rafmacalaba/datause-encoder-data huggingface dataset:
- Sebastian, K. (2009). Agro-ecological Zones of Africa. https://doi.org/10.7910/DVN/HJYYTI, Harvard Dataverse, V2.

- Sedova, B. and M. Kalkuhl (2020). Who are the climate migrants and where do they go? evidence from rural India. _World Development 129_ , 104848.

- Shah, M. and B. M. Steinberg (2017). Drought of opportunities: Contemporaneous and long-term impacts of rainfall shocks on human capital. _Journal of Political Economy 125_ (2), 527–61.

- Shaw, L. H. (1964). The effect of weather on agricultural output: A look at methodology. _American Journal of Agricultural Economics 46_ (1), 218–30.

- Shee, A., C. G. Turvey, and L. You (2019). Design and rating of risk-contingent credit for balancing business and financial risks for Kenyan farmers. _Applied Economics 51_ (50), 5447–65.

- Simonsohn, U., J. P. Simmons, and L. D. Nelson (2020). Specification curve analysis descriptive and inferential statistics for all plausible specifications. _Nature Human Behaviour 4_ , 1208–14.

- Stallings, J. L. (1961). Weather and crop yields. _American Journal of Agricultural Economics 43_ (5), 1153–60.

- Steegen, S., F. Tuerlinckx, A. Gelman, and W. Vanpaemel (2016). Increasing transparency through a multiverse analysis. _Perspectives on Psychological Science 11_ (5), 702–12.

- Suri, T. (2011). Selection and comparative advantage in technology adoption. _Econometrica 79_ (1), 159–209.

- Survey and Census Division, National Institute of Statistics, Niger (NIS) (2014). National Survey on Household Living Conditions and Agriculture 2011. Public Use Dataset. Ref: NER ~~2~~ 011 ~~E~~ CVMA ~~v~~ 01 M. Downloaded from `https://microdata.worldbank.org/index. php/catalog/2050` on 6 September 2019.

- Survey and Census Division, National Institute of Statistics, Niger (NIS) (2016). National Survey on Household Living Conditions and Agriculture (ecvma) 2014. Public Use Dataset. Ref: NER 2014 ~~E~~ CVMA ~~v~~ 02 ~~M~~ . Downloaded from `https://microdata.worldbank. org/index.php/catalog/2676` on 6 September 2019.

- Tack, J., A. Harri, and K. Cole (2012). More than mean effects: Modeling the effect of climate on the higher order moments of crop yields. _American Journal of Agricultural Economics 94_ (5), 1037–54.

- Tanzania National Bureau of Statistics (TNBS) (2011). National Panel Survey 2008-2009, wave 1. Public Use Dataset. Ref: TZA 2008 ~~N~~ PS-R1 ~~v~~ 03 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/76` on 6 September 2019.

- Tanzania National Bureau of Statistics (TNBS) (2012). National Panel Survey 2010-2011, wave 2. Public Use Dataset. Ref: TZA 2010 ~~N~~ PS-R2 ~~v~~ 03 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/1050` on 6 September 2019.

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# B.2. Bayesian Structural Time Series

To estimate the causal impact of the Covid-19 lockdowns on traffic congestion in sample of selected cities, we use a time-varying-parameter (state-space) model known as Bayesian Structural Time Series (BSTS). As in Brodersen et al. (2015), we formally represent the BSTS model in the following block of equations: where _εt ∼N_ (0, _σt_<sup>2)and</sup><sup>_ηt∼N_(0,</sup><sup>_Qt_)areerrortermsindependentofallotherun-</sup> knowns, with _ηt_ = ( _ut_ , _vt_ , _wt_ ) containing independent components of a Gaussian white noise.

