rafmacalaba/datause-encoder-data huggingface dataset:
Table 10: Fertilizer data

||Problems|Solutions/Assumptions|
|---|---|---|
|1|The survey asks if a household received inputs<br>from the government or a cooperative, but it<br>does not ask if they were received through the<br>FISP or FSP program, or if the household<br>participated in these programs.|**Main Assumption:**A household is considered a<br>participant of the FISP or FSP in the period 2008/2009 if<br>it did all of the following:<br><br>Grew maize.<br><br>Used fertilizer and/or purchased seeds.<br><br>Obtained seeds and fertilizer from the<br>government or from a cooperative.|
|2|Related to the previous problem, we cannot<br>differentiate households that received benefits<br>from FSP from those that benefitted from FISP.|**Main Assumption**: Given that households which<br>benefitted from FSP received a smaller package than<br>FISP participants, and that FSP is targeted to household<br>that grow on smaller areas, scenario 2 described below,<br>partiallyaccounts for thisproblem.|
|3|The survey does not allow respondents to record<br>more than one source of fertilizer and seeds.|**Main Assumption:**We assume that the household only<br>received fertilizer and seeds from the government if it<br>reported doing so. All expenditures associated to these<br>inputs were recorded aspaid to thegovernment.|
|4|The survey does not specify the amount of<br>fertilizer and/or seeds that households received<br>from the government or cooperatives.|**Main assumption for Scenario 1:**All households<br>received one full FISP package of seeds and fertilizer.<br>**Main assumption for Scenario 2:**All households with<br>more than 1 hectare of maize received one full FISP<br>package. Households with less than 1 hectare received a<br>proportional amount.|
|5|Some households reportedly received fertilizer<br>and seeds, but the reported expenditure is too<br>high with respect to the area cultivated and the<br>cost of a FISP package (2.1% of beneficiaries).|We eliminate ―outlier‖ expenditure values from the<br>sample. An outlier is defined as a value outside of the<br>range formed by the mean +/- three times the standard<br>deviation of the expenditure variable (54 cases).|

_Source: Authors_

# **5. Results**

# **5.1. The Analysis of Beneficiary Participation**

# **5.1.1. Education**

Access to public primary education in Zambia is relatively uniform across consumption, which is the yardstick used to describe different socioeconomic groups in this analysis. This is particularly true across the lower four quintiles of the consumption distribution, and it is only among the richest quintile that the number and share of beneficiaries decline significantly. The breakdown is very different for secondary education, where the number and share of beneficiaries increase rapidly with consumption levels, and for 22
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capital, not just as a resource that provides communities with access to water. The cultural value assigned to new projects makes it difficult for DWEs to advocate maintaining existing projects. As the DWE in Maji Safi told me: “Unless donors say: ‘you can only use this money for maintenance’, I guarantee that funds will be used to build new infrastructure. Somewhere along the way, politics get involved and you find yourself building another project.” As this remark suggests, DWEs feel subject to the political pull of building new projects, even though they are aware that maintaining existing infrastructure could better service rural communities.

