rafmacalaba/datause-encoder-data huggingface dataset:
are based on the consumption of users already participating in the market (that is, households with positive consumption levels), so they might be underestimating potential welfare gains due to the entrance of new consumers, which is more likely to occur among households in the bottom of the income distribution.

**Table 4. Expenditure on mobile telecommunication and potential distributive impact of higher competition**

||**All**|Poor|est||Hou|sehold in|come dec|iles||Ric|hest|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||1|2|3|4|5|6|7|8|9|10|
|Average pc income<sup>a</sup>|3,499|558|1,012|1,326|1,634|1,987|2,395|2,933|3,772|5,338|14,041|
|Absolute impact<sup>b</sup>|6.43|1.00|1.77|2.09|2.80|3.55|4.71|5.54|7.47|10.71|24.72|
|Relative impact<sup>c</sup>|0.18|0.18|0.18|0.16|0.17|0.18|0.20|0.19|0.20|0.20|0.18|

_Source:_ World Bank estimates using ENIGH 2014. a. According to current income (MX pesos of August of 2014). b. The average impact on well-being (moving from concentrated to competitive market) c. The average absolute impact over the average income times 100.

Concerning the impact on poverty and inequality, Table 5 suggests that introducing reforms to enhance competition in the mobile telecommunication market could help reduce different measures of poverty such as the poverty headcount. The results show the upper-bound expected effects, equivalent to 0.39 percentage points of poverty reduction of simulating a movement from a concentrated market with four firms to a perfectively competitive market. The scenario of moving towards 12 firms yields a poverty reduction of 0.11 percentage point. However, the expected impact of entrance of new firms in the mobile communication market on inequality is negligible, even under the upper-bound scenario. This occurs partially due to a small share of expenditure on mobile telecommunication services and since the share of expenditure in mobile communication across the income distribution is relatively stable. In contrast, when the relative shares of expenditure in a specific good differ across the distribution, the relative impacts on inequality tend to be larger.

**Table 5. Market concentration in mobile telecommunication, poverty, and inequality**

||<br>Headcount|Poverty indicat<br>Poverty gap|ors<br>Squared<br>poverty gap|Gini|Inequality indic<br>Atkinson<br>(epsilon=0.5)|ators<br>Entropy<br>(theta=0.0)|
|---|---|---|---|---|---|---|
|Baseline|53.35|21.09|11.01|0.497|0.2096|0.4328|
|Moving into competition|53.24|21.03|10.97|0.497|0.2096|0.4328|
|Difference|-0.11|-0.06|-0.03|0.0000|0.0000|0.0000|

_Source:_ World Bank estimates using ENIGH 2014.

As suggested in Figure 6, the simulated reduction in the poverty headcount reduction as 2 new firms enter the market is just 0.062 percentage points, followed by a reduction of 0.065 percent when there are 8 firms instead of the original 4, reaching a reduction of 0.11 percent when there is a total of 12 17
<!-- Start of picture text --> iS AZE<br>s<br>= MMR<br>ge ARM CHN<br>z BLR KAZ<br><ee=) a iediM nooTWaaacsSERBon <8 =Gus LEA oust KOR<br>& Se Ea, SAE undiiane SOy czeMy 'KGocp<br>a wGfA ML pct SUE ORR ee Ss x<br>8 | carotene RT ERG NO<br>a<br>Ow<br>co)aD ZWE<br>£<br>$<br><F<br>6 7 Log GDP 8per capita. (constant9 201510 US$) 11 12<br>——— gdp_pc_gr= 4.4824- .2log gdp pc<br>Note: countries in red = BB; countries in blue = EM; countries in green = OECD<br>>~~)4 oa<br>s<br>=i} ETH CHN<br>BDio MMRKab inp og = IRL<br>=)&o CRM ERS RWABEKAGON25 wesBee WV PNGOLAUBBiH ESRABIERiea,DOM MASKaaTURANPOs EST anyKOR SGP<br>Bo© WEREOSB“LSOeMMRTENTE ANDUKBA AreeapapoF, NEBERSCHaB. ERosMEX HRY PRTVv gp JPRGBRBSaA re cue<br>8 EN MOG ii HT! be st ARCs SFA no Lux<br>s sige WE AGO JOR crc<br>a KWT<br>a<br>aw<br>oO" SYR<br>o YEM<br>2 LBY<br>g sso<br><2<br>6 ¥ Log GDP per8 capita. (constant 201510 US$) 11 12<br>———-_ gdp_pc_gr= 2.9807- .116 log_gdp_pc<br>Note: countries in red = BB; countries in blue = EM; countries in green = OECD<br><!-- End of picture text -->
- 27 -

