rafmacalaba/data-use-mentions-extended huggingface dataset:
before 1990. This indicates that roughly 3 out of every 4 episodes in our sample took place in the 1980s, the years of 1986 and 1987 witnessing the largest number of episode starts. Table 1 presents the summary statistics for the 24 inflation episodes. Over the full sample, the average length of inflation episodes is roughly 15 quarters and 21 of them last for more than 8 quarters. Average rise in inflation, from trough to peak, is about 26 percent. In Table 1, we decompose our sample into three periods: 1984-1989, 1990-1995, and 1996-2001. The first period captures the period during which access to global capital markets was relatively limited. For the 12 episodes ending this period, the average length and the rise in inflation are about 13 quarters and around 44 percent, respectively.

<u>Table1: Summary Statistics for Inflation Episodes</u>

|||E<br>|pisodes Ending<br>||
|---|---|---|---|---|
||Full Sample|1984-1989|1990-1995|1996-2001|
|Number of episodes|24|12|8|4|
|Length (quarters)|14.83|13.33|18.13|12.75|
|Initial inflation rate|9.82|10.99|9.62|6.71|
|Ending inflation rate|35.85|54.61|19.87|11.56|
|Risein inflation|26.03|43.62|10.25|4.85|

Note: Figures are average values over the respective periods. Inflation figures come from our series obtained through nine-quarter centered moving average process.

There are 8 episodes between 1990 and 1995 with an average length of 18 quarters and an average rise in inflation of roughly 10 percent. It is interesting to note that during this period, which is characterized by increased access to capital markets, the average length
eligible individuals in village _v_ . The targeting differential can range from Tv = −1, where the program in village _v_ fully covers all non-eligible individuals with no benefits going to eligible individuals, to Tv = 1, where only eligible individuals are beneficiaries and they are all covered by the program. We find that the targeting differential has improved in the 2010/11 season compared to the start of the program, and that the targeting in study villages in Arusha is worse compared to Morogoro but much better than in Kilimanjaro. In fact, targeting in Kilimanjaro by 2010/11 was only 6 percent (0 percent would be consistent with lack of targeting, that is, when eligibility status plays no role in who becomes a beneficiary). In contrast, the targeting in Arusha by 2010/11 is 30 percent.

Table OA5 in the Online Appendix uses data collected at baseline to compare baseline characteristics of eligible households in the three regions of Arusha, Kilimanjaro and Morogoro. Households in Arusha appear to be richer and more educated than those in either of the other two regions. This perhaps explains the overall higher use of improved seeds and chemical fertilizer prior to 2008/09 in our study villages compared to villages in Kilimanjaro or Morogoro. Taking together the results of Table 2 and Online Appendix Table OA5, households and villages may not be readily comparable across regions but the trends in targeting over time are roughly similar.

Table 3 uses data from the eligible survey to compare the characteristics of eligible households that became beneficiaries to those that did not in lottery villages (columns 3-4) and in non-lottery villages (columns 7-8). Column 1 of Table 2 reports the data sources, column 2 the mean of the characteristics of all eligible farmers interviewed in lottery villages, and column 6 reports the analogous mean for eligible farmers interviewed in villages without the lottery. P-values associated with a t-test that the characteristics of beneficiaries and non-beneficiaries are different from zero in lottery and no lottery villages are reported in columns 5 and 9, respectively, and calculated
# Figure B.11: Direct effects on pre- and post-intervention bills

<!-- Start of picture text --> Timely payments Timely and past-due<br>only payments<br>(a) Payment rates in levels<br>% paid the bill % paid the bill<br>COVID-19 Intervention COVID-19 Intervention<br>60 60<br>billing periods bill billing periods bill<br>55 55<br>50 50<br>45 45<br>Treated (pooled) Treated (pooled)<br>40 40<br>Pure Control Pure Control<br>35 35<br>30 30<br>25 25<br>20 20<br>2019m1 2019m4 2019m7 2019m10 2020m1 2020m4 2020m7 2020m10 2021m1 2019m1 2019m4 2019m7 2019m10 2020m1 2020m4 2020m7 2020m10 2021m1<br>Billing Period Billing Period<br>(b) Difference relative to pure control group<br>Treatment Treatment<br>effect (p.p.) effect (p.p.)<br>COVID-19 COVID-19<br>6 6<br>billing periods billing periods<br>5 5<br>Oct 2020: Oct 2020:<br>4 4.4 p.p. 4 4.2 p.p.<br>3 3<br>2 2<br>1<br>1<br>0<br>0<br>-1<br>-1<br>2019m1 2019m4 2019m7 2019m10 2020m1 2020m4 2020m7 2020m10 2021m1 2019m1 2019m4 2019m7 2019m10 2020m1 2020m4 2020m7 2020m10 2021m1<br>Billing Period Billing Period<br><!-- End of picture text --> _Notes_ : These figures show the effect of the communication campaign on payment rates of pre- and post-intervention bills. The left panels only consider timely payments, defined as bills paid before the 27th of the corresponding month (i.e., any payment made after the 27th is considered unpaid). Hence,
mark>BRANCHLESS BANKING</mark> _3) Agent Banking_  Minimum standards to operate as an agent and services offered by agents