Water point mapping could provide the information needed to effectively advocate a change in focus within local government. Water point mapping exercises require districts to report on the number of water points that are functioning within the district on a monthly basis to the MoWI. These data are collected by ward governments and sent to district officials, who then forward their findings to the MoWI. Once the MoWI has collected the data, they circulate within higher levels of the state, rather than being shared with districts to inform their programs and progress. For example, water point mapping could tell the district how they compare to other districts in the region, or could be used to show the percentage of water points that work or that need to be rehabilitated. While DWEs potentially know this information from collecting data and sharing information with other DWEs in their regions, the concentration of data provided from water point mapping exercises throughout the region could be used a as representational tool to advocate for more funds for rehabilitating projects. By representational tool, I am suggesting that not unlike a report, the data from water point mapping can be used to create a picture of the problems with water infrastructure in districts. Water point mapping is potentially an affective tool for districts to understand how well they are maintaining water points compared to other districts in the country. However, through water point mapping exercises, data flows upwards to 57
|||||**Tabl**|**e 4. Effect**|**of Busines**|Dependent varia<br>**s Environment Obsta**|ble: emplo<br>**cles on E**|yment grow<br>**mploymen**|th<br>**t Growth**|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variable|1|2|3|4|5|6|7<br>8|9|10|11|12|13|14|15|16|17|
|Access to finance|-0.002***||||||||||||||-0.004***|-0.002***|
||(0.000)||||||||||||||(0.001)|(0.000)|
|Informal sector competition||-0.003***|||||||||||||-0.003***|-0.003***|
|||(0.000)|||||||||||||(0.001)|(0.001)|
|Inadequate education|||0.005***||||||||||||0.007***|0.007***|
||||(0.001)||||||||||||(0.001)|(0.001)|
|Electricity||||0.002***|||||||||||0.001*||
|||||(0.000)|||||||||||(0.001)||
|Customs and trade|||||0.005***||||||||||0.005***||
||||||(0.001)||||||||||(0.001)||
|Access to land||||||0.003***|||||||||0.003***||
|||||||(0.001)|||||||||(0.001)||
|Political instability|||||||-0.001||||||||-0.002**||
||||||||(0.001)||||||||(0.001)||
|Courts|||||||-0.000||||||||-0.001||
||||||||(0.001)||||||||(0.001)||
|Crime||||||||0.001**|||||||0.000||
|||||||||(0.001)|||||||(0.001)||
|Tax rates|||||||||-0.001*||||||-0.001*||
||||||||||(0.001)||||||(0.001)||
|Tax administration||||||||||-0.000|||||-0.000||
|||||||||||(0.001)|||||(0.001)||
|Licensing and permits|||||||||||0.001||||0.001||
||||||||||||(0.001)||||(0.001)||
|Corruption||||||||||||-0.000|||-0.001*||
|||||||||||||(0.000)|||(0.001)||
|Transport|||||||||||||0.002***||0.000||
||||||||||||||(0.001)||(0.001)||
|Labor regulations||||||||||||||0.002***<br>|-0.001<br>||
|||||||||||||||(0.001)|(0.001)||
|Adjusted_R_-squared|0.123|0.122|0.124|0.121|0.125|0.122|0.121<br>0.117|0.121|0.122|0.122|0.122|0.122|0.121|0.121|0.130|0.127|
|Number of observations|35,837|35,466|36,216|36,554|32,967|35,399|35,814<br>32,794|36,278|36,287|36,154|35,350|35,435|36,222|36,297|26,574|34,359|
|Number of countries|96|96|96|96|96|96|96<br>95|96|96|96|96|96|96|96|95|96|
|<br>_Note:_Standard errors (in pa|rentheses)|are robust|to heterosk|edasticity|and cluster|ed on count|ries. Model 1 (col. 1-1|5):_EG_=_b_|0 +_b_1Indi|vidual Obst|acle +_b_2F|irm Charact|eristics +|Country Fix|ed Effects|+_e_1.|
|* Significant at the 10 perce<br>_Source:_Authors' estimation<br>Model 2 (col. 16):_EG_=_b_0<br>is tested and rejected.|nt level. **<br>s based on<br>+_b_1All 15|Significant<br>data from<br>Obstacles|at the 5 per<br>World Ban<br>+_b_2Firm|cent level.<br>k Enterpris<br>Characteris|*** Signific<br>e Surveys (<br>tics + Cou|ant at the 1<br>2006-10).<br>ntry Fixed E|percent level.<br>ffects +_e_2. The hypot|hesis that|the coeffici|ents for ac|cess to fina|nce and inf|ormal sect|or competiti|on differ||

24
# **References**

Alessandri, P., & Bottero, M. (2020). Bank lending in uncertain times. _European Economic Review_ , _128_ . https://doi.org/10.1016/j.euroecorev.2020.103503

- Amin, M., & Soh, Y. C. (2020). Does Greater Regulatory Burden Lead to More Corruption? Evidence Using Firm-Level Survey Data for Developing Countries. _The World Bank Economic Review_ , _0_ (0), 1–17. https://doi.org/10.1093/wber/lhaa007

- Arellano, C., Bai, Y., & Kehoe, P. J. (2019). Financial frictions and fluctuations in volatility. _Journal of Political Economy_ , _127_ (5), 2049–2103. https://doi.org/10.1086/701792

