rafmacalaba/data-use-mentions-extended huggingface dataset:
missing observations and reapply the battery of tests to ensure consistency of the series.

Finally, output price deflators are constructed by matching wholesale BPS price indexes available at the 5 digits level IHPB classification (Indeks Harga Perdagangan Besar) with KBLI. Moreover, we are able to obtain different capital deflators depending on the type of asset. We distinguish general price deflators from machinery and equipment, vehicles, and buildings. For all deflators, 2010 is used as the base year.

# **3.2 Mexican Data**

The main source of plant-level data is the Encuesta Anual de la Industria Manufacturera (Annual Manufacturing Industry Survey, EAIM), conducted by the Instituto Nacional de Estad ́ıstica y Geograf ́ıa (National Institute of Statistics and Geography, INEGI). The survey provides yearly statistically representative information on production, employment, investment by asset type at both national and NAICS-6-digits level for the period 2009-2015. We match this data with the Encuesta Mensual de la Industria Manufacturera (Monthly Manufacturing Industry Survey, EMIM), which includes the same sample of manufacturing establishments as the annual survey in order to obtain the monthly production of each plant at the beginning of the period, which is useful to construct energy price series as explained below. All variables included in the analysis are winsorized at the 1% and 99% levels.

Unlike in the Indonesian data, an important limitation of the EAIM survey is the data on energy consumption does not include any information on quantities and the data only distinguishes between electricity consumption and other fuels. This makes it more challenging to to construct plant-level energy prices by source. In order to address this limitation, we collected detailed data on official electricity and fuel prices from different sources. Further detail on this procedure is provided in the following sections.

17
The experimental approach focuses on the households that are located between 5-16km of a 3G site. One can split this group into two categories – those relatively close to a site and those relatively far away. Figure A1 above shows that the majority of treated households were within 5km of site.<sup>39</sup> After this point, the likelihood of being covered for each incremental kilometer of distance is relatively stable, suggesting that these households may not have been the primary targets for operators to cover. Meanwhile, Figure A3 suggests that when looking at the untreated group, there is a degree of randomness in the likelihood of being treated when 5-16km from the nearest site (as the likelihood is not obviously declining as distance increases). We therefore use 5km as a threshold to determine the point at which households are unintentionally covered.

If the main reason why some households more than 5km from a site received coverage and some did not was driven by exogenous factors, then we can use this to isolate the impact of 3G coverage. In order to assess this, we used the Terrain Ruggedness Index (TRI) sourced from EarthEnv.<sup>40</sup> The TRI represents the mean of absolute differences in elevation between a focal cell and its eight surrounding cells – a higher value therefore represents more rugged terrain (for example mountain areas with steep ridges). The dataset we use is aggregated to pixels of 10km, which is merged with our survey dataset. Initial analysis we carried out shows that the average TRI is greater for untreated households than treated households that are between 5-16km from the nearest 3G site.<sup>41</sup> This suggests that exogenous factors such as terrain are indeed a relevant factor in determining whether households have coverage when more than 5km from the nearest 3G site.

# _Results_

As in our main specification, we estimate the following equation: (1) i 1D i 2 i i ii ii ii ii ii ii YY = αα + ββ + ββ XX +
Table D.13. Effects of Guerrilla Territorial Control on Political Attitudes

|||_Total Sum of Questio_|_ns per Item/Scope_||
|---|---|---|---|---|
||Political|Engagement with|Non-Democratic|Trust in|
||Participation|Politicians|Engagement|Institutions|
||(1)|(2)|(3)|(4)|
|Guerrilla control|1.449|-0.380**|0.181|-4.112***|
||(1.098)|(0.184)|(1.183)|(1.403)|
|Observations|242|248|172|241|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|
|Dependent mean|12.960|0.383|4.780|11.720|

_Note:_ The table presents the results of equation 1 for our outcomes related to political discontent and distrust. Column 1 shows the political participation scope, which includes questions that measure whether the citizen votes, attends protests, and attends government meetings. Column 2 reports the engagement with politicians’ scope, which measures the extent to which citizens contact state authorities and/or bureaucracies to solve issues and attend government/political meetings. Column 3 shows the nondemocratic engagement scope, which measures the extent to which citizens approve the use of alternative or violent means to engage in politics. Column 4 reports the trust in institutions item, which measures the extent to which citizens trust different types of Salvadoran institutions, including the police, the powers of state, and local government. The table uses the simple sum of questions by each item as dependent variables. The unit of observation in all Columns is the census tract. The information was obtained from the Latin American Public Opinion Project survey (LAPOP). Controls
otherwise. These bank branches refer to Airtel partner banks and the distance was calculated as the closest distance from any point on the cluster boundary. The third stratification variable was based on a strategic priority rating based on business potential provided by Airtel, ranging from one to four. In clusters with greater priority, more time was spent rolling out agents than in areas with low priority.

