rafmacalaba/datause-encoder-data huggingface dataset:
|Description|ERA5 climate<br>reanalysis gives a<br>numerical<br>description of the<br>recent climate,<br>produced by<br>combining models<br>with observations.<br>Total precipitation is<br>the accumulated<br>liquid and frozen<br>water, comprising<br>rain and snow, that<br>falls to the Earth’s<br>surface. It is the sum<br>of large-scale<br>precipitation and<br>convective<br>precipitation.|The CHIRPS data<br>archive is a quasi-<br>global (50S–50N)<br>gridded 0.05°<br>resolution times series<br>covering precipitation<br>from 1981 to near-real<br>time.|The SPEI is a multi-<br>scalar drought index<br>based on climatic data.<br>It is a ratio of actual to<br>potential<br>evapotranspiration.|This spatially explicit<br>index is an indicator of<br>crop performance<br>based on the<br>availability of water to<br>the crop during a<br>growing season.|The Soil Water Index<br>quantifies the<br>moisture condition at<br>various depths in the<br>soil. It is mainly driven<br>by precipitation via<br>the process of<br>infiltration. Various t-<br>values are available: 1,<br>5, 10, 15, 20, 40, 60,<br>100. The T- value<br>simulates the<br>infiltration time into<br>deeper soil layers and<br>hence indicates<br>qualitatively the depth<br>of the SWI estimate.<br>T40 was selected as<br>being generally<br>representative, based<br>on expert advice.|The Terra Moderate<br>Resolution Imaging<br>Spectroradiometer<br>(MODIS) Vegetation<br>Indices Monthly<br>(MOD13C2) Version<br>6.1 product provides<br>a vegetation index<br>value at a per pixel<br>basis. The NDVI is<br>referred to as the<br>continuity index to<br>the existing National<br>Oceanic and<br>Atmospheric<br>Administration<br>Advanced Very High<br>Resolution<br>Radiometer (NOAA-<br>AVHRR)-derived<br>NDVI.|
|---|---|---|---|---|---|---|
|Temporal<br>coverage|1979 to present|1981 to near-real time<br>precipitation time<br>series|1901–2018|2000–2021|2007–2021|2000–2021|
|Spatial coverage|Global|Global|Global|Regional|Global|Global|
|Resolution|Reanalysis: 0.25°<br>(~28 km)|0.05° (~5.5 km)|0.5° (~5.5 km)|0.1° (~11 km)|0.1° (~11 km)|0.5° (~5.5 km)|
|Output<br>information|24-hour aggregation<br>since the beginning<br>of the forecast|mm/per unit of time|Z-values, monthly|WRSI per raster cell;<br>current WRSI data give<br>range of 0–100 percent<br>of total|An index describing<br>the relation between<br>surface soil moisture<br>and profile soil<br>moisture as a function<br>of time(percent)|Vegetation density,<br>monthly|
|Data set source|https://cds.climate.c<br>opernicus.eu/cdsapp<br>#!/dataset/reanalysi|https://data.chc.ucsb.e<br>du/products/CHIRPS-<br>2.0/africa_dekad/tifs/|https://spei.csic.es/|https://edcftp.cr.usgs.g<br>ov/project/fews/dekad<br>al/|https://land.copernicu<br>s.eu/global/products/s<br>wi|https://lpdaac.usgs.g<br>ov/products/mod13c<br>2v061/|

38
15 status of their child, b) know how it has been changing over the last few months, c) can read the growth chart, and d) can remember any advice that the worker has given them.

# V. MAKING USE OF THE PROCESS

The approach to field supervision illustrated above can be useful in impro:ing program quality in two distinct ways: use of the findings to make design or implementation changes to the program; and use of the process to reorient the way supervision is done and to change.worker behavior.

# Using The Findings

Are the findings valid? The systematic routines and questions summarized above are aimed at gaining as clear an understanding of what is happening in the field as is possible from brief visits. Clearly they'cannot substitute, in terms of depth of insight, for the findings of longer term participant observation studies in villages and health centers. But equally, such studies cannot be carried out on a routine basis over large areas.

Despite the inevitable limitations of brief visits, experience in several sectors with using structured interviewing as a rapid rural appraisal technique suggests that the insights gained are often more valid than those from large scale questionnaire surveys, which may be the only practical alternative. Such surveys may be based on statistically valid samples, but are not open-ended enough to fully explore program quality. They are also expensive and quickly out-dated.

