rafmacalaba/datause-encoder-data huggingface dataset:
<mark>developing and maintaining content as well as delivering this content on a timely basis at a large scale. These transaction costs are especially high in regions where agricultural productivity is low because low productivity is more likely to be associated with low overall economic development. In less developed regions with small-scale farm structures, poor public infrastructure, and insufficient human capital investments, there are higher transaction costs of providing relevant advice and services. As a result, low supply of agricultural advice and warning leads to a vicious cycle of inefficient production. In a more general framework, Lio and Liu (2006) investigate the impacts of information and communication technology on agricultural productivity using panel data for 81 countries during the period 1995-2000. They show gains in using low cost tools for improving the efficiency of delivering agricultural production advice, but caution that the returns from ICT in agricultural production of the richer countries are about two times higher than those of the poorer countries.</mark>

# **5. Enabling efficient logistics**

<mark>Digital technologies also improve agricultural supply chain management. With globalized food systems, ensuring food safety has become more complex. These trends have catalyzed innovations to trace the food supply from producer to the consumer, important for developing countries that want to reach new export markets. Smallholder farms turn to cooperatives and aggregators who use digital tools to improve collection, transportation, and quality control. By opening up new specialized market opportunities, the internet improved consumer protection and farmers’ livelihoods. Cunden and van Heck (2004) have argued that digital technologies have not only extended traditional market mechanisms into the electronic realm, but have also done so in areas where there are limited physical markets for buyers and sellers to gather in one place to begin with. Similarly, Site and Salucci (2006) argue that such technologies are especially useful in deep rural areas because of poor public infrastructure to facilitate rural farmers’ access to local and global markets.</mark> <mark>Transporting produce often requires coordination between producers, truckers, and, at times, warehouse owners and aggregate traders. Many producers, especially in remote and rural areas, must carry their produce themselves, often by foot, to the nearest collection point. Coordinating transportation is also important for larger traders who aggregate produce for sale in urban areas</mark> 18
Table 2. Warrantage summary statistics

|sample: LotteryParticipants|Househo|ldlevelrandom|ization|Villag|e level randomiz|ation|
|---|---|---|---|---|---|---|
||Control<br>N=262|Treated<br>N=266|p-valueof<br>test:|Lowaccess<br>N=287|Highaccess<br>N=241|p-value of<br>test:|
|Prior to lottery|<br>(1)|<br>(2)|G)|(4)|(5)|(6)|
|E[# bags to stock]|3.031|3.271|0.233|3.429|2.822|0.003|
|Atstorage (forwarrantage)|||||||
|Household storedany bags|0.011|0.944|0.000|0.341|0.647|0.000|
|#bags stored|0.065|3.004|0.000|1.334|1.900|0.007|
|# different crops stored|0.019|1.068|0.000|0.425|0.693|0.000|
|Householdstored...|||||||
|Sorghum|0.004|0.301|0.000|0.115|0.199|0.007|
|Maize|0.011|0.590|0.000|0.240|0.378|0.001|
|Millet|0.000|0.034|0.003|0.007|0.029|0.051|
|Groundnuts|0.004|0.117|0.000|0.056|0.066|0.611|
|Rice|0.004|0.165|0.000|0.052|0.124|0.003|
|Beans|0.000|0.026|0.008|0.007|0.021|0.169|
|Quantity stored (in kgs) of...|||||||
|Sorghum|0.866|76.796|0.000|32.211|47345|0.161|
|Maize|4.721|177.995|0.000|87.026|97.954|0.509|
|Millet|0.000|7.612|0.021|0.753|7.506|0.042|
|Groundnuts|0.134|8.293|0.000|3.844|4.722|0.591|
|Rice|0.275|28.267|0.000|7.136|23.000|0.004|
|Beans|0.000|3.428|0.012|1.026|2.561|0.265|
|Loan related|||||||
|Householdtookwarrantageloan|0.011|0.380|0.000|0.181|0.216|0.321|
|<br>Loanamount (inFCFA)|355.725|8474.737|0.000|4977 282|3813.278|0.272|
|Destorage (conditional onstorage)|||||||
|Wasthecreditfullyreimbursed?|0.67|1.00|0.00|1.000|0.978|0.290|