Aterido, R., Beck, T., & Iacovone, L. (2013). Access to Finance in Sub-Saharan Africa: Is There a Gender Gap? _World Development_ , _47_ , 102–120. https://doi.org/10.1016/j.worlddev.2013.02.013 Aterido, R., Hallward-Driemeier, M., & Pagés, C. (2011). Big Constraints to Small Firms’ Growth? Business Environment and Employment Growth across Firms. _Economic Development and Cultural Change_ , _59_ (August), 609–647. https://doi.org/10.1086/658349 Bae, K. H., & Goyal, V. K. (2009). Creditor rights, enforcement, and bank loans. _Journal of Finance_ , _64_ (2), 823–860. https://doi.org/10.1111/j.15406261.2009.01450.x Banerjee, A. V., & Duflo, E. (2014). Do firms want to borrow more? Testing credit constraints using a directed lending program. _Review of Economic Studies_ , _81_ (2), 572–607. https://doi.org/10.1093/restud/rdt046 Bardasi, E., Sabarwal, S., & Terrell, K. (2011). How do female entrepreneurs perform? Evidence from three developing regions. _Small Business Economics_ , _37_ , 417–441.

30
do this we employ the U.S. Dept. of Housing and Urban Development's actuarial model used in underwriting the insurance it provides for qualifying reverse annuity mortgages. This model allows us to determine the maximum amount of credit that, for instance, a typical elderly Russian woman living alone could safely borrow against the value of her apartment. It estimates the amount that can be borrowed, under conservative assumptions about house price appreciation and interest rates, such that an insurance premium collected from all borrowers would provide a sufficient cash reserve to cover losses from cases for which the loan principal plus accrued interest would exceed the value of the house at loan termination. An analysis of the program shows that over the past decade that the HUD program has operated on an actuarially sound basis (Rodda and Lim, 2000). The model has been simplified and adapted with Russian single year mortality tables.

The model's primary function is to provide an estimate of the share of house value, based on actuarial tables and assumptions about house price behavior, that could be safely borrowed for each combination of borrower age and expected interest rate-- what is terrned the principal limit factor. This factor can be seen as the present value of the annuity expressed as a percentage of the property value; see Szymanoski (1994) for a discussion of the details of the model. This ratio can then be multiplied by the property value to give the dollar limitation for a specific loan. By pooling premiums and estimating future losses based on house appreciation, interest accrual, and mortality rates, the model estimates the actuarially fair share of house value that can be converted into debt for the insurance premium rate charged.

Table 2 illustrates the derivation of the principal limit factor for a 75 year old female borrower at interest rates between 8 and 12 percent, with the highest rate approximating the current dollar interest rate for mortgages in Russia.<sup>2 5</sup> With the underlying assumptions that annual mean nominal house price appreciation will be 5 percent - so that real interest rates are about 5 to 7 percent -- with a standard deviation of 10 percent, the result is a principal limit factor of 0.495. That is, the table indicates that for a small fee a borrower can insure lenders that the present value of the payments on a reverse mortgage equal to about half the value of a house will not exceed the initial property value.

**Table 2. Principal Limit Factors for Russian Women**

|**Interest**<br>**Rate**<br>__<br>_|**AgeofBorroweratLoanO**<br>_<br>__<br>_<br>_<br>_|**rigination**|
|---|---|---|
|Rate_|__<br>**65**<br>.<br>**75**|**85**|
|**8.0%**|0.511<br>_<br>0.629<br>_|_<br>0.747|
|**10.0%**|0.359<br>_0.495_<br>|=<br>0.645|
|**12.0%**|0.255<br>0.390|0.557|

