rafmacalaba/datause-encoder-data huggingface dataset:
World Bank in Sierra Leone; UN in Sudan; Australia in Timor Leste; and UN in Togo may be able to provide good examples.

# **5. Conclusion**

The two main purposes of this paper are to identify (i) the key aid effectiveness challenges in FCS and (ii) good performance examples which can provide useful lessons to help address aid effectiveness challenges. The identification of challenges is the critical first step to improve aid effectiveness, and exploring strong performance by peers in critical areas is a practical way to find adaptable approaches. As both recipients and providers are key stakeholders for aid effectiveness – recognized as the delivery and management of aid – this paper provides analyses from both angles.

This paper relies on the results of the PD survey due to its country coverage, issue coverage and availability of historical data. In order to enable us to use the results for comparison, the results are standardized. While the data sources and methodology are useful, they have limitations. Therefore, the results of this paper need to be carefully interpreted and strong performance on a particular indicator by a given country/development partner many be the result of factors other than good and replicable practices.

Bearing that in mind, the results show that:

- In all regions except MNA, the performance of FCS significantly lags behind that of non-FCS, especially on indicator 3 (aid flows are aligned with national priorities (aid on budget)); indicator 7 (aid is more predictable) and indicator 5b (use of country procurement systems).

- Despite the poor FCS performance in general, several countries can provide good examples which other FCS can learn from (e.g. indicator 7 (aid is more predictable) in West Bank and Gaza).

- The comparison between 2005 and 2010 shows that FCS‘ below average performance in 2010 is matched by below average improvements over this period.

- On average, among development partners, MDBs and other MOs such as the World Bank, EU institutions and United Nations perform better than bilaterals and vertical funds in both FCS and non-FCS contexts, although performance differs across countries, indicators,  and institutions.

- In FCS, good institutional-level examples abound, and include IADB‘s performance on indicators 7 (aid is more predictable), 9 (use of common arrangements and procedures) and 10b (joint country analytic work).

- At the partner country level, good development partner examples also exist and include the indicator 7 performances of Japan in Democratic Republic of Congo, of the African Development Bank in Guinea-Bissau and of the World Bank in Sierra Leone.

- The identification of key aid effectiveness challenges and good performance examples are the

- initial steps for both partner countries and development partners to improve their aid effectiveness.

18
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affected by e-filing the most; for instance, tax accounting costs, filling out and submitting returns and also TC for VAT. This is important for the implementation of the policy: A policy to establish an effective e-filing system should avoid a requirement for firms to file paper returns or associated documentation, and should take the opportunity to mitigate any other source of complexity; otherwise it may lead to an increase in TCC for SMEs.

It also important to note that reduction in total TCC may not even occur in the short-run. The empirical evidence suggests there is a “learning by doing” process, and therefore the more firms have a chance to experience the system, the bigger the reduction in TCC they will enjoy. Secondly, the upfront investment (capital investment, learning or educating tax staff for the system) is sunk in the long-run and thus may be assumed to be irrelevant for e-filing decisions after they have been incurred.

Additionally, in a broad sense, e-filing also has a potential for costs savings on the public sector side of the tax system, reducing tax administration costs. In particular, computerization of all tax records may reduce errors and decrease the effort of tax authorities in sorting and analyzing the tax files, and administrating the system, besides the potential reduction in corruption and the informal interactions between tax payers and tax officials. Our results do support the claim that firms who report facing corrupt tax officials are more likely to sign-up for e-filing. One may interpret this result as firms that prefer to avoid corrupt officials can achieve this by electronic filing. In brief, e-filing can potentially help countries in other important ways (than directly helping firms reduce TCC) and some of these benefits can only be realized in the medium to long-run.