The model in equation 3 links the observed traffic congestion index time series _yt_ to three components: a local linear trend component _μt_ , a seasonal component _τt_ , and a regression component _β_<sup>_T_</sup> **x** _t_ , where **x** _t_ is a vector of TCI of cities in the control group as well as other controls (such as temperature and precipitation). The second equation of the model displays the current level of the local linear trend _μt_ , which depends on its previous level _μt−_ 1 and its expected increase between _t −_ 1 and _t_ —hence the term _δt−_ 1. The latter is often thought of as the _slope_ of the linear trend at time _t −_ 1 and may obey a random walk as specified in the third equation of the model (Scott and Varian, 2014). The local linear trend model is useful for short-term predictions as it is relatively flexible to account for variations in the local context (Brodersen et al., 2015). The fourth equation of the model depicts the joint contribution of the number of seasons _S_ to the observed congestion index data _yt_ . We set _S_ = 7 to capture the day-of-week effect in the daily traffic congestion index series. Thus, the _seasonal_ component is incorporated as a set of 17
# **5 Unemployment Duration**

The BHPS contains a detailed module on each employment/unemployment episode from every individual in the sample. Merging this data with the previous yearly household survey makes it possible to run a duration model of unemployment spells in order to find the main determinants of its duration. The sample is right-censored: information is not available on spells finishing after wave 16. Some random censoring also occurs as individuals are randomly lost during the 16 waves.

Over the 16 years of the sample, we have data on 5700 unemployment spells<sup>13</sup> . The average duration of a spell is 23 months, but there is a great heterogeneity as half of the spells end before the 12<sup>_th_</sup> month and 70% of them before the 24<sup>_th_</sup> month. The distribution is distinctly skewed to the right, artificially pushing the mean to 23 months. Table 5.1 summarizes some descriptive statistics on the spells. Figure 5.2 shows the survival function of spells, decomposed by gender.

## **5.1 Determinants of duration**

The differences between genders is very clear: women spend on average less time in unemployment. Selection issues are very likely to be the cause of this difference. Participation rates are higher for men than for women<sup>14</sup> , so it is possible that working women differ significantly from their non-working peers, whereas this is less the case for men. Differences across regions are also evident. Unemployment duration is much higher in Scotland, Northern Ireland and Wales than in London and in the South. Age is a major determinant as younger individuals have shorter spells than older ones, in both genders. Finally, individuals with a higher educational achievement have on average shorter spells (15.4 months) than the low achievers (32.1 months).

Figures 5.1 and 5.2 provide visual evidence of two clear discontinuities in the rate of return to work. The first discontinuity is found in the months 10 and 11, in which the rate of return to work is significantly higher than in the immediate preceding or following months. The main suspect for causing this discontinuity is the reduction in benefits occurring on the 12<sup>_th_</sup> month. The same story applies for months 22 and 23, as the benefits are also reduced on the 24<sup>_th_</sup> month. For some individuals, monetary incentives seem to play a significant role in the rate of return to work, as the anticipation of a drop in benefits pushed them to return to a paid job. These results are in line with the literature on job search behavior. The theoretical model of Mortensen (1977) predicts a rise in the hazard

> 13Spells longer than 12 years are (arbitrarily) taken out of the sample. They are outliers irrelevant to the present analysis and bias the results

> 14Women’s participation rates have increased from 70% to almost 75% between 1991 and 2006 (the years covered in this survey). Men participation rates are above 85%

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geographical spread of fires in the same year (see Column (3)).

Table 3: Impact of the intervention on the share of forests affected by fire in November in each of the post-intervention years, as well as on the number and size of the forest fires.

|Month|Year|(1)<br>Share of fre<br>afected grids<br>in November|(2)<br># of ignitions|(3)<br>Avg. fre spread|
|---|---|---|---|---|
||2014|–0.060|–0.867|–0.295|
|||[0.470]|[0.745]|[0.678]|
||2015|–0.082**|–0.471|0.331|
|||[0.035]|[0.416]|[0.971]|
||2016|–0.086**|–1.324**|–2.108**|
|N||[0.014]|[0.043]|[0.032]|
|ov.|2017|–0.101*|–0.445|–1.829***|
|||[0.071]|[0.136]|[0.000]|
||2018|–0.059|–1.237|1.730***|
|||[0.373]|[0.268]|[0.002]|
||2019|–0.006|–0.410|–0.481|
|||[0.305]|[0.452]|[0.364]|

_Note_ : Estimates obtained using the Synthetic Control Method, using the 30% confidence criterion; _p_ - values are presented in parentheses. or better confidence to construct the outcomes. Treatment start from October 2014. P-values from the placebo analyses, in which treatment effects are standardized by pre-treatment RMSPe, are in brackets. Average fire spread is measured in km<sup>2</sup> . *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 10.

