rafmacalaba/datause-encoder-data huggingface dataset:
between one grade and the next. Again, this is consistent with what has been reported using data from other countries (see Kane and Staiger 2008; Jacob, Lefgren, and Sims 2010; Rothstein 2010; and Andrabi et al., 2011). These estimates combine to give a cumulative effect of 0.123 SD.

As seen from Table 8, the magnitudes and patterns of the estimated correlation coefficients are consistent with the assumptions made in Section 4.3, namely the correlation in teacher content knowledge between any two grades is positive, ρ�,�� �0 for all g and g′; and (ii) the further apart any two grades, the lower is the correlation in teacher content knowledge across grades; i.e., ρ�,�� is decreasing in |g�g′|. We can therefore interpret the sum of the reduced form coefficients, β<sup>�</sup> � �β<sup>�</sup> �, as a lower bound on the cumulative effect. Moreover, this lower bound is fairly tight: Adjusting the structural estimates for the fact that student test scores are measured halfway through year 4, so that CE��α�0.5 �α∑���� γ<sup>�</sup> �0.094, we see that this number is 13% larger than the sum of the reduced form coefficients, β<sup>�</sup> � �β<sup>�</sup> � �0.083 (Table 4, column 5).

Second, as discussed in Section 4.4, introducing previous teacher test scores as a regressor not only reduces the overall bias, but also shifts a disproportionate amount of the remainder on the corresponding coefficient: The bias in β<sup>�</sup> � is 0.015 or 50% of the true αγ. In contrast, the bias in β<sup>�</sup> � is just 0.004 or 15% of the true α (again, adjusting for the fact that student test scores are measured half way through the year). This compares to a bias of 0.038 or 130% of the true α, if β<sup>�</sup> � had been estimated based on a contemporaneous specification.

# _5.4. Implications of the structural parameters_

In this subsection, we use the structural parameters to carry out analyses which further shed light on the magnitude of the role of teacher knowledge in student learning. First, we use the structural estimates of the returns to teacher content knowledge to simulate the learning impacts of increasing the content knowledge of all teachers to a minimum threshold. Building on the development accounting approach (see Caselli 2005) we then use an aggregate production function for learning to calculate the extent to which shortfalls in teacher knowledge account for the observed low levels of student achievement.

Second, and inspired by the “misallocation” literature (see Banerjee and Duflo 2005; Hsieh and Klenow 2009), we simulate the impacts of reallocating students to teachers in a way that balances the benefits of assigning more students to better teachers with the (potential) costs of the larger class sizes that would result.

