rafmacalaba/datause-encoder-data huggingface dataset:
|**Transports**|59.77|31.86|3.72|0.70|3.95|100|
|---|---|---|---|---|---|---|
|**Other Services**|46.19|23.09|10.79|7.78|12.15|100|
||||**2005**||||
|**Agriculture**|86.10|11.39|1.87|0.57|0.06|100|
|**Industry**|51.30|35.20|8.83|3.36|1.31|100|
|**Commerce**|72.13|17.27|7.07|2.34|1.20|100|
|**Transports**|66.04|22.85|7.53|2.39|1.19|100|
|**Other Services**|40.72|25.08|14.54|9.28|10.37|100|
||||**2011**||||
|**Agriculture**|78.29|14.90|5.09|1.33|0.40|100|
|**Industry**|45.27|33.05|13.47|3.96|4.25|100|
|**Commerce**|64.25|23.71|8.72|2.52|0.80|100|
|**Transports**|46.34|23.49|15.43|10.64|4.10|100|
|**Other Services**|46.55|23.25|12.87|8.62|8.71|100|
|**Source: ESAM2001**|**, ESPS2005, E**|**SPS2011**|||||

The foregoing is illustrated by a more aggregate picture. The services sector’s share of total employment rose slightly from 31.5 to 33.8 percent between 2004 and 2011 (Figure 7). Notwithstanding the sector’s dominance as a driver of GDP growth during 2006-2011, its expansion was not reflected in the growth of employment. While the services sector grew at an annual average rate of 4.9 percent between 2004 and 2011, employment in the sector grew by an average of only 1 percent per year.

# **FIGURE 7: DISTRIBUTION OF EMPLOYMENT BY SECTOR, 2004-2011 (%)**

<!-- Start of picture text --> 100<br>90<br>31.5 33.8<br>80<br>70<br>60 15.2 14.7<br>50<br>40<br>30<br>53.3 51.5<br>20<br>10<br>0<br>2004 2011<br>Agriculture Industry Services<br><!-- End of picture text --> Source: World Bank (2007), Senegal Competitiveness Report (2011) 19
# **2.4. Outcome variables**

Our analysis examines several outcome variables related to labor, health, and food consumption. The labor supply variables include: farm hours (includes hours on own farm, hours on community farm, hours processing crops, hours herding, and hours processing livestock); wage employment hours; self-employment hours in non-farm work; hours spent gathering firewood and fetching water; and total hours, which is the sum of hours spent in all categories of work just mentioned. These variables correspond to hours spent in each activity in the 7 days prior to the day the household is surveyed. Labor intensity in rural Africa settings is quite difficult to measure due to the predominance of farming and the seasonality in labor hours. The survey did not try to construct an annual estimate of labor hours. We do not include hours spent in housekeeping and child care, partly because these data will be prone to large measurement error.

The average number of hours worked per week reported in Table 2 is below 30 hours and this may appear low. These numbers are actually quite similar to national survey estimates from neighboring countries. For example, they are similar to the hours for individuals in rural households in the Malawi 2004/05 Integrated Household Survey and the Kenya Integrated Household Budget Survey 2006/07 (results not reported). This can be explained in at least two ways. First, in rural areas the main economic activity is farming which leads to large seasonal variation in labor supply (Cleave, 1974) such that the timing of surveys matters. In both the Kenya and Malawi surveys noted above, the field work is spread over 12 months. In the case of the KHDS, the field work was over about 6 months in the first half of the year, which covers some periods of peak labor demand for the long rainy season but was not conducted solely in that period. High seasonal variation is likely to be less of an issue in Kagera, though, with relatively more continuous cultivation of bananas and coffee than in regions with seasonal farming (those with more prominent bimodal or uni-modal rainy seasons). Second, hours will be low in areas with low land holdings per household member such as Kagera (which may be evidence of surplus labor).

We use the following health outcomes: body mass index (BMI); an indicator for self-reporting of an illness in the four weeks prior to survey; an indicator for chronic 10
year born (Yb), year of course completion (COMP), and figration status (IM). **We** d'scuss each of these variables, in turn.