Table 2 Institutions, design and construction<sup>costs per hectare of wet season</sup> irrigated land (in pesos)

||Parti|cipatory|Non-participatory|
|---|---|---|---|
||sy|stems|systems|
|Type of cost|(n|<br>24)|(n<br>22)|
|Institutional costs||||
|Profile preparations|36|($ 1.80)|-|
|Community organizer||||
|salaries and field||||
|supplies|293|($14.65)|<br>-|
|Community organizer||||
|training and supervision|<br>82|($ 4.10)|-|
|Financial and system||||
|management training for||||
|irrigation associations|87|($ 4.35)|-|
|Total institutional costs|498|($24.90)|<br>-|
|Design&construction costsb||||
|Chargeable to farmers|12,666|($633.30)|12,000 ($600.00)|
|Not chargeable to farmers|2,484|($124.20)|<br>3,599 ($179.95)|
|Total design and<br>construction costs|15,150|($757.50)|15,599 ($780.00)|
|Total development costs|15,648|($782.40)|15,599 ($780.00)|

a. Costs have been standardized in terms<sup>of 1984 pesos: P20-US$1.00.</sup> b. Includes costs of materials, labor,<sup>equipment rental and technical</sup> supervision.

Source: de los Reyes and<sup>.)pillo</sup> in Korten and Siy, Jr (1988:113).

The difference between the resources mobilized<sup>under the</sup> participatory system versus the non-participatory<sup>are startling. Farmers'</sup> mean equity contribution to construction<sup>costs under the participatory schemes</sup> has been 357 pesos compared with only<sup>54 pesos under the non-participatory</sup> (see Table 1). Similarly, comparing<sup>loan repayment rates, we find that the</sup> mean percentage of amortization due<sup>that has been paid equals 87 percent for</sup>
# **A Appendix: Tables**

## **Table 1 Variable**

|Variable|Defnition|Source|
|---|---|---|
|_ARETs,t_|Monthly abnormal returns defned as _ARETs,t_ =_rs,t −E_[_rs,t_]<br>where _rs,t_ are stock returns between t-1 and t, and _E_[_rs,t_]<br>are expected returns, calculated relative to a market index|Datastream|
|_CONCs,t_|Percentage of shares held by the largest 5 owners|SFC|
|_INSTs,t_|Percentage of shares held by institutional investors|SFC|
|_FOREIGNs,t_|The percentage of shares held by foreign investors|SFC|
|_B/Ms,t_|Book-to-market ratio, computed as the total net assets,<br>divided by the total market value of equity at the end<br>of the quarter|SFC|
|_SIZEs,t_|Firm size computed as the log of total assets at the end<br>of the quarter|SFC|
|_ROAs,t_|Return over assets|SFC|
|_LEVs,t_|Leverage computed as total liabilities divided by total<br>assets|SFC|
|_SPREADs,t_|Bid-ask spread of stock defned as (_ask −bid_)_/_(_ask_+_bid_)_/_2|Datastream|
|_TURNOV ERs,t_|Number of shares traded, divided by the number of shares<br>outstanding|Datastream|
|_Hs,f,t_|PFA holdings on stock s divided by the number of shares<br>outstanding (monthly and quarterly)|SFC|
|_AFFILs,f,t_|An indicator variable for each stock-PFA pair. It equals<br>if the PFA and the frm belong to the same business group|SFC|

26
25

# **Appendix A1: Dealing with attrition**

A significant amount of households were lost in the third round of the household survey and there are some differences between the interviewed and lost households (Table A1-1). For example, households whose fields are further away or who lived in remote villages were less likely to be interviewed in round 3. Smaller households are more likely to be dropped from the sample. Male headed households and households whose members are elected officials or member of a social or economic group are more likely to remain in the sample.