- Exclusivity of agent contracts

- Financial institution liability for agent actions

- _4) E-money_  Allowing e-money activities (issued by either financial or non-financial institutions)

- License requirements (interoperability, internal controls, consumer protection mechanisms) for non-financial institution e-money issuers

- Safeguards for customer funds

- <mark>MOVABLE COLLATERAL</mark> _<u>5) Warehouse receipts</u>_

> 2 See Annex I for the list of countries included in the EBA sample. The 62 countries are chosen to represent all country groups based on income level, geographical position and role of the agriculture sector as defined by World Bank (2007). For full information on EBA data and underlying methodology, see World Bank (2017).

5
households is influenced by the recall period. Evidence from the various surveys shows that the difference between the short (7-day) and long (12 month) recall period in the number of economic activities measured is significant, with the shorter recall period consistently understating reports of secondary economic activities (see Table 9). A comparison of the share of the working age population reporting secondary activities by recall period a lower proportion captured with the 12 month period in all surveys, with the gap widest in 2005/6, where the proportion reporting a secondary activity in the 7 day recall question was _19 percentage points_ lower than the 12 month recall in 2005/06. This is despite the longer recall question being improperly framed in 2005/6 so the responses are not fully comparable (response options included inactivity ones). A similar result was found in the 2009/10 UNPS survey, when the questions were worded exactly the same. It is possible that the extent of secondary activities varied over time in Uganda, with a peak in 2005/6, but we can’t really be sure.

18
# **4.3 Identifying Supra-Regional Trends**

Nearly all the metrics discussed thus far are founded on static concepts. As such, there is an unintended and potentially misleading implication that all industries are equally viable candidates for development, from both regional and supra-regional perspectives. This conclusion could lead a regional policymaker to work toward the recruitment or promotion of a targeted industry that simply might not be competitive in a given region. One broad brush approach that has allows the decomposition of changes in industrial structure into components that reflect national (supraregional) trends, industry mix, and region-specific characteristics is called Shift-Share Analysis. This method has been in use for decades and owes its longevity to simplicity of implementation, interpretation, and extension.<sup>8</sup> Shift-Share Analysis has been used in a variety of policy contexts, including the identification of regional comparative advantages and other region-specific, relative strengths and weaknesses.

# **_Shift-Share Analysis_**<sup>**_9_**</sup>

Once again, we use regional employment by industry as our indicator of industrial structure, although personal income or output data by industry would be equal informative when these data if and when these data are available. This method, however, requires industrial structure data for two different time periods. While there are numerous extensions, including dynamic shift-share (Barff and Knight 1988) and the incorporation of spatial structure (Nazara and Hewings 2004), the presentation below reflects the method in its most fundamental form.

With data for two different time periods, we use time _t_ − 1 as a reference to the beginning period and time to the ending period. Our industrial structure changes will thus refer to the period spanning time to time . Our variables are defined as follows:

> <sup>_e_</sup> _tr_<sup>- total employment in region</sup> at time

8 More sophisticated methods called structural decomposition analyses have been developed using input-output accounts as empirical foundations. See Lahr and Dietzenbacher (
Policy Research Working Paper 9188

# **Abstract**

Despite informality being the norm in conflict-affected countries, most estimates of the impact of conflict on economic activity rely on formal sector data. Using high-frequency data from Afghanistan, this paper assesses how surges in conflict intensity affect not only the formal sector, but also informal and illicit activities. Nighttime light provides a proxy for aggregate economic activity, mobile phone traffic by registered firms captures fluctuations in formal sector output, and the land surface devoted to poppy cultivation gives a measure of illicit production. The unit of observation is the district and the period of reference is 2012–16. The same dynamic specification and controls are used for the estimation in the three cases, making the results comparable across sectors. Controls include the presence of combat troops and the level of foreign aid at the local level, which both influence local living standards in Afghanistan. The results show that an increase in conflict-related casualties has a strong negative impact on formal economic activity in the following quarter and a positive effect on illicit activity after two quarters. The impact on aggregate economic activity is negative, but more muted.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gacevedo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction
time trends. Appendix Table 2 shows analogous estimates using total (sum of life and non-life) IP.