**25** In 2000 interest rates on dollar-denominated mortgages were in the 1 0- _15_ percent range for most banks (Pastukova and Rogizhina, 2001, Table 6.1); and since then macroeconomics conditions have improved.
Figure 12: Robustness: Combined wage effects in the baseline vs. moderate technological change scenario <!-- Start of picture text --> 45 degree line 45 degree line<br>CHE<br>GBR<br>AUSCANESP<br>GRCPRTLUX HUN JPN<br>ITANLDDNKPOLFRA<br>POL SVKAUTTURCZE<br>CHEGBRAUSPRTHUNCANDNKCZE<br>LUXESPITAGRCTURFRANLDJPNSVK<br>AUT<br>−30% −20% −10% 0% 10% 20% 30% −30% −20% −10% 0% 10% 20% 30%<br>Baseline scenario Baseline scenario<br>Young, HS Old, HS Young, LS Old, LS<br>The figure contrasts equilibrium wage responses in the baseline scenario to those from a scenario where we allow for<br>moderate technological change<br>13: Robustness: Combined wage effects in the baseline vs. strong technological<br>45 degree line 45 degree line<br>LUXCHEESPITAGBRAUTAUSGRCTURPRTFRANLDHUNCANJPNSVKPOLDNK DEU CZENORSWE SVK AUT TURDEUITACZESWENORNLDDNKPOLFRAGRCPRTLUXHUNAUSJPNCANESPGBR CHE<br>−30% −20% −10% 0% 10% 20% 30% −30% −20% −10% 0% 10% 20% 30%<br>Baseline scenario Baseline scenario<br>Young, HS Old, HS Young, LS Old, LS<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>−10% −10%<br>Moderate technological change scenario ́<br>−20% −20%<br>−30% −30%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>−10% −10%<br>Strong technological change scenario ́<br>−20% −20%<br>−30% −30%<br><!-- End of picture text --> Figure 13: Robustness: Combined wage effects in the baseline vs. strong technological change scenario The figure contrasts equilibrium wage responses in the baseline scenario to those from a scenario where we allow for strong technological change 41
14 and Dahl also report higher income elasticities in the developing world than in the industrialized nations. Estimates of price elasticity of demand for electricity are in general lower than for all energy taken together. In general price elasticities range from -0.34 to - 1.2, and appear to be lower in Europe than in the U.S.(Pindyck p. 120). Wilder and Willenborg compare their estimate to that of four other studies and found a similar range for price elasticities of -1.0 to -1.31.

# IJ. **Residential Energy Consumption in Poland -- Some Empirical** Evidence

The purpose of this section is to answer some of the questions raised by the features of energy pricing and consumption patterns outlined above. First, who in Poland would be hurt and by how much if household energy prices were to rise to "efficient" levels? Second: How effective are Poland's present energy pricing policies at accomplishing Poland's social welfare goals?

# _Data and methodology_

The data set used to carry out the empirical analysis in this section is the 1993 household budget survey. This data set consists of information on the expenditures of 16,044 Polish households surveyed between January and June 1993. Expenditure shares by income and social group are calculated as "per equivalent adult", based on the OECD scale (first adult = 1; other adults = 0.7; children aged under 14 = 0.5). The survey expenditure data has been weighted by socio-economic grouping to properly represent the composition of the nation. Regional price differences have also been accounted for by weighting expenditures (and income) according to a regional price index.

Total expenditures (rather than income) are used as a basis for this analysis. Due to widespread under-reporting of income in household survey instruments, total expenditures appear to be a better means of characterizing the well-being of the population than is reported income. Expenditure surveys thus also shed more light on household's poverty
# **5.3 Robustness**

The trade-offs characterized in Theorem 1 are valid under subset _U_<sup>_σ_ ̄</sup> . In general, the trade-offs characterized by _Domination_ and _Weak Pareto_ may depend on the set _U_ considered. We show that some of their key characteristics are preserved on larger sets of preferences. For robustness, we consider the whole set _U_<sup>_B_</sup> .

**Theorem 2.** _The additive index PU B satisfies Domination and Weak Pareto if and only if PU B is a hierarchical index with global line z_<sup>_∗∗_</sup> _defined as_

_for all_ _<u>y</u> ≥ za, whose poverty score function p is such that for all_ ( _y,_ _<u>y</u>_ <u>)</u> _,_ ( _y_<sup>_′_</sup> _,_ _<u>y</u>_<sup>_~~′~~_</sup> ) _∈ Xz∗∗\XA,_ _Proof._ See online Appendix S9.

