rafmacalaba/datause-encoder-data huggingface dataset:
_36_

## _WATER WHEN IT COUNTS_

p-value=0.031). In the post information period, the coefficient of availability to requirements is 1.727, and we reject that the slope is equal to 1 with a p-value of .023, suggesting that allocations as assessed by availability may have become _overly_ responsive to requirements in response to the feedback. If requirements are not correlated with rain, as we would expect in irrigation schemes where planting decisions need not be tied to rainfall, differences in rain in the pre- and post- feedback periods would be expected to shift the axis of the post-information line in Figure 11 rather than the slope. Since it is the slope that changes and not the constant, this is further evidence that it is the information rather than an external factor, such as increased rainfall, that improves the ability of farmers to at least meet minimum requirements. This improvement is observed among farmers who receive the general reminders as well as those who receive the individual feedback, suggesting that individualized feedback based on metering is not instrumental in achieving the improvement.

# **VI. Conclusion**

Using high-frequency data collected in three irrigation schemes in central Mozambique reveals that a major source of water inefficiency comes from applications of rules of thumb. In this context, these rules of thumb are likely established to satisfy equitable decision rules pertaining to the scheduling of extracting water within a water user group. Yet, these rules fail to consider the precise water requirements of specific crops within the scheme over time. As a result, a significant portion of farmers tend to over-water at earlier stages of the crop cycle, jeopardizing the amount of water available at later stages of the crop cycle when water requirements are at their peak.

We show that providing farmers with an auxiliary rule of thumb related to the water
16

# **REFERENCES**

Abiad, A., E. Detragiache and T. Tressel, 2010, _“A New Database of Financial Reforms,” IMF Staff Papers_ , Vol. 57, pp. 281–302.

- Asmundson, I., forthcoming, “More World Trade Flows: An Updated Dataset” (Washington: International Monetary Fund).

- Bahar, D., R. Hausmann, and C. Hidalgo, 2014, “Neighbors and the Evolution of the Comparative Advantate of Natios: Evidence of International Knowledge Diffusion,” _Journal of International Economics_ , Vol. 92, pp. 111–123.

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- Feenstra, R., and J. Romalis, 2014, “International Prices and Endogenous Quality,” _Quarterly Journal of Economics_ , May, Vol. 129, pp. 477–527.

- Feenstra, R., R. Inklaar, and M. P. Timmer, 2015, “The Next Generation of the Penn World Table”, _American Economic Review_ 105(10): 3150–82, available for download at <u>www.ggdc.net/pwt.</u>

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- Hausmann, R., and B. Klinger, 2006, “Structural Transformation and Patterns of Comparative Advantage in the Product Space,” HKS Faculty Research Working Paper Series RWP06-041 (John F. Kennedy School of Government, Harvard University).

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(Keats 2018), and a multi-country study covering Ethiopia, Malawi and Uganda (Moussa and Omoeva 2019) highlights education's intermediating role in reducing sexual activity during adolescence, increasing contraceptive use before a first pregnancy, delaying age at marriage, and lowering ultimate fertility.

A further set of analyses has focused on impacts in other dimensions. These include increased investments in child health and a reduction in malnutrition in Uganda (Keats 2018), and decreased child mortality in Malawi (Makate and Makate 2016).<sup>4</sup> In addition, evidence from Tanzania (Delesalle 2019) shows positive impacts on household consumption, including in agriculture and non-farm self-employment activities, and from Ethiopia, Malawi and Uganda (Moussa and Omoeva 2019) that shows positive impacts on adult female labor force activity. A study in Kenya (Ajayi and Ross 2020) shows positive impacts on both traditional and mobile banking (i.e., measures of financial inclusion) and in familiarity with financial terms.

At the same time as it has been shown to have these positive effects, FPE has also been cited as a potential contributor to a worsening of the average quality of education delivered in SubSaharan Africa (see, for example, the discussion in Wedgwood (2007) for the case of Tanzania). Many observers have pointed out the low average quality of learning outcomes in Sub-Saharan Africa (e.g. Jones and others 2014; Spaull and Taylor 2015; World Bank 2018), which may be linked to the quality of education service delivery (Bold and others 2017; World Bank 2018). The question then becomes whether the implementation of FPE led to a deterioration in quality. The difficulty in recruiting a large number of skilled teachers, and building the required physical infrastructure, to match the expected increases in student enrollments was not unforeseen. Munene (2016) includes a number of case study examples in which this was a first-order challenge in the lead-up to, and during the implementation of, FPE in several countries.

