rafmacalaba/datause-encoder-data huggingface dataset:
# **1. INTRODUCTION**

Mobile phones have spread dramatically: there are over 4.5 billion mobile phones in developing countries (ITU, 2011). In addition to improving communication, these devices make it possible to provide new forms of credit. Telecoms often extend credit to help smooth phone consumption itself: as incomes rise, consumers are expected to transition from prepaid to postpaid plans, which are essentially rolling credit accounts. Phone networks can also be used to mediate loans for consumer durables, such as pay-as-you-go solar energy systems.<sup>1</sup> And through mobile money, phone networks can provide loans directly to consumers at close to zero transaction costs. This can enable forms of credit different from microcredit or traditional bank lending (consider M-Shwari in Kenya, or independent smartphone lending apps).

However, all of these applications face a fundamental problem: how can a lender assess whether a potential borrower will repay? Few developing country residents have traditional credit scores. Few interact with formal institutions at all: 2 billion people lack bank accounts (Demirguc-Kunt, Klapper, Singer, & Van Oudheusden, 2014). And in person methods, like interviews or peer groups in microfinance, are costly for small loans and remote households.<sup>2</sup> This paper develops a method to predict repayment of the poor at scale. Its key insight is that most poor consumers do have at least one rich record of formal interaction—their interaction with a mobile phone itself. The way that a person uses a mobile phone can predict whether they will repay credit.

This project develops and assesses a low cost method to predict repayment of credit using mobile phone metadata, which are already being collected by mobile phones. From raw phone transaction records it extracts signals plausibly related to repayment, and uses a machine learning approach to combine these signals into a prediction of repayment. Using only minor tweaks to off-the-shelf machine learning methods,

> 1 Typically, if approved for a household solar energy loan, consumers receive the device and pay installments via mobile money. If consumers miss payments, their device is remotely switched off via the mobile phone network.

> 2 Nonpayment is particularly problematic in developing societies, as creditors have little recourse if a borrower were to default: borrowers have little in the way of collateral, and systems for legal enforcement are limited.

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electricity consumption but only a weak relationship between electricity consumption and GDP. For example, using a panel of Middle Eastern countries, Narayan and Smyth (2009) show that a one-percent increase in GDP leads to a 0.95 percent increase in electricity consumption, but a one percent increase in electricity consumption raises GDP by only 0.04 percent. In addition to the co-integration methodology dominating the literature, Inuwa et al. (2019) estimate the GDP-electricity consumption elasticity for the Economic Community of West African States with the System GMM estimator and produce a coefficient of 0.02.

The contribution of this paper to the literature is threefold. First, the paper uses a larger sample of countries than other studies that focus on regional data, and the additional information improves the estimators. Also, it provides elasticities for three sectors of agriculture, manufacturing, and services, which is important given the substantial variation in electricity intensity across sectors and its importance for sectoral growth. For example, electricity constraints might be less important for agriculture than manufacturing that relies primarily on energy-intensive technologies in production. In addition, sectoral elasticities help account for variations in the sectoral composition of GDP across countries and support better understanding of the implications of electricity constraints on GDP growth in various settings. Finally, the paper uses the panel GMM estimation approaches to address endogeneity and reverse causality bias in OLS estimates.

The estimation results indicate that electricity consumption has a positive and significant impact on the value added in all three sectors when using the sample of all countries, including high-income and nonhigh-income groups. However, the coefficients become insignificant for agriculture and services once high-income countries are excluded from the sample. These changes in estimation results reflect substantial differences in production technologies in agriculture and services between high- and non-highincome groups. Agriculture in developing countries primarily includes subsistence farmers who use manual labor to perform tasks, while agriculture relies on electricity-intensive technologies in advanced economies. At the same time, sectors with low-skill labor and manual tasks, such as trade, dominate services in developing countries, while they account for a relatively low share of services in advanced economies.

The rest of the paper is structured as follows: Section 2 describes the empirical methodology, section 3 discusses the data and descriptive statistics, section 4 provides the results, and section 5 is a conclusion.

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and gender) and unobservable (social networks, concern for children, child ability) dimensions. In this paper we use an instrumental variables strategy which we describe below. Third, almost all papers in this literature are confined to looking at the contemporaneous or short-horizon effect of child labor. While there is interesting evidence from Brazil, an upper-middle income country, using retrospective rather than longitudinal data on child labor (Emerson and Souza, 2007), there is no direct evidence on the longer-run impact of child labor in low-income countries.