# **3.2 Agent Rollout**

The Airtel Money agent rollout took place in treatment clusters between January and June 2017. A professional services firm assisted with identifying potential agents.<sup>3</sup> This firm also helped agents with the logistics of signing-up to become Airtel Money agents and provided them with the necessary equipment, training, and marketing materials. However, the firm did not provide support in signing up households for Airtel Money services. The research team closely supervised the professional services firm and quality-checked information provided by them.

During the rollout, the professional services firm activated 400 agents who reportedly undertook a successful first transaction. To verify activation, we matched the phone numbers associated with these agents with Airtel Money transactions data. Only 370 of the 400 agents were found in Airtel’s data, which could potentially be due to typos in the recorded phone numbers.

Based on the matched 370 phone numbers, panel A of appendix figure A1 shows the number of active agents in the Airtel Money transactions data from January 2017 to November 2017. The number of active agents increased steadily, reaching a high of 285 in August 2017 with a relatively stable number of agents through November 2017. Not all agents transacted each month, which is why the

> 3 As a starting point to identify agents, information from the 2015 business listing exercise was used. The following information was collated into an index: (i) number of employees; (ii) type of business (service, retail, others); (iii) annual turnover; (iv) proximity to closest bank branch; (v) business closed
<br>**High 2020 Teacher Stock**<br>**Scenario – lower attrition**|**Low 2020 Teacher Stock**<br>**Scenario – higher attrition and**<br>**higher PTR**|
|Teacher attrition rate for<br>projection period (up to<br>2040)<br>1. Countries with data for at<br>least one year between 2015<br>and 2019.<br>-> Use country data (median<br>of 2015 to 2019), except<br>Malaysia (use 2%).<br>1. Use 0.4 for all countries (0.4<br>is the lowest rate observed in<br>the region between 2015 and<br>2019).|1. Countries with data for at least<br>one year between 2015 and<br>2019.<br>-> Use country data (median<br>2015 to 2019) + 1%.<br>2. Countries without data|
|2. Countries without data<br>between 2015 and 2020.<br>-> Use 2.38% (median of<br>available data for EAP<br>region, excluding Myanmar,<br>between 2015 and 2019.|between 2015 and 2020.<br>-> Use 3.4% (higher than base by<br>1%), except for Indonesia. Use<br>7.9% (6.9% + 1%) for Indonesia.|
|PTR in 2040<br>25 or lower<br>25 or lower|30 or lower|

# IV. Main findings from the simulation: Stock and flow of primary teachers in EAP MICs in 2030

## A. Projected changes in teacher position needs vary widely across the region

Significant heterogeneity exists across the 22 EAP countries with
The results also suggest that the _Pilkada_ electoral reform is an important complement to the fiscal decentralization that originated in large part due to concerns about the secessionist sentiments in a number of outlying provinces of the country.

A few more important questions emerge from our analysis that merit exploration in future work. The first is related to our finding that changes in the fiscal decisions of district governments between pre- and post- _Pilkada_ periods are strongly affected not only by whether the district had direct elections, but also by the _expectation_ of direct election in the near future. In districts where direct elections are imminent, district governments tend to have higher current expenditures on public works. On the one hand, this finding has the positive implication that even the expectation of a direct election in the predictable and near future is sufficient to change the incentives of district governments, perhaps toward greater accountability and better performance. On the other hand, the question that arises is whether the electoral reform could also have the perverse effect of inducing spending cycles across the 434 districts of Indonesia (as of 2005) aligned with the electoral cycle, due to the manipulation of district expenditures for the purpose of re-election. While it is far too soon to address this question, future analysis using a longer series of electoral and fiscal data has the potential of doing so.

The second unresolved question relates to the fact that while the impact of electoral reforms is distributed among expenditures in a number of sectors including education, the health sector is a notable exception. Is the lack of impact on health expenditures a reflection of citizens‘ priorities, or are there other systemic factors that have made it hard for district governments to raise investments in health?

Our analysis is also inconclusive about whether electoral reforms lead to better quality and availability of services provided by the district government. While we do not find any impact of direct elections on human development outcomes in the two years since direct elections, these results may have explanations other than an absence of actual impact. The few indicators we measure
the savings would amount to $1.4 billion dollars annually. Based on the volume of Medicare patients in the H-CUP sample, we estimate that $690 million of these savings would accrue annually to the Medicare program alone. If we expand the list of tradable procedures to include coronary artery bypass grafts, which are already being consumed abroad in growing numbers, the estimate of annual savings would exceed $2 billion.

> 25  It is conceivable that foreign prices for these services could increase in the short run with greater consumption by visiting foreign patients, but in the long run, higher revenues could be used to create additional capacity in foreign hospitals, thereby mitigating price increases.

