rafmacalaba/datause-encoder-data huggingface dataset:
purchases or similar arrangements to be the primary mode for procuring any of these crops for processing.

58. Before examining the actual crop procurement arrangements for Kenya's fruit and vegetable processors, it is important to note that real institutional structures are not strictly determined or caused by the techno-economic factors emphasized by transaction cost economics. Institutional patterns in particular countries and industries are also shaped by historical and political factors as well as characteristics of the participating firms and farmers. In this article, limitations of space do not enable us to provide full coverage of the evolution of institutional arrangements in Kenyan horticulture and the broad set of factors shaping such processes.

_59._ Table 6 below denotes the actual institutional arrangements for the procurement of these crops during the mid-1980s. The only exception is that of carrots for which the indicated institutional arrangements are for 1980, the last year that the major vegetable dehydration factory was in continuous operation.

_Table 6: Crop Procurement Arrangements for Erporr-oriented Processing (Percentage Slhare of Raw Material Volutme, 1985/86)_

|Crop|Spot<br>Market|Market<br>Recipr.|Frwd. Mkt.<br>Contract|Interlinked<br>Contract|Vertical<br>Integ.|
|---|---|---|---|---|---|
|**Pineapple**|**0**|**0**|**0**|**_5_**|**9**|
|**French beans**|**_5_**|**0**|**0**|**_95_**|**0**|
|**Caffots ***|**0**|**0**|**0**|95|5|
|**Passion Fruit**|60|0|20|20|0|
|**Orange**|**80**|**0**|20|0|**0**|

> * **For 1980.**

**_Source:_ Author's Field Survey**

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<!-- Start of picture text --> Programmatic Parties 4 @H<br>Ethnic Tension ++——_<br>Rule of Law H—e-——<br>Corruption 1—@—_+—<br>Bureaucratic Quality '——-®-"—+—<br>Executive Elections +=<br>Legislative Elections +8<br>Democracy 7 8<br>System > 'e=8—<br>Log Land Area OO<br>Log Per Capita GDP H——®—_—<br>0 5 10 15 20<br>Sample Value<br><!-- End of picture text -->
**Table 6**

## **Impact of political accountability on regulatory performance** **<u>(developed</u> countries)**

|Variable|local accountability<br>_reg_|global acco<br>_institutional_|untability<br>_checks_|
|---|---|---|---|
|_ml_<br>_cel_<br>|NA<br>+<br>|_−_<br>_−_<br>|NA<br>NS<br>|
|_eff_|NA<br>|NA|NA<br>|
|_p_<br>_res_|NS|_−_|NA|
|_p_<br>_cel_|NA|NA|NA|

_Note_ : NA and NS stand for not applicable and not significant respectively.

# **6 Conclusion**

The quality of political institutions has long been emphasized in both the academic and the institutional spheres as being a crucial determinant of economic performance. This paper is a first attempt to draw lessons from the recent conceptual literature concerned with the role of the economy-wide governance in the shaping of regulatory outcomes and feed them into the more empirical approach that directly examines the impact of sector-wide governance on regulatory performance. Our “integrated” empirical approach rests on the idea that political accountability is a key factor in the interface between political and regulatory structures. This approach is illustrated for the case of telecommunications in developing and developed countries by analyzing the impact of political accountability variables on regulatory performance variables in two time-series-cross-sectional data sets.

In this paper we have used two sets of variables to capture political accountability, local accountability variables and global accountability variables. Local accountability variables include most of the features related to “regulatory governance,” namely, unbundling of regulation from policy making, autonomy and independence of the regulator, accountability of the regulator, clarity in the allocation of mandates and attributes among government institutions, legal aspects, transparency of regulatory practices, and participation in the regulatory process. These variables were synthesized in a regulatory governance index. Global accountability variables include variables concerning corruption, bureaucracy, law and order, expropriation, 26
<!-- Start of picture text --> The regional composition of the EA distribution of consumption expenditure, by region<br>ce ae<br>1<br>@9<br>8<br>+om 8<br>TMN<br>X-axis:<br>2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ventilePPP-adjustedof<br>= aad 1 2011 USD<br>2 9 = Y-axis<br>2 Share of<br>© Q ventile population<br>1 . .<br>~ 2 Population-weights<br>N<br>o<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>HB cn =n IDN 4 PHL I other East Asia<br><!-- End of picture text -->
- 16 -

To summarize our results on the channels of the growth impact of milex, we find empirical evidence that increased milex is associated with greater efficiency in the developing countries. We do not find significant impact of milex through the levels of investment, education or the balance of payments.