Our strategy is to examine the long-run consequences of child labor using the Kagera Health and Development Survey in Tanzania, which consists of five waves, spanning 13 years. We study the relationship between child labor (measured by hours spent on household chores and in economic activity) in early waves (1991-1994) and outcomes (such as education, whether or not the individual is farming, choice of cash versus subsistence crop, marital status, and labor productivity) in the final wave (2004). We instrument for child labor using crop and rainfall shocks. In previous work (Beegle, Dehejia, and Gatti, 2006), we have shown that crop shocks are predictive of child labor; we argue below that they, along with rainfall shocks, are plausible instrumental variables (i.e., that they satisfy conditional exogeneity and the exclusion restriction).

We find that child labor is causally associated with reduced educational attainment (as measured by years of schooling and by an indicator for completion of primary school). Interestingly, this result appears to be entirely driven by the sample of boys, for whom a one standard deviation (5.7 hour) increase in child labor implies nearly one year less of schooling. Boys who worked when young are more likely to be farming (as opposed to earning a wage). We do not find that child labor is associated with discernible differences in the choice of crop (cash 2
_Source:_ Own calculations using rotating panel from ENOE between 2<sup>nd</sup> quarters of 2008, 2009 and 2010. _Note:_ a. Level of instruction is compared to "No level of instruction and primary." b. Geographic regions are compared to "Capital." c. Age is compared with "From 18 to 25." Omitted control groups are (1) with primary or less schooling; (2) Capital region; (3) Less of 26 years of age. Marginal effects on transition probabilities from employment to joblessness computed on the average using STATA.

In summary, these results indicate that there were only small differences in how the recession and the recovery affected different groups. By a small margin, those more likely to stay in a job during the economic crisis were the males, high skilled, household heads, and middle-aged workers.

# **3.2. Wage growth along the cycle**

Average wages in real terms have declined for all groups. According to personal characteristics there seems to be a general decrease in wages (table 3.9). Males experienced higher decreases than females, up to 5.3 percent compared to 4.9 percent. By educational level, higher wage loses are concentrated in both tails, those with no instruction and those with higher instruction. Middle-skilled workers also experienced wage loses, although comparatively smaller. All age groups experienced wage loses, especially those in the middle groups of 36-45 and 46-55. Considering geographic location, wage loses at the beginning of the crisis were concentrated in the capital and central states. At the end of the period of analysis losses were higher in states bordering the United States with high manufacturing employment, the Yucatán Peninsula with high tourism employment, and the north central states.

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# **Figure 6: Indonesia’s real per capital GDP 1993-2005: shock and recovery after decentralization**

<!-- Start of picture text --> 8.0<br>7.5<br>7.0<br>6.5<br>in Million Rp 6.0<br>5.5<br>5.0<br>4.5<br>4.0<br> National GDP Per Capita<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> Source: Authors’ calculations based on GRDP data from BPS. Real data (constant 2000 prices).

Economic activities and resources are concentrated in Java (Box 1). Java, Bali and Sumatra in general have relatively more advanced economic activities than other parts of the country, with manufacturing, trade and services dominating the economies on these three islands. Sulawesi, Maluku and Nusa Tenggara, on the other hand, are much less industrialized, with agriculture making up the main sector of their economies. Indonesia’s population is heavily concentrated on Java, and Java generally has relatively better infrastructure compared with the rest of the country.

# <mark>Box 1:</mark> **<mark>Uneven distribution of resources and economic activity across Indonesia</mark>**

- <mark>Java represents only six percent of the country’s land area but accommodates almost 60 percent of the population.</mark>

- Java dominates the Indonesian economy producing 52 percent of the GDP; Sumatra produces 28 percent of GDP and Kalimantan 11 percent. Eastern Indonesia (Sulawesi, Nusa Tenggara, Maluku and Papua) collectively account for only 10 percent of GDP.

- Eastern Indonesia is lagging behind in terms of the quantity of its infrastructure as measured by access to electricity and telephones. In Papua, Maluku and Nusa Tenggara, less than 10 percent of households have access to a telephone and less than 40 percent of households have access to electricity.

- Java dominates all sectors except for oil, gas and mining. Sumatra and Kalimantan contribute 18 percent and 22 percent to this sector respectively.

- Manufacturing is the dominant sector in western Indonesia whilst agriculture is the dominant sector in eastern Indonesia.

Appendix 2 presents some facts and figures on the distribution of resources and economic activities across Indonesia.

