rafmacalaba/datause-encoder-data huggingface dataset:
<mark>by-year estimates. Columns 1, 2, 5, and 6 use a global approximation to the conditional expectation based on a cubic polynomial and restrict the sample to grids within 3 km and 2 km of the discontinuity. Columns 3, 4, 7, and 8 use a quadratic local regression to approximate the conditional expectation and set a bandwidth of 4.7 km (following Imbens and Kalyanaraman 2012) or 3 km. Standard errors robust against heteroskedasticity and serial correlation within cells are reported in parenthesis. Estimates with</mark><sup>***</sup> <mark>are significant at the 1%, those with</mark><sup>**</sup> <mark>are significant at the 5%, and those with</mark> * <mark>are significant at the 10%.</mark> _<mark>Source: Authors’ analysis based on data from United Nations Office of Drugs and Crime, UNODC.</mark>_ Table 2. Fuzzy RD Estimates of the Local Average Treatment Effect of Spraying on Cultivation around the 10 km Cutoff.

|Sample:|Fro|m 2006 to 2|010|Fro|m 2008 to|2010|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|||Panel<br>|A: Linearg<br>|lobalpoly<br>|nomial<br>||
|Effect of spraying:|-3.508 <sup>**</sup>|-3.084 <sup>**</sup>|-2.918 <sup>**</sup>|-2.257 <sup>**</sup>|-1.869 <sup>*</sup>|-2.313 <sup>*</sup>|
||(1.403)|(1.369)|(1.455)|(1.050)|(1.014)|(1.203)|
|Excluded instrument F<br>ii|41.6|42.7|35.7|44.8|47.7|35.0|
|statstc<br>||Panel B:<br>|Quadratic<br>|globalpo<br>|lynomial<br>||
|Effect of spraying:|-3.756 <sup>***</sup>|-3.472 <sup>***</sup>|-3.260 <sup>**</sup>|-2.392 <sup>**</sup>|-2.102 <sup>**</sup>|-2.561 <sup>**</sup>|
||(1.345)|(1.338)|(1.418)|(1.019)|(0.996)|(1.186)|
|Excluded instrument F<br>ii|41.8|42.7|35.8|44.6|47.7|35.1|
|statstc<br>|<sup>**</sup>|Panel<br><sup>*</sup>|C: Cubicg<br><sup>*</sup>|lobalpoly <br> <sup>*</sup>|nomial<br><sup>*</sup>||
|Effect of spraying:|-4.144|-5.394|-7.298|-3.085|-4.843|-4.539|
||(2.112)|(3.229)|(4.383)|(1.673)|(2.803)|(2.781)|
|Excluded instrument F<br>statistic|15.1|8.0|5.6|15.9|7.8|7.0|
|Sample ofgrids:|3_km_<br>|2.75_km_<br>|2.5_km_<br>|3_km_<br>|2.75_km_<br>|2.5_km_<br>|
|Observations:|13265|11885|10560|7959|7131|6336|
||<sup>***</sup>|Panel<br><sup>***</sup>|D: Local<br><sup>**</sup>|linear regr<br><sup>**</sup>|ession<br><sup>**</sup>|<sup>**</sup>|
|Effect of spraying:|-2.980|-3.183|-3.628|-1.790|-1.933|-2.526|
||(1.019)|(1.163)|(1.537)|(0.765)|(0.889)|(1.206)|
|Excluded instrument F<br>|74.0|58.2|34.5|84.8|64.8|37.6|
|statistic<br>||Panel E<br>|: Localqu<br>|adratic reg<br>|ression<br>||
|Effect of spraying:|-3.179 <sup>***</sup>|-3.591 <sup>**</sup>|-3.791 <sup>*</sup>|-1.900 <sup>**</sup>|-2.402 <sup>**</sup>|-3.241 <sup>*</sup>|
||(1.233)|(1.516)|(2.178)|(0.944)|(1.167)|(1.926)|
|Excluded instrument F<br>statistic|30.0|20.8|9.8|30.5|21.5|9.1|
|Bandwidth:|_b_<br>_IK_<br>|4<br>_b_<br>_km_<br>|3<br>_b_<br>_km_<br>|_b_<br>_IK_<br>|4<br>_b_<br>_km_<br>|3<br>_b_<br>_km_<br>|
|Observations:|22340|18495|13265|13404|11097|7959|
1975-89 period, provides the most conclusive evidence to date on loiR,-term protection conferred by maternal tetanus immunization. Figure 7 shows three-year moving averages in 4-14 day mortality according to 1974 vaccination status for births to mothers residing in the Matlab comparison area. While mortality rates were comparable in the period prior to the vaccine trial (197374), a sharp differential in mortality by vaccination status is observed subsequent to the 1974 trial. Although) some narrowing in mortality rates between groups is evident over time, the mortality rates for the diffeerent groups did not converge over time. Separate analysis by the authors indicated that relative to the unimmunized group, the reduction in 4-14 day mortality among the group receiving two injections of tetanus toxoid was highly statistically significant (p <.01) for 8 years and remained significant (p <.05) through 1989 (with the exception of 198586), a period of 15 years. Thus, even in 1989, the 4-14 day mortality rate for the two injection group was 6 per 1000, compared to 23 per 1000 in the unimmunized group.