**Table A1-1:Characteristics** **<u>of the interviewed and dropped households</u>**

||Attrition<br>|round2|Attrition<br>|round3|
|---|---|---|---|---|
||Not||Not||
||interviewed|Interviewed|interviewed|Interviewed|
|Household size (# of persons)|5.010|5.343|4.957|5.410|
|Dependency ratio|0.511|0.540|0.542|0.538|
|Gender household head (% male)|0.857|0.875|0.791|0.893|
|Age household head (year)|47|54|49|54|
|Percentage of illiterate household members|0.070|0.070|0.073|0.069|
|Member of hh has elected position in village<br>|0.189|0.271|0.166|0.290|
|Member of hh is a relative of people with elected positions in<br>village<br>|0.083|0.080|0.048|0.088|
|Member of hh is a member in a primary society, or other<br>economic group<br>|0.116|0.353|0.239|0.365|
|Member of hh is a member in a religious, youth, women or other<br>social group|0.363|0.374|0.304|0.389|
|Time spent on agricultural (month)|5.121|7.553|6.218|7.716|
|Time spent on non-farm (month)|0.948|0.627|0.708|0.627|
|Maximum distance to land (km)|2|3|2|3|
|Land owned (ha)|0.810|0.810|0.607|0.810|
|Whether plant maize|0.877|0.830|0.812|0.836|
|Whether plant coffee|0.442|0.588|0.435|0.614|
|Whether plant beans|0.684|0.671|0.661|0.674|
|Whether plant bananas|0.637|0.787|0.629|0.814|
|Per adult equivalent household income (1000Tsh)|106|109|105|111|
|Value of houses and compound (1000Tsh)|300|1000|600|1000|
|Belong to Chagga group|0.568|0.753|0.685|0.758|
|Belong to Pare group|0.300|0.172|0.179|0.178|
|Christian|0.817|0.875|0.835|0.881|
|Distance to town (km)|16|12|16|12|
|Village has a market (%)|0.205|0.279|0.282|0.274|
|Village can receive cell phone signal (%)|0.662|0.836|0.814|0.831|
|Whether there is extension agent in village (%)|0.625|0.627|0.549|0.644|
|Village has dispensary,health centre or hospital(%)|0.636|0.517|0.538|0.518|

