rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Agriculture consumes over 60% of South Africa’s (SA) available water supply, most of which is used in irrigation. While the dominance of agriculture in water use is typical for most countries, this disproportionate allocation has special significance for SA where water is scarce and the country is rapidly approaching a water stress situation. Nevertheless, the contribution of agriculture to the country’s total gross domestic product (GDP) is small and continues to decline, falling to an estimated share of less than 3% by 2007 (StatSa, 2008). The same applies to the sectors’ employment capacity which fell to less than 9% of total formal employment by 2002. This transition is typical of countries which have been successful in diversifying economic structure away from primary production (resource extraction and farming) toward manufacturing and services’ provision activities. However, agriculture remains an important economic activity in terms of its economy-wide multiplier effects, its multi-sector linkages and its contribution to food security in general and the livelihoods of the rural poor in particular.

Other important water related features of the SA agricultural economy include the high protection the sector enjoyed in the past for reasons of food security and other political concerns. Agriculture received a direct price subsidy on water use and on investment in irrigation infrastructure as well as non-price protection (i.e. water quota system) that remains largely in place today. More over, previous water allocation regimes were biased in favor of large scale white farmers seriously disadvantaging other segments of the rural population of mostly small holder black farming families. Previous water management regimes and policies also paid little attention to ecological needs and protection of the health of freshwater ecosystems.

Since 1994 however, the SA economy at large and the agriculture and water sectors in particular have witnessed radical policy reforms, many of which are still under implementation. Major macroeconomic reforms have been introduced to correct the grave socio-economic injustices of the past particularly in terms of provision of basic services (e.g. water and sanitation, housing, health and education) and income and employment opportunities to millions of previously service-deprived communities. These shifts in public policy and investment priorities have major implications for water use and 2
**Table 5: Global Agricultural Trade Flows**

||||||Exporter|($ billion)||||
|---|---|---|---|---|---|---|---|---|---|
|||Low|Middle|All||||Other|Total|
|Importer|Period Avg|income|income|developing|EU-15|Japan|NAFTA|industrial|Imports|
|Low income|1990-91|0.7|1.7|2.4|0.5|0.1|0.9|0.3|4.2|
|Countries|2000-01|3.0|7.2|10.3|3.1|0.1|2.0|1.4|16.9|
||2006-07|6.2|15.6|22.0|4.3|0.2|4.1|1.6|32.2|
|Middle income|1990-91|3.2|20.1|24.2|10.4|1.5|17.4|5.2|58.5|
|Countries|2000-01|9.8|71.2|81.4|32.1|3.4|44.3|14.0|174.2|
||2006-07|19.2|158.7|178.8|63.3|5.3|68.2|21.2|335.7|
|All developing|1990-91|3.8|21.8|26.5|10.9|1.5|18.2|5.5|62.7|
|Countries|2000-01|12.8|78.4|91.7|35.2|3.5|46.3|15.3|191.1|
||2006-07|25.4|174.3|200.8|67.6|5.5|72.3|22.8|367.8|
|EU-15|1990-91|7.3|43.5|51.4|133.8|0.4|15.8|8.6|209.9|
||2000-01|9.0|49.9|59.2|142.5|0.4|14.8|8.9|225.3|
||2006-07|15.2|104.3|119.9|267.4|0.6|18.0|15.1|420.1|
|Japan|1990-91|1.6|19.5|21.0|4.1|..|21.6|5.1|51.7|
||2000-01|2.2|23.8|26.0|5.7|..|22.4|5.9|59.6|
||2006-07|2.6|29.2|31.8|7.1|..|21.8|7.2|67.3|
|NAFTA|1990-91|1.3|20.1|21.5|8.5|0.5|23.4|3.5|54.0|
||2000-01|3.0|32.5|35.5|13.5|0.7|48.1|4.8|95.8|
||2006-07|5.0|58.9|63.9|22.9|1.0|70.5|6.8|152.5|
|Other industrial|1990-91|0.3|2.4|2.7|6.2|0.1|1.5|1.0|11.6|
|Countries|2000-01|0.4|3.4|3.8|7.0|0.1|1.6|1.7|14.1|
||2006-07|0.9|6.7|7.7|13.8|0.1|2.2|3.2|26.8|
|Total Exports|1990-91|14.2|106.6|122.4|162.9|2.6|76.9|23.4|384.5|
||2000-01|27.3|186.4|214.5|203.2|4.7|123.6|36.3|573.7|
||2006-07|48.9|370.3|420.7|377.7|7.2|168.8|54.7|1,013.8|

Note: The classification of income group is based on the World Bank World Development Indicators 2007. Source: Based on import data from UN COMTRADE Statistics.