# IV. OTHER ECONOMIC IMPACTS OF MILITARY EXPENDrTURES

Military expenditure is though **t** o hurt economic variables beyond growth. One frequent concern is that increased milex crowds out government social and infrastructure spending. Other economic variables believed to be negatively affected by military spending are the inflation rate, investment, and the balance of payments. The paper also analyzes the impact of military expenditures on these variables.

# A. Ipac on Government Social and Infrastucture Spendin.

Resources used for the military cannot be used for education, health or infrastructure. Accordingly, it would appear that milex must have an opporunity cost of less spending on these three categories which both contribute to growth and poverty alleviation.19 Data from the IMF's Government Financial Statistics were used to test this hypothesis.' The results are shown in Table 8, and are of two types: (i) the government expenditure shares in GDP regressed on the share of milex in GDP; and (ii) government expenditure category shares in total (central) government expenditure regressed on the share of milex in GDP. The share of exenditure categories in GDP more clearly indicates the impact of milex on the overall level of funding for other public services.

The most important result (regression 1, Table 8) is the positive relationship between the share of central government expenditure in GDP and the share of milex in GDP. The coefficients are statistically significant and numerically large. The coefficients imply that an increase in milex is
**Figure 4. Countries with good regulatory practices safeguarding personal data, by country income group (in percentages)**

<!-- Start of picture text --> High income Upper middle income Lower middle income Low income<br>Data Protection Law enacted<br>Data Protection Authority 100% No exception to limitations on<br>data collecting/processing by<br>created<br>80% governments<br>Individual right of redress to<br>60% Necessity and proportionality<br>object to use of their personal<br>test for government exceptions<br>data 40%<br>20%<br>Regulatory limitation on Existence of purpose limitation<br>0%<br>algorithmic decision-making requirement<br>Individual right to challenge<br>Existence of data minimization<br>accuracy and rectify personal<br>requirement<br>data<br>Regulation of data sharing with Existence of data storage<br>third parties limitations<br>Existence of requirements to<br>incorporate Privacy by Design<br><!-- End of picture text --> _Source:_ Author’s calculation based on results from the diagnostic.

There is significant potential for implementing further improvement in two areas: data protection by design (or by default) principle and automated decision-making. Data protection by design seeks to deliver the maximum degree of protection by ensuring the automatic protection of personal data in any given information technology system or business practice, from the design stage right through the life cycle (Cavoukian 2011; UK Information Commissioner’s Office 2020b). On a global scale, this requirement is found in 18 percent of the countries assessed—36 percent of high-income countries, as in the case of Uruguay,<sup>6</sup> 14 percent of upper-middle-income countries, and 23 percent of lower-middle-income countries. Not one low-income countries has adopted a similar provision. For instance, art. 12 of Uruguay’s Ley de Proteccion de Datos Personales n. 18331 requires data controllers to adopt technical measures such as _privacidad desde el diseño_ and _privacidad por defecto_ to protect personal data.<sup>7</sup> A similar requirement can

> 6 - See Ley de Proteccion de Datos Personales n. 18331, art. 12. Accessible at: https://www.impo.com.uy/bases/leyes/18331 <u>2008/12.</u>

> 7 - See Ley de Proteccion de Datos Personales n. 18331, art. 12. Accessible at: https://www.impo.com.uy/bases/leyes/18331 <u>2008/12.</u>

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impacts and out‐of‐pocket health expenditure were not included in the estimate but were likely to be high.

For the present study, data from two detailed household surveys carried out in six wards have been used as seen in Table 2. The first survey of 1168 households focused on the losses due to the extreme weather event of July 2005 (Patankar et al., 2012). Detailed information was obtained on flood‐ specific impacts and costs of repairs/replacements of household assets and equipment. Similarly, the second survey of 200 households (carried out in 2014 as part of the present study) focused on the impacts of recurrent floods and ability to respond. Under this survey, questionnaires were used to understand more about vulnerability and impacts of recurrent floods and ability to respond, including the possibility of relocation to another area to escape recurrent floods. The questionnaire focused on (a) household income, expenditure and assets, (b) household characteristics, (c) exposure and impact of recurrent floods, (d) preparing for floods and (e) relocation as an option.