‘Notes: The sample is restricted to all households who participated in the lottery. All variables are unconditional unless otherwise specified. Column (3) and (6) present p-values corresponding to the test of whether the means are the same between treated and control households and between households, and households in Low Access and High Access villages respectively.
# **3.1 R&D**

The R&D series from 1960-2000 extend the compilations by Lederman and Saenz (2005) which use national surveys that use a common definition of expenditures that includes fundamental and applied research as well as experimental development. In line with our discussion of the dual role of R&D the category considers not only the traditional investments for development of new technologies expected in advanced countries, but also investments in the adoption and adaptation of existing technologies more likely to be labeled as R&D into developing countries. The series are constructed combining data published by UNESCO, the OECD, the Ibero American Science and Technology Indicators Network (RICYT) and the Taiwan Statistical Data Book, following the definitions convened in the OECD Frascati Manual.

Though it would be desirable to study the evolution and rate of return of private R&D, we work with aggregate R&D for several reasons. First, the data sources divide R&D not into private and public, but rather into productive and non-productive sectors, the latter accounting for roughly 20% of the total. The definition of “productive sector” includes both public and private for profit and not-for profit firms while “non-productive sector” includes R&D financed or undertaken by the executive branch of government. Since the productive sector may well include public utilities or other state owned enterprises, focusing on this breakdown is less interesting than the public/private sector split would be.

Second, this division seems to occasionally lead to some critical issues in categorization. For instance, if a public company finances its R&D from retained earnings, this will count as productive sector R&D. If instead that R&D is financed by a transfer from the Treasury to the firm, it counts as “non-productive” R&D. For several countries in our sample, there were striking shifts in composition from one year to the next suggesting sensitivity to accounting practices. In contrast, the total R&D series were reasonably stable. The final consideration is more prosaic: many developing countries tabulate only the aggregate values and as they 10
Table A4: Heckman selection model

|Dependent variable:|Loan availability<br>(1)|Loan application<br>(2)|
|---|---|---|
|Constrained by informal|-0.0566***<br>[0.0187]|-0.0534**<br>[0.0210]|
|Account|0.0155<br>[0.0221]|0.00799<br>[0.0247]|
|No need|-0.0495***<br>[0.0177]|-0.0413**<br>[0.0198]|
|Age|-0.0121|-0.00371|
||[0.0129]|[0.0145]|
|Size|0.0342***|0.0373***|
||[0.00773]|[0.00864]|
|Export|0.0394*<br>[0.0230]|0.0405<br>[0.0256]|
|Manufacturing|0.0346*<br>[0.0191]|0.0469**<br>[0.0214]|
|Retail|0.00516|-0.0176|
||[0.0303]|[0.0336]|
|Listed company|0.0491<br>|0.0731<br>|
||[0.183]|[0.208]|
|LLC|0.0559|0.103|
||[0.182]|[0.206]|
|Sole proprietorship|0.0229|0.0820|
||[0.181]|[0.205]|
|Partnership|0.0328|0.0878|
||[0.181]|[0.206]|
|Ltd Partnership|0.0284<br>[0.182]|0.0884<br>[0.206]|
|Model|Heckman|Heckman|
|Time FE|yes|yes|
|Geographic area FE|yes|yes|
|Additional controls|yes|yes|
|Observations|10386|10444|
|Selected|1911|1969|
|Not selected<br>|8475|8475|
|Wald _χ_<sup>2</sup>|296.31|498.86|
|Inverse Mill’s ratio|-0.0309*|-0.0308|

_Notes:_ Heckman selection model. In this table, we explicitly model the probability of being included in our analysis in a two-step Heckman-type selection model (Heckman, 1976; Lewis, 1974; Gronau, 1974). The selection equation models a firms’ probability of belonging to the panel (i.e., being interviewed in two consecutive waves of the WBES survey) depending on firms’ age, size, and belonging stratum (the intersection of sector and country, excluded in the main specification). The inverse Mill’s ratio is included as an additional regressor in the original specification (reported in the bottom panel). All regressors are timed consistently with previous analyses. Unreported controls follow the specification in Table 4. Measures are defined in Table B4. Robust standard errors in brackets. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