The economic crisis marked a structural break in the growth performance of Indonesia’s districts (Table 3). District-level growth before the economic crisis was fast, with the median district between 1993 and 1997 growing by 5.6 percent. In 1999-2001, the median growth rate across districts fell dramatically to 3.5 percent, and the variation of growth rates across districts was much greater, probably reflecting the differential impact of the crisis, as well as data issues in the 12
# **Table 12.  Financial Statements of SPMS**

|_Particulars_|_1998-99_|_1999-00_|_2000-01_|
|---|---|---|---|
|**Income**||||
|Interest on loans|988,204<br>|1,290,594<br>|2,390,795|
|Service charges/Fees|36,175<br>|96,819<br>|107,729|
|Donations|-<br>|6,900<br>|106,399|
|Sundry Income<br>Total|62,072<br> <br>1,086,451<br>|59,083<br> <br>1,453,396<br>|42,466<br>2,647,389|
|**Expenditure**||||
|Interest|331,813<br>|563,226<br>|1,283,772|
|Salary and benefits|414,554<br>|382,254<br>|326,308|
|Travel|65,039<br>|46,431<br>|62,246|
|Office administration|92,916<br>|99,648<br>|184,726|
|Printing and Stationery|80,103<br>|45,950<br>|160,641|
|Training|81,273<br>|121,254<br>|109,592|
|Audit fee|12,000<br>|12,600<br>|21,827|
|Sundry expenses|36,139<br>|152,023<br>|225,977|
|Profits|(27,386)<br>|30,010<br>|272,300|
|Total|1,086,451<br>|1,453,396<br>|2,647,389|
|**Equity and Liabilities**||||
|Reserves and Surplus|2,185,836<br>|2,298,420<br>|2,376,496|
|Member Deposits|2,124,333<br>|3,117,571<br>|4,233,458|
|Loans|4,451,110<br>|9,379,741<br>|18,000,377|
|Specified Funds|284,094<br>|581,526<br>|1,355,298|
|Total|9,045,373<br>|15,377,258<br>|25,965,629|
|**Assets**||||
|Land and Fixtures|338,914<br>|614,676<br>|1,157,230|
|Deposits and advances|640,224<br>|1,878,087<br>|3,096,808|
|Loans|5,674,371<br>|9,513,461<br>|19,998,159|
|Cash|2,391,864<br>|3,371,034<br>|1,713,432|
|Total|9,045,373<br>|15,377,258<br>|25,965,629|

Item heads in original statements reorganized and relabeled for easier comparison.

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3. Change in a firm’s full-time permanent employment between June/July 2009 and the end of 2007 fiscal year. This is the difference in natural log _(ln)_ of full-time permanent employment between the first wave of the Financial Crisis Survey and that in the 2007 BEEPS. Ideally, we would like to examine full-time temporary employment because temporary workers are most likely to be the first to go when the firms get squeezed. This is not possible, however, because in the 2007 BEEPS, the vast majority of firms claim that they do not employ temporary workers (their reported number of fulltime temporary workers is zero).

The details of the explanatory variables are below:

# 1. Sensitivity to demand shocks:

Establishing demand sensitivity is difficult. A traditional approach is to measure the correlation between a firm’s sales and aggregate output, and take it as a proxy for demand sensitivity. In our view this is not satisfactory because we are not sure if demand shocks drive the correlation between sales and aggregate output. The simple correlation can capture many other shocks, such as productivity shocks and access to credit shocks. For example, an improvement in a one sector’s technology (e.g. an introduction of new smart phones) would stimulate sales of that industry and at the same time increase total output.

We follow the approach of Tong and Wei (2008) to define the proxy for demand sensitivity. The proxy is at the sector level, based on U.S. data.<sup>7</sup> It is the change in stock index by sector in a 10-day period following the September 11, 2001 attack, which is presumably a demand shock. The idea is that the terrorist attack caused temporary psychological setback, which can be considered as a negative demand shock to consumers, as firms’ technology and banks’ lending capacity remained intact.<sup>8</sup> More demand sensitive sectors would then see stronger declines in the stock prices following the attack. For each sector, the proxy is calculated as the difference between the log of the sector’s stock index on Sep 10, 2001 and that on Sep 21, 2001 (i.e., log[priceSEP10]-log[priceSEP21]). A higher value for the proxy indicates higher demand sensitivity. See table A3 in the Appendix for detailed sensitivity for the industries.

The use of stock index changes around the September 11, 2001 attack does carry some drawbacks. Firstly, airline stocks were disproportionately hit. Second, stocks in defense industry may have seen some

> 7 The use of U.S. based industry data on demand and credit sensitivities and extrapolating to other countries is popular (see for example Rajan and Zingales (1998); Raddatz (2006); Claessens, Tong and Wei (2012)). This is to overcome the lack of data in many countries. As noted by Rajan and Zingales (1998), the estimator will be unbiased if the industry ranking of demand sensitivity or liquidity needs is preserved across countries.