24
Table 10: Disaster fatalities, aid and institutional quality

||Storms|Floods|Earthquakes|
|---|---|---|---|
|Ln(_AIDj,t-1_)|0.687|-0.144|-0.567|
||(0.546)|(0.122)|(0.716)|
|_CORR CONTjt_|0.517|-1.163***|-1.683*|
||(0.524)|(0.382)|(0.867)|
|_CORR CONTjt* AIDj,t-1_|-0.186|-0.054|0.915|
||(0.573)|(0.274)|(0.753)|
|Ln(_AIDj,t-1_)|0.288|-0.297|-0.296|
||(0.500)|(0.329)|(0.353)|
|_GOV EFFjt_|0.537|-1.033***|-2.199***|
||(0.430)|(0.320)|(0.510)|
|_GOV EFFjt* AIDj,t-1_|-1.015|0.072|-0.403|
||(1.103)|(0.335)|(0.304)|
|Ln(_AIDj,t-1_)|1.133**|-0.523|0.263|
||(0.440)|(0.522)|(0.493)|
|_POL STABjt_|0.354|0.264|-2.939***|
||(0.322)|(0.363)|(0.790)|
|_POL STABjt* AIDj,t-1_|0.496|-0.097|1.968***|
||(0.320)|(0.443)|(0.599)|
|Ln(_AIDj,t-1_)|0.842*|-0.362|-0.608|
||(0.481)|(0.279)|(0.586)|
|_REG QALjt_|<br>0.120|<br>-1.084*|<br>-1.713***|
||(0.618)|(0..568)|(0.436)|
|_REG QALjt* AIDj,t-1_|0.011|0.340|1.299*|
||(0.732)|(0.400)|(0.786)|
|Ln(_AIDj,t-1_)|-0.092|-0.613**|-0.805|
||(0..687)|(0.247)|(0.927)|
|_RULE LAWjt_|0.552|-0.608**|-0.903|
||(0.433)|(0.257)|(0.957)|
|_RULE LAWjt* AIDj,t-1_|-1.143|-0.681**|1.060|
||(0.791)|(0.293)|(0.952)|
|Ln(_AIDj,t-1_)|0.831***|-0.359|-1.958|
||(0.304)|(0.328)|(1.341)|
|_VOICE ACCjt_|-0.301|-0.501*|0.051|
||(0.408)|(0.300)|(0.642)|
|_VOICE ACCjt* AIDj,t-1_|<br>-0.532|<br>-0.196|<br>0.943|
||(0.572)|(0.765)|(0.817)|
|Ln(_AIDjt-1_)|0.814*|-0.483|-2.959***|
|_,_|(0.465)|(0.447)|(0.478)|
|_DEMOCRACY_|-0125***|-0012|0099***|
|_jt_|.|.|.|
||(0.054)|(0.026)|(0.030)|
|_DEMOCRACYjt* AIDj,t-1_|0.039|0.010|0.181|
||(0.054)|(0.033)|(0.053)|

_Notes:_ Negative binomial estimates. Dependent variable is _DEATHijt_ .. Standard errors (in parentheses) are adjusted for clustering on country-level. Covariates from the baseline specification (equation (5)), continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

29
effects of policy changes in a world where, as observed by Folbre (1997), there is neither a benevolent head of household nor a benevolent head of state.

In developing economies where there are no outside markets, the assumption may be less appropriate. Udry (1996), for example, presents an analysis of African agricultural households in six Burkina Faso villages, in which there are no outside markets for either labor or land. He finds that yields are substantially lower on plots controlled by women than on similar plots controlled by men planted with the same crop, in the same years in the same households. This is taken as evidence of non-Pareto efficiency in agricultural production and is used as the basis for rejecting the assumption in general.<sup>30</sup> However, the results need to be interpreted in context, and with some caution. It would appear that the Pareto inefficiencies stem from the nature of the property rights in these villages. If, as efficiency would seem to require, men were allowed to work the women’s plots, there is the risk that they would cease to be women’s plots, as the paper points out. Thus the inefficiency is the price paid for the maintenance of existing control rights. The question then is whether, given the further constraints represented by this kind of consideration, the household allocation was Pareto efficient, i.e. the appropriate concept would seem to be that of _constrained Pareto efficiency._ In any concrete application of the multi-person household modeling approach, such constraints should be explicitly formulated and incorporated, even if this is not done in a more general treatment of the model.

# **4 Multi-person models with household production**

This section outlines the structure of multi-person household production and demand models, paying particular attention to the valuation of domestic output and the importance of across-household heterogeneity, especially in respect of female labor supply and domestic work, in deriving measures of household welfare. Section 4.1

> 30 Jones (1986) also obtains results that support the rejection of Pareto efficiency in the allocation of resources within the agricultural household.

22
the number of men. In terms of trip generation _rates_ – or number of trips made per person<sup>2</sup> per day – men in AMBA are estimated to make an average of 2.63 trips per day each, compared to 2.42 trips among women. The gender imbalance in trip generation rates is consistently observed across all age groups and is the largest in the eldest category, where men make about 18 percent more trips per day per person compared to women.