In addition to the above surveys, we have also used the data obtained from 30 housing societies<sup>9</sup> in order to understand the adaptation options exercised by households collectively, source of finance for such measures and the decision‐making process involved therein (Patankar et al., 2015).

Table 1: Methodology and data sources for the study

||**Research questions**|**Methodology**|**Data Sources**|
|---|---|---|---|
|**Exosure**| Who and what is<br>exposed to recurrent<br>floods| Examine demographics<br>of flood‐prone wards| Census 2011 data on<br>Mumbai<br> Ward‐wise data obtained<br>from planning<br>department of MCGM on<br>residential, commercial<br>and other land uses|
|**p**| What is the extent and<br>spread of poor and<br>non‐poor households<br>around flooding spots| Land use identified in<br>500m buffer zone around<br>chronic flooding spots<br>and 200m zone around<br>localized spots| Existing Land Use (ELU)<br>maps of planning<br>department of MCGM<br> GIS based land use maps<br>created for buffer zones<br>around chronic and<br>localized flood spots|
|**Vulnerability**| What have been the<br>impacts of extreme<br>weather event of July<br>2005| Sampling for households<br>in flood‐affected wards<br> Administering<br>questionnaires to sample| Primary data collected<br>from 1168 households<br>under 2010 household<br>survey|

9 A housing society is a group of housing units, whose object is to provide its members with the open plots of land, dwelling units or apartments and common amenities and services. Member households elect representatives who take decisions on their behalf and look after the common services and their maintenance.

8
quartiles. Consistent with other patterns of asymmetry already reported, farmers' revisions to their self-reported plot size are more pronounced among those farmers who underestimated their plots.

Incomplete updating is apparent in the scatter plot and nonparametric kernel regression displayed in Figure 3, which plots the percentage measurement error in the post-treatment (postharvest) survey round on the vertical axis, against the pre-treatment measurement error. If the information treatment fully eliminated NCME, the scatter plot would be a horizontal line at the zero mark on the vertical axis. Only 13 percent of treated households report plot size accurately after receiving the GPS measure of the plot. Notably, those observations are heavily concentrated among respondents whose initial measurement error was modest, within roughly the [-50,50] interval. Meanwhile, only another 28 percent of households exhibit some correction of mistaken beliefs, as shown by the clustering of points around the zero value on the vertical axis and within the range bounded by zero and the post-planting measurement error. A plurality of farmers (37 percent) did not change their incorrect beliefs, which remained identically wrong over time, as depicted by the observations along the 45-degree line. It is striking that nearly three times as many farmers did not change their mistaken beliefs at all than fully updated to the correct plot size based on measurement they witnessed on an observable variable. The nonparametric regression indicates a strong, positive correlation between pre- and post-treatment measurement errors, but of shallower slope than the 45-degree line, signaling incomplete adjustment to corrective information, indicating partial learning failures.

Figure 3 reveals two other linear relationships in the pre- and post-treatment data. These reflect the propensity of respondents to report round values for plot area. To see this, define the self-reported plot size from survey round t (PP or PH), SRt. true value, T (which does not vary over time, thus no subscript), and rounded values, Ri (with i indexing different positive integer multiples of 0.5, e.g., 0.5, 1.0, 1.5, 2.0). Then, for respondents who adjust from SRPP=R1 to SRPH=R2 with R1≠ R2, R1≠T and R2≠T, a linear relationship between measurement error in postharvest and post-planting emerges with a slope R2/R1≠1 as one varies T. For R1>R2 (R1<R2) the resulting linear relation has a shallower (steeper) slope than the 45-degree line. Since most of the rounded values take one of four values, these other lines demonstrate both respondents' propensity to round and the incomplete/incorrect updating of beliefs as they move from one incorrect, round self-report to another. The same share of treated households that fully corrected their pre-treatment erroneous self-report of plot size, 13 percent, instead switched from reporting one rounded, 17
# **4 Simulation experiments**

