rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Discrete time transition model of reported days in customs - <u>policy</u> data included<sup>_a_</sup>

|**Days (I**|**mports)**|**Day**|**s (Exports)**|
|---|---|---|---|
|**Variable**<br>**Spec3**|**Spec4**|**Spec3**|**Spec4**|
|**Basic controls**||||
|lngdppc<br>-0.542|-0.661|-0.414|0.054|
|(1.299)<sup>_b_</sup>|(1.473)|(1.449)|(1.556)|
|lngdppc2<br>0.010<br>|0.022<br>|0.029<br>|0.001<br>|
|(0.080)|(0.090)|(0.089)|(0.096)|
|lngdp<br>0.155<br>|-0.081<br>|-0.077<br>|-0.094<br>|
|(0.160)|(0.200)|(0.177)|(0.219)|
|lnsqkm<br>-0.204|-0.152|0.021|-0.005|
|(0.117)*|(0.130)|(0.126)|(0.132)|
|landlocked<br>-0.755|-0.928|-0.656|-0.694|
|(0.3612)**|(0.443)**|(0.388)*|(0.412)*|
|island<br>0.311|0.131|0.369|0.309|
|(0.417)|(0.440)|(0.465)|(0.482)|
|wto<br>-0.283|-0.492|0.906|0.887|
|(0.517)|(0.642)|(0.548)*|(0.588)|
|oecd<br>1.177|1.515|1.068|1.469|
|(0.729)|(0.847)*|(0.842)|(0.922)|
|**Trade Facilitation Indicators**||||
|Information<br>-0.390|-0.396|-0.034|0.052|
|(0.459)|(0.511)|(0.533)|(0.542)|
|Involvement<br>-0.200|-0.073|-0.059|-0.048|
|(0.268)|(0.303)|(0.324)|(0.335)|
|Adv. Rulings<br>-0.113<br>|-0.066<br>|0.444<br>|0.362<br>|
|(0.309)|(0.355)|(0.344)|(0.387)|
|Appeals<br>-0.332|-0.344|-0.414|-0.485|
|(0.470)|(0.490)|(0.561)|(0.549)|
|Fees & charges<br>0.388|0.462|0.158|0.106|
|(0.353)|(0.387)|(0.399)|(0.398)|
|Documents<br>0.143|-0.050|0.539|0.530|
|(0.491)|(0.548)|(0.578)|(0.624)|
|Automation<br>0.662|0.756|0.247|0.242|
|(0.328)**|(0.397)*|(0.389)|(0.430)|
|Procedures<br>0.404|0.374|0.027|0.287|
|(0.604)|(0.706)|(0.652)|(0.716)|
|Coop. - internal<br>-0.044|0.248|-0.020|0.055|
|(0.228)|(0.286)|(0.274)|(0.307)|
|Coop. - external<br>-0.065|-0.011|0.080|0.029|
|(0.265)|(0.337)|(0.361)|(0.388)|
|Consularization<br>0.034<br>|0.058<br>|-0.029<br>|-0.051<br>|
|(0.206)|(0.223)|(0.256)|(0.257)|
|Governance<br>0.914|1.026|0.918|0.960|
|(0.482)*|(0.516)**|(0.499)*|(0.515)*|
|**Trade Infrastructure**||||
|Phys. Infra.|0.393||-2.218|
||(1.958)||(2.177)|
|ICT|5.280||2.103|
||(2.253)**||(2.246)|
|Bus. Environment|-5.347||1.465|
||(3.155)*||(3.566)|
|Ephron’s Pseudo-R2<br>0.2977|0.3489|0.5677|0.5817|

Notes:<sup>_<u>a</u>_</sup> Combined results from estimates over 80 imputations. _b_ standard error is reported in brackets below estimated parameter, *** indicates 1% significant, ** 5% significant and * 10% significant. WGI data included in the specification, but results not reported to save space. There were no significant coefficients on the WGI variables.

23
to turn temporary work arrangements into jobs with permanent contracts.