The dependent variable (EARN) rolates to individual earnings. The NII supplied information on recorded earnings for the identified sample of course completer.; this m*ssure might **appear** to be the tethxically *ppropriate one **as** it includes all those with earnings ga-, **and** thus **takes** account automatically of unemployment which may differ by training mode. Yet, these gaps, particul-arly when zero annual earnligs result, may reflect, rather more, such factors **as** temporary withdrawal from the labor force because of study, illness or periods spent abroad, than reflecting unemployment. Including only those individuals with positive earnings for any annual regression run provides a way of overcoming the pr. A of lengthy earnings gaps; but again, not all earning gaps of less than a year refl&ct unemployment. Both measures were tried in alternative regression runs. Since similar results were obtained, we present only those regressions in which EARN is measured by positive earnings. We used natural (positive) earnings as the regressor, rather than the more usual log specification, in order to keep our results comparable with those of Borus, who used the natural earnings measure. However, since all of our earnings observations relate to one small part of the age cross-section profile, there is less reason to fear that the earnings distribution in our sample departs from normality; the case for using log earnings thus becomes less compelling.

Training mode (TP) was entered as dummy variables: APP (apprenticeship), IND (industrial school), TEEN (teenager course), with VOC (vocational secondary school) in the constant term. Trainees had qualified in one of the three major training trades (TT) taught in the programs under - study electrician trades "ITEC), automobile mechanics (AUTO) and metal working trades (METAL). TT, entered as dummy variables, was included in the regressions to eliminate any differential effect of training occupation on subsequent earnings. Length of course (LEN), measured in units of a year, 9
not getting these scholarships, whilst teachers complained that parents inappropriately enroll under-age children, that can’t and don’t attend school, just to lay claim to the scholarships. The second issue that attracted attention was a new government mid-day meal program. Actual learning levels attracted the least attention, and the facilitators had a difficult time steering the conversation away from scholarships and school meals to the broader issue of learning.

Follow-up surveys were undertaken in the same 280 villages in March 2006, 3-6 months after the information campaigns were implemented, and the most surprising fact emerging is that the campaigns did not lead to any substantial improvement in citizens’ lack of knowledge of VECs. Less than 10 percent of citizens are aware of the VECs both before and after the interventions. We also find no effect on public school performance. This contrasts with a dramatic increase in private efforts to improve learning of lagging children in response to information provided—local youth volunteered to hold additional classes outside school, parents of illiterate children in particular chose to participate in these classes, and consequently the children made great strides towards literacy. However, we don’t even have anecdotal evidence that these local volunteers were assisted in their efforts by local government structures—neither the village government head, nor the village public school teacher, nor any member of the VEC. Indeed, according to anecdotes provided by Pratham’s facilitators in the field, the public school teacher and the _Pradhan_ in some villages felt threatened by the volunteer activities and attention drawn to learning failures in public schools.

# **4. Implications for international development assistance**

Much of the reforms being pursued by donors in support of decentralization consist of providing greater revenues or transfers, and building capacity of local governments through training programs such as in public financial management. The arguments here suggest that these reforms on their own might not be efficient or effective in delivering improved public services for actual development outcomes, if the local jurisdictions being supported are politically captured. Greater funds devolved to local governments are more likely to go towards clientelist transfers rather than improvement of public goods. Capacity building can be a waste when local politicians have no incentives to develop technocratic skills for better service delivery.

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The net effect of _corruption_ (i.e., the bribe tax) on TFP is then given as In equation (3) a significant coefficient for _β3_ will indicate that the effect of corruption on productivity depends on the degree to which the firm is engaged in time consuming relations with the state. In particular, a positive coefficient for _β3_ would indicate that a high _time tax_ is accompanied with less negative - or even positive - effects of _corruption_ on productivity, thus providing evidence in favour of “efficient grease”, with corruption helping to mitigate the effects of burdensome regulation. The same result could also be consistent with a setting where briberevenue maximizing bureaucrats may use red tape (the time tax) as a screening device to give production licenses to high-productivity firms (Banerjee, 1997). In such a model, every firm pays the same amount of bribes while high productivity firms spend more time with bureaucrats.