Moreover, data limitations certainly limit our ability to go deeper in the analysis. Firstly, taxpayers perhaps need up to 3-4 years to learn and effectively use the e-filing system (as was the case in South Africa’s experience). However, some of the surveys were performed in the first or second year of the policy and this may underestimate the benefits from e-filing, while focusing excessively on its cost. Nevertheless, this also helped us to observe possible short-run consequences of the policy (e.g. Nepal and Ukraine) versus long-run (e.g. South Africa). Yet, due to 53
because of the length of pregnancy and timing of the survey, parents adjust their labor market decisions at least a year before the birth of their first child. This could also be related to the impact of pregnancy itself. Because of this, the core of our results use estimates in which the comparison year is set at _g −_ 2 (two years before the birth of a child, or a year before pregnancy), rather than _g −_ 1 as is usually the case.

With regard to the comparison group, further discussion is needed to evaluate the validity of the parallel trends assumption. According to Callaway and Sant’Anna (2021), we can consider two types of parallel trend assumptions. One type assumes that the potential outcomes of the treated and never-treated units follow the same trends, while the other type assumes that the potential outcomes of the treated units follow the same path as units not yet treated, which may or may not include the never-treated units.

Although the use of never-treated units is generally a good option to address the potential bias due to anticipation effects, it may not be appropriate if the never-treated group has substantially different trends compared to the eventually treated units. In this paper, we provide suggestive evidence that never parents are substantially different from individuals who would eventually become parents. This fact may lead to strong violations of the parallel-trends assumption. To circumvent this issue, we use not-yet-treated units as our comparison group, excluding those who did not become parents during the study period. When doing so, both the conditional and unconditional parallel trends assumptions appear to hold, as we show in the Results section.

# **4 Results**

## **4.1 Main Estimation Results**

We start our analysis by focusing on the impact of first parenthood on working status under the assumption of unconditional parallel trends, using never-treated individuals as the comparison group. Figure 2 provides the event study estimates for both prospective fathers and mothers. This figure also reports the 95% uniform wild-bootstrap confidence intervals.

In this preliminary specification, we observe a decline in employment status for both mothers and fathers. However, the negative impact is more pronounced among mothers, with only a 16
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20
Figures 1 and 2 graph the index values against each other using 2005 Indian data and 2011 U.S. city data (60% allocation), respectively. The bubble size in each graph represents the size of the local manufacturing basis, and the horizontal and vertical axes provide the median value for each index. In Figure 2, we winsorize U.S. values at the levels of the Indian winsorized values in 2005 so that the scales are comparable. Both graphs show a negative correlation, such that higher diversity is associated with reduced specialization. The Indian correlation is weaker at -0.2 than the U.S. correlation of -0.4. Visually, the U.S. data exhibit a much more regular pattern in terms of this trade-off than the Indian data do.

In advanced economies, specialization patterns of cities are quite stable over time (Henderson, 1997). The substantial trend discussed in Table 1a suggests that this finding may not hold in India. Since 1989, India has experienced rapid economic growth, major infrastructure investments, a greater openness to foreign trade, and the adjustment of many industrial policies regarding location choice (e.g., reduced effort to have industry locate in lagging regions, development of special economic zones). These and other factors may weaken the persistence of district specialization and diversity levels.

Tables 2a-3b show the limited persistence of Indian specialization and diversity levels. For the specialization index in Panel A of Table 2a, there is a 0.2-0.3 correlation of district values across adjacent surveys that are about five years apart. By itself, this correlation over a short time period is pretty small. Across two surveys, or about ten years in duration, these correlations are neither economically or statistically meaningful. Thus, there is rapid change in specialization levels for the period that we consider. Panel B shows a similarly rapid decline for the diversity index, although there is less additional attenuation over the ten-year span. Table 2b looks pretty similar when we isolate urban areas of districts. In both tables, there is some evidence that the patterns may be showing greater persistence in the later periods than earlier, which we quantify next.