# Number of Doses

# One Dose

Con iderable interest has centered upon the degree of protect.,n provided by one dose of tetanus toxoid, since an effective single dose would greatly simplify the delivery of maternal tetanus immunization. The findings on this issue with respect to mortality impact have been at best inconsistent, and not infrequently contradictory. For example, the New Guinea study found no differences in the neonatal tetanus rates between the small numbers of births to mothers with one injection vs. no injections, with both groups experiencing markedly higher neonatal tetanus rates than those who received 2 or 3 tetanus injections. The Colombian study, on the other hand, reported somewhat lower neonatal tetanus rates among births who received 1 vs. 0 tetanus toxoid injections, although this difference was not statistically significant and the rate for the 1 dose group was still considerably higher than the group who received 2-3 injections (40 vs. 0 per 1000). The small numbers of cases included in both of these studies, however, make definitive conclusions difficult.

Once again, the most persuasive evidence on the issue of a single tetanus toxoid dose comes from the Matlab study area. However, even within the Matlab study area, the findings on this issue have not been entirely consistent. The effect of one dose is closely linked to the issue of duration of protective immunity. As shown in Figure 8, the study by Black, et al. found that one injection of tetanus toxoid was associated with significantly lower 4-14 day mortality 9-20 months after 24
46

|**Country**|**V**<br>**1999**|**V**<br>**2009**|∆**V**|∆**P**|∆**C**|**Data**<br>**source**|
|---|---|---|---|---|---|---|
|Uzbekistan|-2.0|-1.4|0.6|0.0|0.6|IEA|
|Vanuatu|1.1|3.8|2.7|0.0|2.7|EIA|
|Venezuela, RB|-18.5|-16.1|2.4|0.3|2.0|IEA|
|Vietnam|-3.8|0.2|4.0|-0.9|4.9|IEA|
|Yemen, Rep.|-26.9|-12.8|14.1|7.5|6.6|IEA|
|Zambia|2.1|2.1|0.0|0.0|0.0|IEA|

_Sources:_ EIA 2012, IEA 2012, WDI 2012, and author’s calculations.

_Note_ : Reporting to the first decimal point is for formatting purposes and does not imply that numbers exceeding 10 percent have three significant figures.