In order to quantify the magnitude of our estimates, we ask what the predicted increase in IP would be if a country were to move from the 25<sup>th</sup> to the 75<sup>th</sup> percentile of the distribution of each one of the predictors. For example, what would be the predicted change in life IP if a country moved from the 25<sup>th</sup> to the 75<sup>th</sup> percentile of the distribution of institutional quality (i.e., governance index). The numbers are shown in Table 3a. Appendix Table 3a shows analogous comparative statics using estimates of total IP from Appendix Table 2.

In Table 3a, we can see that the estimates of our preferred specification (from Column 2 of Table 2) imply that an increase in GDP per capita that moves a country from the 25<sup>th</sup> to the 75<sup>th</sup> percentile of income per capita distribution (roughly a USD 20,000 increase, or one standard deviation) is associated with an increase in life IP of 141 percent over an average of 1.2% of GDP. The same increase in GDP per capita is associated with an increase of 62 percent in non-life IP, over an average of 1.6% of GDP. Interestingly, changes in institutional quality of the same magnitude as changes in income per capita (around 1.5 SD of the governance index, which moves a country from the 25<sup>th</sup> to the 75<sup>th</sup> percentile of the distribution) are associated with similar changes in life and non-life IP (125 and 54 percent, respectively). Thus, our estimates suggest that, even controlling for income per capita, institutional quality is strongly correlated with
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between 2001 and 2006, through a reduction in search costs (i.e., mobile phones reduce price dispersion). Similarly, Aker and Mbiti (2010) highlight the magnitude of the effects in rural areas, for example, in Ghana, farmers in the countryside are now using mobile phone services (e.g., text messages) to learn about the prices of corn and tomato in Accra.

Mobile broadband has also allowed the dissemination of technologies such as mobile banking that have the ability to promote inclusive development (Baabdullah et al., 2019) through a decreasing effect of poverty and income inequality (Asongu and Nwachukwu, 2018). By using data from 93 developing countries for 2011, Asongu and Nwachukwu (2018) find that mobile banking has a significant and positive correlation with the Human Development Index (HDI) with the magnitude of the association varying among different country income groups (low, middle, upper). Mbiti and Weil (2016) analyze the Kenyan mobile banking M-Pesa and conclude that the success of M-Pesa has decreased the prices of money-transfer competitors, increased transactions among individuals, served as a saving mechanism, and increased the probability of being banked.

Finally, to foster significant welfare and development gains, ICT adoption should be accompanied by increased digital literacy. Gebremichael and Jackson (2006) discuss the concept of “information poverty” and claim that to foster development and to not fall behind the developed world, SSA countries and other developing nations should bridge the digital divide and alleviate information poverty. To do so, governments should focus on targeting the determinants of ICT adoption such as the education level of the household head (Mwanthi, 2009).

14
.19 0.0356`|
|`CIa_1 2.19206`|`0.4763 4.60 0.0001`|
|`dumm1986 7.19174`|`3.288 2.19 0.0357`|
|`dumm2008 7.16930`|`3.263 2.20 0.0349`|

``` sigma = 3.54329 ``` ``` Equation for: DInterestrt ```

||`Coefficient Std.Error t-value t-prob`|
|---|---|
|`Drm2_1`|`2.68178 1.574 1.70 0.0975`|
|`Drgdp_1`|`-4.20947 2.275 -1.85 0.0729`|
|`DINFL_1`|`0.0292526 0.01732 1.69 0.1004`|
|`DDISC_1`|`0.550912 0.09303 5.92 0.0000`|
|`Drm2_2`|`-3.90089 1.442 -2.70 0.0106`|
|`DInterestrt_2`|`0.183695 0.1110 1.65 0.1072`|
|`CIa_1`|`-0.497069 0.1093 -4.55 0.0001`|
|`dumm1992`|`-5.41473 0.9314 -5.81 0.0000`|
|`dumm2009`|`-2.67578 0.6491 -4.12 0.0002`|
|`dumm2010`|`0.991003 0.5750 1.72 0.0939`|
|`dumm2013`|`2.82036 0.6527 4.32 0.0001`|