Tables 3a-b show an alternative approach for measuring persistence. We develop a transition matrix to follow cohorts of districts over time. In Panel A of Table 3a, we start by grouping districts into quintiles of specialization in 1989. For example, the top row is for the 20% of districts that have the lowest specialization in 1989, while the fifth row documents the 20% of districts with the highest 1989 specialization index values. Moving across the columns, 12
<!-- Start of picture text --> Figure 6c: Access to Adequate Care by Household Wealth<br>Prevalence of Adequacy of Care<br>By wealth<br>89<br>81<br>72<br>64<br>56<br>54<br>52 50 51 52<br>39 37 40 36 39 40 38<br>34<br>BGD, HK '10 BOL '08 IDN '10 NPL '11 ZWE '10<br>BGD, IFPRI '11 ETH '11 KHM '10 PER '12<br>Resource Poor Resource Rich<br>Source: Author estimates.<br>Note: (1) In Peru and Bolivia early initiation of breastfeeding is within 100 minutes, not within 60 minutes as in others.<br>(2) Bangladesh (IFPRI) does not include information on early initiation of breastfeeding<br>(3) Indonesia does not include information on complementary feeds for 6 to 8 month olds.<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text -->

# **3.2.3 Adequate Environment and Health**

In general children lack access to improved water and sanitation infrastructure. Figure 7a highlights the lack of access to improved sanitation at the household and community level. Even if a child has access to improved sanitation in their home, the great majority of the children live in communities where less than 75% of the households of the community have access to improved sanitation (Figure 7a). Only in the IFPRI sample of Bangladeshi children do more than 50% of the children live communities where 75% of the households have access to improved sanitation. In Ethiopia only 3% of the children live in such communities and in Cambodia only 11%. However, except for Ethiopia, more than three‐fourths of children in the surveys have access to improved drinking water. That is, the majority of the children drink water that has been treated to reduce disease‐producing contaminants.

