rafmacalaba/datause-encoder-data huggingface dataset:
rely on liquid assets such as livestock to mitigate negative effects. In our sample, just over half the households own agricultural land, and 60 percent own some type of livestock.<sup>33</sup> In Figures 13 and 14, we do find evidence that landed households are able to protect their consumption in a time of adverse vegetation anomalies, whereas the consumption of the landless decreases. However, the trends in consumption are no different between landed and landless households when temperature anomalies are positive. Livestock ownership does not seem to protect a household’s consumption in times of adverse weather shocks. Our findings are in line with Christiaensen and Subbarao (2004), and Kazianga and Udry (2006), who show livestock ownership as ineffective in mitigating consumption losses due to drought in Kenya and Burkina Faso, respectively.

Afghanistan’s population is primarily rural (78 percent), who are either directly or indirectly dependent on agriculture or rearing livestock. Among households in urban areas, we may expect better resilience to adverse effects of weather due to their access to diversified livelihoods outside agriculture. In Figure A.3, we find that the adverse effects of low NVDI and high temperatures are concentrated in rural areas, while in urban areas the effect is noisier, suggesting that local weather extremes do not impact livelihoods in the same way.

# **6 Vulnerability to Poverty**

Beyond the observed impacts on household welfare, we want to understand long-term household vulnerability to poverty in the context of increasingly extreme climate in Afghanistan. Even for households not affected by the 2018 drought, similar or more severe shocks could drive them into poverty in the future. The identified households may live close to the edge of poverty and are vulnerable to backsliding.

Drawing from Hill and Porter (2017), we estimate the vulnerability to absolute poverty for Afghan households due to drought. We combine the historical weather data with the estimated impacts of droughts on consumption to predict future consumption based on a posited threshold for the probability of falling into poverty and the estimated likelihood of future shocks based on the past weather shocks.

For simplicity, we rely on the linear estimates of NDVI anomalies on log consumption. As is clear from Figure 8a, imposing a linear relationship through the range of z-scores may underestimate the effect of severe droughts (NDVI z-score <-1), so these estimates should be considered a lower bound of household vulnerability to weather shocks.

> 33Over four-fifths of cultivating households in IE-LFS 2019-20 possess livestock, and livestock rearing is the second-largest income-generating activity for over two-thirds of the farming households.

17
In contrast to Membrillal, Bellavista residents do not recommend infrastructure and services but rather credit and improved community organization. Credit is needed for land and animal acquisition, agricultural inputs and storage facilities. Credit would help families to store their products and wait for better prices before selling them in the market. For example, the price of corn at harvest is 6000 sucres per 100 pounds but the price rises to 22000 sucres thereafter. Community members also feel that communal organization has seriously slipped recently, partly reflecting that many male members have migrated to the urban centers in the Coast.

# _.3. Oriente_

Infrastructure and basic services projects are dominant in the answers of the inhabitants of Villano. They clearly prioritize the completion of the road connecting the village to Archidona of which 1.5 km is missing. This will improve market access considerably. Services like water and health rank second -- the community is seeking assistance in the construction of a water system from the Municipal Council of Archidona and Integrated Health Program of the agricultural cooperative of which it is a member. A community center, under construction by the Federation of Indigenous Organizations of Napo, is viewed as a symbol of well-being in the community and families anxiously await its completion. Finally, credit is viewed as a fourth important item to overcome poverty.

## 8. Flnal Comments

Our aim with this qualitative assessment was to learn about the views of poor rural households on their living conditions, strategies in times of change and recommendations for anti-poverty programs. Although the scope of this assessment was not even close to a 'representative survey', we have nevertheless obtained several very interesting insights into the rural life of seven communities which could well be indicative of the rural areas in general. Further, while the presented qualitative analysis cannot subsitute for more quanitative studies using a representative survey like the Living Standard Measurement Survey, the presented results can be viewed as complementary: they can help researchers formulate questions and research subjects which can then be applied in quantitative analyses.