**_Figure 1_** _: Total trips and trip generation rates on a weekday in October 2021, by gender and age_

<!-- Start of picture text --> A. Total trips per day B. Trip generation rate<br>Men Women Men Women 3.2<br>5,000,000 2.9<br>2.7<br>2.5 2.6<br>4,000,000 2.4 2.3 2.4<br>2.2 2.2<br>3,000,000<br>2,000,000<br>1,000,000<br>0<br><18 18-28 29-44 45-63 >64 <18 18-28 29-44 45-63 >64<br><!-- End of picture text -->

# **Secondly, the CDR analysis also suggests that women have shorter individual trips compared to men.**

About 66 percent of the trips made by women in AMBA according to the October 2021 data are shorter than 5 kilometers, compared to 61 percent of men’s trips. The higher distances traveled by men compared to women persist across the income spectrum although the difference declines somewhat at the higher income deciles. The average trip distance differences between men and women are also relatively consistent across travel modes as reported in the ENMODO 2018 household travel survey although are more distinct in the case of travel by car or by public transport than in the case of the non-motorized modes. Similarly, data from a survey implemented by the team in AMBA with current private motorized vehicle users broadly corroborates this pattern: 9.2 percent of women have commutes or other types of “typical” trips that last less than 10 minutes, compared to 5.2 percent of men.

# **Gender norms may play a role in women’s travel decisions in AMBA and, specifically, the distances**

**traveled** . Even if the analyzed data does not explicitly ask questions about intrahousehold resource allocation between women and men, from the ENMODO 2018 household travel survey it can be inferred that gender roles contribute to defining women’s decisions of when are how far to travel. The more family members there are in the household, the farther men seem to travel, possibly to access better economic opportunities, while for women with one family member the distance decreases once they add an additional one.

**Thirdly, public transport appears to serve more women than men, especially in northern AMBA.** Women constitute the majority of public transport users, with the total number of daily public transport trips made by women (4.07 million) in October 2021 being 13 percent higher than those made by men (3.59 million), and public transport represents a greater share in women’s overall mobility compared to men’s

> 2 There are a total of 7.34 million men and 7.75 million women in AMBA. The largest difference in population size by gender is in the older age group (64 and older), with nearly 990,000 women but only about 650,000 men.
# **Summary**

In adopting its new unified banana policy the European Union chose some of the worst features of previous EU member countries' policies. The policy essentially maintains the trade access preferences which some EU member countries previously granted to banana suppliers in former colonies and tenitories as a fonn of aid. Earlier research (Bananarama I and II) has shown that the previous systems of preferences, and the import quotas used to underpin them, were highly inefficient as mechanisms for granting aid. They cost EU consumers dearly while providing only very small advantages to - banana suppliers in former colonies and territorial mainly ACP (African, Caribbean and Pacific) countries. At the same time the preferences imposed costs on efficient banana exporting countries of Latin America which are also developing countries. The main beneficiaries of the policies turned out to be marketers of fiuit coning from countries with preferences The EU missed a good opportity to greatly rationalize and reduce the cost of its previously distortionary policies. Earlier research indicates that the efficiency of providing aid to preferred suppliers could have been increased greatly by pursuing virtual free trade policies involving a relatively small tariff and through targeted, diect aid.

The new polcy relies on EU-wide quotas to restict supply and raise internal EU prices. Prohibitive over-quota tariffs make the quota effective. A tariff also applies to quota imports.

The main difference arising from the adoption of the new policy is that the costs of the policy are borne by consumers in all EU countries rather than consumers in only some EU counties. Moreover, import restrictions applying against non-preferred supplying countries have been tightened. This has imposed extra costs on efficient export suppliers in Latin America.

Despite the opportumties missed, the new policy has unleashed some new pressures for change. Foremost among these is the emergence of a well focused debate on the inefficiencies of EU banana policy. Groups with well articulated arguments are now pressing for reforms of the EU policy. Pressures are being exerted through GAIT. Other forums will also be used. This debate could well culminate in a substantia **l** iberalization of EU policy.

