rafmacalaba/datause-encoder-data huggingface dataset:
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# _111.2_ **_Adjustments_** _to_ **_the Data_**

## **1112.1** Foreign wssistance

Data on disbursements of foreign assistance are difficult to obtain, and the poor quality of aid data leads to inconsistencies between the aid recorded in the balance of payments accounts and that recorded in the fiscal accounts. Sometimes the discrepancies arise from purely definitional differences, for example, depending on whether or not debt relief is included. More often, however, it reflects the difficulties of accurately measuring and accounting for aid receipts. For some types of aid, expenditure is incurred overseas, and payment made directly by donors, so that it never passes through the Tanzanian government accounts. In Tanzania, such aid would be foreign technical assistance and direct project assistance, usually in the form of specific commodities, which together accounted for roughly half of total foreign assistance in the late 1980s. As a result, the balance of payments accounts, which are based on donor reporting, record substantially larger inflows of aid than the fiscal accounts.

In the analysis below, two adjustments have been made to the fiscal data to get a more accurate picture of total Government expenditures and the split between recurrent and development expenditures. The first adjustment is necessary to get a more precise measure of the fiscal deficit; the second is necessary to get a more accurate measure of public (and hence also total) investment, which is critical for the measurement of the productivity of investment in Tanzania.

The fiscal data are adjusted in two stages. First, the expenditure in the development budget is adjusted upward to include all project aid captured in the balance of payments. This adjustment allows the fiscal accounts to reflect more accurately the level of total government expenditure in
Policy Research Working Paper 9235

# **Abstract**

Facing a fiscal crisis, the Islamic Republic of Iran decided to increase gasoline prices at the end of 2019. This paper estimates the impact of the price increase on household welfare and government revenue, using the most recent Household Expenditure and Income Survey conducted by the Statistical Center of Iran in March 2018–March 2019. The paper looks at the direct and indirect impacts of the reform and quantifies the compensatory cash transfer program the government instituted. Despite very regressive gasoline subsidies benefitting the rich the most, the increase in gasoline prices is found to affect the poor to a greater extent due to larger negative indirect impacts as well as their relatively low incomes. In total, poverty is estimated to increase by about 2.9 percentage points, with the direct impact accounting for a third of this increase. The proposed government scheme, if targeted perfectly to the poorest 18 million households, would fully compensate the poorest bottom 50 percent of the population and reduce poverty to below pre-reform levels. The annual cost of the program will be around 338 trillion rials, which accounts for 77 percent of the estimated total savings from the subsidies reform (439 trillion rials).

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 aatamanov@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 and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
it demonstrates performance comparable to that of traditional credit bureau models, including for unbanked individuals who cannot be scored by traditional methods.

Since the online posting of this paper’s proposal (Björkegren, 2010) and working paper (in 2015), our approach has received substantial attention (NPR, 2015), and is already widely implemented. Combined with mobile money, it forms an essential component of a new ecosystem of digital financial services. The first digital credit product (M-Shwari) was launched in Kenya in 2012, evaluating risk using a simple scoring rule based on mobile money usage. In a few short years, it has been followed by over 68 other digital credit products, which use risk scoring approaches similar to ours. Digital lending smartphone apps have attracted over $190m in venture capital funding. So far, most of these loans are small, but they are already widespread: in Kenya alone, over $2b has been disbursed, and 27% of the population has taken out a digital loan (Totolo, 2018). More Kenyans have loans through these new digital platforms than through traditional banking, or microfinance (FSD, 2016).

Our method consumes raw metadata from mobile phone usage, which are already being collected at close to zero cost. These records can yield rich information about individuals, including mobility, consumption, and social networks (Blumenstock, Cadamuro, & On, 2015; Gonzalez, Hidalgo, & Barabasi, 2008; Lu, Wetter, Bharti, Tatem, & Bengtsson, 2013; Onnela et al., 2007; Palla, Barabási, & Vicsek, 2007; Soto, Frias-Martinez, Virseda, & Frias-Martinez, 2011). This paper shows how indicators derived from this data can predict the repayment of credit.