A simulation study is conducted to compare the performance of our approach, ELL, and a purely surveybased estimator in predicting FGT poverty measures. We focus on the poverty headcount ratio and the poverty gap with three generic poverty lines that render 25%, 50%, and 75% of the population poor. The simulation setting is based on Tarozzi and Deaton (2009). In particular, the target population in the census is a village with _N_ = 15 _,_ 000 households, divided into 150 clusters _kc ∈{_ 1 _, . . . ,_ 150 _}_ , each of size 100. In each simulation run, an artificial household survey is drawn from the census by selecting randomly ten households from 100 randomly selected clusters. First, both data sets are generated by the following process with homoscedastic errors: Note that the explanatory variable is generated so that it differs in expectation between clusters. Such a situation with large and systematic differences in the averages of covariates across clusters (e.g., average levels of education or dwelling characteristics) is frequently observed in practice. This setting is ideal for the ELL method, which exactly models the data generating process. A linear regression based on the target population yields an _R_<sup>2</sup> of 0.55 while the new method with an _R_<sup>2</sup> of 0.08 has considerably lower explanatory power.

A second setting mimics a real-word situation where the census is dated and a more recent household survey (with an underlying true census which is not observed) is available. Here the model which explains consumption in the same way as the first setting for both the census and the survey, but the explanatory variable for the more recent survey is generated by where the sampled 100 clusters in the survey have the same values for _kc_ as they have in the old census. For both estimators, the _R_<sup>2</sup> obtained from the first-stage regression for all generated surveys is on average similar to the _R_<sup>2</sup> based on the census in the first setting.

Note that in both settings, estimators purely based on the survey have desirable properties as the surveys are representative of the respective village population at the time of data collection. In real-word situations, however, a survey is not necessarily representative at the village-level.

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Figure 1: Effects on aggregate poverty of urban-rural redistributlon (Poverty line less than mode) <!-- Start of picture text --> <///RInZIlZ@X(uFina Indx (rba I<br>nItel hOeadeoaunt index<br>rural Final Index (urban tl<br>urban I<br>urban 2<br><!-- End of picture text -->

**Figure 2!** Effects on aggregate poverty of urban-rural redistribution (Poverty **line** greater than modJe)

<!-- Start of picture text --> /.lr elna hedeount Indox<br>. I Initial headcount Index<br>rural/ .<br>/<br>urban<br>z-x Z Z*X<br>poverty line<br><!-- End of picture text -->
|**Table 5: Impact on Partner Household Level**|**Outcomes**<br>Sample|Control|ITT|LATE|
|---|---|---|---|---|
|Outcome|Size|Mean|Impact|Impact|
|Log Household Income|282|9.447|‐0.077|‐0.109|
||||(0.097)|(0.140)|
|Log Household Income per capita|282|8.213|0.030|0.043|
||||(0.101)|(0.144)|
|Log Household Expenditure|282|9.126|‐0.018|‐0.025|
||||(0.097)|(0.137)|
|Log Expenditure per capita|282|7.892|0.090|0.126|
||||(0.115)|(0.165)|
|Household Size|282|3.720|‐0.295|‐0.415|
||||(0.234)|(0.354)|
|Household Earned Income|282|7809|155|218|
||||(1098)|(1542)|
|Remittances received|282|466|153*|215**|
||||(78)|(97)|
|Number of Vehicles|282|0.678|0.052|0.073|
||||(0.082)|(0.119)|
|Household has a Cellphone|282|0.364|‐0.053|‐0.075|
||||(0.085)|(0.119)|
|Household has a Computer|282|0.119|0.032|0.045|
||||(0.062)|(0.088)|
|Household has a DVD player|282|0.028|‐0.005|‐0.007|
||||(0.015)|(0.021)|
|Household has a Microwave|282|0.063|‐0.020|‐0.029|
||||(0.024)|(0.035)|
|Household has a Washing Machine|282|0.287|0.076|0.107|
||||(0.081)|(0.118)|
|Household has a Bank Account|282|0.951|0.060**|0.084**|
||||(0.026)|(0.036)|
|Household has an ATM card|282|0.951|0.062**|0.087**|
||||(0.027)|(0.038)|
|Total Savings|282|545|194|273|
||||(201)|(286)|
|Number of Foods in Diet|282|7.308|‐0.269|‐0.378|
|Notes:<br>Robust standard errors in parentheses, *, **<br>10, 5, and 1 percent levels respectively.<br>All regressions include controls for age, gen<br>type of relative of the PAC applicant, and fo<br>Regressions weighted for population propo|, *** indicate si<br>der, island of bi<br>r whether the p<br>rtions of compli|gnificance a<br>rth, ballot y<br>artner co‐re<br>ers and non|(0.194)<br>t the<br>ears entered,<br>sided with th<br>‐compliers.|(0.282)<br> <br>e applicant.|

