rafmacalaba/datause-encoder-data huggingface dataset:
4

# _:hIn.I:_ F9 PHW LENDING _PROGRn_ oouitment in US$ millions

|Country|Project|Objectives|Amount|statuab|
|---|---|---|---|---|
|**AXCA**<br>Benin|NeLtlh<br>aPopulation|o<br>Improve**management**of health system<br>o<br>Strongthon key health progras<br>o Rehabilitate and revitalise rural<br>health facilities|5.0<br>(TDA)|Appraised 6/18|
|Mosembiquo|Health**&**Nutrition|o<br>lprove<br>**prlmary**health**care**servlies|27.0<br>(IDA)|Appraised 7/68|
|**Nigeria**|**l-o Health**|**o Help**too State improve PNC**serviLeo**<br>and initiate a population evarenes<br>andfamily<br>plalnLig<br>servlea<br>program|27.6<br>(hZRD)|Board presentation<br>scheduled<br>111/8|
|Zaire|AIDS|eSupport<br>for<br>medlum-torm<br>National<br>AIDS<br>Control<br>Program|6.2<br>(IDA)|Board<br>approved<br>_9/88_|
|ASIA|||||
|Cbina|Uealth III|o Develop and implement regional health<br>development programsfor<br>three<br>*edlum-<br>sized cities (Improve effoieLony<br>affordability and qualLty through<br>*ectoral policy reforms in management,<br>financlngandplaning)<br>o DeveLop emergency care systems tn<br>urban centers|110.0<br>(IDA)|Appraisal 12188|
|Indonesia|Health III|o<br>Improve healthincertain provinces<br>o<br>Assist in design and implementation of<br>polieLes to support decentralLsatLon<br>and resource mobilization|38.0<br>(15RD)|Appraised 1018S|
|Turkey|Health<br>I|o<br>Improve<br>health conditions in<br>underprivileged<br>provinees<br>o<br>Strengthen<br>national<br>health<br>program<br>o<br>Supportinstitutlonal<br>development|80.0<br>(IJRD)|Appraised 6/88|
|YemenPDR|Health<br>I1|o<br>Extend<br>PbCservLeos<br>o<br>Develop<br>FPservices<br>o<br>Strongthen<br>sector<br>management|4.3<br>(IDA)|Appraised<br>3/8S|
|LAC|||||
|Sreatl|NE SsiLc Bealth11|o<br>Improve<br>health<br>sorviees<br>insix<br>states<br>o<br>Strengthen MOB's state secretarlats of<br>health through technieal assistance|275.0<br>(IBRD)|Negotiations<br>1/89|
|Bra-tl|Amason Basin<br>MalarLi|o<br>Controlmalaria<br>through;<br>**a)**civil works forsanitation<br>and<br>source<br>control<br>b)<br>vector<br>control<br>chemotherapy<br>c) institution bulding_|150.0<br>(IBRD)|Appraisal 10/88|
|TOTALS|||723.1||

a _As_ of 7/13/88 b As of 10/17/88
# **1 Introduction**

Location and institutions are increasingly recognized as crucial determinants of economic performance (reflected, for example, in the 2009 World Development Report on Economic Geography and the New Economic Geography’s rise to prominence). This burgeoning interest in location and institutions has also manifested itself in the growing literature on the impact of the investment climate on firm performance (see e.g. Dollar et al., 2005, World Bank, 2005a), which attempts to explain spatial disparities in economic outcomes by variation in the geographical, institutional and regulatory environment in which firms operate. Dollar et al. (2005) have demonstrated a strong association between firm performance and the quality of the investment climate; firm’ productivity and growth are correlated with infrastructure (e.g. access to electricity), export bottlenecks (e.g. days to clear customs) and access to finance (e.g. having an overdraft facility).<sup>1</sup> Conversely, cities with lower customs clearance times, reliable infrastructure, and good financial services attract more foreign investment. Cross-country variation in country’s growth rates is highly correlated with increased trade integration. A better investment climate thus seems to promote international economic integration and to stimulate growth (Dollar et al., 2006).