We found that rural life is everything but static. Almost none of the rural households in the seven very different communities survive on subsistence agriculture alone. New income opportunities are sought, ranging from traditional animal husbandry to piecework, specialization in cash crops to migration and from weaving to the start of small businesses. The choice of this array of income sources depends primarily on the family composition but also community support and mutual assistance between families.

Of most importance is that the rural poor believe that practical solutions to overcome their poverty are at hand. Families distinguish very clearly between factors they have to improve within their community from those where outside help is needed; infrastructure and training are the main categories suggested for outside assistance.

30
of bank lending spreads. LIQREQ is a dummy variable that takes value of one if the authorities enforce liquidity requirements, and zero otherwise. RESTRICT is a variable that indicates the degree to which restrictions are imposed on banks to engage into nonbank financial activities, including real estate, insurance and securities. ENTRY is a variable that indicates the restrictions on entry into banking and is measured as the fraction of entry applications denied. From the same database, we draw the variable STATE which is the share of state ownership in the banking system of the country. To control for the impact of the structure of the banking system, we use the bank-5 concentration ratio from the same database. Our bank concentration variable CONC is defined as the ratio of deposits of the largest five banks in the country to the total deposits held by banks in the country. We also use a broader measure of restrictions on banking from the Heritage Foundation. This variable, which we will refer to as FBANK, measures the degree of freedom in banking, and captures the degree of (i) government ownership of banks; (ii) restrictions on the ability of foreign banks to open branches and subsidiaries; (iii) government influence over the allocation of bank credit; (iv) government regulations in banking; and (v) freedom of banks to offer different types of financial services, securities, and insurance policies.

We also control for the degree to which credit information is publicly available. Using data from the World Bank Credit Registries database, we construct a dummy variable PCR that takes value of one if the country has a public credit register, and zero otherwise. Finally, we will check whether the results are affected by the degree of dollarization in the country. The interest rate spreads we calculate do not distinguish between local currency and foreign currency. In countries where a large share of deposits 11
design or fieldwork protocols include Beegle et al. (2012), Browning, Crossley and Winter (2014), Engle-Stone et al. (2017), Jolliffe (2001), Pradhan (2009), Schündeln (2018), and Winter (2003). Most of these papers find a downward bias in estimated mean consumption compared to a benchmark. Consumption by logic (and instrument/questionnaire design) has a lower positive, nonzero bound, but has no symmetric upper bound. In part for this reason, measurement error in consumption, whether downward biased or mean preserving, tends to reduce the positive skewness in the distribution. Korinek et al. (2006) find higher non-response rates among the rich. Similarly, Banerjee and Piketty (2005) find that a substantial part of the HHS - NAS gap in India can be attributed to missing top incomes from surveys. Farfán et al. (2017) find significant underreporting of food-away-from home which would have the effect of compressing the positive tail of the distribution. Gibson et al. (2015) find that reporting error is negatively correlated with true consumption (accounting for which would require changing the distribution). The weight of the evidence is that measurement error in surveys is not distribution neutral and typically biases downward both mean consumption and the density of the upper tail of the distribution.

Within the limited literature of reconciling household surveys and national accounts there is sometimes a presumption that national accounts are the benchmark for comparison (i.e. they are free of measurement error). This inclination towards national accounts estimates over household surveys has been somewhat heightened recently by the assertion of Pinkovskiy and Sala-i-Martin (2016) that GDP per capita is better correlated than survey data with data on nighttime lights and non-monetary development outcomes, such as schooling and health status. Yet since the founding of national accounts in the 1940s, there has been an acknowledgement of measurement error in national accounts by those working in this field. As one of the seminal papers on this, Stone et al. (1942) introduced the idea of using balancing tables (balancing estimates from differing approaches) as a tool to reduce measurement error in national accounts estimates. More recently, Aruoba et al. (2016) uses this approach to estimate measurement error in U.S. GDP estimates. Charmes (2012) discusses potential sources of measurement error in national accounts by providing an overview of how it struggles to capture informal economic activities. One specific type of informality, illegal activities are particularly challenging to measure in national accounts, and sometimes more readily picked up in household surveys.