Interestingly, Theorem 2 shows that the iso-poverty-score curves and the relative part of the global line must be straight lines even when the set _U_<sup>_B_</sup> contains preferences whose indifference curves are _not_ straight lines.<sup>39</sup> There are two key differences between Theorems 1 and 2. First, the global line of the latter is _z_<sup>_∗∗_</sup> instead of _z_<sup>_∗_</sup> . This reflects the fact that there is no upper bound on the sensitivity to relative income in _U_<sup>_B_</sup> . Second, below the global line, the poverty score function in Theorem 2 only depends on own income. This does not mean that relative income plays no role, but its role is limited to the definition of the global line. This second difference reflects the fact that _U_<sup>_B_</sup> contains utility functions that exhibit extreme forms of concavities. This difference disappears on the subset _U_<sup>_C_</sup> of utility functions in _U_<sup>_B_</sup> whose indifference curves are weakly convex in the space of bundles. On _U_<sup>_C_</sup> , the iso-poverty-score map characterized by _Domination_ and _Weak Pareto_ corresponds to that illustrated in Figure 5.b, except that the global line is then _z_<sup>_∗∗_</sup> because no upper bound is placed on the sensitivity to relative income in _U_<sup>_C_</sup> .<sup>40</sup>

> 39For the relative part of the global line, the explanation follows from the fact that the global line is maximal. When _<u>y</u> ≥_ _<u>y</u>_<sup>_~~z~~_</sup> , the shape of the global line is defined by the utility function most sensitive to relative income. On _U_<sup>_B_</sup> , the strict monotonicity to own income when holding relative income constant constrains the sensitivity to relative income. Graphically, the slope of the indifference curve passing through a bundle cannot exceed the slope of the ray passing through this bundle and the origin.

> 40Details can be found in online Appendix S10.

32
manages to stay above the poverty line (case (b)). But ~~within each of these cases,~~ the ATP-based Doorslaer (2003) developed their arguments using income. But then in the empirical illustration, ~~The case of zero borrowing (or saving)~~ Assume the individual derives utility from nonmedical consumption in just two periods (the current period (period 0) and the future (period 1)), but needs to be in good health to be able to (we relax this assumption in a moment), and chooses to consume the same amount in both periods, health shock. (The results that follow can be generalized to the case where point ais off the 45 spending.) The individual starts on indifference curve J, 0 0 5 equal to the distance OB. If we observe income (OA) and medical spending (BA) in our data, we can 0 INC = ~~—©~~ INC >= ~~—_~~ If we do not observe income, but do observe nonmedical consumption (OC), we can compute the consumption-based catastrophic payment ratio. We add medical spending (BA) to nonmedical consumption (OB) to get ‘consumption gross of medical expenditures’ (OA), which is our proxy for 0
14 Even competitive elections can be influenced by violence, allegations of fraud, contestation of results, and are typically dominated by clientelistic practices of winning political support through promises of provision of private or group benefits (Keefer and Khemani, 2004). Such clientelist practices can have deep roots in historical institutions, as discussed in the previous section. Furthermore, there is also substantial variation in performance in growth and human development across autocratic regimes, which to a large extent overlaps the variation among democratic regimes (Besley and Kudamatsu, 2007). Autocratic and intermediate regimes can still be under political pressures to deliver on development to middle and poorer groups—as exemplified by countries such as Ethiopia and Rwanda that fall into this intermediate category.

A growing body of evidence on citizen participation in elections and other political processes in Africa is being generated through the use of the Afrobarometer surveys.<sup>9</sup> Broad arguments are made on the basis of this data that citizen political behavior in Africa cannot be reduced only to responding to clientelist appeals and ethnic identity, but that performance of governments in managing the economy and delivering development matters as well (see for example, Bratton et al, 2011, and Young, 2009). Average self-reported voter-turnout in the 2008 round of the Afrobarometer survey for both “full democracies” and even intermediate cases, as rated by Polity IV, is higher than 70 percent (see Table 1, below). Such widespread intentions to participate in political processes likely encompass a variety of citizens, some of whom demand broad public services and economic development. In section 3 below we report evidence from experiments undertaken by civil society organizations in Africa to shift citizen demands away from clientelism and towards broad public goods.

# _2.4  Accountability and civil society organization_

There also appears to be a large shift in Africa in organization among citizens. Village-level group formation in Africa increased dramatically over the 1990s when participatory approaches were emphasized in international development paradigms, promoted through aid, and adopted deliberately by country governments to deliver projects to communities.<sup>10</sup> Bernard et al (2008), for example, show a rapid proliferation of village organizations (VOs) in a sample of

> 9 Working papers using the Afrobarometer data can be found at http://www.afrobarometer.org/index.php?option=com_docman&Itemid=39

> 10 Here, we focus on community organizations intended to solve failures in the delivery of local public goods; instead of marketed-oriented organizations that have existed primarily in agriculture (Bernard et al, 2008).