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# **Tables**

**Table 1.** Baseline descriptive statistics and balance between treatment groups.

|||T1|T2|C||||
|---|---|---|---|---|---|---|---|
||Overall|<br>(extension)|<br>(extension<br>+ video)|<br>(control)|T1 - T2|T1 - C|T2 - C|
|**Panel A: Household characteri**|**stics**|||||||
|Age HH head (in years)|47.03|46.27|47.32|47.22|-1.05|-0.95|0.10|
||[14.61]|[14.61]|[14.54]|[14.64]|(1.02)|(0.85)|(0.94)|
|Gender HH head (1=male)|0.85|0.86|0.84|0.85|0.02|0.01|-0.00|
||||||(0.02)|(0.02)|(0.02)|
|Education HH head|2.15|2.19|2.42|2.03|-0.23|0.15|0.39|
|(grades compl.)|[3.36]|[3.36]|[3.61]|[3.24]|(0.37)|(0.32)|(0.28)|
|<br>Non-farm family business|0.19|0.21|0.18|0.18|0.03|0.02|-0.00|
|(1=yes)|||||(0.04)|(0.03)|(0.03)|
|<br>Off-farm wage employment|0.19|0.18|0.23|0.18|-0.05|-0.00|0.05|
|(1=yes)|||||(0.03)|(0.02)|(0.03)|
|No. of HH members|3.06|3.08|3.11|3.03|-0.03|0.05|0.08|
|over age 14|[1.31]|[1.34]|[1.31]|[1.29]|(0.11)|(0.10)|(0.08)|
|No. of organizations involved|4.47|4.53|4.38|4.49|0.15|0.05|-0.1|
|(0-12)|[1.87]|[1.91]|[1.78]|[1.90]|(0.21)|(0.19)|(0.18)|
|Basic assets score (0-4)|1.84|1.79|1.91|1.83|-0.13|-0.04|0.09|
||[0.89]|[0.84]|[0.90]|[0.90]|(0.10)|(0.09)|(0.09)|
|No. of TLU owned|3.39|3.26|3.48|3.42|-0.22|-0.16|0.06|
||[2.83]|[2.61]|[2.93]|[2.87]|(0.29)|(0.26)|(0.25)|
|Radio owned (1=yes)|0.29|0.27|0.29|0.30|-0.02<br>|-0.03<br>|-0.01<br>|
||||||(0.03)|(0.03)|(0.03)|
|Cellphone owned (1=yes)|0.52|0.53|0.53|0.51|-0.00<br>|0.02<br>|0.02<br>|
||||||(0.04)|(0.03)|(0.04)|
|Contracted any credit (1=yes)|0.34|0.38|0.35|0.32|0.03|0.06**|0.03|
||||||(0.04)|(0.03)|(0.06)|
|Eligible for formal credit<br>(1=yes)|0.73|0.71|0.73|0.75|-0.02|-0.04|-0.02|
||||||(0.05)|(0.04)|(0.05)|
|Food insecurity score (0-12)|2.34|2.29|2.08|2.46|0.21|-0.18|-0.39|
||[2.99]|[3.05]|[2.71]|[3.07]|(0.31)|(0.27)|(0.26)|
|Walking dist. to nearest FTC|33.30|33.34|32.94|33.42|0.40|-0.09|-0.48|
|(min)|[25.55]|[26.21]|[25.31]|[25.40]|(3.84)|(3.03)|(3.29)|
|Walking dist. to nearest|27.36|25.56|22.34|30.14|3.22|-4.58|-7.80**|
|(all-season) road (min)|[29.47]|[23.66]|[25.98]|[32.50]|(3.34)|(3.02)|(3.56)|
|Walking dist. to nearest market|74.17|67.03|67.91|79.65|-0.88|-12.63*|-11.74*|
|(min)|[48.15]|[44.82]|[46.86]|[49.31]|(7.84)|(6.96)|(6.47)|
|**Panel B: Agricultural producti**|**on characte**|**ristics**||||||
|Total land size (in ha)|1.34|1.37|1.40|1.30|-0.03|0.07|0.10|
||[1.11]|[1.16]|[1.18]|[1.06]|(0.18)|(0.15)|(0.14)|
|Grows main crop (1=yes)|<br>0.94|<br>0.94|<br>0.95|<br>0.93|<br>-0.01|<br>0.01|<br>0.02*|
||||||(0.01)|(0.02)|(0.01)|
|No. of adopted quickwins (0-5)|1.40|1.51|1.53|1.30|<br>-0.02|<br>0.21*|<br>0.23**|
||[0.99]|[1.00]|[0.97]|[0.99]|(0.14)|(0.13)|(0.11)|
|Compost applied (1=yes)|0.36|0.34|0.39|0.37|-0.05|-0.03|0.02|
||||||(0.06)|(0.05)|(0.05)|
|Blended fertilizer applied|0.014|0.009|0.021|0.014|-0.011|-0.004|0.007|

28
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Section 2. It allows us to obtain clear predictions of the effects of changes in expected returns on the current account balance.

Our model is an extension of Merton’s (1971) model to examine the dynamics of the current account. Merton’s model assumes agents with logarithmic utility functions and i.i.d. asset returns. It thus implies that long-term investors behave as mean-variance optimizers, choosing the same portfolio as a short-term investor. Given the assumptions, the optimal portfolio allocation is characterized by constant portfolio shares. To obtain a model with dynamic portfolio choice, we change two central assumptions of Merton’s model. We assume agents with an Epstein-Zin utility function and non-i.i.d. asset returns.

# **3.1 The Environment**

The model is set in discrete time. We consider a partial equilibrium analysis in which agents face exogenous asset returns. There is an arbitrary set of traded assets. We also assume that all individuals are identical and have access to the same information set regarding the current state of the world. This common-knowledge assumption is standard in international macroeconomic models and it implies that capital flows in our model do not result from differences of opinion on future asset returns or risks.

# **3.2 A Representative Country**

Consider a country populated by identical and infinitely lived individuals whose preferences are represented by Epstein-Zin (1991) recursive preferences defined over their consumption stream: 9