> 8 Tong and Wei (2008) also conduct a number of checks to make sure that this index reflects the relative sensitivity of a firm’s stock price to an unexpected shock in consumer demand, and is not contaminated by a firm’s sensitivity to liquidity or other shocks.

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cut-off grade is set ex-post based on the distribution of grades, which mitigates the risk of score bunching around the cut-off.

To address potential spatial correlation in learning outcomes within schools, we cluster standard errors of regressions that use test scores as the outcome at the school level.

# **3.2 Weather Data**

We utilize the ERA5-Land dataset to obtain weather information at a spatial resolution of 9 km grid spacing. This dataset, which is available from 1950 onwards, provides hourly information on surface variables such as daily maximum temperature and daily total precipitation for each school in our sample.

To ensure the accuracy of our weather data in relation to the experiences of students, we construct a 10 km buffer around the school’s geographic location<sup>11</sup> . We base this on the assumption that students typically reside close to their schools. This assumption is supported by findings from the Living Standard Measurement Study (LSMS) surveys, which indicate that about 90% of students in Ethiopia walk to school, and take less than one hour to reach their school from their homes. Additionally, we re-weight the climate data by population size (from Center for International Earth Science Information Network - CIESIN (2018)) within the buffer, to account for variations in population density.

Overall, our use of the ERA5-Land dataset and the construction of geographic buffers allows us to accurately capture the relationship between extreme temperatures and learning in our study. Our results are robust to variations in buffer size (of 5 km and 20 km) and population density.

# **3.3 Other Data**

## **3.3.1 Household Survey**

We use the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) data for Ethiopia (also known as the Ethiopia Socioeconomic Survey (ERSS)) in order to investigate the time-use patterns of household members as well as the impacts of shocks to agriculture on food consumption within the household (CSA, 2012, 2014, 2016). The LSMS-ISA is a household survey conducted in eight countries in Sub-Saharan Africa (SSA), focusing predominantly on rural households and their agricultural activities. There have been four waves/ survey rounds conducted for Ethiopia in 2011-12, 2013-14, 2015-16, and 2018-19. For our analysis, we specifically concentrate on the second and third waves of data. This selection is based on the sampling consistencies across these waves, ensuring the reliability of the collected data.

The LSMS-ISA surveys employ a two-stage random sampling design. First, enumeration areas (EAs) are selected at random, followed by a random selection of households

> 11To understand the distribution of hot days within these 10 km buffers, refer to Figure A4 in the Online Appendix. It provides a map of the variation in temperatures that is not explained by school, year, and stream fixed effects.

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Sources: Per capita GDP and average real growth 2000-07 are from the World Bank ECA database. Real GDP growth for 2008 and forecast for 2009 is from World Economic Outlook, 2009, except 2009 forecast for Tajikistan which is the latest forecast from the IMF country team.

The paper is organized as follows. Section 2 examines the nature of the external shock facing Tajikistan and analyses how this will be transmitted to the domestic economy, to living standards and poverty and to the fiscal position. Section 3 evaluates whether there is both macroeconomic and fiscal space to allow a more expansionary fiscal stance. We argue that external donor support will be necessary to create the macroeconomic and fiscal space for a more expansionary fiscal stance. Section 4 discusses the optimal characteristics of a fiscal program in terms of the objectives to both stimulate output and protect the poorest and most vulnerable. Section 5 makes recommendations for specific fiscal policy measures to be included in the program. Section 6 provides a conclusion.

# **2. The nature of the shock and economic crisis facing Tajikistan**

Since the first half of the 2000s, remittance transfers from migrant workers have risen at a rapid rate to become by far the most important source of foreign exchange earnings for the Tajik economy. Gross remittance inflows in 2008 were estimated at $2.2 billion: this amounts to the equivalent of 43 percent of GDP, 78 percent of merchandise imports and more than three and half times the combined total of all goods and services exports.<sup>3</sup> The Kyrgyz Republic and Moldova are also dependent on remittances, which amounted to 31 percent of GDP and about 50 percent of imports in both countries in 2008, as is Armenia to a lesser extent (see table 2).

> 3 Official data indicate net migration of 388,000 people in 2008, equivalent to 6 percent of the population. Unofficial estimates are 150 percent to 200 percent higher (World Bank, 2008: 5).

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