# **_3.3 Institutional Quality: Interaction between Firm-level and Country-level Effects_**

As a further step in our analysis, in order to check whether the effect of corruption on firm level productivity differs depending on country characteristics, we extend equation (1) and interact _corruption_ with country level measures of institutional quality that may be hypothesized to be relevant in determining individual incentives for corrupt behaviour. For this we use two alternative indicators. First is the _Transparency International Corruption Perception Index (CPI)_ for the year 2008, which allows investigating whether the prevalence of corruption at the country level has implications for firm behaviour and, consequently, productivity performance. The CPI captures the perceived levels of public-sector corruption in a given country and is a composite index, drawing on different expert and business surveys. It may be interpreted to reflect the possibility of social effects as described earlier, whereby in a more corrupt environment individual entrepreneurs would have stronger incentives to behave corruptly. The CPI ranges from zero (highly corrupt) to ten (highly clean). It varies across countries and is fixed across sectors for a given country.

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Policy Research Working Paper 10301

# **Abstract**

Despite the relevance of house prices for a variety of stakeholders as well as for macroeconomic and monetary policy making, reliable, publicly available house price data are largely absent in emerging markets and developing economies. Filling this void, this paper presents a systematic approach to collecting, analyzing, and assessing private property prices in emerging markets and developing economies. The paper uses data scraped from five countries’ largest real estate websites where private properties are listed for sale, to obtain price data and property attributes to establish a comprehensive data set that allows for both intra- and inter-country comparison of residential property prices. It then outlines the usability of these data by employing random forest estimation to predict the price of a standard housing unit—the basic house price—that is comparable across countries. While this approach is also applicable to filling wide data gaps in the provision of private property prices in developed economies, the paper focuses on how this approach can be applied to emerging markets and developing economies, where private property price data are particularly scarce.

This paper is a product of the Development Impact Measurement Department, International Finance Corporation. It is part of a larger effort by the World Bank Group to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/prwp. The authors may be contacted at dbehr@worldbank.org; khaider@ifc.org; ssingh13@ifc.org; and azaman@ifc.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table IX reports the effects of a trade reform that lowers tariff rates from ten percent to five percent for industries with high levels of import penetration. As indicated in Table IX, this would raise _σε_<sup>2foroneyearfollowingthereformfromameanlevelof0.08to0.013(here</sup> we use our regression results from equation (10’) reported in Table VI). The corresponding welfare cost of this change is calculated to be 0.98 percent of permanent consumption if the co-efficient of risk aversion is _γ_ = 1, and this cost increases to 1.96 percent of lifetime consumption if we choose _γ_ = 2 instead.<sup>34</sup> Now consider the indirect effects of trade policy as measured by the interaction terms in (10’). As noted above, an exchange rate appreciation of ten percent raises _σε_<sup>2forayearfrom0.008to0.011ifthetarifflevel istenpercent.This</sup> translates into a welfare cost of 0.59 percent of lifetime consumption if _γ_ = 1 and 1.18 percent if _γ_ = 2. If the tariff rate were lowered to five percent, however, _σε_<sup>2risesto0.014andthe</sup> corresponding welfare costs are 1.18 and 2.36 percent of lifetime consumption, respectively. Finally, if the tariff rate is ten percent, a cyclical downturn in the economy (a drop in _g_ by five percent) raises _σε_<sup>2forayearfrom0.008to0.010,andthecorrespondingwelfarecostis</sup> calculated to be 0.39 percent of lifetime consumption if _γ_ = 1 and 0.78 percent with _γ_ = 2. In contrast, if the tariff rate were lowered to five percent, _σε_<sup>2risesto0.013instead,andthe</sup> corresponding welfare costs are 0.98 and 1.96 percent of lifetime consumption, respectively. Thus, our calculation suggest that both the short-run direct effects of tariff reforms and the indirect effects of the level of the tariff in amplifying the effects of macroeconomic shocks are economically significant.

As we have mentioned before, the limited time series dimension of our income data might lead us to over-estimate the amount of permanent labor income risk Mexican workers face.

> 34Although the welfare formula (15) is non-linear in _γ_ , this non-linearity is not very pronounced for moderate degrees of risk aversion. For example, if _γ_ = 4, then the welfare cost of this short-run change in _σε_<sup>2is4</sup><sup>_._16%oflifetimeconsumption.NoticealsothattheresultsreportedinTableIXassume</sup><sup>_n_=4since</sup> we use quarterly risk and preference parameters and the increase in income risk lasts for four quarters (one year).