Several studies have documented reductions in quality associated with FPE. For example, using data from the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) and school census data, Valente (2019) finds that after fees were removed in Tanzania, student-teacher ratios increased and observed teacher training, experience, and subject-specific

> 4 A number of these studies include a “first stage” in which a measurable impact of FPE on school enrollment or attainment is documented.

4
Table 3 quantifies the sensitivity of an array of statistics of interest for welfare analysis. The first four columns refer to inequality measures: the Gini index (column 2), two members of the Generalized Entropy family of indices (Mean Log Deviation and Theil index, in columns 3 and 4), and the Atkinson index (with the inequality aversion parameter set equal to two, column 5). The remaining columns refers to Foster, Greer and Thorbecke (1984) poverty measures: the headcount ratio (H), the poverty gap index (PG), and the poverty gap squared index (PG2). The results show that the exclusion of outliers decreases estimated inequality according to all measures. The Gini index is the least sensitive among the inequality measures shown in Table 3, although before-after differences can be large enough to cast doubt on the validity of estimates obtained from the raw data: the Gini index for Nigeria, Mozambique, Malawi, and Mexico is bumped down by 5 to 6 percentage points, a salient difference by all metrics. The Theil index is the most sensitive measure among those we examined, showing differences as large as 28 percentage points. Overall, these results align with the theoretical analysis in Cowell and Flachaire (2007). Differences observed for three poverty measures – the poverty headcount, poverty gap, and poverty gap squared – are smaller across the board, in line with the theoretical findings available in the literature (Cowell and Victoria-Feser 1996b). However, our illustrative example uses relative poverty lines, which may affect results.

The empirical magnitude of the effects documented in Table 3 is consequential, and can be appreciated focusing on the case of Gini index (the more conservative scenario in our setting). Figure 6 (top panel) shows the point estimates of the Gini index calculated on the RuLIS ‘raw data sets’, that is on data as available from the data provider. Countries are ranked low (Kyrgyzstan) to high (Bolivia) accordingly. The bottom panel of Figure 6 shows the estimated Gini indices, for the same countries, after excluding outliers (as identified by `outdetect` ). Even a cursory inspection of the two graphs bring to light a number of rerankings: countries move up or down the ladder as a consequence of how outliers are treated.

30
**Table 1. Summary Statistics**

||Pool|ed|Urb|an|Rur|al|
|---|---|---|---|---|---|---|
||mean|sd|mean|sd|mean|sd|
|Rural|0.576|0.494|0.000|0.000|1.000|0.000|
|Harmony|1.015|0.715|1.033|0.724|1.000|0.708|
|No conflicts|0.253|0.435|0.274|0.446|0.237|0.426|
|Ln(couple annual income)|8.752|1.195|9.860|0.795|7.937|0.668|
|Parent Matched|0.338|0.473|0.145|0.352|0.480|0.500|
|Parent Matched norm|0.351|0.188|0.155|0.053|0.495|0.100|
|**Township Characteristics**<sup>**a**</sup>|||||||
|Ln(avg individual income at township level)|8.151|1.075|9.226|0.720|7.361|0.387|
|Average years of schooling at township level|7.812|2.371|10.288|0.941|5.993|1.134|
|**Husband Characteristics**|||||||
|Age|34.892|6.291|36.539|6.071|33.682|6.174|
|Schooling|8.718|3.218|10.657|2.754|7.293|2.757|
|Mother schooling|2.196|3.163|3.678|3.714|1.108|2.103|
|Father schooling|4.310|3.719|6.184|3.808|2.933|2.977|
|Communist Party member|0.204|0.403|0.344|0.475|0.102|0.302|
|**Couple outcomes**|||||||
|Number of children|1.646|1.025|1.275|0.915|1.918|1.016|
|Three or more children|0.162|0.369|0.063|0.244|0.235|0.424|
|Reward for providing old age support|0.447|0.497|0.506|0.500|0.405|0.491|
|**Wife traits**|||||||
|Schooling|7.350|3.859|10.067|2.540|5.353|3.418|
|Ln(wife annual income)|7.223|0.821|7.742|0.418|6.846|0.836|
|Wife submissiveness|1.036|0.882|0.721|0.760|1.271|0.893|
|Wife career unimportant|0.184|0.387|0.089|0.285|0.253|0.435|
|No good male friend|0.386|0.487|0.249|0.432|0.487|0.500|
|Cannot reject sex|0.469|0.499|0.382|0.486|0.534|0.499|

a The average here was computed based on sample information but excluding the individual in the computation.