The validity issue for this kind of field supervision is therefore not so much whether it reflects reasonably well what is going on in a given local area, a.s whether the local area you are visiting really reflects the successes and constraints of the program you are reviewing in general. Do not therefore pay too much attention to the findings of an individual field visit. Make field visits to several areas which you think should show differences in client knowledge or attitudes, in worker motivation, in health resource availability or in local managerial capacity, and only then begin to draw general conclusions. Cross-check the validity of the opinions you are forming with people knowledgeable about the area--especially experienced local health managers, and field researchers in the social sectors who may have worked in the area.

ADplving the findings. It is important not to use your interview findings to correct the behavior of workers on the spot. This is a common and tempting practice; most supervisors cannot resist demonstrating they know better, or showing their authority. But guiding the individual worker is the job of the worker's immediate supervisor, not yours. Correcting workers on the spot has three undesirable consequences. It may undermine your image as open-minded inquirer rather than inspector. It may undermine the authority of the worker's immediate supervisor. Most important, if you correct the health
MAPS II sub‐plot crop cutting based plot‐level maize production estimates are computed by multiplying the crop cut sub‐plot production across the 64m<sup>2</sup> area covered by the 8x8m subplot by the ratio of the entire GPS‐based plot area in m<sup>2</sup> to 64m<sup>2</sup> .

Furthermore, half of the target household population within each of the pure stand and intercropped domain in each EA was selected at random prior to the start of the MAPS II fieldwork for a full‐plot crop cut. This rare approach to crop production measurement entailed the harvesting of the entire plot area, shelling the resulting harvest, weighing it in the field, and capturing its moisture level. This operation was conducted by the enumerators with help from the EA‐specific crop cut monitor and the crop cut assistant(s) recruited from within the households. On the MAPS II plots selected for full‐plot harvest, the harvest of the designated 8x8m subplot was weighed separately from the full‐plot harvest to allow for comparative yield analysis. The full‐plot harvests were only weighed in the EAs as their transport to and additional drying and reweighing at a central location was deemed logistically infeasible. Moisture readings taken from the maize grain harvested from the full plot harvests were used to standardize the production quantity to 12 percent moisture. A total of 211 plots had full‐plot harvests. Gourlay et al. (2017) detail the approach to full plot harvests. Although farmers were not told the final weight of their harvest, it is likely that the process of harvesting and bagging the maize improved their self‐report production values compared to plots without full plot harvests. Therefore, the analyses that use self‐reported maize production per hectare rely only on 252 plots without a full plot harvest.

# **2.3.4. Satellite‐Based Maize Yield Measurement**

Images from the Sentinel‐2A Multispectral Instrument, processed to top‐of‐atmosphere reflectance (Level ‐1C) were accessed within the Google Earth Engine platform. Clouds and shadows were masked from the images using a random forest classifier trained on points visually selected from images throughout the region. Five vegetation indices (VIs) were then calculated for each pixel using the equations shown in Table 2. The average value of all bands and VIs within each plot polygon were then extracted for image date for further analysis. In addition, for comparison with the Sentinel‐2A images, an image acquired by Terra Bella’s Skysat sensor on May 29, 2016 was accessed. Skysat measures radiance in blue, green, red, and near‐infrared channels at 1m resolution. As with the Sentinel‐2 data, clouds and shadows were masked using a random forest classifier trained on several images in the region, including those used in BL17.

10
32

**Table 5 Structure of the Interstate Pipeline Transportation Market, 1994-95**

(percent)

||1994|1995|
|---|---|---|
|Primary transportation|71|69|
|Share arranged by local distribution companies|-|63|
|Share arranged by marketersa|-|37|
|Secondary transportation|29|31|
|Firm|13|17|
|Interruptible|16|14|
|Share arranged by local distribution companies|26|24|
|Share arranged by marketersa|74|76|
|All transportation|100|100|
|Share arranged by local distribution companies|-|51|
|Share arranged by marketersa||49|
|-<br>Not available|||
|**a**Includes end users.|||

_Source:_ Interstate Natural Gas Pipeline Association of America.

The most active players in the primary market are local distribution companies, which arranged 63 percent of primary deliveries in 1995; marketers and end users arranged the remaining 37 percent. The ownership of transportation contracts has a similar structure: local distribution companies held about 66 percent of interstate pipeline capacity in 1995, marketers 14 percent, end users 8 percent, other pipelines 7 percent, and producers 3 percent.

Transactions in the secondary transportation market are dominated by marketers, which arranged transportation for 76 percent of secondary deliveries in 1995. Local distribution companies' share was only 24 percent and is steadily decreasing. Their share in the overall transportation market is 51 percent, and marketers' share is 49 percent.