Despite mounting evidence that the investment climate strongly varies both across and within countries, and that its impact on firm performance depends on firm size (see e.g. Sleuwagen and Goedhuys, 2002, Pages et al., 2007), almost all the work on the investment climate thus far has focused on relatively large manufacturing firms in urban areas (notable exceptions include Kinda and Loening 2008, Deininger et al. 2007 and Deininger et al. 2008). Primarily because of a lack of data (Cook and Nixson, 2000, Ayyagari et al, 2003), relatively little is known about the determinants of rural non-farm enterprise performance. It is therefore not clear to what extent the conclusions derived from urban investment climate

> 1 Having access to an overdraft facility is only correlated with firm growth, not with Total Factor Productivity (at least not when capital and other variables have been controlled for).

-2-
ographic coverage, the extent of low-lit areas, and sensor recalibrations. Temporal variations unrelated to economic activity give rise to large noise in nighttime light data, but year fixed effects in regression analyses can control for such noise if it is common across countries. We found that the quarterly correlation between VIIRS nighttime light growth and GDP growth is stronger for EMDEs than for advanced economies.

Different from annual DMSP nighttime light data, monthly VIIRS nighttime light data can be used to study economic short-term impacts of different shocks. Efforts in this direction are already leading to very interesting new insights. Among others, studies have looked into the impact natural disasters like the 2017 earthquakes in Nepal (Beyer, Chhabra, Galdo, and Rama, 2018), domestic policy measures like the demonetization in India (Chodorow-Reich, Gopinath, Mishra, and Narayanan, 2020) or lockdown measures during the COVID-19 pandemic (Beyer, Jain, and Sinha, 2020), and trade shocks like the recent tariff war between China and the United States (Chor and Li, 2021). Other possible applications are too numerous to list. For all of them, it is necessary to translate changes in nighttime lights measured in nanowatts into changes in economic activity measured in US dollars (or any other currency).

In this paper, we presented a novel framework to estimate the elasticity between nighttime lights and GDP, which is fundamental for this translation. Our framework uses varying degrees of measurement errors in nighttime light data across countries to identify the elasticity. We estimate it to be 1.55 for EMDEs and find only small differences across model specifications and country groups. This elasticity can now be used to translate changes in nighttime lights into changes in economic activity. For example, if the economic impact of a shock is studied with a difference-in-differences approach, a 10 percent decline of nighttime lights in the treated group with respect to the control group reflects an economic impact of 6.5 percent of GDP.<sup>30</sup> We concluded the paper with two applications. First, we constructed a nighttime light-adjusted measure of GDP growth and showed that more developed countries and those with higher statistical capacity tend to have smaller measurement errors in their national accounts. Widespread efforts to strengthen statistical offices and systems in EMDEs are hence indeed likely to contribute to better economic measurement. However, we also found that low voice and accountability results in higher overestimation of GDP growth, highlighting a crucial role for public scrutiny in ensuring proper measurement. A proper debate about GDP growth in academia, think tanks, and the media is

> 30To translate changes in nighttime lights to changes in GDP, the inverse elasticity, that is 1 _/β_ , needs to be used.

28
**Table 15. Average Earnings, Earnings per Hour, and Hours Worked by Sample Group** Matching group: Age 17-65. **Three** nearest neighbors based on propensity (1998 pesos)

|_Cohort_|_Control_<br>_Group_|_Earning_<br>_CONALE_<br>-|_s_<br>_Difference_|_Ea_<br>_Control_<br>_ru_|_rnings per_<br>_CONAL_|_hour_<br>_E Diffe_|_Hour_<br>_Control_<br>_ru_|_s worked pe_<br>_CONAL_|_r week_<br>_E Difrence_|
|---|---|---|---|---|---|---|---|---|---|
|90 - 93|1910.5|2660.0|749.5|10.6|14.3|3.7|44.5|46.4|1.9|
|91 - 94|1851.1|2170.4|319.4|11.0|12.4|1.3|43.8|45.6|1.8|
|92 - 95|1883.6|2262.3|378.7|10.7|14.9|4.2|43.5|46.0|2.4|
|93 - 96|1980.1|2288.8|308.8|10.7|12.3|1.6|45.3|46.9|1.6|
|94 - 97|1826.9|1655.1|-171.9|10.1|9.6|-0.5|44.3|45.0|0.7|
|Total'|1890.4|2208.7|318.3|10.6|12.7|2.1|44.3|46.0|1.7|
|_ENE982_|2046.0|||11.6|||43.5|||
|_ENE 98, LS'_|1266.3|||6.6|||44.8|||
|_ENE 98, US'_|2088.5|||11.2|||45.8|||