There are many straightforward indicators of behavior that are plausibly related to repayment of credit. For example, a responsible borrower may carefully manage their balance over time so usage is more smooth. An individual whose usage repeats on a monthly cycle may be more likely to have a salaried income. Or, an individual whose calls to others are returned may have stronger social connections that allow them to better follow through on entrepreneurial opportunities.

From raw transaction records, we extract approximately 5,500 behavioral indicators. Determining how to extract relevant behaviors from unstructured transaction records is the critical step in standard 3
account the social and historical context in which those perceptions are formed and the timing when the survey questions were asked. Other caveats relate to the different interpretations respondents might have of the question asked and their perception of trust given dissimilar risk preferences (i.e. those who are more risk averse may be less likely to trust others).

Secondly, we need to identify the other correlates of trust. We collected data on a number of determinants of trust that we identified in the existing literature and discussed in the previous section. Those include HIV prevalence rates, governance indexes, measures of the quality of institutions (in particular regarding the control of corruption) and measures of social distance such as income inequality, ethnic and linguistic fractionalization. We also control for population density because spatial distance may negatively affect the formation of networks and the flow of information. However, areas with high population density can overwhelm public services and are likely to display more heterogeneity and lower trust. What is more, population density has been associated with higher levels of crime. In addition, people living in rural areas are said to be more trusting than people living in large urban areas (Collier, 2002; Delhey and Newton, 2005). Note that Japan is a country with high population density, large urban areas and low levels of crime. Therefore, the strength and sign of the correlation between population density and trust are by no means clear. Like Alesina and La Ferrara (2002), we include measures of educational achievement as determinants of trust. Using individual level data for the United States, they find that “successful people” in terms of income and educational achievement tend to trust more.

We also included the log of initial GDP (as measured by Dollar and Kraay, 2002) as a possible determinant of trust in some specifications. In the Zak and Knack (2001) moral hazard model, trust is decreasing with wealth but increasing in wages<sup>6</sup> . Following this logic, the impact of the log of initial GDP (the proxy for those two economic factors at the national level) on trust is ambiguous.

Finally note that there is empirical evidence conducted for America (Glaeser et al, 2002) and Britain (Li et al, 2005) showing that generalized trust tends to increase with

> 6 The intuition here is that investors have more incentives to monitor brokers’ behavior to protect their wealth but in the model, wages are considered to be the opportunity cost of investigating a broker so if this cost is high, there are more incentives to trust the broker.

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Table 3: Growth of Monetary Aggregates and Inflation Tax

|Annual average growth of<br>financial Assets<br>(percent per annum)|Real capital loss<br>(percent of GDP)|
|---|---|
|Real money balance<br>-3.3<br>Real deposits in local<br>currency<br>7.7|'.87<br>1.44|
|<br>Bank deposits in foreign<br>currency<br>13.7||

Source: Central Bank of Egypt

# B. Interest Rates and Workers' Remittances INs

At present, workers' remittances constitute the largest single source of foreign exchange earnings in Egypt.. The official figure **ini** the balance of payments is $3.5 billion in 1989, more than total exports of goods ($2.5 billion), and more than the sum of the next three largest sources of foreign exchange ( Suez Canal, oil and tourism). It is often stated that even this figure is low compared to over 1.5 million Egyptian workers abroad, that these workers' savings are several times higher than this figure but that the remittances have been discouraged by the low level of domestic interest rates. However, this argument 'ooks at only one of the several determining factors in the decision to send h,. e remittances. In reality, there are two main motives for sending remittances: subsistence for family members and investment. The former is largely independent from policy variables such as interest rates or exchange rates. In fact it is possible that interest rate increases, together with a devaluation may reduce this part of the remittances as a devaluation would make it cheaper to support a family, ceteris paribus.