Together our results on better fire containment and no reduction in the number of forest fires started complement the findings of Edwards et al. (2020a). These authors evaluate the effect of a payment for ecosystem service program in Indonesia, in which villages were offered a large cash transfer if they did not set any fires within their villages’ boundaries. Although payments induced villagers to better monitor their peers and to prevent them from setting fires, Edwards et al. (2020a) find that payments did not decrease the number of villages with one or more fires, and tree coverage did not increase either. Our findings suggest that forest conservation programs can be effective in increasing villagers’ fire prevention effort and in containing fires, but not so much in preventing fire setting and limiting the subsequent damages. Even so, it is yet to be understood why better fire containment may not improve tree cover neither in Indonesia nor Burkina Faso.

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# Teacher Oualifications

All elementary school teachers must have completed a four-year college course toward the degree of Bachelor of Elementary Education. In general, however, elementary school teachers have no subject area of specialization. The science component of the elementary teaching program, comprised of 11 units of science (three courses) and 6 units of mathematics (also three courses), amounts to less than 8% of the whole program. Programs for improving elementary science teaching exist, but reach relatively few teachers. For example, four-week residential inservice training courses offered by ISMED have space for only 2-3 groups of 20 teachers annually (Ministry of Education, Culture and Sports and National Science and Technology Authority, 1985).

# Student Performance

Studies of student performance reveal that science achievement is low in both elementary and secondary school. For example, a recent study of incoming first year high school students (Gonzalez, Co and Peralta, 1985) fo;nd that even the most able students had science scores below the 50% achievement level. Students from private and public city schools were among the top performers in science, with elementary school graduates from the Metro Manila region scoring highest. The study also revealed that the elementary school graduates scored poorly on questions requiring higher cognitive skills of application, analysis and problem-solving. Preliminary analyses of 17 of 24 countries participating in the Second IEA Science 16
that researchers have used different measures of institutions, and that they often reach different conclusions on their importance or lack thereof.<sup>2</sup> Some studies rely on comprehensive and aggregate measures of institutions: Rodrik, Subramanian and Trebbi (2004) use the Kaufman, Kraay and Zoido (2002) “Rule of Law” index, a composite indicator of numerous variables from various sources measuring different aspects of the protection of property rights and strength of legal and judicial systems. Knack and Keefer (1995) use an index of several indicators (including one on risk of government expropriation) from the International Country Risk Guide (ICRG). Others rely on more specific aspects of institutions: Mauro (1995) uses a corruption indicator from Business International, while AJR (2001) use the ICRG expropriation risk measure and an index of executive constraint from Polity IV.

Other authors prefer more _de jure_ or objective measures of institutions. For instance, Glaeser et al. (2004) argue that most indices of institutions used in the literature are outcomes of institutions rather than objective measures of institutions, which, in their view, should be durable and rule-based. Thus they prefer _de jure_ measures of institutions such as differences in electoral laws (proportional representation vs. majoritarian elections) and judicial independence. But this approach also suffers from the omission of a key component, enforcement (Woodruff, 2006).<sup>3</sup> The World Bank’s “Doing Business” project developed a series of objective measures pertaining to creditors' rights, minority shareholder protection, and other issues (Djankov et al., 2003; 2007). Again, they are not without their problems, such as the omission of enforcement quality, so there are some anomalous rankings. For example, on legal formalism, Vietnam scores better than France and Germany (Woodruff, 2006), and it is unclear whether and to what extent these measures–often asked about a very specific procedure or type of firm–are relevant to the experience of different types of firms and people. Furthermore, even with the same underlying theoretical construct, different measures are often constructed using different methods, with varying degrees of measurement error, which can result in significant biases in their estimated effects. Thus, an important, though often ignored, issue for the literature of institutions and development is measurement error in institutional indicators.