Note.- Medians of variables maximum distance to land, per adult equivalent household income, value of houses and compound, distance to town are reported. Means of all other variables are reported.
**Table 4. Regression estimates of the relationship between the availability of childcare and time allocation on primary activities**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||With|individual co|ntrols|With individ|ual + househol|d controls|With individ|ual + househol<br>controls|d + regional|
||Market<br>work<sup>a</sup>|Unpaid<br>work<sup>a</sup>|Other<br>activities<sup>a</sup>|Market<br>work<sup>a</sup>|Unpaid<br>work<sup>a</sup>|Other<br>activities<sup>a</sup>|Market<br>work<sup>a</sup>|Unpaid<br>work<sup>a</sup>|Other<br>activities<sup>a</sup>|
|_Ref: A. No child or youngest child is 6 years_||||||||||
|_and older_||||||||||
|B. Youngest child is 0–5 years and|-1.688***|1.854***|-0.166|-1.932***|1.829***|0.103|-1.786***|1.785***|0.0004|
|childcare available|(0.485)|(0.300)|(0.276)|(0.455)|(0.298)|(0.367)|(0.454)|(0.297)|(0.366)|
|C. Youngest child is 0–5 years and no|-3.149***|2.125***|1.02***|-3.143***|2.025***|1.117***|-3.064***|1.997***|1.066***|
|<br>childcare available|(0.426)|(0.282)|(0.382)|(0.418)|(0.282)|(0.373)|(0.413)|(0.281)|(0.371)|
|Age|0.360**|0.108|-0.467***|0.331*|0.122|-0.453***|0.296|0.126|-0.421***|
||(0.167)|(0.110)|(0.138)|(0.185)|(0.114)|(0.151)|(0.182)|(0.114)|(0.150)|
|Age squared|-0.006**|-0.001|0.007***|-0.005*|-0.001|0.007***|-0.005*|-0.002|0.006***|
||(0.003)|(0.002)|(0.002)|(0.003)|(0.002)|(0.002)|(0.003)|(0.002)|(0.002)|
|_Ref: Education: No education_||||||||||
|Class 1 to 5|-0.911**|0.664***|0.247|-0.522|0.428*|0.094|-0.601|0.433*|0.169|
||(0.446)|(0.252)|(0.348)|(0.446)|(0.260)|(0.343)|(0.442)|(0.258)|(0.340)|
|Class 6 to 8|-1.525***|0.442|1.083**|-0.805|0.139|0.666|-0.705|0.113|0.592|
||(0.567)|(0.335)|(0.474)|(0.586)|(0.326)|(0.490)|(0.578)|(0.325)|(0.482)|
|Class 9|-2.006***|1.491***|0.515|-1.592**|1.158**|0.434|-1.625**|1.174**|0.451|
||(0.620)|(0.504)|(0.581)|(0.656)|(0.500)|(0.632)|(0.657)|(0.494)|(0.630)|
|SSC and above|-1.028|0.341|0.687|-0.271|-0.137|0.407|-0.459|-0.082|0.542|
||(0.667)|(0.355)|(0.562)|(0.736)|(0.438)|(0.624)|(0.741)|(0.442)|(0.617)|
|Wear burkha to travel outside|-1.056***|0.440*|0.616*|-0.925**|0.291|0.634**|-0.827**|0.233|0.594*|
||(0.390)|(0.231)|(0.324)|(0.371)|(0.231)|(0.321)|(0.376)|(0.233)|(0.326)|
|Head’s age||||-0.012|-0.009|0.021|-0.010|-0.010|0.020|
|||||(0.027)|(0.014)|(0.022)|(0.026)|(0.014)|(0.021)|
|Head is female (=1)||||1.441**|-1.342***|-0.098|1.542**|-1.329***|-0.213|
|||||(0.639)|(0.337)|(0.510)|(0.630)|(0.333)|(0.500)|
|_Ref: Head’s education: No education_||||||||||
|Class 1 to 5||||-0.914**|0.666***|0.248|-0.941**|0.672***|0.269|
|||||(0.411)|(0.254)|(0.332)|(0.411)|(0.253)|(0.330)|
|Class 6 to 8||||-1.125**|0.485|0.640|-1.117**|0.489|0.627|
|||||(0.468)|(0.302)|(0.403)|(0.464)|(0.302)|(0.405)|
|Class 9||||<br>0.986|<br>0.072|<br>-1.058|<br>0.838|<br>0.151|<br>-1.00|
|||||(0.992)|(0.646)|(0.739)|(0.967)|(0.630)|(0.742)|
|SSC and above||||-1.139|1.072**|0.066|-1.168|1.058*|0.110|
|||||(0.726)|(0.540)|(0.678)|(0.744)|(0.541)|(0.678)|
|HH receive international remittance=1||||-1.262|1.489**|-0.226|-1.033|1.372**|-0.339|
|||||(1.059)|(0.648)|(0.743)|(1.092)|(0.644)|(0.781)|