W At the time this paper was prepared (April 1990), the Gulf crisis had not started. Since August 1990, the effects of interest rate changes on workers' remittances have been further reduced as these remittances have fallen substantially.
30 FERNANDES-KLENOW-MELESHCHUK-PIEROLA-RODR ́ıGUEZ-CLARE the associated IME for each percentile in Figure 15 – the pattern of the IME across percentiles is remarkably close to what we see in the data.

Table 14 shows the elasticity of variable and fixed trade costs with respect to distance (controlling for origin and destination fixed effects). Now both types of trade costs are strongly increasing in distance. Surprisingly, however, we still get a negative correlation between fixed and variable trade costs.

Overall, our estimated full lognormal-Melitz model does a very good job in fitting the EDD data and in solving the puzzles associated with the Pareto model. The lognormal model generates an IME that is close to the one we see in the EDD and implies fixed trade costs that decrease with distance. The implied pattern for the IME across different percentiles is also very similar to what we see in the data.

# **5. Counterfactual Analysis**

In this section we study whether the counterfactual implications of the Melitz-lognormal model estimated as in the previous section differ from those of the Melitz-Pareto model estimated as described below. To conduct counterfactual analysis, we need to close the model. We do so in standard fashion by assuming that labor is the only factor of production, with wage _wi_ and perfectly inelastic labor supply _Li_ in country _i_ , by assuming that entry costs are in terms of labor, and that fixed exporting costs are in terms of labor of the exporting country. To make the model be perfectly consistent with the data, we allow for trade imbalances via exogenous international transfers, as in Dekle et al. (2008). Formally, letting _Xi_ =<sup>�</sup> _l_<sup>_Xli_denote total sales by country</sup><sup>_i_</sup> and _Yi_ =<sup>�</sup> _j_<sup>_Xij_denote total expenditure, trade imbalances are equal to international transfers</sup> ∆ _i_ – that is, ∆ _i_ = _Xi − Yi._

## **5.1. Exact Hat Algebra in the Generalized Melitz Model**

Here we show how to extend the “exact hat algebra” for counterfactual analysis in Dekle et al. (2008) to the Melitz model with a general productivity distribution (not necessarily lognormal) and allowing for firm-level demand and fixed-cost shocks.

We start by introducing some notation. Let _μφ,i_ , _μα_ and _μf,ij_ denote the mean of ln _φi_ , ln _α_ , ̃ and ln _fij_ , respectively, and let _f_<sup> ̃</sup> _ij ≡_ ln _fij − μf,ij_ , _φij ≡_ ( _σ −_ 1)(ln _φi − μφ,i_ ) + ln _α − μα_ and
32 to trade with geographically close countries, as the experience of small Hungarian and Polish fims illustrates."' Their share in exports, though increasing, remains lower than their share in aggregate output. In brief, privatization and organizational restructuring of SOEs together with the development of infrastructure facilitadng access to foreign markets may provide strong stimuli to exports.

More mesearch is needed on the following issues to provide an answer to the question of the sustainability of export growth following the introduction of radical transformation programs: First, the extent to which SOEs successful in marketing their products in the OECD have been affected by the decLine in investment activity. The evidence is scarce but there are some indications that export-oriented enterprises face a fimancial squeeze,'<sup>9</sup> and that export performance has not been reflected in higher profitability. If this is so, it is doubtfiul that export-oriented SOEs have invested in upgrading their production capacities, which will add to the difficulties of privatizing SOEs.

Second, the supply side of the export upswing. Among the questions relevant to the sustainability issue are: what manufactured goods contributed to the export drive of the CEE-3, to what extent did revealed comparative advantages change during the recent export drive, and did exports originate mainly from productive capacities of more recent vintage?

bird, the characteristics of the major CEE-3 export markets, that is, whether these OECD markle: are stagnating, contracting, or expanding?

Fmially, the impact of rising foreign direct investment in the CEE-350 by OECD firms on the international competitiveness of the troika.

