rafmacalaba/datause-encoder-data huggingface dataset:
programs are designed on paper to benefit the poorest agricultural producers. In relative terms, it becomes clear that these programs are significantly more likely to benefit wealthy farmers than poor farmers. Moreover, participant contributions are made on a fixed-cost basis, which is inherently advantageous to wealthier participants. An effective strategy for enhancing the targeting of agricultural-subsidy programs must utilize some form of means-testing, either as a participant criterion or a method to scale contributions, or both. Without better-designed and more conscientiously implemented targeting mechanisms, public spending on health, education and fertilizers will not be able to further the government‘s larger objectives for pro-poor and progressive development policy.

# **References**

Breceda, K., J. Rigolini, and J. Saavedra. 2009. "Latin America and the Social Contract: Patterns of Social Spending and Taxation." _Population and Development Review_ 35(4): 721–48.

- Cuesta, J., and J. Martinez-Vazquez. 2012. ―Analyzing the Distributive Effects of Fiscal Policies: How to Prepare (Analytically) for the Next Crisis?‖ In _Fiscal Policies and the Financial Crisis_ , ed. O. Canutto, O. Dobson-Blanco, and M. Bramhatt. Washington, DC: World Bank.

- Goñi, E., H. Lopez, and L. Serven. 2008. ―Fiscal Redistribution and Income Inequality in Latin America.‖ World Bank Policy Research Working Paper No. 4487, Washington, DC.

- Giugale, M., A. Narayan and J. Saavedra (forthcoming) ―Opportunities for Children in Africa. A Study of Twenty Countries in sub-Saharan Africa", World Bank.

- IOB. 2008. _Primary Education in Zambia_ . IOB Impact Evaluation No 312. Policy and Operations Evaluation Department. The Hague.

- ILO, 2008. _Zambia, Social Protection Expenditure and Performance Review and Social Budget_ , Geneva. Available at: www.ilo.org/publns.

- Masiye F, Chitah B M, Chanda P and Simeo F (2008)  ̳Removal of user fees at Primary Health Care facilities in Zambia: A study of the effects on utilisation and quality of care,‘ EQUINET Discussion Paper Series 57. EQUINET, UCT HEU: Harare

- Ministry of Agriculture and Co-operatives (2011) Farmer Input Support Programme: Implementation Manual, 2011/2012 Agricultural Season

- Ministry of Agriculture and Co-operatives (2009) Farmer Input Support Programme: Implementation Manual, 2009/2010 Agricultural Season

- Ministry of Community Development and Social Services (undated) The Targeted Food Security Pack: Implementation and Impact 2000-2004, National Steering Committee, Republic of Zambia: Lusaka

- Ministry of Education (2008) Educational Statistical Bulletin 2008, Republic of Zambia: Lusaka Ministry of Education (2007) Educational Statistical Bulletin 2007, Republic of Zambia: Lusaka Ministry of Education (2006) Educational Statistical Bulletin 2006, Republic of Zambia: Lusaka Ministry of Finance and National Planning (2011) Sixth National Development Plan, January 2011, Republic of Zambia: Lusaka

- Ministry of Finance and National Planning (2010) Financial Report for the Year Ended 31<sup>st</sup> December 2009, Republic of Zambia: Lusaka

38
24

**Table 8: Effects on household budgets of increasing energy prices by 80%. (loss in consumer surplus as percentage of total expenditures).**

|Category|Heat|||jElectr|icity||Gas|||J<br>All e|nergy||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Elasticity|0||.5|||0|_.5_||-|||_5_|| -1|||||
|I (poorest)|1.2|1.0|.7|**2.9**|2.3|1.7|**1.1**|.8|.6|5.9|4.7|_3.5_|
|2|1.7|1.4|1.0|2.8|2.2|1.7|1.3|1.1|.8|7.2|5.7|4.3|
|3|2.1|1.6|1.2|2.8|2.2|1.7|1.6|1.3|1.0|8.1|_6.5_|**4.9**|
|4|2.1|1.6|1.2|2.6|2.0|_1.5_|1.7|1.4|1.0|8.6|6.9|5.2|
|5 (richest)|1.8|1.4|1.1|2.2|1.7|1.3|1.8|1.4|1.1|8.2|6.6|4.9|
|All|1.7|1 .4|1.0|2.6|2.1|1.6|**_1.5_**|1.2|.9|7.6|6.1|4.6|

Source: Authors' simulations.