19
which to compare the model-based forecasts. Because FEWS NET data do not assign a probability to future outcomes, we only focus on the _LA_ loss function of Equation 1. Table 3 contains the results.

The most striking feature of Table 3 is the extreme imbalance between false alarms – an FPR of around 2% – and failure to predict – an FNR of over 74%. This clearly indicates that the historical performance baseline is characterized by a strong aversion to predicting transitions into food crises that subsequently do not occur. In the context of our evaluation framework, this can be interpreted as evidence of a very high implicit weight on false positives and a low weight on false negatives, i.e. a low value of w.<sup>21</sup> The low false alarm rate does not mean that those warnings that were raised, did also most likely predict a transition correctly. To the contrary, only 35% of the projected transitions into crisis were followed by actual transitions. Comparing the frequency of transitions into crisis in both the historical assessments and outlooks also highlights the conservative nature. Although transitions into crisis account for 5 _._ 3% of the historical assessment sample, only 3 _._ 2% of the binarized outlooks projected a transition, see Table B.1 in supplementary Appendix B.

Table 3: Historical performance baseline calculated from binarized FEWS NET outlooks.

||**Error **|**Types**||**_LA_**||
|---|---|---|---|---|---|
||**FNR**|**FPR**|**w=1/3**|**w=1/2**|**w=2/3**|
|**Near-term**|74.3%|1.4%|25.7%|37.8%|
Policy Research Working Paper 9322

# **Abstract**

This paper develops and estimates a dynamic model of individuals’ and couples’ labor supply, savings, and retirement decisions to analyze how the design of a privatized pension system affects gender pension gaps. Chile has one of the longest running nationwide private retirements accounts systems in the world, operating since 1980. It has served as a model for many countries and was reformed in 2008 to alleviate old- age poverty and reduce gender pension gaps. The paper estimates the dynamic model using pre-reform data and compares the model’s short-term predictions with available evidence on the reform’s causal impacts. The analysis finds that household structure is an important determinant of the behavioral and distributional impacts of the reform. The paper evaluates how actual and counterfactual changes in the pension system design affect men’s and women’s economic decisions, pension receipts, and program costs over a longer time horizon. Three design features significantly reduce gender pension gaps: expanding minimum pension benefit eligibility, providing a per-child pension bonus, and increasing women’s retirement age to be equal to men’s.

This paper is a product of the Development Research Group, Development Economics. 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 author may be contacted at cjoubert@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
, would be constructive feedback from the actual stakeholders - i.e. sector organizations or representatives and services professionals that cross borders frequently to deliver services – on the actual regulatory, compliance and even investment climate issues that impose unnecessary costs on them. Conducting country- and sector-specific Mode 4 surveys<sup>8</sup> to elicit such feedback would be another agenda for further work on this subject.

> 7 Authorized economic operators (AEOs) are pre-recognized entities whose compliance with an importing country’s regulations could be certified by the exporting country to facilitate market access.

> 8 One example of this is the surveys undertaken of IT sector exporting firms in India by the country’s Central Bank, which inter alia, enables it to provide estimates of IT services exports by modes of delivery.

15
pdf 5 As cited in _Poverty Reduction Strategy of the Republic of Tajikistan for 2010-2012_ .

> 6 Tajikistan, First National Communication to the UNFCCC (2003), page 65.

> 7 Tajikistan, First National Communication to the UNFCCC (2003)

> 8 However, according to data from the British NGO Merlin, the number of reported cases of malaria in Tajikistan has fallen considerably, to less than 3,000 reported cases per year over the period 2005-2007.

> 9 World Bank Country Environmental Assessment, page 36.

> 10 World Bank (2009) _Adapting to Climate Change in Europe and Central Asia_ Office of the Chief Economist of the Europe and Central Asia region, World Bank Washington DC.

6
firms in six African countries invested more in their workers’ human capital; Mouelhi (2009) also documents an additional human capital investment among internet-connected firms in Tunisia. Internet connectivity can affect human capital development both at home and in school. Bianchi et al. (2020) show that connecting high-quality teachers in urban areas of China with millions of students in rural primary and middle schools improved students’ long-run academic achievement, labor-market outcomes, and internet use. However, the overall evidence on the impacts of connected schools on test scores is mixed thus far. Positive impacts have been documented in Peru (Kho et al., 2018) and Malawi (Derksen et al., 2019); by contrast, work in Peru (Malamud et al., 2019) and Brazil (Bessone and Dahis, 2020) has found no impact on test scores.