Whatever the eventual outcome the new policy and the pressures for change create considerable uncertainty about what- will happen. This has big implications for the macroeconomic outlook for many small island economies. Loss of the aid without compensation could have a big economic and social impact on ACP countries. What would help to reduce this uncertainty and help in the development of such economies is a known dmetable for change with transparent objectives and rules, transition arrangements and economic support for adjustment, social support and diversification.

This paper builds on the previous Banarama papers and documents how changes in EU policy could affect banana exporting countries and draws out the policy implications for these countries. One of the main policy implications drawn out here is that ACP countries 32
- **Credit Card Ownership** (Credit_Card_p): Ownership of a credit card Original variable name in dataset: q3ab Dummy variable equals 1 if respondent has a credit card, equals 0 if he/she does not own a card.

- **Money Borrowed from a Financial Institution** (Borrow_Bank): Any money borrowed from a bank, credit union (or another financial institution, where applicable – for example, cooperatives in Latin America), or microfinance institution

- Original variable name in dataset: q14a Dummy variable equals 1 if respondent has borrowed money from a financial institution, equals 0 if he/she has not.

- **Money Borrowed from a Store** (Borrow_Store): Any money borrowed from a store using installment credit or buying on credit

- Original variable name in dataset: q14b Dummy variable equals 1 if respondent has borrowed money from a store, equals 0 if he/she has not.

- **Money Borrowed from Family or Friends** (Borrow_Family): Any money borrowed from family or friends

- Original variable name in dataset: q14c Dummy variable equals 1 if respondent has borrowed money from family or friends, equals 0 if he/she has not.

- Reasons for not having a bank account:

- Too Far Away (Too_Far)

- Too Expensive (Too_Expensive)

- Not Possessing the Necessary Documentation (Lack_Doc)

- Not Trusting Financial Institutions (Lack_Trust)

- Not having Enough Money to Use the Account (Lack_Money)

- Religious Reasons (Religious)

- Someone Else in Family Has an Account (Family_Has)

Only respondents who do not have a bank account are asked to list their reasons.

Original variable names in dataset: Too_Far (q10a), Too_Expensive (q10b), Lack_Doc (q10c), Lack_Trust (q10d), Lack_Money (q10e), Religious (q10f), Family_Has (q10g) The Life in Transition Survey (LITS, 2010)

- **Bank Account Ownership** (Bank_Account_h): Ownership of a bank account by any household member

- Original variable name in dataset: q225c  Dummy variable equals 1 if any individual in the household has a bank account, equals 0 if no one has an account.

- **Savings Rate** (Savings_h): Any left over money to put into savings in the household in a typical month.

- Original variable name in dataset: q223_t1 Dummy variable equals 1 if household has saved money, equals 0 if household has not.

- **Debit Card Ownership** (Debit_Card_h): Ownership of a debit card by any household member Original variable name in dataset: q225d  Dummy variable equals 1 if any individual in the household has a debit card, equals 0 if no one has a a card.

- **Credit Card Ownership** (Credit_Card_h): Ownership of a credit card by any household number Original variable name in dataset: q225e Dummy variable equals 1 if any individual in the household has a credit card, equals 0 if no one has a card.

- **Borrowed Money** (Borrow): Money borrowed from anyone (e.g. friend, other person or institution) by any individual in the household.

- Original variable name in dataset: q805 Dummy variable equals 1 if any individual in the household has borrowed money, equals 0 if no one has borrowed

24
Table 3: Intervention impact on sanitation loan uptake

||Sanitation<br>Loan|
|---|---|
|SL|0.182<sup>_∗∗∗_</sup><br>(0.0358)|
|Cluster-robust p-value|[0.0000]|
|Strata FE|Yes|
|Interviewer FE|Yes|
|Household covariates|Yes|
|Ratio sample clients/GP size|Yes|
|Control mean|0.0133|
|N|2821|
|_Notes_: SL equals sanitation|loan arm. Stan-|
|dard errors clustered at the|village level are|
|shown in round parentheses.|*, **, *** in-|
|dicate signifcance at the 10,|5 and 1 percent|
|level. Romano-Wolf p-value is|not displayed as|
|it corresponds to cluster rob|ust p-value. Co-|
|variates: Toilet ownership at|baseline, indica-|
|tor for presence of a child aged<br>ratio of number of sampled|0 - 2 at baseline,<br>clients to village|
|size. Data source: household|survey. Depen-|
|dent variable comes form adm|inistrative data.|