The open access regime and unbundling of interstate pipeline transportation have transformed the way in which end users receive their gas deliveries. Before 1985 they received almost all natural gas deliveries through sales from local distribution utilities or a nearby pipeline company. Today they purchase only 52 percent of gas deliveries directly from pipeline or local distribution companies. They purchase the remaining 48 percent in the wholesale market and pay fees to pipeline and local distribution companies for transporting these deliveries to consumption sites.

More than 74 percent of deliveries to end users were transported under firm transportation contracts in 1995, and the remaining 26 percent under interruptible contracts.
||||**Glo**|**bal**|||**Adva**|**nced**|**Eme. D**|**ev. In.**|
|---|---|---|---|---|---|---|---|---|---|---|
||Adva|nced|Eme|rging|Deve|loping|I|Ot|E|D|
||In|Out|In|Out|In|Out|n|u|me|ev|
|Signifcant & positive|17/19|18/19|15/28|22/28|8/38|14/38|15/19|14/19|10/28|17/38|
|<br>Signifcant & negative|0/19|0/19|0/28|0/28|7/38|2/38|0/19|0/19|0/28|1/38|
|<br>Median|0.5|0.61|0.35|0.51|-0.11|0.29|0.5|0.5|0.22|0.34|
|Median t-stat|4.1|6.1|2.3|3.6|-0.71|1.7|5.1|5.0|1.4|1.9|

Table B.3: Sample including developing countries. Summary statistics of the loadings of the different factors. Significance refers to the 95% level, based on 10000 block bootstrap replications.

||Infows-Out|fows|I|nfows-Infow|s|Outfows-Outfows|
|---|---|---|---|---|---|---|
|Adv|Global<br>Eme<br>Dev|Advanced|Glob.-Adv.|Glob.-Eme.|Glob.-Dev.|Glob.-Adv.|
|0.73|0.26<br>-0.06|0.88|-0.13|-0.44|-0.19|-0.33|

Table B.4: Sample including developing countries. Correlations of the loadings of the different factors.

||All fa|ctors|Global|factor|Group|factor|
|---|---|---|---|---|---|---|
||Infows|Outfows|Infows|Outfows|Infows|Outfows|
|All countries|||||||
|Median|0.31(0.06)|0.23(0.06)|0.13(0.04)|0.19(0.05)|0.12(0.04)|0.00(0.00)|
|Mean|0.37(0.03)|0.29(0.02)|0.19(0.02)|0.23(0.02)|0.18(0.02)|0.06(0.01)|
|Advanced|||||||
|Median|0.67(0.08)|0.67(0.06)|0.25(0.10)|0.37(0.10)|0.25(0.09)|0.25(0.09)|
|Mean|0.57(0.04)|0.62(0.04)|0.31(0.05)|0.36(0.05)|0.26(0.05)|0.26(0.05)|
|Emerging|||||||
|Median|0.27(0.10)|0.26(0.08)|0.13(0.06)|0.26(0.08)|0.05(0.06)|NA|
|Mean|0.31(0.05)|0.28(0.05)|0.17(0.04)|0.28(0.05)|0.14(0.04)|NA|
|Developing|||||||
|Median|0.28(0.08)|0.10(0.05)|0.07(0.05)|0.10(0.05)|0.12(0.06)|NA|
|Mean|0.30(0.04)|0.13(0.03)|0.14(0.03)|0.13(0.03)|0.16(0.04)|NA|

Table B.5: Sample including developing countries. Fraction of the variance explained by the estimated factors. Two-standard errors shown in brackets 65
59

# Paper No. 434. Washington, DC: World Bank.

Shalizi, Zmarak, and Jonathan Skinner. 1988. “Tax_iion and output growth." PPA Working Paper No. 73. Washington, OC: Worid Bank.

Shaw, Edward. 1973. Financial Deepening in Economic Development. New York: Oxford University Press.

- Shell, K. 1967. °A model of inventive activity and capital accumulation." in K. Shell, ed. Essays in the Theory of Optirnat Economic Growth. Cambridge, MA: MIT Press.

Skinner, Jonathan. 1988. ‘Taxation and Output Growth in Africa.” PPR Working Paper No. 73. Washington, DC: World Bank.

Smith, Adam. 1776. The Wealth of Nations (edited by Edwin Cannan; New York: The Modern Library, 1937).

- Solow, Robert. 1956. "A Contribution to the Theory of Economic Growth." Quarterly Journal of Economics 70 (Feb.):pp. 65-94,

- Stiglitz, J. 1976. "The efficiency wage hypothesis, surplus labor, and the distribution of income in LDCs." Oxford Economic Papers 28: 185-207.