24
- 106 -

# Table 36: EARNINGS FUNCTIONS (lnW2) FOR PRIVATE FORMAL SECTOR EMPLOYEES

# BY LOCATION

||Abidj<br>Forma|an<br>l|Other Ci<br>Forma|ties<br>l|
|---|---|---|---|---|
|Intercept|10.137|(45.4)|10.645|(23.2)|
|Years of Primary Schooling|.030|(0.7)|<br>-.151|(1.4)|
|Years of Secondary Schooling|.092|(2.3)|<br>-.016|(0.1)|
|Years of Higher Schooling|.395|(4.2)|<br>.187|(1.1)|
|Years of Current Job Experience<br>|.142|(6.0)|<br>.049|(0.8)|
|(Years of Current Job Experience)<br>2|-.0034|<br>(4.3)|<br>-.0023|<br>(0.9)|
|Years of Previous Job Experience<br>|.042|(0.8)|<br>.124|(0.7)|
|(Years of Previous Job Experience)<br>2|-.0043|<br>(1.0)|<br>-.017|(0.9)|
|Years of Apprenticeship|.055|(1.5)|<br>.004|(0.1)|
|Years of VTE|.125|(2.9)|<br>.169|(1.1)|
|CEPE|-.143|(0.6)|<br>.917|(1.3)|
|BEPC|.322|(0.6)|<br>1.717|(2.0)|
|VTE Diploma|-.176|(0.6)|<br>2.349|(2.5)|
|Other Diploma|-.272|(0.6)|<br>2.623|(2.7)|
|Sex (1-female)|-.022|(0.1)|<br>-.501|(1.1)|
|Non-Ivorian|-.302|(1.9)|<br>.171|(0.5)|
|R2|.52||.68||
|F|8.5||4.6||
|n|135||49||

# Absolute t - values in pa.entheses.
YC - **per** capita consumer income

-

- Tj quarterly shift terms

- Fj numbers of various types of livestock on feed

-

- Pj **prices** of various types of livestock meat

E - trade weighted exchange rate

- P, suppore price

Pr - release price D - shift term reflecting the 1983 PIK program.

The demand system was not estimated in the form of (10) because a system. that determines price through an identity equation tends to produce erratic price estimates particularly when demands are inelastic. Alternatively, a demand equation in (10) can be solved for price, (11) Pm = Qi- Qj,Xj) and then the identity can be used to determine Qi. This approach suffers in practice because the coefficient estimates of exogenous variables in the inverted equation are susceptible to spurious correlations with other factors in the system. This can lead to an unreasonably large contribution of these variables relative to other exogenous variables in the system in determining price predictions in practice. The eeproach used in this study is to solve the system in (10) for a partial reduced form price equation which is then used to replace one of the demand equations in (10). This partial reduced form equation can be regarded as a convex combination of equations such as (11) which essentially produces a composite price forecasting equation in the sense of Johnson and Rausser (1982) where the weights are estimated simultaneously with the coefficients of the price equation. The number ot such equations to combine in this manner is roughly determined by the tradeoff between increased forecasting accuracy of combining more forecasting equations 7
despite the increase in their loans. Thus, overlapping membership did not necessarily contribute to over-indebtedness, as this study suggests.<sup>3</sup> However, findings from some other countries suggest that indebtedness and multiple borrowing may be positively correlated. For example, Vogelgesang (2003) finds that increasing competition among lenders and an easy supply of credit is associated with higher levels of indebtedness in Bolivia. On the other hand, some studies find a positive correlation between the length of loan maturity and indebtedness (Gonzalez 2008). Gonzalez (2008) also finds that the households with more experience with particular lenders are less likely to be over-indebted.

It is possible for over-indebtedness to grow if the overlapping households fail to increase their level of income and net savings. An increase in the income of the overlapping households will not be enough to reduce their indebtedness unless it is either used to repay loans, to contribute to net savings, or both. The role of overlapping memberships plus a host of other demand and supply factors are examined using longitudinal household survey data, given growing loan portfolios as well as asset-holding.