Table A1.8: Decomposition analysis for 1999–2009, continued

|**Country**|**Price**|∆**P**<br>**Output**|**GDP**|**Price**|**Oil**|∆**C**<br>**Energy**|**GDP**|**Exch.**|
|---|---|---|---|---|---|---|---|---|
|Albania|-2.4|-1.2|2.7|5.2|-0.6|-2.3|-1.4|-1.5|
|Algeria|-35.1|-4.8|31.6|5.4|-0.1|0.2|-3.7|0.4|
|Angola|-159.1|-116.9|301.6|46.0|18.4|-22.1|-136.0|92.7|
|Antigua and Barbuda|0.0|0.0|0.0|7.2|0.0|0.1|-2.1|0.0|
|Argentina|-4.4|0.3|0.3|3.9|-0.3|-0.3|-4.1|4.1|
|Armenia|0.0|0.0|0.0|2.4|0.7|-0.9|-0.7|-0.8|
|Australia|-1.7|0.1|1.2|2.5|-0.1|-0.4|-0.8|-0.3|
|Austria|-0.1|0.0|0.1|1.5|0.0|-0.1|-0.2|-0.4|
|Azerbaijan|-105.5|-111.6|204.9|16.0|-5.0|-13.9|-8.6|-0.3|
|Bahamas, The|0.0|0.0|0.0|6.6|0.1|2.0|-1.0|0.0|
|Bangladesh|-0.1|-0.1|0.1|2.1|-0.7|0.3|-0.9|0.7|
|Barbados|-0.8|0.6|0.2|5.0|-0.3|-0.3|-1.6|0.0|
|Belgium|0.0|0.0|0.0|2.9|0.2|-0.4|-0.5|-0.7|
|Belize|-3.0|-5.8|2.1|7.9|0.2|2.1|-1.0|0.0|
|Benin|0.0|0.0|0.0|7.5|4.6|0.0|-2.4|-1.9|
|Bhutan|0.0|0.0|0.0|3.1|-1.5|0.8|-1.3|0.3|
|Bolivia|-6.3|-1.0|4.2|6.2|0.7|-0.2|-3.3|1.0|
|Bosnia and Herzegovina|0.0|0.0|0.0|4.7|-0.8|0.1|-2.3|-1.0|
|Botswana|0.0|0.0|0.0|2.9|0.9|-0.7|-2.0|1.1|
|Brazil|-3.5|-1.7|3.2|4.1|-0.3|-0.3|-2.7|0.3|
|Brunei Darussalam|-46.0|4.4|33.0|3.4|-0.1|0.3|-1.7|-0.5|
|Bulgaria|-0.1|0.0|0.1|7.3|0.9|-2.9|-3.5|-1.6|
|Burkina Faso|0.0|0.0|0.0|2.7|0.0|-0.6|-0.6|-0.6|
|Burundi|0.0|0.0|0.0|4.3|-1.1|-0.8|-3.8|2.8|
|Cambodia|0.0|0.0|0.0|6.3|2.2|-2.9|-2.1|0.5|
|Cameroon|-10.8|3.8|6.6|2.6|1.1|-0.5|-0.5|-0.5|
|Canada|-5.2|-1.0|3.3|3.6|0.2|-0.6|-0.8|-0.8|
|Cape Verde|0.0|0.0|0.0|2.5|0.1|0.6|-0.1|-0.6|
|Central African Republic|0.0|0.0|0.0|2.6|0.4|0.0|-0.7|-0.6|
|Chad|-28.8|-59.0|50.0|0.8|0.0|-0.2|-0.3|-0.2|
|Chile|-0.2|-0.1|0.1|4.6|0.5|-0.8|-2.2|0.4|
|China|-3.1|-0.4|3.7|4.4|-0.9|-0.3|-1.3|-0.7|
|Colombia|-9.6|1.5|7.9|3.4|-0.5|-0.8|-2.3|0.6|
Finally, we selected two districts, Makassar and Balikpapan, which differ in the quality of their governments’ PFM systems but have similar values on other variables. As a measure of PFM quality, we use the average procurement budget (capital investment, goods and services) as a proportion of total expenditure for 2007 to 2011.<sup>6</sup> The budget data for 2007 and 2008 is actual budget realization and the data for 2009 to 2011 is planned expenditure.<sup>7</sup> Since the narratives of the case studies did not include sufficient detail on the relationship between the quality of Makassar’s and Balikpapan’s PFM systems and the uptake of reform, we chose to eliminate the discussion of this relationship from this paper. In a forthcoming paper (Turkewitz et al. 2013), we use quantitative, time series data on the quality of districts’ PFM systems and procurement to illustrate the relationship between PFM systems, the uptake of reform and procurement performance.

In each of the six localities, teams from Bandung Trust (B-Trust) and The Asia Foundation carried out fieldwork for a period of two weeks. The research teams used a snowball sampling approach to identify key individuals involved in procurement for the study. Using instruments, they conducted in-depth interviews and focus groups with key respondents from the local executive and legislative branches, government departments, the private sector, journalists, academics and civil society. The teams collected government documents, data and information, as well as policy documents, newspaper clippings, reports and books.

A short description of the local governments included in our case studies is below:

- _North Luwu_ is a remote district (population 450,000) of South Sulawesi that was a sub-district of Luwu district until 1999. Relatively large geographically, North Luwu has large forest reserves (55% of the district is zoned as protected forest) and natural resource opportunities that

> 6Our measure of PFM quality average expenditure on capital investment and goods and services is an approximate but best available measure.