- . 1985. "Maney, credit, and banking lecture: Credit market and the control of capital." Journal of Money, Credit and Banking 17:133-152.

. 1989. "Financial markets and development.’ Finance and Economic Development 5: 55-68.

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- , and , 1983. ‘Incentive effects of terminations: Applications to the credit and labor markets," American Economic Review 73:912-927.

Stockman, Alan C. 1981. “Anticipated inflation and the capital stock in a cash-in-advance economy." Journal of Monetary Economics 8 (3,Nov): 387-393.

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- Summers, R. and A. Heston. 1988. "A new set of international comparisons of real product and price levels estimates for 130 countries, 1950-1985." Review of Income and Wealth. 34 (Mar.):p. 1-25.

Swan, T.W. 1956. "Economic growth and capital accumulation." The Economic Record. 32 (Nov): pp. 334-361.

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and . 19889b. ‘Three decades of industrislization." World Bank Economic Review 3 (2): pp. 145-181. L. 1969. "Development patterns: A simulation study." Quarterty Journal of Economics 83: 220-241. . 1983. Structuralist Macroeconomics: Applicable Models for the Third W. did. New York: Basic Books incorporated.

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Thomas, V., K Matin and J. Nash.

1989b. “Lessons in trade policy reform." Policy and Research Series.

Trela, irene and J. Whalley. 1990. "Taxes, outward orientation, and growth performance in Korea." PRE Workin Paper No. 519. Washington, OC: World Bank.

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40 Table 7

# Poverty Profile of Household Groups in the Model

|Household|Poverty Ratio|Contribution to<br>National Poverty|
|---|---|---|
|Export Croppers|44.04|24.03|
|Food Croppers in the Savannah|63.68|18.89|
|Other Food Croppers|34.46|11.84|
|Government Employees|8.69|2.76|
|Formal Sector|1.79|0.36|
|Small Businesses|46.81|27.06|
|Inactive|37.07|_15.06_|
|All|37.16|100.00|

# **Principles of Targeting** 33

Over the past decade there has been major debate on the social costs of implementing adjustment programs in developing countries. It has been argued that adjustment policies have led to a deterioration in general welfare and that poor households were severely hit. As a result, a series of actions were recommended to mitigate the effects of adjustment on the poor whether by protecting them from temporary shocks or by increasing their effective 33 This section draws heavily on Besley and Kanbur (1988).
# 1. **Introduction**

Foreign investors can play an essential role in the development of a domestic debt market. Foreign investors can enlarge the investor base, and contribute to the process of financial innovation in domestic market. If foreign investors provide financial services as well as capital flows, the import of financial services results in additional efficiency gains through increased competition and the spread of good practices. In addition, the availability of foreign investors willing to lend can dampen business cycles by reducing the need of households and firms to contract consumption and investment spending when hit by negative shocks to domestic production and income. At the global level, capital flows created by foreign investors permit a more efficient allocation of world savings and direct resources to their most productive uses. Global capital flows produce opportunities for intertemporal trade, portfolio diversification, and risk sharing.

Despite these large potential benefits, there also exist risks associated with foreign investors. Recent crises suggest that foreign investors can make host economies more susceptible to volatility. Such concern may be all the more warranted if the economies have weak fiscal policies, unsound banking systems, and highly distorted domestic markets. Under some circumstances, the presence of foreign investors can increase the extent of herding and play a key role in crisis contagion. Foreign investors can amplify the effects of policy distortions. The entry of foreign financial institutions can erode margins already narrowed by competition stemming from domestic financial liberalization. Foreign investors can facilitate gambling for redemption in domestic markets by offering access to elastically supplied offshore funding.

In developing a domestic debt market, developing countries face the challenge of reaping the potential benefits that foreign investors can offer while minimizing the associated risks. Since neither the benefits nor the risk of foreign investors will exist without their interest, a developing country's first step should be to create a general environment that will have appeal to foreign investors. Most developing countries have yet to undertake the liberalization of capital account transactions. In sequencing capital account liberalization, there is a danger in removing most restrictions on capital account transactions before major problems in the domestic financial system are addressed. For liberalizing foreign portfolio investment in domestic debt instruments, development of essential infrastructure is necessary.

# **2. Conceptual frameworks**

Countries that have received largest portfolio inflows have experienced the largest increase in market capitalization.<sup>2</sup> There seems to be considerable scope for increasing the share of emerging securities market assets in the portfolios of OECD institutional investors.<sup>3</sup> From both the demand and supply sides, there would thus appear to be a great

> 2 World Bank, 1997.