# VII. CONCLUSION

This paper provides analysis to support the following findings. Furst, the decision to move quicldy to a market-based economy was closely linked with export performance. This is illustrated by the dramatic difference between the export performance of the most reformed Central European troika and that of the 4s According to the Hungarian Ministry for International Economic Relations, the growth of small and medium-sized finns engaged in exports was one of the factors accounting for the fall in trade with developing countries (see 'Hungary: Foreign Trade,' Oxford Analvtica, 27 August 1992). Polish private firms also export mainly to EC markets (see CPO-1992 and MWGZ-1992).

49 For instance, the Hungarian minister of international economic relations, Bela Kadar, noted that SOEs facing bankruptcy exported products worth around US$1 billion in 1991. See Radio Free Europe - Daily Report, June 30, 1992. In 1991 the Polish staowned industrial sector recorded significant losses, but it is unclear whether the SOEs heavily involved in exports were profitable.

50 For a statistical survey of foreign investment in Central and Eastern Europe, see East Eurnean Investment, June 1992:14-25.
oaffi Mwso **-** No A *** m .** Table 5. Use of ORS(%) by type of<sup>diarrhea</sup>

||"Severe" vs. "mild|" diarrhea||
|---|---|---|---|
||NATIONAL<br>KAP 1988|MENOUFIA<br>KAP1988|MENOUFIA<br>CS 1990|
|SEVERE|69.7|71.6|66.2|
|MILD|25.2|38.7|36.8|
|"|Dehydrated" vs."not|dehydrated"||
||MENOUFIA<br>KAP 1988|MENOUFIA<br>CS 1990||
|DEHYDRATED|83.4|89.2||
|NOT<br>DEHYDRATED|32.8|32.7||

Source: references 22, 23, 21.

Ethnographic methods are clearly superior to<sup>cross-sectional surveys in reporting actual</sup> case management of diarrhea, as the<sup>latter may tend to obtain answers that reflect the ideal</sup> behavior supported by media messages. The<sup>results of an ethnographic study' conducted in 6</sup> communities with 56 cases of diarrnea<sup>foliowed daiiy strongly suggest improper</sup><sup>**uiilizaiOw**oL</sup> ORS. Such improper use would, in turn,<sup>greatly reduce the expected impact on both diarrhea</sup> associated and infant mortality.

# 3- Investigation of Causality

The two previous steps dealing with theoretical<sup>quantification and implementation analysis</sup> provide some sobering insights. The first relates<sup>to the complex mechanisms through which ORT</sup> may exert its impact on mortality. The simplest<sup>curative mechanism that is implied by the early</sup> focus of the program and data on incidence<sup>and nutritional status has a lower theoretical potental</sup> than that implied by the more complex mechanism<sup>passing through health status. The</sup> implementation data - particularly ORS<sup>udlization and quality of use -further reduces the</sup> expected demographic impact of ORT.

This section investigates mortality trends and attempts<sup>to answer the question: is the pace</sup> of change after 1984 significantly different from<sup>the pace of change prior to the project period?</sup> A regression discontinuity analysis is adopted'. This procedure examines time series data involving a one group pre test - post test design<sup>in which data are available for a number of</sup> times before and after program implementation.<sup>The trend in the series is examined as a</sup> function of time, which is used as a surrogate<sup>for other causal factors. A dummy variable is</sup> 13
resources: we find that the occurrence of famines has nothing to do with fiscal revenue, or the tradition of violence in the region, either. There is, however, evidence that famines reflect a warlord’s incentives, in that famines could rise with potential military threat: the likelihood of famine was higher if an adjacent warlord belonged to another military bloc, if the adjacent warlord had better military training, or if the adjacent warlord had powerful allies. We conclude that a ruler’s willingness to contain famines remains a function of his calculation of the likelihood of holding his territory.