In terms of _type of energy,_ the welfare loss to all consumers of raising _electricity prices is_ much higher than for other fuels, given its generally larger share in overall consumption. Assuming no change in consumption, raising power prices gives rise to a welfare loss equal to 2.6 percent of total budgets, compared to 1.7 percent for district heat and 1.5 percent for gas. However the impact on the poor is especially notable. Raising power prices affects the poor more than raising the prices of other types of energy. The poor incur welfare losses equal to 2.9 percent when power prices are raised, compared to 1.2 percent for heat and 1.1 for gas.

In terms of _socio-economic group_ and occupation, farmers and mixed families are hurt the least by an overall 80% increase in the price of energy --largely because they do not consume district heat. Assuming a zero elasticity of demand, their welfare drops by about 5.2%, while pensioners are hurt the most -- 9.4%. Workers are also hurt significantly -- 7.1%. These results suggest that the "constituency" for keeping prices low --workers and pensioners -- is quite large in Poland and that there may be a major political dimension to raising prices in Poland.
ranked all 366 households by their wealth index score and report a cumulative distribution of subsidies by cumulative percentage of sample households.

# **_Calculation of the total average cost of electricity and water services_**

Another critical parameter when calculating the subsidy received by each household _i_ for service _j_ is AC _j_ , or the total average cost of providing service _j_ in Addis Ababa. We calculated AC _j_ using data from the utility companies and the following formula: In this expression, OM is annual value of operation and maintenance (O&M) costs, _K_ is the annual capital cost, _R j_ is the ratio of capital costs to O&M costs, _P_ is the annual production level, and _L_ is the level of annual losses during production and distribution.<sup>20</sup> These data were provided directly by various departments of the utility companies: the EEU’s Budgetary Control Office Finance Department and Planning Department; and the AAWSA’s Finance Department and Project Planning Department. For both water and electricity, we also collected data on capital costs, which were sometimes inconsistent among departments. Even so, the average information on capital costs helped us verify that the data underlying _R_ (the ratio of capital costs to O&M costs) were a viable proxy. For electricity provision, <mark>O&M costs were considered to be 5% of the total cost and the full financial costs (including all investment costs) were considered to be 95% of the total cost; for water provision,</mark> t <mark>he full financial costs of water production (including all</mark> <mark>investment costs) were considered to be 3 times higher than the O&M costs. Because</mark> our estimates are annual costs, we collected data from the most recent years 2012–2015 for the purposes of our

> 20 Energy and water losses play an important role in the calculation of costs because they increase the average unit cost. In Addis Ababa, during the last three years, there have seen losses of 18%–22% in electricity provision. Also, in 2015 there were losses of 35%–40% in water production. (Data collected in 2016 from EEU, and AAWSA.)

13
# _Figure A3_

# _NDVI Anomaly, Northern Zone_

<!-- Start of picture text --> Percent<br>20<br>10<br>0<br>-10<br>-20<br>Jul-02 Jul-03 Jul-04 Jul-05 Jul-06 Jul-07 Jul-08 Jul-09 Jul-10 Jul-11 Jul-12 Jul-13 Jul-14<br><!-- End of picture text -->

**Source** : NASA and authors’ calculations

_Figure A4 NDVI Anomaly, Southern Zone_ <!-- Start of picture text --> Percent<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>Jul-02 Jul-03 Jul-04 Jul-05 Jul-06 Jul-07 Jul-08 Jul-09 Jul-10 Jul-11 Jul-12 Jul-13 Jul-14<br><!-- End of picture text -->

**Source** : NASA and authors’ calculations

43
Table 6 presents estimation results of a more parsimonious specification whereby we assume that the effects of market expansion and rising transport costs are both fully instantaneous having no lagged component at all. But the findings are broadly consistent with those in Table 5. Here also the main result is that all three predictions of the Melitz‐Ottaviano model hold up only in data on the production of cinder blocks. This is in the sense that TFPR increases with market expansion in that industry while declining with an increase in transport costs or with an increase in sunk costs of entry, all indicated by appropriately signed and statistically significant elasticity estimates.

TFPR increases whenever market size expands in Table 6, not only in the cinder blocks industry but also in the production of cooking oil and in bakeries. But unlike the reading in Table 5, TFPR declines with increasing transports costs in the production of cooking oil. An increase in sunk costs of entry also has the predicted effect in grain mills as well as in the production of cinder blocks.