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**Table 7. Regressions with CIM and aid**

|**(p**|**Model 1**<br>**Model 2**<br>**ure change,OLS) (pure changes,IV1 Aid)**|**Model 3**<br>**(pure changes,IV2 Aid)**|**Model 4**<br>**(IV1 Aid)**|**Model 5**<br>**(IV2 Aid)**|
|---|---|---|---|---|
|Aid|0.0009<br>-0.003|-0.002|-.012*|-.01*|
|(log)|-0.0037<br>-0.005|-0.005|-0.006|-0.006|
|Initial CIM|||-.29**|-.29**|
|(in year 1985)|||-0.14|(-1.97)|
|Population|-0.059<br>-0.06|-0.06|-.07*|-.07*|
|(‘85~’00)|-0.043<br>-0.049|(-1.33)|-0.04|-0.04|
|GDP|.039***<br>.038**|.039**|.04**|.04**|
|(‘85~’00)|-0.013<br>-2.37|-2.43|-0.017|-0.017|
|Constant|.063***<br>0.08|.08***|.35**|.34**|
||-0.019<br>-0.028|-0.027|-0.14|-0.14|
|# obs|94<br>87|87|87|87|
|R<sup>2</sup>|0.27<br>0.26|0.27|0.36|0.36|
|(uncentered)|||||

Notes:

- 1) Dependent variable CIM is Contract-Intensive Money, 1985~2000.

- 2) instrument 1: log population in 1975, log gnp in 1975

instrument 2: log population in 1975, log gnp in 1975, dummy for Central America, East Asia, Europe and SubSaharan Africa 3) robust standard error is reported in the brackets below the coefficient

- 4) ***designates significance at .01 level, ** at .05 level, * at .10 level.

20
# **Box 3: Alternative Regulatory approaches**

**Performance-Based Regulations—** specify required outcomes or objectives rather than the means by which they must be achieved.  Thus firms and individuals can choose processes that are more efficient and less costly, which promotes the use of new technology on a broader scale.  Such type of regulation is increasingly used in health, safety, consumer protection, and environmental regulation.  Drawbacks include measurement problems related to desired outcomes, higher administrative and monitoring costs, greater responsibilities for small companies to develop appropriate compliance strategies.  Most countries have resorted to the use of guidelines or “safe harbors” in conjunction with performancebased regulation.  Guidelines provide information on appropriate compliance strategies, while safe harbors allow the benefits of certainty of compliance associated with prescriptive regulation to be attained, while also allowing more innovative firms to take advantage of the benefits of such regulation.

**Process Based Regulations** —require businesses to develop processes that systematically control and minimize production risks. These processes are used in businesses with multiple and complex sources of risk, where ex post testing of the product is either ineffective or expensive.  Process based regulation is predominantly used in health, food safety, and environmental regulation.

**Co-regulation—** businesses take the lead in regulation through endorsement and adherence to codes of practice.  This type of regulation is highly cost effective for the government.  Drawbacks include the possibility for encouraging anti-competitive activities by business or professional organizations.

**Economic Instruments—** taxes, subsidies, tradable permits, vouchers and the like.  Economic instruments allow businesses to achieve regulatory goals in the least costly manner and provide market incentives which reward the use of innovation and technical change.

**Information and Education—** most widely used approach to regulation in OECD member states; empower consumers to adopt actions or make informed choices to change their behavior.  Examples include campaigns aimed at reducing speeding when driving, anti-litter behaviors; reducing the use of drinking water; eco-labeling of products.

**Guidelines** —issued by regulatory authorities, setting out processing or providing interpretations to aid understanding of government objectives by businesses and citizens.  Guidelines may accompany existing regulations, but also are increasingly used as stand-alone documents.  Guidelines, for example, are widely used in the area of consumer protection in Denmark.

**Voluntary Approaches** —initiated by industries, sometimes formally sanctioned or endorsed by government.  They include voluntary initiatives, voluntary codes, voluntary agreements, and selfregulation.  An example of a voluntary arrangement is the chemical industry’s Responsible Care Program, used in 40 countries, which promotes the adoption of rules for sound environmental management practice.

_____________ _Source_ : OECD 2002.

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