**_1._ Sample: Workers** in **the matching roup.**

**2. Sample: All workers.**

3. **Sample: Workers with lower-secondary complete and** 3 **years of experience (18 and** 19 **years old).** 4. **Sample: Workers** with **upper-secondary complete and 1-5 years** of **experience (22-26 years old).** Table 16. Average Earnings, Earnings per Hour, and Hours Worked by Sample Group Matching group: Age 17-65. Three nearest neighbors based on propensit (1998 Pesos)

|_Cohort_|_o Control_<br>_Group_|_Earning_<br>_CONALE_|_s_<br> <br>_Difference_|_Ea_<br>_Control_<br>_Group_|_rnings per_<br>_CONAL_<br>_P_|_hour_<br>_E_<br>_Diference_|_Hour_<br>_Control_<br>_Group_|_s workedp_<br>_CONAL_<br>_P_|_er week_<br>_E_<br>_Diference_|
|---|---|---|---|---|---|---|---|---|---|
|90 - 93|2135.9|2878.9|743.0|11.5|15.3|3.8|44.2|45.3|1.1|
|91 - 94|1860.4|2177.5|317.1|10.4|12.5|2.1|44.4|46.0|1.6|
|92 - 95|2003.5|2757.3|753.8|11.1|16.5|5.4|43.9|44.7|0.8|
|93-96|2081.3|2561.7|480.4|_11.5_|13.6|2.1|43.7|46.8|3.0|
|94 - 97|1772.5|1733.5|-39.0|9.7|11.0|1.3|45.4|45.8|0.3|
|Total'|1977.1|2454.5|477.3|10.9|13.9|3.0|44.3|45.7|1.4|
|_ENECE952_|1984.4|||11.2|||43.8|||
|_ENECE 99, LS'_|1253.1|||6.6|||44.8|||
|_ENECE 99, US'_|2142.2|||11.4|||46.0|||

**1.** Sample: **Workers** in **the matching group.**

2. **Sample: All workers.** 3. **Sample: Workers with lower-secondary complete and 3 years of experience (18** and 19 **years old).** 4. **Sample: Workers with upper-secondary complete and 1-5 years of experience (22-26 years old).** 38
Policy Research Working Paper 9881

# **Abstract**

This study empirically investigates the drivers of inflation in Ethiopia using monthly data from July 1998 to September 2020. It explores short-run and long-run effects of domestic and external determinants of inflation—including demand-side, supply-side, and structural factors—using the cointegration and vector error correction methodology. Four measures of inflation are considered: cereals, food, nonfood, and all items consumer price index inflation. A key contribution to the existing literature is the investigation of the role of the fiscal sector in modeling inflation, a topic that has been neglected in the existing studies on inflation in Ethiopia. The empirical results show that disequilibria in the monetary sector, grains sector, and food markets have long-run effects on inflation. In the short run, inflation is driven by structural factors (notably, cereal output gaps and imported inflation) as well as demand-side factors (notably, money growth and public sector borrowing). The results hold when the analysis is limited to the high growth period from 2005 onward, following the end of the International Monetary Fund program in the country. The evidence provides valuable insights in the context of ongoing macroeconomic policy reforms in Ethiopia.

This paper is a product of the Macroeconomics, Trade and Investment 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 msanchezmartin@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
# - 52 -

- National Council of Applied Economic Research (NCAER). 1972. All Indla Household Survey of income, Saving and Consumer Expenditure. New Delhl: NCAER.

- National Council of Applied Economic Research (NCAER). 1975. Changes in Rural Income In India 1968-69, 1969-70, 1970-71. New Delhi: NCAER.

- National Council of Applied Economic Research (NCAER). 1980. Household Income and Its DIsposition. New Delhi: NCAER.

- National Council of Applied Economic Research (NCAER). 1986a. Changes In Household Income, Inter-Class Mobillty and Income Dlstributlon In Indla -- A Longitudinal Study: 1970-71 - 19801-82. New Delhi: NCAER.