More importantly, different measures of institutions may very well reflect distinct theoretical constructs, with some mattering more than others for development outcomes. This point is forcefully

> 2 See Woodruff (2006) for a survey of corruption measurement.

> 3 Peru, for instance, receives a perfect seven based on judicial independence from La Porta et al. (2004), which is based on objective measures of institutions. Yet, “Peru's judicial opinions were available for purchase, the sales agent was Fujimori's right hand man” (McMillan and Zoido, 2004; Woodruff, 2006).

3
where _T_<sup>_t_</sup> _is_<sup>is the test-score performance of student</sup><sup>_i_in school</sup><sup>_s_at time</sup><sup>_t_,</sup><sup>_B_are student</sup> background characteristics and _S_ are school characteristics. This value-added specification is considered preferable to simple contemporaneous specifications because it accounts for the cumulative character of the education process by controlling for prior performance at time _t_ –1 (cf. Hanushek 2002). Given the proxy of pre-school reading performance we derived from the PIRLS questionnaire, we can implement a version of this value-added specification using the PIRLS data. This is not a perfect solution because ideally, we would either have the students tested at the start of fourth grade or cumulative measures of school inputs _S_ from first to fourth grade. However, given relative constancy of school factors over the course of the first four years of schooling, the approximations introduced by this specification should be limited.

Treating the input factors as additively separable and considering the imputed character of some observations, we implement the following microeconometric estimation equation of the education production function: where _T_<sup>_t_</sup> _is_<sup>is the PIRLS test score of student</sup><sup>_i_in school</sup><sup>_s_in fourth grade,</sup><sup>_Tt_–1</sup> _is_<sup>is the measure</sup> of pre-school reading performance, _B_ is a vector of student background data, _S_ is a vector of data on school characteristics and _ε_ is a student-specific error term. The parameter vectors β 1 to β 4 will be estimated in the regressions.

The vector _D_ contains one dummy for each variable in _B_ and _S_ that has missing data. The dummies equal 1 if a value was missing and thus imputed and 0 for original data (cf. Section 2.1 above on the data imputation). By including these imputation controls in the estimation, observations with missing data on each variable are allowed to have their own intercepts. This ensures that none of the results are driven by imputed data. While data imputation introduces measurement error in the explanatory variables, which should generally make it more difficult to observe statistically significant effects, the imputation controls ensure that the results are robust against possible bias arising from data imputation.

As an additional procedure to test for the robustness of our treatment of missing data, we also estimated our models under disregard of any missing data individually for each variable, re-running the regressions as many times as there are variables with missing data, for the two Latin American countries. We did not observe a single qualitative change of our results under these alternative specifications without each variable’s imputed values, so that our presented results do not hinge on the data imputation.

13
the state level. The survey reports information on the year of marriage, age at marriage, age at cohabitation, age at the consummation of marriage, and age at first birth of ever-married women between ages 15 and 49. We construct a retrospective state-year-level dataset of around 415,000 marriages between 1990 and 2014 across Indian states using this information. This allows us to compare outcomes for women across Indian states married before and after the Gujarat riots of 2002. For a sub-sample of women, the survey also reports information on their socioeconomic statuses, such as their employment status, exposure to intimate partner violence, and their decision-making power, which we use to analyze the effect of the Gujarat riots on women’s post-marital outcomes.

The variables used for analyzing women’s socioeconomic outcomes post riots include women’s years of education and indicators for whether they experienced physical and emotional violence by their husbands. We define physical violence as an indicator for whether the woman was slapped, pushed, kicked, dragged, burned, threatened using weapons, beaten up, physically injured in other ways, or sexually abused by her husband. Emotional violence is an indicator of whether the respondent was humiliated, insulted, or threatened by her spouse. We also include indicators for whether women are employed, have autonomy over their own income, participate in decisions related to household purchases, and make their own decisions about visiting relatives and friends. The sub-sample used for analyzing socioeconomic outcomes consists of around 70,700 women married during 1990-2014.<sup>1</sup> Table 1 reports the sample means of key variables for women married before 2002 across Gujarat and other Indian states. There are some differences between the characteristics of women married before 2002 in Gujarat and other Indian states. For example, in terms of religion, Gujarat has a higher fraction of the Hindu population and a lower fraction of Muslims. Gujarat is also wealthier on average than the other states. The average marriage age for women from Gujarat or other Indian states is approximately 18 years. While the fraction of women married before 18 is lower in Gujarat than in other Indian states, the difference between the two groups is not statistically significant.