``` sigma = 0.502194 ``` ``` log-likelihood 31.986552 -T/2log|Omega| 344.153029
no. of observations 55 no. of parameters 26
```

52
limited funds from family and friends, the other nonformal financing sources consist of suppliers' credits in the form of trade credit, buyers' advances (either in cash or in raw materials and supplies), moneylenders, various "club" systems pooling membership savings for loans, credit cooperatives and NGOs. The costs of financing facilities in the nonformal sector are typically twice or more than the prevailing interest rates charged by institutions in the formal sector. Moreover, a number of developing-country governments control interest rates and influence the allocation of credit, usually in favor of large-scale modem industry and exporters. This either drives up interest rates to MSBs or, if lending rates are controlled, enables even fewer small firms to obtain credit. The smaller the enterprise, the more likely it is to be denied credit from formal sector institutions.

28. Data available from the experience of various countries indicates that the MSB sector is an important, large and growing subsector of many countries in terms of value added generated and number of the labor force absorbed.<sup>10</sup> Equally important are the subcontracting linkages between MSBs and the larger industrial firms in urban areas as well as between them and the agricultural sector in rural areas. It can then be argued that it is possible to achieve relatively high impact on productivity and output of MSBs by means of additions to equipment used in production and application of new technology to operations. Lease financing is especially suited for this role of providing the financial resource for acquisition and use of equipment and new technology by MSBs, leaving the borrower's nonformal sources available for working capital needs. Box 3 below summarizes financing options available to small businesses and microenterprises with and without a leasing industry.

15elei~s tt 10 MSBs employ a substantial portion of the econonmically active population in the informal sector, particularly in developing countries. In the Statistical Yearbook of the International Labour Organisation (ILO), the pertinent classification would be employerlself-employed for own account and the noncompensated household members employed in family enterprises categories.
# **3.1. Data and descriptive statistics**

To explore possible links between political and economic empowerment, we use individual data from a complete enumeration of all adult residents in 190 villages in 13 states implemented in 2014/15 as part of the long-running ARIS-REDS panel.<sup>6</sup> Information was collected on 275,677 individuals in 91,984 households. Of these 23,350, generally the most disadvantaged ones, had a job card allowing household members to apply for work under NREGS. To obtain a conservative estimate of reservation-induced effects, we limit our analysis to these households.

In addition to standard demographic and socio- economic characteristics at individual and household level, the survey obtained detailed information on actual and desired labor market participation at individual level. For individuals who participated in NREGS, data were gathered on key features of program implementation including whether dated work receipts were issued, payment was deposited directly in beneficiaries’ own account, if they were paid less than the statutory wage and, if yes, whether a complaint was lodged. For a subsample of states with traditionally high levels of discrimination against women, an extra module was administered asking about individuals’ involvement in key household-level decisions. In addition, a village questionnaire was also administered to, among others, elicit characteristics of all village leaders elected from 2005 together with election details, including if the election was ‘reserved’.

Table 1 illustrates the timing of panchayat elections in sample states. Most states held elections in 2005/06 so that the local government had been recently elected when NREGS was launched in 2006-2008. Another round of elections was held in 2010 or 2011 and the village council leaders elected then had just completed their terms when our data were collected. Random assignment of female leadership reservation to villages provides an opportunity to assess if exposure to female leadership in the current or immediately preceding election period improved women’s ability to take advantage of labor market opportunities in NREGS or the private sector although we are unable to
# _Core Results_

Table 2 reports regressions of changes in the $1.25/day and $2/day poverty headcounts against percentage changes in food prices using OLS and the IV GMM. For both regressors the standard errors are clustered at the country level, and, though not reported, both models include time dummies to control for generic international economic trends.

The most striking feature of regressions 1 through 4 in table 2 is that all the coefficients are negative and significant at the 5% level or higher, implying that increases in real food prices tend to reduce poverty rates at both the $1.25/day and $2/day poverty lines. The magnitude of the estimated coefficients is similar across the two poverty lines but very different across estimators. Specifically, the IV GMM estimates are several times larger than the robust regression estimates, suggesting that the OLS estimates might be biased upwards, perhaps because increases in domestic food prices are often driven by negative domestic shocks such as droughts or broader economic crises.<sup>8</sup> Even so, the IV GMM point estimates also need to be interpreted cautiously. While the exclusion assumption is supported by the hypothesis tests in the bottom of table 2, the KleibergenPaap rk Wald F statistic suggests that our instrument (international food prices) is relatively weak, which is to be expected since international price transmission into domestic markets is quite variable and since the basket of nine international prices is not always relevant to domestic consumption and production patterns.<sup>9</sup> Consistent with a weak instrument problem, the standard errors on the IV GMM coefficient estimates are relatively large. This suggests we should be cautious in attaching too much importance to an economic interpretation of the magnitudes of these coefficients. Bearing that caveat in mind, the six percent food price increase in the median country from 2005 to 2008 would predict a 1.76 percentage point decline in $1.25 poverty in the average country based on the least squares regression result and
into which urban goods and services informal firms need and use at their workplace. Finally, we use big data to proxy the correlation between urban spaces and informal job types in urban Mozambique, including poverty, urban density and cell tower density. Again, the urban variables observed where workers live, which may not be where they are working.

# **7.1. Access to Urban Infrastructure and Services**

A small share of owners of informal firms identifies a business need for public utilities, with significant differences by firm type. Less than 20 percent of firm owners sampled in the IES state that they need water or electricity services (Figure 21). However, low average electricity needs are driven by the large LE-SE sector; 40 percent of the other two firm types

> 62 The three country IES sample found that 60% of informal businesses operated outside the household premises. We do not have similar data for the 26-country GMD.

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