> 7We considered using the proportion of “unspent budget” (SILPA) and the value of the administrative budget (personnel, interest, subsidy and contingency) as proxies, but decided not to use these as indicators for two reasons. Our first batch case studies found that a higher SILPA may indicate a higher level of competition in procurement rather than the district’s inability to manage its budget. The administrative budget was not used since it is already reflected by look at the average proportion of procurement in the district budget (i.e., a higher administrative budget corresponds to a lower procurement budget).

11
Table 4. Out-of-sample performance in Guatemala and Honduras based on K-fold cross validation

|**Transition from**<br>|Mean<br>**primary to lo**|SD<br>**wer secondary**:**Gua**|Min<br>**temala**|Max|
|---|---|---|---|---|
|_Sensitivity_|||||
|In sample|81.27|0.52|80.10|82.40|
|Out of sample|69.26|0.72|67.95|70.67|
|_False-Positive P_|_roportion_||||
|In sample|20.94|0.47|19.90|21.98|
|Out of sample|24.51|0.52|23.54|25.74|
|**Transition from**<br>|**primary to lo**|**wer secondary**:**Hon**|**duras**||
|_Sensitivity_|||||
|In sample|78.26|0.16|77.94|78.64|
|Out of sample|72.27|0.19|71.87|72.73|
|_False-Positive P_|_roportion_||||
|In sample|19.04|0.18|18.68|19.48|
|Out of sample|21.73|0.19|21.37|22.17|

26
Starting with the top row, our study is set during a period in which plant counts and output are growing for the organized sector, but employment levels are shrinking overall. This is true in general, and the divergence is even more pronounced for entrants. Accordingly, labor productivity for establishments is rising on average.

Looking at differences in growth patterns by distance from the GQ network, non-nodal districts within 10 km of the GQ network demonstrate growth that exceeds that in districts 10-50 km from GQ in every column. Moreover, in five of the seven columns, the growth in these very proximate districts also exceeds that in districts over 50 km away from the network. For convenience, we tabulate this ratio near the bottom of Panel A. The share changes in Panel B also tend to be quite strong considering the big increases in the nodal cities that are factored into these share changes.

Distance from the NS-EW highway system provides an interesting contrast, even at the level of these descriptive statistics. First, it is clear that we should be careful about assigning all of the changes observed for the GQ upgrades in nearby districts to the project, as districts near the NS-EW network also tend to exhibit growth differences compared to districts 50+ km away as a raw statistic despite the fact that the upgrade of NS-EW has only just begun by our sample end point. Second, districts near the GQ network appear to have especially increased plant counts and labor productivity compared to districts near the NSEW corridor. These patterns are not conclusive, perhaps most importantly because districts can be close to both highway systems as shown in Figure 1 (about 8% of GQ is also part of NS-EW), but they provide a suggestive starting point for our work.

# **Section 4: Empirical Analysis of the Impact of Highways on Economic Activity**

This section analyzes the impact of highway construction on manufacturing activity across districts. We use simple linear models with outcome variables expressed in logs, with the exception of TFP, which is expressed in unit standard deviations. Estimations include district and year fixed effects. These district fixed effects absorb long-run levels in manufacturing activity by district (along with any other fixed trait), while the year fixed effects absorb aggregate changes in the Indian manufacturing sector.