Trade among industrial countries still dominates global agricultural trade, most of it within the trade blocs such as EU and NAFTA<sup>9</sup> . This intra-bloc trade accounts for more than a third of global agricultural trade. In 1990/01 industrial country agricultural exports to other industrial countries totaled $233 billion. Of that, $134 billion was intra-EU trade (almost 58 percent) and $23 billion (10 percent) was intra-NAFTA trade. Agricultural trade among industrial countries that are not members of these trade blocs accounted for only $76 billion. In 2006/07, their total exports to each other had increased to $456 billion and still 58 percent of it was intra EU-15 trade and intra NAFTA trade had increased to 16 percent of intra industrial country trade. 64 percent of EU-15 imports were from other EU countries in 1990/01, and this ratio stayed the same despite the advances made by developing country exports.<sup>10</sup> Intra NAFTA trade of 46 percent also stayed the same.

Trade among developing countries is also increasing, with almost 55 percent of their agricultural imports coming from other developing countries. However, only 47

> 9 Here we are not including the new members of EU because there is a change in their trading status in the middle of the periods and thus they are included in middle income developing countries. 10 If we include 12 new EU countries in the intra EU trade, it has increased.

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5 the most sensible one for country authorities to pursue. Critically, the data in this new survey provide the raw material for research that should help confirm, refute, or refine this privatemonitoring view.

# **II. The Survey**

The Survey of Bank Regulation and Supervision Around the World assembles and makes available a database to permit international comparisons of various features of the bank regulatory environment. BCL (2012) and the World Bank website list the questions as they appear in the current survey, previous surveys and responses are on the World Bank website and the CD in BCL (2006).<sup>4</sup> Although the current version has over 300 questions, compared with only about 60% much of the expansion was in the form of making explicit separate categories for responses or otherwise clarifying issues. Most questions could be answered “Yes/No” though in many cases we requested that in case of doubt the authorities attach governing regulations or laws. Some of these explicitly or implicitly refer to Basel II, such as those enquiring as to the plans for the implementation of Basel II, and if so then the variant of the first pillar to be adopted. Similarly, some of the questions relating to capital, provisioning, and supervision have been modified to keep abreast of current thinking and emerging practice in these areas.

We will not go into detail about the survey here given the earlier explanations provided in BCL (2012, 2006, 2004, and 2001). The latest survey continues to group the survey questions and responses into the same twelve sections as previously, namely,

- Entry into banking

- Ownership

- Capital

- Activities

- External auditing requirements

> 4 Available at

~ <u>http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20345037 pagePK:642 14825~piPK:64214943~theSitePK:469382,00.html</u>
presence of vocational programs or the availability of higher human capital due to stronger schools and tertiary institutions.<sup>5</sup> District-time fixed effects should address this concern.

Second, the sector-specific proportion of women among employees may be correlated with other characteristics that matter for performance, both across plants and across sectors over time. For example, nearly everywhere in the world women tend to work in lower productivity sectors. Similarly, the evolution of performance over time may be different for female-dominated sectors, and this could also affect the demand for preschools. The inclusion of plant and sector-time fixed effects should absorb this unobserved heterogeneity across plants, sectors and time.

Thus, once we control for sector-time, district-time, and plant fixed effects, it is plausible that the observed differences in productivity associated with an additional preschool per 1,000 children between plants with higher and lower shares of female workers are due to the impact of preschools: plants with a higher share of female workers benefit from a higher “dose” of preschool treatment.