> 3 See Blommenstein, 1997.
# **<u>Table 4 continued:</u>**

||**_Pooled dyna_**<br>**_status, Y_** **_t_**|**_micprobit of cur_**|**_rent employm _**|**_ent_**|**Simplepr**|**obit of employme**|**nt status in**|**1994**|
|---|---|---|---|---|---|---|---|---|
||Unemployed|Informal sector|Self|Public sec|tor<br>Unemploy|mentInformal sector|Self|Public sector|
|||employment|employment|employme|nt|employment|employme|nt emploment|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Family backg**<br>**_Fathers'_**|**round variable**<br>**_schooling_**|**s:**|||||||
|Primary|||||-0.108|0.062|-0.030|-0.036|
||||||(0.83)|(0.48)|(0.20)|(0.25)|
|Secondary|||||-0.307|0.200|-0.273|-0.316|
||||||(2.01)*|(1.27)|(1.27)|(1.72)|
|Some tertiary|||||-0.081|-0.214|-0.500|0.137|
||||||(0.30)|(0.65)|(1.00)|(0.47)|
|Mother's s|chooling (com|pleted:|||||||
|Primary|||||0.017|0.123|-0.065|-0.170|
||||||(0.08)|(0.63)|(0.28)|(0.74)|
|Secondary co|mpleted||||-0.478|0.358|0.597|0.367|
||||||(1.16)|(0.89)|(1.31)|(0.83)|
|Father's oc|cupation (refer|ence group small|farmer):||||||
|Non farm busi|ness owner/ow|n account worker|||-0.203|0.314|0.592|-0.168|
||||||(2.05)*|(3.50)**|(6.16)**|(1.58)|
|paternaloccup|ation==public s|ector worker|||-0.012|-0.005|0.087|0.078|
||||||(0.13)|(0.05)|(0.74)|(0.78)|
|Mother is a ho|memaker||||-0.413|0.331|0.202|0.022|
||||||(6.58)**|(5.81)**|(3.21)**|(0.33)|
|Ethnicity dumm|ies<br>Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Religion dumm|iesYes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|-2.016|-0.722|-1.576|-2.374|-0.574|0.730|-0.824|-2.625|
||(15.27)**|(5.71)**|(10.91)**|(15.76)**|(3.87)**|(5.32)**|(5.39)**|(15.44)**|
|Observations|8513|8513|8513|8513|3312|3312|3312|3312|
|Log likelihood|-1910|-1881|-1239|-1289|-1251|-1479|-1176|-1081|
|Pseudo R-sq.|0.63|0.66|0.72|0.72|0.40|0.32|0.30|0.39|

Absolute value of z statistics in parentheses

* significant at 5%; ** significant at 1%

30
# **_4.1.1 Model & estimation strategy_**

To compare and contrast the performance of rural and urban enterprises and to examine how the investment climate impacts on the productivity and technology choice of enterprises we use a standard Cobb-Douglas production function approach,<sup>IC</sup> where value-added _V_ is modeled as a function of capital, _K_ , labour inputs, VV= KK _L_<sup>ββ</sup> , and TFP, which is in<sup>KK</sup> LL<sup>ββLL</sup> ee<sup>ββEEE+ββI</sup> turn modeled to be a function of characteristics of the enterprise, _E,_ such as its sectoral affiliation and characteristics of the management, as well as investment climate characteristics, _IC_ .<sup>11</sup> Our estimable equation thus becomes where _v_ is a zero-mean random error term, assumed to be uncorrelated with the regressors in the model. A key objective is to examine to what extent the differences in labor productivity can be attributed to technology choice, increasing returns to scale, TFP and factor intensity. If internal economies of scale are important, one would expect the sum of the coefficients on capital and labor, to be larger than one (e.g. + > 1). The I coefficient vector can be KK LL interpreted as measuring the impact of different investment climate characteristics on total ββ ββ ββ factor productivity.

This approach has well-known limitations. From a conceptual point of view, we are only comparing the direct impact of the investment climate on firm productivity across rural and urban areas, while we know that the investment climate may also impact on allocative

> 11 We also experimented with the more flexible translog production function, which can be interpreted as a second-order Taylor approximation to a more general production function. Based on this framework, we did not reject the Cobb-Douglas restrictions for any of our estimations. We therefore proceed with the Cobb-Douglas framework, which is remarkably robust across African firm-level data. This facilitates interpretation of the results, and retains comparability with both micro- and macro-approaches to determining the impact of the investment climate (see e.g. Dollar et al., 2005) on firm performance.

-13-