11
The growth records of these twvo groups are very different, as shcown in the following table. The resumption of aggregate growth for each group coincided with real exchange rate adjustments.

**Table 2: Real Growth Rates in Africa, 1981-1995 (antnual averages)**

|Country Group|1981-1985|1986-1993|1994-1995|
|---|---|---|---|
|Non-CFA Zone Countries||||
|GDP Growth|0.4|4.4|3.0|
|Per-Capita GDP Growth|-2.5|2.1|-0.2|
|CFA Zone Countries||||
|GDP Growth|4.3|-0.4|2.8|
|Per-Capita GDP Growth|0.6|-3.2|-0.8|
|Source: Klau (1998)||||

Outside of the CFA zone, most Sub-Saharan African currencies experienced large real appreciations in the 1970s and early 1980s, estimated at about 44 percent on average. The CFA-zone overvaluations started a bit later, in the mid-1980s.

There is strong evidence that the overvaluation of real exchange rates contributed a great deal towards Africa's poor economic performnance. Ghura and Grennes (1993) analyzed the relationship betw-een the real exchange rate and macroeconomic performnance in 33 Sub-Saharan African countries between 1972 and 1987. They found that misalignment, or overvaluation, was associated with lower levels of growth of real GDP per capita, lower levels **cif** exports, lower levels of imports, lower levels of investment, and lower levels of savings, even when correcting for other causes.

Klau (1998) showed that the CFA franc overvaluation of the late 1980s and early 1990s was one of the principal causes of output decline in those countries. In both CFA zone and non-CFA zone countries, he showed that real devaluations are associated with short-run and long-run improvements in economic activity, as suggested in the table.

Real devaluations are also associated with improvements in African export perfornance. Sekkat and Varoudakis (1998) analyzed the real exchange rate and manufactured exports in three sectors in 11 African countries from 1970 to 1992. They found that an overvaluation was negatively associated with exports in all three sectors, textile products, chemical products, and metal products, for the non-CFA zone countries.

The next section presen-ts case studies of Ghana, one of the non-CFA reformers, and the CFA countries as a group.

French franc. Their monetary arrangements help guard against large losses of reserves and shortages of foreign exchange. Membership in the zone has changed somewhat over time, but in general has included Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Congo, Coinoros, C6te d'Ivoire, Equatorial Guinea, Gabon, Mali, Niger, Senegal, and Togo.

16
- Generous tax credit and tax allowances are permitted.

- The MOUs, which specify the fiscal conditions for oil operators, often provide numerous fiscal incentives and protect operators‘ profits at the government‘s expense when oil prices go down.

- At the same time, a minimum tax liability of 15 percent is mandated in the law.

- The royalty rates are neither linked to price of oil nor to the rate of return or profits.

85. Clearly, a comprehensive tax reform is needed in the oil sector. The pending PIB is supposed to replace the existing laws governing the petroleum sector. The bill aims to increase the government‘s take in JVs to 95 percent from its present level of 85–90 percent and its take in PSCs to about 70 percent from its current level of 60 percent. Although the law covers fiscal provisions, it does not include a reform of the tax system applied to the oil and gas sector.<sup>28</sup> 86. Compared with its peers in West Africa and practices in other oil-producing countries, Nigeria‘s royalty and profits tax rates are high, but at the same time, Nigeria gives substantial incentives and allowances. For example, it makes the fiscal regime competitive by giving a 50 percent tax allowance, and it provides for a sliding scale of royalty, depending on water depth and differences in production costs. The absence of cost-recovery limits ensures that investors recover their investments in good time. Additionally, MOUs include other incentives for the companies and further reduce the tax liability.

# _ii. Revenue Administration_

87. Numerous shortcomings in revenue administration have been identified in the Nigeria Extractive Industries Transparency Initiative (NEITI) financial audit report 1999–2004 issued in 2006. The very quantity of oil that is the basis of all royalty and profits tax assessment is uncertain. Department of Petroleum Regulation (DPR) production data often differ from the data provided by the companies. Measurement of crude oil is also a problem.