However, the possibility of endogenous placement of preschools remains a concern. For example, the government may incentivize preschool construction in districts with high levels of female-dominated industries with increasing productivity. Alternatively, the private sector may respond to demand for childcare in districts with large share of highly productive firms in female concentrated industries – as they expect women working in such firms to have higher demand for childcare, and be able to cover its costs. To address this concern, we carry out two separate checks for parallel trends. First, we carry out a placebo test using the data from 2000, 2001 and 2002, prior to extensive preschool expansion, and estimate: where Growthd captures change in preschool density between 2003 (when the NSEA was passed) and 2014. Posttp is a dummy equal to 1, if year _t_ is greater or equal to year _p._ We estimate

> 5 It could also be the case that the election of a particularly efficient district-level government would benefit firms, for example due to a better local business climate, and attract private preschool investments at the same time.

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as fair and have a positive effect on productivity through their impact on workers’ morale. In these models morale can depend on wage changes as well as on the wage level. In the former case, the theories imply downward wage rigidity at any level of pay and are of direct relevance to our study.

The importance of different versions of the efficiency wage theory for explaining wage rigidity has been analysed using surveys based on interviews with company managers. The shirking model of Shapiro and Stiglitz (1984) found little support in the US and Sweden (Campbell and Kamlani, 1997; Agell and Lundborg, 1999). In contrast, most existing surveys attribute instead a prominent role to the negative effects of wage cuts on morale and labor productivity (Campbell and Kamlani, 1997, Bewley, 1995, 1999, 2004, Agell and Lundborg, 2003).

Some surveys also indicated that if there is a need to reduce the labour cost in a given firm, company managers prefer laying some people off to lowering the wage level. Bewley (1998, 1999) argues that this is because layoffs can be carried out selectively, whereas when all workers’ wages are cut there are negative consequences for morale. Workers care about a fair treatment and in some instances they would be ready to accept pay cuts if this avoided a large number of layoffs. However, the costsavings associated with small wage cuts may not be large enough to prevent firms in distress from laying off some workers and firms therefore tend to avoid cutting pay at all (Bewley, 2004).

Another strand of this literature focuses on the impact on employment turnover when a firm reduces wages. Better workers will be the most likely to leave the firm as a reaction to wage cuts and the studies by Bewley (1999) and Campbell and Kamlani (1997) find strong support for this adverse selection hypothesis as a reason for avoiding cuts. According to Campbell and Kamlani (1997), the best workers are valuable because pay in general does not increase in proportion to workers’ productivity, and the adverse selection channel applied to quits becomes all the more relevant when workers have accumulated substantial firm-specific human capital. Notably, surveys based on US managers find little support to the adverse selection hypothesis as applied to hiring (Bewley, 2004).

Analysis based on behavioural experiments also confirms the importance of fairness considerations in wage-related decisions. Lab and field experiments show that higher wages lead to an increase in effort. Interestingly, it is shown that the response to a wage cut, which is considered an unfair act, is stronger than the response to a wage increase of the same size, which is seen as a fair act (see Fehr et. al 2008). The analysis also shows that the impact of fairness considerations on performance is higher in long-term employment relationships.

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Second, non‐core items are partitioned into optional modules. Different methods can be used for the partitioning into optional modules. In the simplest case, the remaining items are ordered according to their food share and assigned one‐by‐one while iterating the optional module in each step. A more sophisticated method would take into account correlation between items and partition them into orthogonal sets per module. This would lead to high correlation between modules supporting the total consumption estimation.

Conceptual division into core and optional items should not be reflected in the layout of the questionnaire. More complicated partition patterns can result in a set of very different items in each module. However, the modular structure should not influence the layout of the questionnaire. Instead, all items per household will be grouped into categories of consumption items (like cereals) and different recall periods. Therefore, it is recommended to use CAPI technology, which allows hiding the modular structure of the consumption module from the enumerator.

Third, optional modules will be assigned to groups of households. Assignment of optional modules will be performed randomly stratified by enumeration areas to ensure appropriate representation of optional modules in each enumeration area. This step is followed by the actual data collection.