# _G. Effects on productivity and prices across the size distribution of establishments_

A comparison of corresponding entries of the last columns of Table 5 and Table 7 shows that the effects of increase in market size on productivity vary in magnitude across the size distribution of cinder block producers. Specifically, for any given increase in market size, industrywide average plan level TFPQ and product prices both increase at proportionately higher rates among smaller and less capital intensive producers. This follows from the fact that market expansion is seen to increase TFPR significantly in the relevant entries of the two tables with a proportionate increase that is always higher in Table 7. The latter point implies that the effect of market expansion on TFPR as read from Table 5 is smaller in larger (in terms of employment size) or more capital‐intensive establishments.

Similar comparison of entries of the tables relating to the cost of licensing indicate that the effects of any given rise in sunk entry costs on TFPQ and on product prices of cinder blocks production are always more pronounced in smaller and less capital intensive producers. For the same increase in license fees is seen to produce greater decline in TFPR in Table 7 than that it would 19
29 Ngtes

# 1. The results of the project are detailed in Byrd and Lin (1990).

2. In the course of the project, five qualitative and quantitative surveys and numerous interviews were carried out. Data were collected and analyzed on a sample of 122 rural industrial firms, 1,174 employees, and 67 rural townships to complement aggregate statistics at county, provincial, and national levels.

3. A specialized village typically contains a number of firms, some of which may be very small, involved in common activities, for example, the making of bamboo combs, ropemaking or tire repair. There may be some institution for common marketing.

4. Despite rapid growth of private firms, by 1987 they still produced only oneseventh of the total industrial output of the TVP sector. Moreover, in most cases, the extent to which private firms could be established and could grow was still very much determined by the attitude of local government and its willingness to permit their use of land and other factors and to guarantee bank loans.

5. Strictly speaking, the right to use land rather than its ownership was assigned on an individual or household basis.

6. With interest rates held below market-clearing levels, China tends to experience excess demand for funds. Rural bank deposits are considered local resources, and communal governments exert a large influence on their use. Nevertheless, more industrialized areas such as Wuxi have, for some years, been net borrowers from other areas.

7. Dollar (1989) provides an interesting analysis of the impact of changes in incentives on the performance of Chinese state-owned enterprises after 1978.

8. In some ca.';es the distinction between a community and a firm may be minor. For example, e. ceramics factory in one sampled village employed virtually the entire labor force as well as some outsiders. Such a "company town" is clearly highly vulnerable to market shifts. 9. TVPs have also benefited, relative to state enterprises, from more favorable tax treatment, although this advantage is being eliminated. The effect of mid1989 policy changes, which apparently discriminate against TVPs in favor of state firms, remains to be seen.

10. The gap system of credit allocation ties local bank credit limits to local deposit mobilization. This inhibits the transfer of final resources between communities.

11. Regional income inequality in the United States has fallen greatly over the last 50 years as a result of increased interregional mobility. For discussion of this topic, see Barro and Sala I Martin (1989). In rural China, by contrast, it has remained high due to limited mobility.
a mortgage application. Hence demand for a neighborhood is the weighted average of demands conditional on the choice set: where _j_ indexes neighborhoods {1, 2, ..., J}, _x_ is the vector of household characteristics, and _z_ the vector of all neighborhood amenities. The probability of a choice set _C_ is a product of the probabilities of approval of mortgage applications: In turn, approval probabilities are driven by mortgage originators’ standards. Explicitly endogenizing the choice set presents the unique advantage of allowing an analysis of the impact of changes in lending standards on each neighborhood demand.

In turn, preserving the equilibrium of the city requires an adjustment of housing prices in response to the rise in neighborhood demand. Changes in lending standards thus produce a shift at each quantile of the housing price distribution. As the marginal impact of a relaxation of lending standards depends on household characteristics (e.g., race and income), housing price shifts vary across quantiles regardless of whether lending standards depend on neighborhood characteristics. Estimates based on micro data of the San Francisco Bay area for 2000-2006 suggest that the relaxation of lending standards led to a compression of the price distribution: housing prices rose more for the lower quantiles than for the higher quantiles of the housing price distribution. Such compression of the price distribution is consistent with a gentrification of lower-priced neighborhoods as the number of mixed Black-White neighborhoods is declining both in general equilibrium simulations and in observed data.