Estimations report standard errors clustered by district, weight observations by log total district population in 2001, and have 1,248 observations as the cross of four surveys and 312 districts. We winsorize outcome variables at the 1%/99% level to guard against outliers. Our district sample is 12
|**Table 2: Correlates of the Take‐up Decision among the Treatm**|**ent Group**<br>Take‐up W|hatAventure|Attend M|asterclasses|
|---|---|---|---|---|
||Full Model S<br>|tepwise Model <br>|Full Model <br>|Stepwise Model<br>|
|Baseline Investment Readiness Score|0.494**|0.573***|‐0.217|‐0.238*|
||(0.194)|(0.169)|(0.144)|(0.132)|
|Incorporated/Registered in Croatia|0.424||‐0.757**|‐0.771***|
||(0.397)||(0.296)|(0.286)|
|Incorporated/Registered in Serbia|0.409||‐0.331|‐0.393|
||(0.354)||(0.261)|(0.245)|
|Sector is business and productivity|0.128||0.040||
||(0.254)||(0.262)||
|Sector is lifestyle and entertainment|0.191||0.010||
||(0.366)||(0.327)||
|Uses Cloud Technology|0.137||0.577**|0.610**|
||(0.372)||(0.275)|(0.259)|
|Uses Big Data|0.740||0.278||
||(0.521)||(0.266)||
|Place in value chain is developer|‐0.181||‐0.360*|‐0.309|
||(0.292)||(0.215)|(0.207)|
|Place in value chain is service provider|‐0.211||‐0.108||
||(0.294)||(0.235)||
|Age of firm (years)|‐0.005||‐0.026||
||(0.056)||(0.044)||
|Early stage firm|‐0.147||0.024||
||(0.335)||(0.238)||
|Number of employees|0.008||‐0.005||
||(0.013)||(0.010)||
|Age of main founder|‐0.020|‐0.018|‐0.008||
||(0.014)|(0.013)|(0.012)||
|Main founder has post‐graduate education|‐0.141||0.487**|0.485**|
||(0.278)||(0.221)|(0.212)|
|At least one founder is female|‐0.674**|‐0.455|‐0.001||
||(0.302)|(0.337)|(0.270)||
|Company has a global focus|0.280||0.073||
||(0.305)||(0.225)||
|Have accepted outside financing|0.211||0.210||
||(0.324)||(0.252)||
|Has an outside private investor|‐0.123||‐0.121||
||(0.581)||(0.373)||
|Have participated in mentoring/accelerator program before|0.558||0.399|0.488*|
||(0.579)||(0.308)|(0.276)|
|Sample Size<br>Notes: Robust standard errors in parentheses. Coefficients ar<br>*, **, *** indicate significance at the 10, 5, and 1 percent leve<br>90.1% of firms took up WhatAVenture, and 43.7% attended at|174<br>e marginal effe<br>ls respectively<br>least one mast|174<br>cts from probit<br>erclass.|174<br>estimation.|174|

35
sector.<sup>25</sup> Formal mechanisms such as SALM committees are necessary to ensure that the coordination and exchange of information are sustainable in the long term and do not rely on personal or _ad hoc_ arrangements. For this reason, ALM committees (ALCO) are common practices in banks and other financial institutions, while dedicated committees have been in place in the public sector for the coordination of other policies, such as debt management committees (debt, monetary and fiscal policies) or macro prudential committees (financial sector).

The survey explored the existence and composition of an ALCO and/or risk management coordination committees at all levels. Overall, half of the respondents indicate that they have a high‐level risk management committee. Only four (Canada, Switzerland, U.K. and Uruguay) reported ALCO‐like committees to manage sovereign assets and liabilities (see graph 6).

Others mentioned coordination at least once a year between the minister of finance and the governor of the CB, or in the absence of a committee, the existence of a specific team, often in the Treasury, responsible for advising the government on ALM issues.

<!-- Start of picture text --> Graph 6 ‐ High Level Risk Committee<br>14 13<br>12 11<br>10<br>8<br>6<br>4 3<br>2<br>0<br>Yes No Other<br><!-- End of picture text --> Source: Responses to SALM survey Responses varied on the composition of the high‐level risk committees. The heads of the Treasury (5) or of the debt management office (6) are often mentioned as chair or member of the committee. Fewer countries report on the participation of the minister (3) or deputy minister (3). The governor of the central bank was mentioned once, with other officials from the central bank identified in a few more cases (3) as members of the committee. This confirms the sensitivity of the issue of the independence of the monetary authority in implementing an ALM approach in

> 25 This would not be necessary in the theoretical case of one single entity being responsible for managing jointly all the assets and liabilities of the sovereign. None of the respondents present such a situation.

26
for girls, the program, and the interaction of the two, respectively.

Relative to their counterparts in control villages, program-village households were more likely to desire that their boys become doctors (+5.8 percentage points) and engineers (+2.6 percentage points), and less likely to desire that they become security personnel (-5.0 percentage points). They were also more likely to desire that their girls become teachers (+6.5 percentage points), and less likely to desire that they become housewives (-14.9 percentage points). Program-village households desired higher attainment for their boys and girls (+1.5 and +1.7 years, respectively). In terms of age of marriage, program-village households had similar preferences to control-village households, for boys and girls.

Program-village boys were more likely to desire public sector employment (+12.2 percentage points). Program-village children did not desire more years of education than control-village children. However, children in both program and control villages desired more years of education than was desired by households (11.3 years for children versus 7.4 years for households, in control villages).