Fourth, household consumption will be estimated by imputation. The average consumption of each optional module can be estimated based on the sub‐sample of households assigned to the optional module. In the simplest case, a simple average can be estimated. More sophisticated techniques can employ a welfare model based on household characteristics and consumption of the core items. Six techniques are presented in the next section and perform their estimation on the data set from Hargeisa.

Single imputation of the consumption aggregate under‐estimates the variance of household consumption. Depending on the location of the poverty line relative to the consumption distribution, this can either consistently under‐ or over‐estimate poverty. Multiple imputation based on boot‐strapping can mitigate the problem but will render analysis more complicated. Single as well as multiple imputation techniques are used for the evaluation of the methodology.

# **Module Construction**

Consumption for a household is estimated by the sum of expenditures for a set of items 5
that students are acquiring a large amount of real human capital even if they are also investing for the signaling value. Finally, the appropriate response in the signaling case is probably not to discourage tutoring—as through the ban that Korea imposed in 1980—but to address the problem at its source. For example, the government could revise university admissions policies so that they place less reliance on a single examination that makes for a tempting ability signal.

# **7. Policy Implications**

The analysis in this paper has provided evidence that, both in theory and in practice, under certain caveats discussed in Section 5, tutoring can play a positive role in raising education outcomes as a complement to the formal school systems. Our earlier discussions also suggest that in the absence of corruption, private tutoring increases the welfare of households and society overall. However, private tutoring may place poorer households at some disadvantage compared to richer households, particularly when corruption distorts the tutoring market; corruption also reduces the efficiency of the tutoring equilibrium. Thus while the discussion above holds rich policy implication for private tutoring for both developed and developing countries along several dimensions, this final section explores what these findings imply for education policy in developing countries, with a focus on the control of corruption.

## **7.1. Government policy and the control of corruption:  A taxonomy**

Before turning to policy recommendations, it is useful to consider what policies toward private tutoring have been implemented by governments in the past. Bray (2003) categorizes governments into four types, depending on their stance and action toward private tutoring: banning (type I), ignoring (type II), recognizing and regulating (type IIII), or actively encouraging (type IV). Table 4 is constructed mainly from Bray (2003, 2006) and Silova and Bray (2006b), with examples of the countries and territories falling into these categories, as well as several typical measures implemented by these countries on private tutoring. It should be noted that type III governments differ in the degree to which they react to private tutoring, in that not all the measures listed are implemented by each country. Nevertheless, the listed measures provide a good overview of how governments have reacted to private tutoring.

The studies by Bray and Silova offer helpful further detail on these different groups of governments. First, they note that the type I governments all failed in their attempts to ban private tutoring, for different reasons. Bans in Cambodia and Myanmar have failed because those countries’ institutions are too weak to implement the policy, while in Mauritius and Korea, the bans faced too much opposition from vested interests. The latter two countries responded by subsequently lifting the ban and regulating private tutoring instead.

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Table 2: Take up of the reserved space under positive opportunity cost, overall and by presence of men