Price responses to increases in lending supply typically occur in markets with low supply elasticities. While markets for consumer goods exhibit high elasticity, housing supply and the provision of higher education services can exhibit low elasticity and thus experience large and heterogeneous price responses. While Saiz's (2010) estimates suggest a large variance of housing supply across metropolitan areas, Ouazad and Rancière (2016a) finds substantially smaller neighborhood-level supply elasticities for the metropolitan area of San Francisco. Thus, changes 19
# Table 17A. Benchmark factors' remuneration with respect to total value added in Jensen and Tarr (2010) (in percentage)

||Child<br>(age 10 to<br>14)|Female<br>(no formal<br>education)|Female<br>(not finished<br>primary school)|Female<br>(not finished<br>secondary<br>school)|Female<br>(secondary<br>or higher<br>education)|Male<br>(no formal<br>education)|Male<br>(not finished<br>primary<br>school)|Male<br>(not finished<br>secondary<br>school)|Male<br>(secondary or<br>higher<br>education)|<br>Subsistence|Capital|Land<br>All<br>females|All males|**Total**<br>**Value**<br>**added**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**IRTS Goods and Services**|**0.0**|**0.0**|**0.0**|**0.6**|**0.4**|**0.1**|**0.3**|**1.9**|**1.1**|**0.4**|**10.3**|**1.1**|**3.3**|**15.1**|
|**CRTS Goods and Services**|**0.3**|**0.9**|**1.0**|**6.4**|**1.7**|**1.0**|**3.3**|**7.1**|**4.5**|**25.3**|**29.5**|**4.1**<br>**10.0**|**15.8**|**84.9**|
|**Business Services**|**0.00**|**0.00**|**0.01**|**0.13**|**0.15**|**0.02**|**0.08**|**0.56**|**0.58**|**0.0**|**4.3**|**0.0**<br>**0.3**|**1.2**|**5.9**|
|Telecommunication||||0.011|0.020|0.002|0.005|0.076|0.107||0.53|0.03|0.19|**0.7**|
|Insurance|0.000|0.000|0.000|0.003|0.002|0.000|0.002|0.008|0.005||0.04|0.01|0.02|**0.1**|
|Banking|0.001|0.002|0.005|0.045|0.039|0.005|0.028|0.148|0.098||0.80|0.09|0.28|**1.2**|
|Professional business services|0.001|0.002|0.006|0.050|0.043|0.005|0.031|0.164|0.108||0.88|0.10|0.31|**1.3**|
|Air transport||||0.002|0.004|0.000|0.001|0.016|0.023||0.23|0.01|0.04|**0.3**|
|Road transport||||0.015|0.028|0.002|0.007|0.104|0.148||1.50|0.04|0.26|**1.8**|
|Railway transport||||0.001|0.001|0.000|0.000|0.005|0.008||0.08|0.00|0.01|**0.1**|
|Water transport||||0.006|0.011|0.001|0.003|0.041|0.058||0.29|0.02|0.10|**0.4**|
|**Dixit-Stiglitz Goods**|**0.01**|**0.03**|**0.03**|**0.47**|**0.25**|**0.04**|**0.17**|**1.33**|**0.50**|**0.4**|**5.9**|**0.8**|**2.0**|**9.2**|
|Processed food||0.005|0.002|0.014|0.080|0.007|0.010|0.096|0.061|0.33|1.40|0.10|0.17|**2.0**|
|Beverages & tobacco products||||0.001|0.003||0.000|0.009|0.134|0.11|0.60|0.00|0.14|**0.9**|
|Textile & leather products|0.005|0.025|0.024|0.438|0.144|0.011|0.123|0.656|0.157||1.49|0.63|0.95|**3.1**|
|Wood paper printing|0.000|0.000||0.003|0.005|0.013|0.010|0.151|0.037||0.74|0.01|0.21|**1.0**|
|Manufacture of basic & industrial chemicals||||||||0.175|||0.05|0.00|0.17|**0.2**|
|Manufacture of fertilizers & pesticides||||||||0.028|||0.01|0.00|0.03|**0.0**|
|Petroleum refineries||||||||0.044|0.005||0.13|0.00|0.05|**0.2**|
|Rubber plastic & other manufacturing||||0.010|0.005||0.001|0.026|0.009||0.18|0.01|0.04|**0.2**|
|Glass & cement||0.000|0.000|0.000||0.001|0.001|0.074|0.014||0.33|0.00|0.09|**0.4**|
|Iron steel & metal products|||||0.003|0.006|0.017|0.058|0.044||0.43|0.00|0.12|**0.6**|
|Manufacture of equipment||||0.003|0.002||0.014|0.017|0.021||0.59|0.00|0.05|**0.6**|

50
above, would take care of this problem.