The purpose of this study is to examine the phenomenon of over-indebtedness among microcredit borrowers in Bangladesh, using a panel household survey over a 20 year period. The paper is organized as follows. Section 2 reviews the literature concerning alternative measures and possible sources of over-indebtedness. Section 3 describes the household panel survey data that is rich in measuring both short- and long-term measures of over-indebtedness. Section 4 analyzes the data in terms of the nature and extent of indebtedness among MFI and non-MFI borrowers and the proximate factors influencing the extent of over-indebtedness over time. Section 5 lays out a deterministic model to estimate the major causes of over-

> 3 Overlapping membership seems a problem when the lenders don’t know who are overlapping members and hence the value of their security is less than they thought, and hence their assets are more risky than expected.

4
Page 27

**Table 7. Pair-wise Correlations**

||**Drop-out**<br>**rate**|**Repetition**<br>**rate**|**Fail rate**|**Excellence in**<br>**Albanian**|**Excellence in**<br>**math**|
|---|---|---|---|---|---|
|**RESOURCES**||||||
|Rural location|0.104|0.051|0.096|-0.296***|-0.313***|
|Availability of public transportation|0.067|0.042|0.099|0.137*|-0.004|
|Days with no electricity|0.045|-0.057|0.004|-0.201***|-0.163**|
|No phone|0.134*|0.039|0.073|-0.280***|-0.384***|
|Library|0.046|-0.039|0.048|0.066|0.242***|
|Teachers‟ years of experience|0.063|0.018|0.083|0.137*|0.091|
|Teachers are motivated|-0.196*|-0.045|-0.078|0.228***|0.088|
|Average wage of teachers|0.004|-0.059|0.024|0.132*|0.113|
|**TOP-DOWN ACCOUNTABILITY AND TRANSPA**|**RENCY**|||||
|N. of RED inspections|-0.167**|-0.029|-0.063|0.209***|0.111|
|Perceptions of corruption in the appointment of teachers|0.191**|0.028|0.050|0.103|-0.017|
|**PARTICIPATORY ACCOUNTABILITY AND PA**<br>**INVOLVEMENT**|**RENTAL**|||||
|N. of SB meetings (Jan-May 09)|- 0.238***|-0.0805|- 0.045|0.043|0.045|
|School directors participates and votes|0.149*|-0.069|0.027|-0.039|-0.070|
|% parents not knowing that there is a SB|0.102|0.073|0.187**|-0.139*|-0.184**|
|% parents not knowing that there are parent class reps.|0.076|0.183**|0.136*|-0.123*|-0.189**|
|% parents not knowing the name of the math teacher|0.068|-0.028|0.159**|-0.236***|-0.175**|
|% parents not knowing the name of the language teacher|0.121|-0.052|0.161**|-0.190**|-0.159**|
|Number of meetings with the math teacher (Jan-May 09)|-0.248***|-0.008|-0.149**|0.204***|0.296***|
|Number of meetings with the math teacher (Jan-May 09)|-0.251***|-0.014|-0.119|0.198***|0.317***|
|“ lack of parental involvement in the school is a problem”|0.107|0.096|0.063|-0.132*|-0.166**|
|**PARENTS’ CHARACTERISTICS**||||||
|Average mother education|-0.227***|-0.184**|-0.178**|0.322***|0.358***|
|Average father education|-0.225***|-0.215***|-0.178**|0.263***|0.364***|
|Average n. of books in the household|-0.150**|-0.129*|-0.126*|0.180**|0.186**|
|% currently unemployed|0.111|0.048|0.024|-0.147*|-0.109|
|% farmers|0.118|0.186**|0.184**|-0.222***|-0.136*|
|Average index of economic status|-0.178**|-0.148**|-0.171**|0.282***|0.306***|

Note: ***significant at the 1% level, **significant at the 5% lever, * significant at the 10% level.

87. The third panel of Table 7 highlights that a large number of measures of participatory accountability and parental involvement are correlated with education outcomes. In particular, students‟ excellence in math and language appears significantly more likely where parents are informed about participatory organs, know the names of the math and Albanian teachers, have more frequent meetings with teachers, and where lack of parental involvement is not considered to be a problem in the school. Parents‟ lack of information is also positively correlated with failure and repetition rates. Interestingly, the drop-out rate seems to be higher in schools where there are fewer SB meetings, where the school director can participate and vote in such meetings, and where parents held fewer meetings with teachers.
<!-- Start of picture text --> +ByXie + Bpj;P + BsS + BrT + Uj + Eie<br>Bui<br>(Bunm = Bupm = Buju = Bunm = Bune = Boupr = Buyr = Baur)<br>(Bunm = Bune Bupm = BM pr. BMujm = Buyr Buum = Bur<br><!-- End of picture text --> <!-- Start of picture text --> Uj<br><!-- End of picture text -->
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE saving measures, meeting the infrastructure targets outlined here would take 36 years (figure 18a-b). If, on the other hand, Tanzania were unable to increase infrastructure spending but were able to eliminate inefficiencies, this would take only 19 years. These simulations help to underscore the value of reducing inefficiencies, which hemorrhage resources from the sector. Eliminating them could bring Tanzania 17 years closer to meeting its infrastructure targets.