|Depende<br>Chose res<br>(1)|nt variable:<br>erved space<br>(2)|
|---|---|
|_Panel A: Overall_||
|Positive opportunity cost<br>-0.164<br>(0.011)|-0.163<br>(0.010)|
|High crowding|-0.008<br>(0.012)|
|Few men in reserved space|0.041<br>(0.009)|
|Constant<br>0.249|0.228|
|(0.005)|(0.007)|
|_Mean dependent variable_||
|Zero opportunity cost<br>0.<br>(0.|245<br>014)|
|_Panel B: Heterogeneous efects by male presence in reserved spac_<br>ˆ|_e_|
|_βM_1: Positive opportunity cost _×_ Few men in reserved space<br>0.096<br>(0.008)|0.096<br>(0.008)|
|ˆ_βM_2: Zero opportunity cost _×_ Few men in reserved space<br>0.280<br>(0.009)|0.280<br>(0.009)|
|ˆ_βM_3: Positive opportunity cost _×_ Many men in reserved space<br>0.074<br>(0.007)|0.075<br>(0.007)|
|ˆ_βM_4: Zero opportunity cost _×_ Many men in reserved space<br>0.216<br>|0.216<br>|
|(0.008)|(0.008)|
|_Mean dependent variable_||
|Zero opportunity cost _×_ Few men in reserved space<br>0.<br>|293<br>|
|(0.|019)|
|Zero opportunity cost _×_ Many men in reserved space<br>0.|194|
|(0.|014)|
|Observations<br>17047|17047|
|Riders<br>363|363|
|Control for high crowding<br>No|Yes|
|Rider fxed efect<br>Yes|Yes|
|_Post-estimate tests for heterogeneous efects_||
|By opportunity cost: zero opportunity cost - positive opportunity cost||
|Few men in reserved space: <sup>ˆ</sup>_βM_2 _−_<sup>ˆ</sup>_βM_1<br>0.184|0.184|
|<br>P-value<br>0.000|0.000|
|Many men in reserved space: <sup>ˆ</sup>_βM_4 _−_<sup>ˆ</sup>_βM_3<br>0.141|0.142|
|<br>P-value<br>0.000|0.000|
|By male presence in reserved space: few men - many men in reserved space||
|Zero opportunity cost: <sup>ˆ</sup>_βM_2 _−_<sup>ˆ</sup>_βM_4<br>0.064|0.064|
|<br>P-value<br>0.000<br>|0.000|
|Positiveoortunitcost: <sup>ˆ</sup>_β−_<sup>ˆ</sup>_β_<br>0022|0021|
|ppy _M_1 _M_3<br>.<br>P-value<br>0.042|.<br>0.050|

_Note:_ A ride is the unit of observation. Observations weighted by the inverse of the number of rides taken by the individual rider. Standard errors in parentheses, clustered at rider level.
**Table 4: Spillover Effects** (Firms Without Foreign Linkages)

|**Dependent variable: Growth**in**Sales**|**OLS**|**Random-Effects**|
|---|---|---|
|**Amemiya Selection Bias Correction**<br>**Variable**|**Yes**|**Yes**|
|**Sector-Specific**<br>**Returns to Scale and Mark-ups**|**Yes**|**Yes**|
|**Spillovers (Share of assets of firms with joint**<br>ventures**and FDI)**|**-0.178****<br>**(3.125)**|**-0.172-**<br>**(2.054)**|
|Dummy**for 1994**|**0.002**<br>**(0.215)**|**0.002**<br>**(0.178)**|
|Dummy**for 1995**|**-0.038****<br>**(-4.201)**|**-0.037***<br>**(-3.934)**|
|Dummy**for 1996**|<br>**-0.036****<br>**(-3.534)**|**-0.035****<br>**(-3.642)**|
|**Observations**|**340**|**340**|
|**F-test**|**0.92**||
|**Hausman test (random**<br>**vs. fixed effects)**||**4.57 (14.45)**|
|**Adjusted R2**|**0.887**|**0.843**|

Notes: Heteroskedasticity consistent (White correction); t-statistics in parentheses; aconstan **t** enn is included in both regressions; ** Significant at the 95% level.

It has been argued that spillovers from joint ventures should be higher than those from FDI (establishment of majority-owned affiliates) as the foreign partner has less ability to control the behavior of the domestic partner, and the latter has a greater incentive to pursue R&D itself (see, e.g., Pack and Saggi, 1997). Internalization through FDI in contrast should offer greater opportunities to limit "technology leakage." If this is indeed the case, it implies that excluding joint ventures from the SPILLOVER measure of foreign "ownership" share and re-estimating the equation should increase the magnitude of the negative spillovers. The evidence, however, does not support this argument (Table 5). Instead, the magnitude of the spillover effect becomes smaller and statistically insignificant, although it remains negative in sign (Table 6). Thus, excluding joint ventures has an offsetting effect. In part this reflects the fact that joint venture firms have higher TFP growth than firms without any foreign partnerships, and this raises the average of the non-FDI group. This result illustrates that the **initial** negative spillover result may not be robust and that tests for spillovers with the methodology used here (and in the literature 18