Throughout Chinese history, famines frequently ended dynasties through peasant rebellions (Chapter 7, Acemoglu and Johnson 2019). But state failures happening at critical historical junctures, e.g., the infusion of communist ideologies in the 1920s, had more significant impacts in the long run. We show that it was in the prefectures with the highest warlord-era famines that the most local residents died in uniform serving the Communist party. This is possible by tracing the birthplaces of the 1.24 million members of various Communist organizations (largely the army) killed in the Communist rise to power. We use the number of war casualties relative to the local population as a measure of Communist penetration. We find that prefectures hit hardest by famines in the decade before 1927 were also home to the most Communist war casualties after 1927. This holds when using a three-stage least square approach: relying on the 13<sup>th</sup> -century re-demarcation by Mongolian rulers to predict warlordera border ruggedness, which is used to predict the warlord-era famines, which in turn predicts the number of communist war casualties. Our findings are suggestive that warlord-era famines deeply affected many peasants who later swelled the communist military force. In a placebo test, we also investigate casualty rates for the non-communist Kuomintang (Nationalist Party, or KMT), ruling party of China after 1927. There we do not find a relationship between warlord-era famines and subsequent KMT casualties.

Exactly why did famines push peasants into the arms of the Communists? Using land ownership surveys between 1918 and 1921, we find that famines raised landless tenancy: famine occurrence raised the fraction of tenant households by 1.7 percentage points in the following year. We also show that as tenant households rose, there was an increase with the 4
under a 'no intervention' scenario to estimate the benefits of introducing an early warning system by calculating the number of deaths avoided.

(iii) _Working hours shift_ . We apply the shift of working hours to daily modelled heat exposure under future climate scenarios produced through the UrbClim simulations. The number of lost working hours averted through this ‘early mornings/late afternoons’ working regime is quantified and valued based on projected economic output.

(iv) _Cool roofs_ . Evaluation of cool roof interventions in India has shown that indoor air temperatures can be reduced by 2°C-5°C depending on building type and treatment type, resulting in reduced energy needs for air conditioning (NRDC, ibid). In Hyderabad, cool roofs reduced cooling energy usage by 14%-26% for previously black roofs and 10%-19% for previously uncoated concrete roofs, resulting in energy savings of 13-14 kWh per square meter for uncoated concrete roofs and 20-22 kWh per square meter for black roofs annually. We assume an annual energy saving of 17 kWh per square meter.

_Table 2: Heat mitigation interventions, their outputs and outcomes, and valuation approach_

|<br>**Intervention**|<br>**Output**|**Intermediate**<br>**outcome**|<br>**Value metric**||<br>**Valua**|**tion approach**||
|---|---|---|---|---|---|---|---|
|**Urban**<br>**greening**|Increase<br>in<br>tree<br>canopy cover|Temperature<br>reduction|Mortality risk<br>change)<br>Labor<br>productivity<br>change)|(%<br>(%|Relati<br>as<br> <br>tempe<br>Labor<br>as<br> <br>tempe|ve risk of mor<br>a<br>function<br>rature<br>productivity<br>a<br>function<br>rature|tality<br>of<br>loss<br>of|
|**Heat-health**<br>**early warnings**|Heatwave<br>early<br>warnings|Individuals<br>adopt exposure<br>reduction and<br>health-<br>preserving<br>behaviors|Mortality risk<br>change)|(%|Relati<br>as<br> <br>tempe|ve risk of mor<br>a<br>function<br>rature|tality<br>of|
|**Working hours**<br>**shift**|Change in working<br>hours<br>for<br>heat<br>affected workers|Reduced heat<br>exposure<br>of<br>workers|Labor<br>productivity<br>change)|(%|Labor<br>as<br> <br>tempe|productivity<br>a<br>function<br>rature|loss<br>of|
|**Cool roofs**|Increase in building<br>area with cool roof<br>treatments|Reduced<br>cooling energy<br>demand|Private cost<br>cooling<br>change)|of<br>(%|Coolin<br>functi|g expenditure<br>on of tempera|as a<br>ture|

12