**Table 12. If all inefficiencies were eliminated, the funding gap would be much smaller**

|US$ million|ICT|Power|Transport|WSS|Total|
|---|---|---|---|---|---|
|Needs|(257)|(910)|(619)|(626)|(2,412)|
|Spending|215|358|408|224|1,205|
|Potential efficiency gains:|41|348|108|19|516|
|Including:||||||
|Capital execution|1.1|1.4|27|13|42.5|
|Operational inefficiencies|40.0|87.0|81|4.1|212.1|
|Including:||||||
|Labor inefficiencies|40.0|11.9|—|-|51.9|
|Losses|—|74.9|—|2.1|77.0|
|Undercollection|—|-|48.2|2.0|50.2|
|Undermaintenance|-|-|33.3|-|33.3|
|Cost recovery|—|260|-|2.3|262.3|
|(GAP) or surplus|(0)|(204)|(103)|(383)|(691)|
|Reallocation potential|32|0|0|0|32|

_Source:_ Derived from Foster and Briceño-Garmendia, AICD Flagship Report, 2009.

_Note:_ Potential overspending is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors.

— = Data not available.

32
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53
share of their export earnings from primary commodities. More specifically, primary commodities account on average for about three-fourths of total exports in most of them, and the share of commodities in some countries' exports exceeds 90 percent (see World Bank (2000, Chapter 4)). Fluctuations in the terms of trade nave also represented a major source of macroeconomic volatility; Figure 1 for instance shows the correlation between the volatility of real GDP and the volatility of the terms of trade (both weighted and unweighted, as defined in Appendix B) for all the countries in the sample over the estimation period. The figure does suggest a positive association between these variables. In addition, there is also some anecdotal evidence that access to world capital markets by many of these countries (which, to begin with, are relatively closed financially) tends to be asymmetric; it is often in "good times" (periods characterized in particular by high commodity prices and improvements in the terms of trade that private capital tends to flow to low-income countries (see World Bank (1999, Chapter 2)).

The remainder of the paper is organized as follows. Section II discusses analytical issues. It reviews briefly the conventional, consumption-smoothing approach to assessing the effects of terms-of-trade shocks on savings, and considers the role of borrowing constraints in explaining an asymmetric consumption and saving response by private agents to this type of shocks. It elaborates, in particular, on the role of expected borrowing constraints in bad states of nature. Section III discusses the specification of the econometric model used to assess the existence of an asymmetric effect of terms-of-trade movements and describes the estimation technique. Section IV presents the empirical results, based on cross-sectional data covering the period 1980-96 for non-oil exporters of sub-Saharan Africa. The last section summarizes the results, identifies some other factors that may explain an asymmetric response to terms-of-trade shocks, and discusses some possible extensions of the analysis.

# 2 Analytical Issues

Early contributions to the analysis of the effect of terms-of-trade shocks on saving include those of Harberger (1950) and Laursen and Meltzer (1950). The Harberger-Laursen-Meltzer (HLM) effect predicts a positive relationship between (transitory) changes in the terms of trade and saving, as a result 3
<!-- Start of picture text --> e<br>© e °<br>Cc<br>=°<br>@3-<br>n<br>oO<br>a<br>2 e e °<br>= e<br>oO 2-<br>dq<br>°°<br>38e e<br>><br>® 4- Ge<br>=] e<br>£e<br>5e<br>fo}<br>qd<br>o-<br>i] i i 1 i<br>0 2 A 6 8<br>Formal sector share of total private sector employment (buyer)<br>@ Buyer sector-county bins<br><!-- End of picture text -->