- The DPR does not have a comprehensive database of those who hold oil mining leases and oil prospecting licenses.

- DPR has not rigorously verified royalty computations prepared by companies and the key related variables in computation—volume and API (American Petroleum Institutemeasuring density of petroleum liquid) gravity—seem to be interpreted and applied subjectively by the companies.

- In the present setup, the responsibility for technical data dealing with physical production and volumes is separated from the responsibility for financial management. This has led to unreconciled differences between the two. These differences are attributed to the institutions involved—DPR, Federal Inland Revenue Service (FIRS),

30
# **Table 3: Impact of PBF on Facility Administration**

|**Variable**|**Baseline**<br>**Mean in**<br>**PBF**<br>**districts**|**Rural H**<br>**Trend**|**ealth Cente**<br>**PBF**<br>**impact**|**rs**<br>**p-**<br>**value**|**N**|**Baseline**<br>**Mean in**<br>**PBF**<br>**districts**|**He**<br>**Trend**|**alth House**<br>**PBF**<br>**impact**|**p-value**|**N**|
|---|---|---|---|---|---|---|---|---|---|---|
|Number of evaluations in past<br>12 months:|||||||||||
|External evaluations of facility|5.9|-1.8|0.1***|0.001|420|3.6|-0.5|0.6**|0.014|276|
|External evaluations of staff|3.6|-1.3|4.4***|0.000|420|3.8|0.5|0.8|0.156|274|
|Internal evaluations of staff|6.7|-4.0|8.5|0.265|418|5.9|3.6|1.3|0.350|262|
|Number of staff meetings in past<br>3 months|9.8|-1.6|4.2|0.518|420||||||
|Solicitation of patient opinion|0.660|-0.078|0.246|0.425|420|0.371|-0.143|0.355|0.385|278|
|Staff leaving the facility in past<br>12 months:|||||||||||
|Number left|0.6|0.1|-0.3|0.782|420|0.4|0.2|-0.1|0.605|278|
|Any left|0.302|-0.091|0.008|0.939|420|0.129|0.138|0.091|0.695|278|

Note: data from health facility assessments. All regressions are the difference-in-differences specification with facility fixed effects and controlling for exposure to the collaborative quality improvement and citizen scorecards interventions. Standard errors are clustered at the district and survey round level with wild bootstrapping. Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 25
<!-- Start of picture text --> wW<br>S<br>- C1<br>wn<br>C2<br>wo<br>N<br>AC3<br>ned ao. a 6 S , e ©C4<br>Toma ke i<br>Ww<br>N<br>2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text -->
participation (e.g. employed, unemployed, homemaker), form of employment, if any (e.g., selfemployed, wage worker, apprentice), and type of employer (e.g. private formal, private informal, public). Information on occupation and sector of activity is the focus of our empirical analysis.

The night-time illumination data come from satellite imagery. They were obtained from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS). The dataset covers most of the earth at a 30 arc-second grid (approximately 1 square kilometer). The luminosity measure for each cell, or pixel, is a digital number in the range 0 to 63 that increases with luminosity. There are 280,000 pixels for Ghana. Recent studies have made use of these data as a high-resolution, local measure of economic development (Bleakley and Lin 2012; Henderson, Storeygard, and Weil 2012; Michalopoulus and Papaioannou 2013). We map the nightlight data into our EAs, and are able to construct a panel dataset for 12,390 EAs and 19 years (1992-2010).<sup>5</sup> For most years, we have observations from 2 different satellite readings. To reduce measurement error and noise, we use the average of the two as nightlight measure for that year. We also use year dummies to correct for differences in satellite calibration over time.

# **3.3 Descriptive statistics**

Administratively, Ghana consists of 10 regions which are subdivided into districts. There were 110 districts in 2000. We keep the 2000 district boundaries throughout the analysis. The average (median) district has a size of about 2,200 (1,350) square km. According to the census data, the population of Ghana was 18.85 million in 2000 and 24.66 million in 2010, corresponding to an annual rate of population growth of 2.7% – close to the African average. Urbanization is equally rapid: in 2000, about 44 percent of the population lived in urban areas, and 51 percent in 2010. These two features make Ghana an ideal candidate to study town formation.

