|(0.014)|(0.015)|
|Observations|3,600|3,173|3,173|3,173|3,173|3,173|
|R-squared|0.383|0.446|0.425|0.444|0.442|0.446|
|Adjusted R-squared|0.073|0.109|0.074|0.105|0.101|0.108|
|Mean dep. variable in Control|0.009|0.040|0.018|0.018|0.023|0.044|
|Pvalue Test group1=group2|0.101|0.009|0.058|0.037|0.080|0.022|
|Pvalue Test group1=group3|0.002|0.001|0.018|0.005|0.008|0.002|
|Pvalue Testgroup2=group3<br>Note: Administrative data from GUFE<br>controlling for strata dummies (dumm|0.032<br>and survey da<br>ies for each tr|0.489<br>ta March 2015<br>iplet). ***, **|0.682<br>. OLS regressi<br>, * indicate sta|0.422<br>on of the outcome variabl<br>tistical significance at 1, 5|0.277<br>e on treatment<br>and 10%.|0.325<br>dummies,|