36
McKay, Robinson, Gonsalves, and Worthen 2006), exploring both the positive and negative aspects of the combatant experience for adolescent girls. Yet while much of the literature on female combatants does address the experience of adolescent girls, there is little micro‐level analysis of conflict’s impact on labor market decisions, welfare, vulnerability to sexual violence, and behavior specific to civilian adolescent girls.

Quantitative research on the impact of conflict on women has focused on heads of households with little information on the experience of adolescent girls or other female members of the household. Conflict’s impact on risk preferences and social capital accumulation have mostly been explored in adults. Welfare measurements related to children and youth tend to comprise of years of schooling and monetary investments in health care. A focus on adult respondents remains the norm. While girls have been included in surveys assessing the extent and impact of various forms of GBV and are the subjects of mostly qualitative research on ex‐combatants, little systematic empirical research focuses specifically on non‐combatant female adolescents.

This gap can partly be explained by the fact that challenges related to collecting data in conflict‐ affected areas are particularly acute when targeting adolescent girls. For purposeful surveys, sampling female adolescent respondents can be more challenging compared to male or female heads of households, particularly in the context of traditional gender norms and low levels of community trust. Existing socio‐economic household data also typically relate to heads of households. Lack of personal identification, which is more common among children and underage youth than adults, is often higher in conflict affected areas and can cause discrepancy in data. Attrition is a major concern due to conflict driven displacement. These challenges can be compounded by difficulties tracking younger household members who are not always registered consistently in population databases.

# 3. Data

The Adolescent Girls Initiative (AGI) was launched by the World Bank in October 2008 as a public‐ private partnership intended to promote the transition of adolescent girls from school to productive employment through innovative interventions that are tested, and then scaled‐up or replicated if successful. The initiative was piloted in eight countries including Afghanistan, Jordan, Lao PDR, Liberia, Haiti, Nepal, Rwanda, and South Sudan. In South Sudan, the World Bank partnered with an NGO, BRAC International, to adapt and pilot its Empowerment and Livelihood for Adolescents (ELA) model which combined a range of innovative social and financial empowerment interventions 8
- 2 -

young or low-income individuals who cannot borrow to consume and offset the compulsory savings.<sup>4</sup> In any case, even if households saving increase, it is difficult to assess whether such an increase is reflected at the national level. This may be due to at least two factors: 1) it is difficult to say whether household savings is or is not offset by government's response, either as a consequence of tax concession or transitional costs of pension reform; and 2) it is difficult to estimate whether at a company level, saving through pension funds is not offset by dissaving elsewhere.<sup>5</sup> Accordingly, capital markets development is pointed out as one of the main potential consequences of contractual savings development.<sup>6</sup> The purpose of this paper is to analyze the effects of contractual savings and nonlife insurance on asset markets, and particularly on stock markets. The focus is on macroeconomics and financial effects of contractual savings development thus, emphasizing the role of pension funds, and life and non-life insurance companies as financial intermediaries. This paper is mainly an empirical study. The evidence of the relationship between contractual savings, non-life insurance and stock markets development is presented for some OECD and developing countries. Finally, we explain how the development of the contractual savings sector promotes financial sector development and economic growth, and mitigates social and financial risks.

**Figure 1: Contractual savings in country financial assets (%,1996)**

<!-- Start of picture text --> 100%<br>70% m ll E<br>20%~~~~~~~~~~~~~~~~~~~~~~2<br>t XMME 2 Al W<br>10%<br><!-- End of picture text --> Source: 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

4 See, for example, Feldstein (1978), Munnell (1976), Loayza, Schmidt-Hebbel and Serven (2000), Samwick (2000), Smith (1990), Bailliu and Reisen (1997), Schmidt-Hebbel and Serv6n, eds. (1999). 5 See Davis (1995).

6 See, for example, Bodie (1990b), Davis (1995), Vittas and Skully (1991), Vittas (1998a, 1998b, 1999).
The subscripts represent industries and time periods. _E_ is the log of employment in industry _i_ at <u>the end of quarter</u> _t_ , _m_ is the log of that industry’s imports from the United States, and _x_ is the log of Mexican exports of that industry to the United States. The inclusion of industry fixed effects, αi, makes this model a typical within-industry model. The time-period specific fixed effect, γt, acquires particular importance in our application, because common effects across industries are especially important for identifying the effects of trade shocks in the context of a broader macroeconomic shock. The parameters βm and βx are the elasticities of interest. If imports are complementary inputs to labor, that is, if both are needed to produce a unit of a final good, then βm > 0 and βx > 0. The error term εi,t is assumed to be the standard regression error that is uncorrelated with the independent variables.<sup>4</sup> We are also interested in allowing for assembly time. The resulting dynamic assembly time specification of the model can be written as:

- (2) Ei,t = βm ∙mi,t + βx ∙xi,t + βm2 ∙mi,t+1 + βx2 ∙xi,t+1 + αi + γt + εi,t,

in which the inclusion of exports one period ahead captures the time lag in the realization of exports. The inclusion of one-period-ahead imports toughens the test of the dynamic model, because evidence in favor of the model requires that βx2 > 0 with βm2 = 0. Also, lagged imports and exports are implicitly taken into account as the variables represent average trade flows during the quarter while the dependent variable is measured at the end of the quarter.

In practice, both models are estimated in differences to take out the industry fixed effects. The final assembly time specification is thus:

- (3) ∆Ei,t = βm ∙∆mi,t + βx ∙∆xi,t + βm2 ∙∆mi,t+1 + βx2 ∙∆xi,t+1 + γt + ∆εi,t.

This equation is evaluated in Section 5.1.

> 4 We do, however, calculate the standard errors of our coefficients allowing for the possibility that the errors within an industry are arbitrarily correlated with each other over time (serial correlation) using the “cluster” option in Stata.

13
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

- We then calculated the total number of adults in the population by subtracting the number of children 14 years old and under from the total population; both series are found in the Bank’s SIMA (current DDP database).

- Next, we estimated the rate at which literate adults participate in the labor force. The assumed “literate labor force participation rate” is very high, but trending down somewhat as literacy increases. The assumption is that there is career training specialization in Cote d’Ivoire. In the largely rural environment, persons planning to remain engaged in the within-household production typically do not invest in an education and hence often remain illiterate while persons planning to participate in the formal labor force seek education.

- This allowed us to estimate the literacy rate among workers. Due to the low but rising literacy rate in the population, and the high proclivity among the educated to participate in the labor force, the labor force is much more literate than the general population of adults.

- We estimated the average number of years in school or average educational attainment for literate persons.  The regular social surveys of Cote d’Ivoire ask this question of a fairly large sample of respondents. In 2002, for example, the average schooling per worker was 7.7 years, higher in urban areas and in industry, lower in rural areas and in agriculture.  There is also some information as to how long students stay in school, conditional upon whether or not they enroll in the first place

- The average schooling per worker in the labor force is the product of the estimated labor force literacy rate times the estimated average schooling of those who are literate.

The accompanying table A-2 below provides the data and estimates used. The estimated average schooling per worker developed here is higher than those typically encountered in the literature (for example, Pradeep Mahajan (2003), Bosworth & Collins (2003) or Cohen & Soto (2001)). Some authors do not provide estimates for Cote d'Ivoire (e.g. Barro et al) while others cite problems with the data from Sub-Saharan Africa. Some seem to rely on the literacy rate of the general population as a proxy for educational attainment of the work force.

21
new activities can most profitably be developed. The term “2<sup>nd</sup> advantage” refers to the fact that many aspects of growth are self-reinforcing. The lumpiness of the growth process suggests the presence of increasing returns so that a location’s advantage in an activity derives, in part, from the very fact that it has a presence in the activity. It is the interaction of 1<sup>st</sup> and 2<sup>nd</sup> advantages that yields the growth patterns we observe, and that makes it so difficult to pin down the causes of rapid economic growth. Increasing returns and cumulative causation have featured in many analyses of economic development, from the development economists of the 1950s through to Murphy, Shleifer and Vishny (1989). We think it important, however, to recognize that key aspects of returns to scale are often found at the micro level – within very narrow sectors of production or small districts of a city. A microeconomics of growth is needed to capture these sectorally and spatially concentrated processes Cross-country comparisons, though useful in identifying some of the important 1<sup>st</sup> advantages, cannot provide policy insights into what will drive growth in a particular location. Instead a bottom-up approach is required, where microeconomic studies are used to build up evidence base on what works in different countries, regions and sectors. This empirical work is facilitated by increased availability of micro data. Regional, city, firm and household data are now available for a wide range of countries. The institutional context in which policies are implemented and accumulation decisions are made affects economic performance (see Djankov and others 2003; Rodrik 2003). This makes it natural to study single countries and makes it less clear what one learns from cross-country regressions where these factors cannot typically be controlled for. Furthermore it is clear that the actors in development work are highly diverse. Policy and institutional innovations are happening all the time and are being implemented by central government, local government, nongovernment organizations and local communities. Evaluating these innovations requires careful microeconomic work at the 2