32
term (World Bank, 2018). These results should not be understood to suggest that CSA is some kind of “magic bullet”. If carefully packaged, some CSA practices may have potential to raise resilience. In our case, CSA includes the use of MT, inorganic fertilizer, and hybrid seed, and the use of MT seems to be the biggest factor in improving resilience (Table S7) even if we do not further investigate the causal chain. Prioritizing what works where and under what circumstances is key in scaling CSA. Zambia’s CSA investment plan identify cereal-legume crop diversification, commercial horticulture, agroforestry and reducing post-harvest losses as some of the most promising CSAs in the country (World Bank, 2018).

We urge some caution in interpretating the results of this paper:

- First, while we attempted to address some key endogenous factors and their effects on vulnerability and resilience—namely, CSA adoption, crop diversity, and access to credit—the success of our approaches depends on how good the instruments used are, and how well the exclusion restrictions are met. These are difficult empirical issues and ones that every economist will question. Collapsing RALS data to cross sectional data that allows us to measure vulnerability and resilience after three-waves complicates matters. We are, however, confident that our approaches—which allow us to better measure vulnerability and resilience and apply instrumental variable probit models—add value to existing studies linking climate shocks, CSA, vulnerability, and resilience.

- Second, we acknowledge that CSA as defined in this paper (minimum tillage, inorganic fertilizer, and hybrid seed) captures only short-term effects. This is because we are not able to assess for how long farmers have used these CSA practices on the same plots. We propose that future research should control for long-term plot-level CSA effects on vulnerability and resilience.

- Lastly, although our income-based poverty measures are presumably broadly aligned with consumption expenditure-based measures, they may be an imperfect substitute for expenditure-based metrics, which are more commonly used in the country.

# 7. Conclusion

This paper assessed the extent to which smallholder farmers in Zambia are exposed to climate shocks using both exogenous and self-reported shock measures. It then evaluated the impacts of climate shocks on vulnerability and household resilience and assessed the extent to which climate-smart agriculture (CSA) practices—defined as minimum tillage, inorganic fertilizers, and hybrid maize seed—influence outcomes. We used a three-wave household panel data obtained from the nationally representative Rural Agricultural Livelihoods Survey (RALS). We restricted our analysis to a subset of households re-interviewed over the three-waves for a total sample of about 6,531 households and applied an instrumented probit regression that 23
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**Table 5: Institutions/organizations that help in reducing corruption**

||**All Uganda**|**Central**|**East**|**North**|**West**|
|---|---|---|---|---|---|
|**High impact**(%)||||||
|Radio|49.27|54.67|43.56|21.19|58.41|
|Presidents' Office/State House|44.19|45.70|40.81|13.21|62.14|
|IGG’ Office|34.81|33.62|29.70|22.74|50.04|
|Commissions of enquiry|34.67|39.69|25.09|12.78|45.07|
|Newspapers|33.46|37.63|29.66|15.68|37.61|
|Church organizations|27.59|29.52|34.59|22.39|18.77|
|Local Councils|23.63|24.31|25.78|15.04|24.81|
|**Medium impact (%)**||||||
|Ministry of Ethics and Integrity|19.20|18.32|13.86|3.11|36.25|
|Television|18.73|22.02|19.35|6.00|17.52|
|General public|18.66|18.97|25.13|12.50|14.87|
|Members of Parliament|16.69|16.95|17.90|9.39|19.10|
|Local politicians|16.16|16.61|17.54|11.09|16.65|
|Human rights groups|14.28|16.81|16.73|7.20|9.73|
|Teachers|13.30|13.74|15.64|9.53|12.01|
|Judiciary|13.19|14.05|12.73|9.75|13.59|
|Women's organizations|12.70|12.98|17.01|5.72|11.68|
|Non-governmental organisations|12.64|13.94|17.17|7.42|7.87|
|Auditor General|11.72|12.54|11.96|7.49|11.97|
|**Little impact (%)**||||||
|Police|8.98|9.77|9.90|6.99|7.29|
|Universities|8.29|8.75|10.06|4.45|7.58|
|National Bureau of Standards|7.00|7.14|9.78|3.88|5.63|
|Professional bodies|6.80|6.63|9.17|2.33|7.42|
|Observations|12,200|5,895|2,475|1,416|2,414|

Source: Authors’ own calculations based on the Second National Integrity Survey 2002 for Uganda.

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