Since the main focus of our analysis is occupational data, we focus on the working-age population, which we define as all adults aged 20 to 60 years. These make up 42.5 percent of the total population in 2000, and 45.4 percent in 2010. The composition of the labor force, labor supply, employment status, and type of employer are summarized in Table 2.

In line with the rapid urbanization of the country, we observe a rapid transition of the

> 5 We additionally construct a grid where each cell contains 3 _×_ 3 pixels. This yields 31,334 grid cells for each of the years.

11
A fourth resurvey of Palanpur took place in 1983-4 when Jean Drèze and Naresh Sharma, in close consultation with Bliss and Stern, lived in the village for fifteen months, once again collecting data for the entire village population.<sup>xi</sup> The most recent re-survey of the village, once again by Drèze and Sharma, was conducted in 1993. This survey was carried out over a shorter period and is consequently somewhat less comprehensive.<sup>xii</sup> A considerable body of research has emerged from the Palanpur research program. A volume edited by Lanjouw and Stern (1998) brings together a set of these studies and attempts to distill the main findings. This volume touches on most of the themes discussed in the earlier book by Bliss and Stern (1982), but includes a more explicit focus on outcomes and processes of change over the entire period from 1957-8 to 1993. The general story, presented below, of economic development in Palanpur and its social consequences, has been adapted from the more detailed and complete discussion that can be found in Lanjouw and Stern (1998).

At the beginning of the last survey (in mid-1993), Palanpur had a population of 1,133 persons, divided into 193 households (Table 1). Hindus represented 87.5 percent of the village population, and Muslims the remaining 12.5 percent. Hindus were divided into six main castes (ranging from 14 to 48 households in size), and three minor castes of three households or less (Table 2). The shares of Hindus and Muslims in the total population, and the relative sizes of the main castes, remained fairly stable throughout the survey period.

At the time of the last survey for which detailed income data were available, 1983/4, the economy of Palanpur was essentially one of small farmers. The proportion of landless households was relatively small by Indian standards and there were no clearly outstanding 12
_ESTIMATING FOOD PRICE INFLATION_ _31_ Table A5—Linear decomposition of _CV_ - _score_ .

||_Dependent variable: CV -score_|
|---|---|
||OLS|
|log Number of markets|_−_0.034<br>(0.028)|
|log Number of food items|0.021<br>(0.032)|
|Number of markets per food item|0.003<br>(0.005)|
|Data completeness|_−_0.019<br>(0.092)|
|Infation rate|0.450<sup>_∗∗_</sup><br>(0.138)|
|Volatility|_−_1.305<sup>_∗∗_</sup><br>(0.361)|
|Max. draw-down|0.024<br>(0.138)|
|Constant|1.018<sup>_∗∗∗_</sup><br>(0.098)|
|Observations|25|
|R<sup>2</sup>|0.627|
|Adjusted R<sup>2</sup>|0.474|
|Residual Std. Error|0.052 (df = 17)|
|F Statistic|3.792 (df = 7; 17)|
|_Note:_|_∗_p_<_0.1; _∗∗_p_<_0.05; _∗∗∗_p_<_0.01|

_Note:_ Simple linear regression estimates that decompose the cross-validation performance score into key characteristics of the imputation problem. Percentage covariates are modeled as numeric digits of the same unit as the dependent variable, e.g. a monthly volatility of 2% enters the regression as a value of 0 _._ 02. The simple results highlight that prediction performance is not significantly related to data dimensions of the problem, instead high volatility and high inflation are determinants of imputation accuracy. Jointly, these two variables explain almost half of the in-sample variation. The signs of the coefficients suggest that when volatility increases, prediction performance deteriorates and data collection becomes more important. When the inflation trend is strong relative to volatility, then the price trend is clearer in the data and imputation accuracy increases, suggesting that robust inflation tracking is possible even when little ground truth data is available. _Source:_ The statistics have been prepared by the author for this paper based on end-of-August (2021) food price data from World Food Program.