- National Council of Applied Ecqpomic Research (NCAER). 1986b. Changes In the Extent of Poverty and Pattern of Household Consumptlon In Rural India -- Based on a Longitudinal Study: 1970-71 to 1981-82. New Delhi: NCAER.

- Natlonal Sample Survey Organizatlon (NSS). 1976. _Tables with_ Notes on _Snall-9cale Manufacture In Rural and Urban Areas._ The National Sample Survey, TwentyThird Round, July 1968-June 1969. No. 28. New Delhl: Department of Statistics, Ministry of Planning.

- National Sample Survey (NSS). 1986a. _A Report on the Third Oulnquennial Survey on Consumr Expenditure: National Sample Survey Thirty-Eighth Round_ (January-December _1983)._ Sarvekshana 9(4):S1-102.

- Natlonal Sample Survey (NSS). Some _Results on the Second Quinquennial Survey_ on _Consuer Expenditure: National Sample Survey 32nd Round._ Sarvekshana 9(3):S51 -S1 84.

- Okelo, J.A. 1973. _Rural Enterprise Survey In Kakamega District, Kenya._ A Report of the Consultant to Danida. (Mlmeo), Nalrobl, Kenya.

- Pal, Rajan and Quizon, Jaime. 1983. _Factor Costs, Income and Supply Shares In Indian Agriculture._ Agriculture and Rural Development Department, Discussion Paper No. ARU 16. Washington, D.C: World Bank.

- Papola, T.S. 1985. _Rural Industrialization and Agricultural Growth: A Case Study on India._ In R. Islam (ed.) Rural Industrlalization and Employment In Asia. New Delhi: International Labor Organization, Asian Employment Program.

- Radhakrishna, R; Reddy, S. Sudhakar; and MItra, Gautam Kumar. 1988. _Deterninants of Labor Force Participatlon and Llving Standards._ Hyderabad: Centre for Economic and Social Studies.

- Richardson, H.W. 1985. _Input-Output and Economic Base Multipliers: Looking Backward and Looking Forward._ Journal of Regional Science.

- Rondinelil, D.A. and K. Ruddle. 1978. Urbanization and Rural Development: A Spatlal Policy for Equitable Growth. New York: Praeger.

- Rondinelli, D.A. 1983. Secondary Cities In Developing Countries: Policies for Diffusing Urbanization. Beverly Hills, CA: Sage Publications.
_MACHINE LEARNING AND HIGH-FREQUENCY SURVEYS IN PAPUA NEW GUINEA 5_ Table 1—Example of the missing data problem.

_b_ 6 _c_ 6 _Note:_ Example of the missing data problem, three hypothetical vectors _A_ , _B_ , and _C_ that represent price series, with elements _at_ , _bt_ and _ct_ being individual price quotes indexed by time periods _t_ . Blank entries represent missing observations. The challenge is to estimate change rates ∆ _P_ of the basket price vector _P_ = _A_ + _B_ + _C_ that spans all _t_ = 1 _, . . . ,_ 6. Element _b_<sup>_∗_</sup> 4<sup>isanexampleoutlierpricewhichneedstobe</sup> removed and replaced with an estimate. _Source:_ Example has been taken from Andr ́ee (2021).

real-time estimates serve as proxies for economic indicators during fluid events when timely official data is not available and cannot be relied upon.

Univariate time series or multivariate regression techniques are also commonly deployed to address data gaps through prediction, but they do not efficiently exploit the information that is available. For instance, a univariate method applied to food item _A_ does not utilize the information available in items _B_ and _C_ . A regression model that explains _A_ based on ( _B, C_ ) is not possible, as there are 0 complete cases for the triplet, even though half of the observations are available in the example table.

Andr ́ee (2021) develops an imputation strategy suitable for the context. At a high level, the strategy starts off with the notion that the most valuable information for imputation is contained in the observed price ratios and should be utilized to fill in the blank spots. Specifically, taking the example of table 1, the problem could be stated as in which the goal is to estimate the L.H.S., using the information in price pairs on the R.H.S., so that ∆ _P_<sup>ˆ</sup> can be calculated. This information is utilized by using chained equations modeling (see van Buuren and Groothuis-Oudshoorn (2011); van Buuren (2012) for an in-depth treatment and implementation of the concept).

A simplified walk-through of the steps is as follows. The exact outline of the algorithm is provided in the appendix.