# **7 Program**

SEF maintained records of all program costs under detailed accounting heads. Figure 1 depicts the distribution of program cost components in fiscal years 2008–09, 2009–10, and 2010–11. The fiscal year runs from July 1 to June 30. In fiscal year 2008–09, the program was launched. However, subsidy payments to phase-1 program schools, those under this evaluation, were only provided in the last quarter of that fiscal year. Consequently, subsidies represented a small percentage of total program costs in fiscal year 2008–09, while fixed costs and other variable costs such as those related to administering the first phase of entry into the program represented large percentages. Costs in fiscal year 2010–11 are until April 2011, when the follow-up survey was administered, but two months short of the end of the fiscal year.

Over the evaluation period, SEF incrementally scaled up the program in phases, which affected the level and composition of costs. In fiscal year 2009–10, SEF administered a second phase of entry, with 97 schools added to the program. By the time of the follow-up survey, SEF had provided eight subsidy payments to phase-1 program schools. As school operators could not charge any fees, subsidy payments represented the sole source of school revenues. Subsidy costs for phase-1 (all) program schools evolved from 30 percent (30 percent) of total costs in fiscal year 2008–09 to 67 percent (72 percent) in 2009–10 to 48 percent (73 percent) in 2010–11.

The scale-up of the program during the evaluation period also affected how cleanly we could assign costs to phase-1 program schools. The cost data allow us to distinguish between subsidy costs for phase-1 and phase-2 program schools, but we could not separate out other types of costs in the same way. The cost data 12
in Tables 8 and 9.<sup>7</sup> Note that results displayed in Tables 7 and 9 include all relevant control variables; Tables 6 and 8 omit the controls.

**<u>Table 9: 2SLS: Effect of mobile activity on aspects of business enterprise</u>**

|<br>DV|<br>(1)<br>Transform<br>Agr.<br>product|<br>(2) Make<br>clothing|<br>(3) Own<br>construction|<br>(4) Any<br>profession<br>for own|<br>(5) Own<br>bus<br>service|<br>(6) Own<br>commercial<br>transport|(7) Own<br>restaurant<br>or bar|(8) Own<br>small<br>activity|
|---|---|---|---|---|---|---|---|---|
|MOBILE|-0.191*<br>(0.0994)|-0.105*<br>(0.0578)|-0.108**<br>(0.0508)|0.0166<br>(0.0458)|-0.141**<br>(0.0660)|-0.0553<br>(0.0471)|-0.0114<br>(0.0465)|-0.169**<br>(0.0759)|
|Constant|0.326***<br>(0.0601)|0.292***<br>(0.0294)|0.0665**<br>(0.0299)|0.372***<br>(0.0247)|0.0487<br>(0.0339)|0.0448*<br>(0.0265)|-0.00152<br>(0.0218)|0.0457<br>(0.0487)|
|# of OBS|||||||||
|R-squared (%)|26.70|19.70|23.60|18.80|20.90|19.80|22.80|23.70|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Cluster FEs|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FESs|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|1<sup>st</sup>Stage Estimates<br>[-0.052,-0.125]**<br>n X R-squared ≥ 51.6<br>√<br>No evidence of_“weak_<br>_instruments”_|||||||||

_Notes_ : Table reports estimates from regressions of the various aspects of engagement in non-agricultural business enterprises on mobile use and activity. Columns differ based on the combination of the dependent variable, model type and fixed effects included. All standard errors are clustered at the region <u>level and shown in parentheses. Significance levels are indicated by *** 1%, ** 5%, *10%.</u> _<u>Unit of Analysis</u>_ <u>:</u> **<u>cluster-level.</u>** The 2SLS estimates on mobile activity MOBILE are mostly significant and negative, as we would expect. Effects are large under the following 3 aspects: households’ ownership of a business service or center (-0.125); ownership of small income generating activities (-0.147); and involvement in the transformation of agricultural products (-0.178). These are then followed by households’ engagement in any sort of construction enterprises and the making of clothing for local sale. We do not find any effects for the other aspects, e.g., having own profession and ownership of a commercial transport business, which we do not find surprising. Most of the Nigerien households are agrarian, which helps to explain why the transformation of agricultural products will show the largest mobile activity impact. Next, households’ ownership of a business service or center is also expected to be largely affected by the SIM deactivation exercise since most of these service centers operate directly on the mobile infrastructures, e.g., the recent and

> 7 The OLS results are included here for mere display, we shall focus discussions below on the results from our preferred 2SLS/Policy Instrument.

‐ 18 ‐