Internet access can also affect labor productivity by improving firm-worker matching, especially in developing economies where frictions in the labor market are large. Empirical evidence is largely from high-income countries, such as the US (Kuhn and Mansour, 2011) and Norway (Bhuller et al., 2020). Lederman and Zouaidi (2020) find a robust, negative relationship between internet usage and long-term, frictional unemployment across countries. Internet access can also increase firm-level productivity by facilitating the adoption tangible inputs such as machines, new technologies, and intermediate materials, and the adoption of intangible inputs such as management, organizational practices, and services. Evidence on the take-up of tangible inputs in response to internet connectivity is limited, but such high-speed connectivity has been shown to affect the organization of production and trade in developing countries. Houngbonon et al. (2022) find that individual firms in Africa are 20 percentage points more likely to undertake process innovation and 12 percentage points more likely to undertake product innovation when fast internet becomes available. Tian (2021) shows that internet access allows firms in urban areas to reorganize production to enhance collaboration and facilitate the division of labor.

Using enterprise-level panel data,
information from a couple of available programs), a web-based search was undertaken, which thoroughly combed through various existing lists and databases maintained by large donors and international agencies operating in different regions. In most cases, a survey non-experimental research method relying on questionnaires or interview protocols, directly contacting project officers and program managers, for data collection, led to a more detailed review of available secondary information and, to collect as much information as possible, some of which was not available on the web. In many cases, interviews took place via e-mail exchange in order to collect all of the information necessary to fill up items within the template.

# **III. The Inventory**

This section provides a summary of the main descriptive statistics of the program inventory. The inventory compiles information about 36 programs supported by IOs (56% by bilateral and 44% by multilateral organizations) (see Annex 1). The main descriptive statistics are as follows<sup>5</sup> :

- **Program Type** : The great majority (83%) of all programs included in the inventory provided access to finance while about half provided some form of training as well as coaching and incubation services. Financial support provided by the programs was mainly in the form of grants (63%) while 20% provided loans. Only 17% of all programs provided both grants and loans (Figure 1).

**Figure 1: Type of programs supported**

<!-- Start of picture text --> 20%<br>17%<br><!-- End of picture text -->

- **Target Region:** The majority of all programs in the inventory were implemented in the South Asia (45%) and Africa regions (42%); which are also the world regions with the highest poverty rates. About 17% of all programs were implemented in the East Asia and MENA regions; while the share of programs in high-middle income regions
Households with 3 working members have income of IQD 250,000 / $211 more per month than households with no working members (Figure 9). Female-headed households, on average, have a much lower average monthly income, at IQD 85,631 / $72. By contrast, the mean income for male-headed households is IQD 156,781 / $132 (not shown).

In general, households have expenditures that are greater than their incomes (Figure 10). Average total monthly expenditure for the sample is IQD 467,665 / $393, significantly greater than the average income of IQD 137,041, which translates to an average per capita monthly consumption of IQD 60,422 / $ 51. The gap between income and expenditure is partly explained by debt repayments (not included in the per capita consumption calculation), which do occur even among very low-income households, and the reliance on gifted in-kind food and non-food items and humanitarian aid to meet basic needs, in place of spending. The sample has relatively large debt – average of IQD 2,481,099 / $2,077. Much of the debt households accrue is to meet basic needs: more than four out of five (84 percent) resort to buying basic goods on credit as a coping strategy.

_Figure 4: Per capita income and consumption (IQD_ <!-- Start of picture text --> 0 50000 100000 150000 200000 250000 300000 350000 400000 450000 500000<br>Per Capita Income (IQD) Per Capita Consumption (IQD)<br>Data Source : CLCI Legacy Database.<br><!-- End of picture text --> Temporary unemployment among this sample is high: 72 percent of households reported no member of the household having worked in the previous 30 days. This ranges from 59 percent in Falluja to 93 percent in Tikrit. Among female-headed households, this increases to a mean of 86 percent. The number remained high over the period of assessment and changes inversely with per
# **Impact of High Inflation on Household Livelihoods in Urban South Sudan**

Alvin Etang, Thierry Hounsa and Utz Pape<sup>1</sup> _Keywords_ : Inflation, livelihoods, poverty, urban, household surveys, South Sudan _JEL Codes_ : E31, I30, O18, C23, O55

> 1 Alvin Etang and Utz Pape are Senior Economists in the Poverty and Equity Global Practice of the World Bank. Thierry Hounsa is a consultant at the World Bank. Many thanks to Luca Parisotto and Ando Rahasimbelonirina for assistance with data work. Advice and comments from Nobuo Yoshida and Emmanuel Skoufias are also gratefully acknowledged. We also thank Pierella Paci for her guidance. Peer reviewer comments from Arden Finn and Nora Dihel are also gratefully acknowledged. The analysis in this paper is based on the publicly available data from three waves of the High Frequency South Sudan Survey, available on www.thepulseofsouthsudan.com. <mark>The findings, interpretations, and conclusions of this paper are those of the author and should not be attributed to the World Bank</mark> or its Executive Directors. The corresponding author Alvin Etang may be contacted at aetangndip@worldbank.org.