- 1) First, the missing price entries are filled based on prior assumptions. These prior assumptions could be based on expert opinion, be random (standard chained equation implementations typically start by filling entries using random draws of observations, see for instance the implementation of van Buuren and Groothuis-Oudshoorn (2011)); or be based on prior modeling (Andr ́ee (2021) used a combination of spatial and time series interpolation techniques, Andree (2022) uses a pattern-matching algorithm that fills gaps
provinces. However, the cross-province inequality in UBN is high—in Formosa, Salta, and Santiago del Estero 31 percent of the population had UBN compared to the City of Buenos Aires where only 8 percent had UBN.

A larger share of the rural than urban population faces UBN. Disaggregate data from the 2001 Population Census on the situation in rural and urban areas is now available. In rural areas 30 percent of the population had UBN in 2001.

The variation in UBN across provinces is large. Data from 2001 reveal that Northeast and Northwest regions have the largest share of the rural population with UBN. For example, in Salta and Formosa more than 50 percent of the rural population has UBN while only around 15 percent of the rural population in the provinces of Buenos Aires and Pampa face this situation.

# Box 2: Children Die of Hunger Although Argentina Is One of the World’s Largest Food Producers

Since October 2001, poverty has increased by 40 percent and the number of unemployed has risen by 450,000. The 2001 crisis magnified long-standing inequalities and has shocked society into recognizing problems of malnutrition, which has often been given little attention. Not all state subsidies to unemployed heads of household and other aid programs have managed to alleviate poverty either before or after the 2001 crisis. It is estimated that up to 25 percent of those under five are suffering from malnutrition. According to estimates, three children per day now die of malnutrition or related diseases, most of them in the northern parts of Argentina. In Misiones, around 50 children died of malnutrition in 2002. In Tucumán, the revelation that eight children died of malnutrition in one week focused attention on the provincial government’s failure to provide adequate poverty relief. Tucumán, which has one of the country’s highest poverty rates, is an example of the pauperization that has occurred in Argentina in recent years.

Poverty in Tucumán has remained at extremely high levels since at least 1930, when the sugar industry on which its economy is based entered into severe decline. Since then, much of the provincial economy has survived on state subsidies to maintain the sugar industry, which is unprofitable but labor intensive, in a densely populated province characterized by high unemployment, and on other federal subsidies that serve to buy votes for both the provincial and national governments. Cases of malnutrition in Tucumán are occurring among the “structural poor.” Doctors in the province note that malnutrition has affected two earlier generations, and the current rise in deaths is attributable to an exacerbation of the poverty situation, rather than being a previously unknown problem. <mark>Source: Oxford Analytica; http://www.oxweb.com</mark> 20
41 Confucian material that many potential Chinese reformers argued had little or no relevance to creating a modern nation state. However, almost every official with any power in China had to have spent a large portion of his adult life studying for the exam, making it unlikely these same officials would find the material irrelevant.

Similar claims can be made about the persistence of damaging policies. The growth inhibiting policies typical of many developing countries, such as a large urban bias, explicit and implicit taxation of agriculture, industrial protection, and a tendency to allocate educational expenditures regressively can perhaps be understood as direct products of the demands of a relatively small educated elite. More education might lead to a reenforcement of these policies as the newly educated protect their gains rather than risk reform<sup>53</sup> .

I want to emphasize I am not equating government, or the magnitude or growth of government employment, with the magnitude of rent seeking, nor am a I saying that the expansion of education in government is necessarily unproductive. On the contrary, the most recently successful of developing countries have had strong and active governments and highly educated civil servants (World Bank, 1994). Wade (1990) asserts that college graduates were as likely to enter government service in Korea and Taiwan as in African economies. The question is not whether the educated labor flows into the government so much as what the educated labor does once it is in the government<sup>5</sup> '.

# CIonhidnn

Recently created data on the growth of years of schooling provide no support at all for the proposition that more rapid rates of growth of education capital produce greater output growth. In fact, 53 Tbis is perhaps the answer to my earlier posed puzzle as to why physical capital works exactly like it should while education capital does not. To paraphrase the NRA, 'physical capital doesn't lobby people, people lobby people.' 54 Wade (1994) demonstrates the importance of institutional environment for govermment performance with an intriguing comparison of government irrigation in India and Korea.