21
# **Contents**

|**S1**|**Road Improvements by Baseline Woreda Nighttime Lights**|**S2**|
|---|---|---|
|**S2**|**RSDP Road Dataset**|**S3**|
|**S3**|**Nighttime Lights Data**|**S4**|
|**S4**|**Trends in Woreda-Level Outcome Variables Over Time**|**S5**|
|**S5**|**Kebele Information**|**S8**|
|**S6**|**Universal Electricity Access Program versus RSDP**|**S9**|
|**S7**|**Two-Way Fixed Efect Models**|**S10**|
|S7|.1<br>Results using Kebeles as Unit of Analysis<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .|S10|
|S7|.2<br>Results using 1x1km Pixels as Unit of Analysis . . . . . . . . . . . . . . . . . . . . . . . . .|S11|
|**S8**|**Additional IV Results Analyzing the Impact of Areas Incidentally Connected to**<br>**the RSDP**|**S12**|
|S8|.1<br>Treated and Control Summary Stats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S12|
|S8|.2<br>OLS Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S12|
|S8|.3<br>First Stage Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S13|
|S8|.4<br>Second Stage Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S14|
|S8|.5<br>Second Stage Results - Diferent Baseline Nighttime Light Groups<br>. . . . . . . . . . . . . .|S16|
|**S9**|**Balance Across Non-Targeted Treated and Control Areas**|**S18**|
|**S10**|**Constructing Minimum Spanning Trees**|**S19**|
|**S11**|**Estimating Travel Time from Road Network Data**|**S21**|
|**S12**|**Market Access Two-Way Fixed Efect Model**|**S23**|
|**S13**|**Diference in Diferences - Additional Results**|**S26**|
|S1|3.1 Results using 1x1km Grid as Unit of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . .|S26|
|S1|3.2 Results when excluding Kebeles within 100km of Addis Ababa<br>. . . . . . . . . . . . . . . .|S27|
|S1|3.3 Heterogeneity of impacts across Woredas with zero and positive baseline nighttime lights . .|S28|
|**S14**|**What land cover type does urban replace?**|**S29**|
|**S15**|**Market Access, Long Diference - Additional Results**|**S30**|
|S1|5.1 Varying Doughnut Sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S30|
|S1|5.2 Varying Values of Theta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S32|
|S1|5.3 Varying Baseline Nighttime Light Groups<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .|S34|

S1
between 2007 and 2018 (see Figure 14). This means that in both 1987 and 1997 sectors that had a relatively high labor productivity subsequently increased their share of overall employment. In 2007, the opposite was the case and relatively unproductive sectors increased their employment share in the ensuing ten years. Also, despite the overall positive correlation between initial labor productivity and subsequent labor reallocation for the periods from 1987 to 1997 and from 1997 to 2007, the employment share of manufacturing as a prominent sector with a relatively high level of labor productivity declined by about 4.6 percentage points between 1997 and 2007. In parallel, the share of employment in the trade services sector, which had a below average level of labor productivity in 1997, increased by about 5.1 percentage points in the ensuing ten years.

**Figure 12: Relationship between initial productivity and changes in employment share, 1987-1997**

<!-- Start of picture text --> 0.8<br>0.6<br>trn<br>0.4 bus<br>0.2 man<br>0<br>agr oth con<br>-0.2<br>-0.4 trd<br>-0.6<br>-0.8<br>-1<br>-20 -15 -10 -5 0 5 10<br>Change in share of employment (%), 1987-1997<br>aggregate productivity in 1987<br>Log of ratio of sectoral productivity to<br><!-- End of picture text --> Source: Authors’ calculations based on data from DOSM Note: Sectors depicted are agriculture, business services, construction, manufacturing, trade services, transport services, and other services.

**Figure 13: Relationship between initial productivity and changes in employment share, 1997-2007**

<!-- Start of picture text --> 0.8<br>bus<br>0.6<br>0.4 trn<br>man<br>0.2<br>0.0<br>trd<br>-0.2<br>agr con oth<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>-20 -15 -10 -5 0 5 10<br>Change in share of employment (%), 1997-2007<br>aggregate productivity in 1997<br>Log of ratio of sectoral productivity to<br><!-- End of picture text --> Source: Authors’ calculations based on data from DOSM Note: Sectors depicted are agriculture, business services, construction, manufacturing, trade services, transport services, and other services.

**Figure 14: Relationship between initial productivity and changes in employment share, 2007-2018**

<!-- Start of picture text --> 0.8<br>0.6<br>trn<br>0.4 bus man<br>0.2<br>oth<br>0<br>-0.2<br>agr<br>-0.4<br>trd<br>-0.6<br>-0.8 con<br>-1<br>-20 -10 0 10<br>Change in share of employment (%), 1987-2018<br>aggregate productivity in 2007<br>Log of ratio of sectoral productivity to<br><!-- End of picture text --> Source: Authors’ calculations based on data from DOSM Note: Sectors depicted are agriculture, business services, construction, manufacturing, trade services, transport services, and other services.