Households might take business loans, which have a higher limit, if they want to borrow more than the sanitation loan. However, the theory suggests that when the label does not matter, they should take the sanitation loan first before taking the business loan. This is feasible in practice since the MFI does not constrain clients to take one loan at a time. The data indicates, though, that a significant proportion of clients do not optimise their portfolio of loans as suggested by the theory: 31 percent of all clients in the treatment area took a business loan of over Rs. 25,000 (the lowest amount that can be taken as separate sanitation and business loans) even when they were eligible for a sanitation loan. This evidence thus supports the notion that the loan label affected loan take up choices.

While the loan label effect could explain the much lower rates of substitution towards the cheaper sanitation loan observed in the data, other factors could also explain the low level of sanitation loan take-up. In particular, the fact that the study area experienced two major macro-economic shocks over the study period, could have depressed sanitation loan take-up. Latur and Nanded saw a severe drought in 2016, which was later followed by demonetization at the end of 2016. Both of these led to a reduction in loan disbursement by the MFI, which is apparent in a slowdown of loan take-up in 2016 and early 2017.<sup>39</sup> Moreover, it could also be the case – as predicted by the theory – that the perceived benefits (costs) of safe sanitation are too low (high) to make it worthwhile to take the sanitation loan to invest in a toilet at this specific interest rate. More generally, we note that the sanitation loan take-up rate is comparable with those found by other randomized controlled trials on micro-finance. Studies by Banerjee et al. [2015], Tarozzi et al. [2015] and Angelucci et al. [2015], which sampled households most likely to be targeted by the relevant micro-finance providers as potential clients, encountered loan take-up rates of 17-19 percent, indicating how challenging it is to predict loan take-up.

> 39While not shown here, we find that take-up of all other loans provided by the MFI also slowed down over this period.

25
first is similar to our logit regressions above, studying the set of variables to characterize the nature of differences between species. The second use is to then use the fitted combination of measured variables to predict the species of new animals or plants for which only this vector of measured variables has been observed. This is particularly useful in cases where the species of an animal can only be truly verified after exhaustive and expensive testing, possibly resulting in killing the animal. Observing certain characteristics of the animal instead allows it to be accurately classified without taking such expensive and extreme measures.

We apply these techniques to classifying another elusive animal, the own-account worker. Logistic discriminant analysis is first used to obtain the combination of observed characteristics which best separates the wage workers from the SME owners. This fitted model is then used to predict the “species” of each own account worker, enabling us to characterize them into wage workers and SME owners. Estimation was carried out in STATA 10.0.

Table 8 shows the results of classifications using different subsets of variables. We use four overlapping groups of variables in the classification analysis. Gender, age and education are included in each of the groups. In addition to these variables, the first group of variables includes the three ability tests: Raven, digitspan and CRT. The second group includes family background variables, including whether the respondent’s mother and father owned a business, the percentage of neighbors who were poor, the percentage of the neighborhood cohort sitting for the O- and A-level exams, and others listed on Table 8. The third group focuses instead on entrepreneurial attitudes: risk, achievement, work centrality, and others. The final group includes combines all of the variables from the first three groups.

We show first the percentage of the SME and wage workers properly classified by each set of variables. Classifying using only age, gender, education and the three ability variables results in a correct classification of 74 percent of the SME owners and 69 percent of the wage workers. Family background alone allows us to classify 74 percent of both wage workers and SME owners correctly. Attitudes by themselves classify a slightly lower percentage of the respondents correctly: 73 percent of SME owners and 70 percent of wage workers. Using both sets of variables combined results in 79 percent of SME