Holmqvist's analysis of the impact of education aid on educational expenditure using cross section data, already discussed in Chapter 2, is an alternative, approach to categorical fungibility. We have already expressed reservations about cross section analysis. 'These doubts aside, his simple methodology is intuitively appealing. However, as demonstrated in Chapter 2, his particular results are determined by a single observation.

# _Simultaneous equation models_

The argument of this report is that broad-based analysis using cross-section data of very general relationship **s** hould be replaced by more careful analysis of time series data of more detailed channels through which aid may affect macroeconomic aggregates. It is also argued that such work should usually be conducted in the context of simultaneous economic models, to capture the full range aid's effects.

Some object to econometric models using developing country data, as the data are not deemed sufficiently good for such analysis. Poor data quality is, however, a more convincing argument against cross-section analysis, since the researcher is more likely to be using numbers pulled out of a database and about which they have little knowledge (changes in definition, lack of comparability between countries, and, most of all, inadequacies in the data). Analysis of a single country allows greater famiiarity with these issues.

there are a few multi equation models of aid impact which may be mentioned (in addition to the two CGE models mentioned above in the discussion of aid and exports). Hill (1988) presents a twenty-two equation output-driven model of Bangladesh, which disaggregates the government and agricultural sectors: a halving of aid flows is shown to lead to a drop in GDP of between _5.9_ and 11.8 per cent. White's (1992d) eleven equation model of Sri Lanka focuses on investment, but is set in the context of a demand-determined macroeconomic model. A reduction in aid is shown to reduce both public and private investment and lead to a fall in GDP of about twice the value of the aid reduction. Radelet (1993) presents a CGE - the results from which are discussed in Chapter 4- **to** analyse if Gambias economic growth comes from rain, aid or policy reform. Ahiuwalia's (1979) model of India - discussed in Chapter _5_ - alows for the incorporation of possible disincentive effet from aid. These models provide a useful basis for further work.

59
both weighted averages (giving greater importance to ‘dominant’ products, using labor hours or production quantities/values as weights) and unweighted averages (giving the same importance to each product).

# **_(4) Defining a threshold_**

Summarizing a continuous variable, such as the share of output intended for sale, into a binary indicator of own-use or market-oriented agriculture requires a threshold at which the market portion of output becomes large enough to classify the agricultural activity as employment. Though the ICLS resolution does not clearly specify a threshold, its emphasis on “mainly intended for sale” suggests a threshold of at least 50 percent (of total farm output). While own-use production work in the 19<sup>th</sup> ICLS framework is not synonymous with subsistence agriculture, similar questions have arisen in the applied economics literature in distinguishing subsistence agriculture from non-subsistence agriculture.<sup>13</sup> The 2008 World Development Report on Agriculture, for example, uses subsistence thresholds ranging from 10 to 50 percent of agricultural production to delineate subsistence-oriented producers from market-oriented producers (World Bank 2007). Others use an absolute threshold based on minimum physiological needs required for survival, like a poverty line anchored in nutrition, or empirically estimate a threshold (Wharton 1969; Cadot et al 2010).

# **3 Data**

This paper pulls together data from four multi-topic household surveys from three countries collected under the umbrella of the World Bank’s Living Standards Measurement Study (LSMS) project. The 2015 Ghana and 2016/17 Malawi Agricultural Labor Surveys are specialized agricultural surveys conducted as part of the LSMS’s program of survey methodological research. Both surveys were designed, among other objectives, to pilot questions about the intended use of farmers’ output to facilitate the operationalization of the 19<sup>th</sup> ICLS standards. In addition, we draw on the 2016/17 Malawi Integrated Household Survey (IHS4) and the 2015/16 Nigeria General Household Survey Panel (GHS-Panel Wave 3), which are the first two nationally representative LSMS-ISA surveys that adopted the revised standards. The following paragraphs describe each data set (see Table A1, Appendix, for a summary description and additional information on each survey).

> 13 As per the 19th ICLS, subsistence foodstuff producers constitute a subgroup of persons in own-use production work. Additional work is planned to operationalize the concept of subsistence in labor statistics.

10