**Figure 15: Relative productivity and share of employment by sector, 2018**

<!-- Start of picture text --> 2.5 30<br>2.0 25<br>20<br>1.5<br>15<br>1.0<br>10<br>0.5 5<br>0.0 0<br>Relative productivity Share of employment (RHS)<br>productivity Share of employment (%)<br>Ratio of sector productivity to overall Construction Other Agriculture Trade Manufacturing Transport Business<br><!-- End of picture text --> Source: Authors’ calculations based on data from DOSM 16
move from one vertex to the other. Table C7 reports the average speed for each one of the roads and the public transit system.

# **C.2 Calibration of Fixed Costs**

Table C8: Estimation of fixed costs

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Outcome:|ln _Mis_|ln _Mis_|ln _Mis_|ln _Mis_|
|ln _Lis_|0.715***|0.879***|0.642***|0.568***|
||(0.014)|(0.077)|(0.017)|(0.054)|
|_γi_|1.799***|1.735***|1.825***|1.853***|
||(0.034)|(0.036)|(0.051)|(0.046)|
|ln _wisLis_||-0.154**||0.070|
|||(0.069)||(0.051)|
|_γ_|-1.005***|-0.559***|-0.598***|-0.810***|
||(0.055)|(0.194)|(0.091)|(0.183)|
|Observations|5,387|5,387|4,374|4,374|
|Adjusted R-squared|0.851|0.853|0.901|0.902|
|Implied _FI_|0.182|0.123|0.117|0.141|
|Implied _FF_|1.366|0.875|0.911|1.124|
|State FE|X|X|||
|Municipality FE|||X|X|

_Notes:_ This table reports the results of a regression relating the number of firms to the number of workers to recover the parameter _β_ and the fixed costs _FI_ and _FF_ for the informal and formal sector respectively. The unit of observation is a sector-census-tract cell. The dependent variable is the log number of firms in each cell. Columns 1 and 2 include state fixed effects, while column 3 and 4 include census-tract fixed effects to control for the price of commercial floor space _qi_ . Even columns add as a control the wage bill for each sector to control for the price per unit of commercial floor space. Standard errors are clustered at the municipality level and reported in parentheses. _∗p <_ 0 _._ 1, _∗∗ p <_ 0 _._ 05, _∗∗∗p <_ 0 _._ 01.

In the model from section 4, in equilibrium, the optimal number of firms is where _L_<sup> ̃</sup> _is_ and _Z_<sup> ̃</sup> _is_ is the amount of labor and commercial floor-space units employed by location _i_ and sector _s_ , _σs_ is the elasticity of substitution, and _Fs_ is the entry fixed cost. Taking logs, I estimate the following equation relating the number of firms to the number of workers for both sectors in the baseline year. This estimation allows me to recover the parameters _Fs_ : 14
# **VII. CONCLUSION**

We used satellite data on evening hour luminosity, which is a good proxy for economic activity across temporal and spatial scales, to measure monthly economic activity in South Asia at the district level. To do so, we divided nationally predicted GDP into agricultural and non-agricultural output and proposed a very simple distribution process that distributes the former based on rural population and the latter based on nightlights. For Indian districts, our measure has a high correlation with measured activity in national accounts. We used our measure to study the 2015 earthquake in Nepal, violent conflict outbreaks in Afghanistan and the 2016 demonetization in India.

We restricted subnational measures of economic activity to administrative units and the district level. Providing the measure at the district level has the advantage that it can be merged with a lot of different data series which are available at this level. However, the measure could be computed for any spatial aggregation one can think of. For example, it can be only a few square kilometers small and it can span different districts, provinces, or even countries. We have also restricted our measure to be monthly, so that we can rely on publicly available data. Nightlight data is also available daily (though not yet publicly), which allows for computing an even more frequent measure. However, there exists a trade-off between the frequency of the measure and the difficulty of the data cleaning process.

Our measure can be improved in several ways. First, we invested a lot into cleaning the data and followed best practices. Nevertheless, the data cleaning could probably be improved by a procedure optimized for computing a monthly measure of economic activity. The method should identify and select the pixels with the largest information content and disregard those that are mostly noise. Second, our distribution of economic activity can be improved. For example, the distribution could acknowledge within-country heterogeneity in agricultural productivity.

23