# **5 Empirical Strategy**

We use a difference-in-differences strategy to isolate the effect of the Hindu-Muslim riots on women’s marital outcomes. In particular, we rely on spatial variation due to the riots that erupted in the Indian state of Gujarat and the temporal variation in marriages before and after the riots of 2002.

> 1The sample size may vary by the outcome of interest depending on the missing observations.

7
# TABLE OF CONTENTS

|I. INTRODUCTION<br>..........................................|3|
|---|---|
|II. BACKGROUND ON THE SAFE MOTHERHOOD INITIATIVE ...<br>.......|_5_|
|Ill. COMPONENTS OF A SAFE MOTHERHOOD PROGRAM .............|6|
|IV. MEASURING SAFE MOTHERHOOD COSTS .....................|10|
|V. COST MEASUREMENT: FURTHER METHODOLOGICAL ISSUES ...<br>...|13|
|VI. A GENERAL MODEL OF COST AND COST-EFFECTIVENESS ...<br>.....|_15_|
|VII. CONCLUSIONS ........................................|18|
|APPENDICIES:||
|1. LINEAR PROGRAMMING, INPUT-OUTPUT ANALYSIS, AND NON-LINEAR<br>MODELLING TECHNIQUES COMPARED ....<br>........|2........<br>21|
|2. TOWARD AN ECONOMIC MODEL OF SAFE MOTHERHOOD ...<br>......|22|
|3. SOME DATA ON COSTS ...................................|24|
|4. DETAILED AGENDA ......................................|28|
|4. WORKSHOP PARTICIPANTS ................................|32|
|5. ANNOTATED BIBLIOGRAPHY ...............................|33|
Given that the port achieves its target of 28 moves per hour, the main issue emerging from the interviews is the lack of consistency across shifts. It was reported that some shifts perform better than others, meeting the agreed target of 28 moves per hour while others don’t. Possible reasons for this inconsistency could be poor supervision or lack of staff motivation. The introduction of performance contracts tied to an incentive scheme was introduced with the aim of encouraging staff to meet specified targets daily in order to improve the improve the movements of cargo and to keep terminal occupancy levels below the specified threshold of 70%. It should also be noted that only 5% of TPT staff have committed to that level of performance. It is possible that the performance contracts are not yielding the expected results and in that regard there may be a need to review their design and improve if necessary.

Stability of port labor was also mentioned as a key concern for stakeholders as it is perceived to have an impact on operations. The high turnover of management staff is listed ias a major cause of instability and a desire for this to be reduced was expressed.

However, the greatest irritation seems to stem from the fact that strikes are widespread in South Africa. In 2010, there was a protracted strike over pay and conditions lasting for almost 3 weeks starting from 10<sup>th</sup> to 28<sup>th</sup> May. The strike effectively closed all port operations and the recovery took months also evident in the dwell time data presented earlier.

# **Improve Port Access**

South Africa’s infrastructure quality is good compared to other SADC countries, and current investment in infrastructure will ensure that this position is maintained in future. However, one major inadequacy is the lack of intermodal facilities and integration between transport modes, which needs to be addressed before South Africa can be seen as a true regional logistics hub (CSIR, 2007).

The port of Durban has good road connections to the extensive national trunk road network but the access roads in its vicinity suffer from congestion. Mainly due to its location inside the Central Business District, port traffic is sometimes in conflict with general traffic particularly during commuter traffic peak hours therefore constraining port access and general movement of traffic.

While road still dominates, rail transport plays a major role in transporting a large bulk of commodities, with some dedicated services. However turnaround times are still very high. With regards to access, DCT is linked to 4 rail tracks with rail mounted gantries for loading and unloading. In 2008, Transnet reported that it handles up to 16 trains per day in and out of the terminal area and this have increased to 22 over the last few years (Arup (EThekwini Municipality report), 2008).

23