# **4.4 Aggregate experience and income effects**

This section estimates the aggregate experience and income gains resulting from the extensive and intensive employment effects previous discussed. Panel A in Table 6 shows aggregate experience gains across different employment types over two years. Treatment participants in vocational schools accrue an additional 1.03 (14.8%) more months of formal sector experience driven by a 1.23 (25.7%) increase in employment with permanent contracts (Col. 1, 3). There is no change in aggregate experience for informal or temporary work (Col. 5, 7).

**Table 6:** Aggregate experience and income effects

**Panel A: Aggregate experience** **<u>(months)</u>**

||For|mal|Perma|nent|Temp|orary|Info|rmal|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Incentive|1.033<sup>_∗∗_</sup>|-0.242|1.234<sup>_∗∗∗_</sup>|-0.116|-0.187|-0.126|-0.002|3.762<sup>_∗_</sup>|
||(0.420)|(0.660)|(0.378)|(0.480)|(0.269)|(0.503)|(0.853)|(2.114)|
|Observations|1539|385|1539|385|1539|385|1170|271|
|R square|0.13|0.08|0.12|0.06|0.03|0.08|0.08|0.25|
|Control Mean|6.96|3.75|4.81|1.97|2.16|1.78|9.57|14.59|
|Std Dev|8.44|8.44|7.39|7.39|5.24|5.24|16.07|16.07|
|Sample|Techn.|Gener.|Techn.|Gener.|Techn.|Gener.|Techn.|Gener.|

**Panel B: Monthly income (pesos)**

||For|mal|Perma|nent|Temp|orary|Info|rmal|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Incentive|237.8<sup>_∗∗_</sup>|-78.4|284.2<sup>_∗∗∗_</sup>|-13.5|-22.4|-29.3|-2.3|413.9|
||(105.9)|(152.7)|(89.2)|(99.5)|(55.2)|(99.7)|(129.1)|(292.5)|
|Observations|1539|385|1539|385|1539|385|1170|271|
|R square|0.16|0.08|0.15|0.05|0.03|0.08|0.08|0.32|
|Control Mean|1654|826|1089|384|446|354|1320|2065|
|Std Dev|2139|2139|1753|1753|1070|1070|2409|2409|
|Sample|Techn.|Gener.|Techn.|Gener.|Techn.|Gener.|Techn.|Gener.|

_Notes:_ Source: IMSS administrative data for formal employment and endline survey data for informal work. Robust standard errors are reported in parantheses. We control for demographic characteristics, socioeconomic status, work experience, time and school fixed effects.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 Panel B shows treatment effects on monthly income (not conditional on the employment status). Vocational school graduates assigned to the treatment group earn on average 238 pesos (14.4%) more from formal work, driven by a 284 pesos (26.1%) increase in income 26
22 Teachers are an articulate group better organized than consumers and taxpayers, and their unions are politically skillful at pressing governments to raise spending and salaries. The fact that publicly financed schools are shielded from market pressures gives unions and other professional groups more power than they would have in competitive private markets. If the higher salaries teachers thereby attain attract a more qualified teaching staff this represents a real cost of quality, not a transfer or rent. But if the same teaching staff remains, at a higher salary, a redistribution occurs, from society at large to the producers rather than the consumers of education. If producers of publicly funded social services receive rents, conventional measures which assume that inputs are being paid their opportunity cost understate the benefits of government spending that accrue to the middle and upper classes and overstate the real resource inputs that accrue to lower income groups (consumers).

Difference Between Distribution of Benefits and Redistribution of Real Income Our third point potentially works in the opposite direction: it is possible that the rich receive a disproportionate share of benefits, yet the service may still be redistributive because they pay an even larger share of the tax bill. This effect is probably more likely to pertain in developed than in developing countries, since in the latter tax systems are often regressive or proportional rather than progressive.

Measuring the distribution of the tax burden is made difficult 'y the fact that the initial incidence is often quite different from the final impact, once shifting to consumers and/or workers is taker. into account. Most empirical studies, therefore, deal only with initial incidence -- and even this cannot be determined with precision. In studies of American and Japanese
data covering 120 developing countries, defined as non-high income by the World Bank, i.e. those with an average GDP per capita below 12,055 2010 US dollars, over 1990-2017. This period covers only post-Cold War years, which had a large impact on arms trade patterns (e.g. Akerman and Seim (2014)), and it also covers the oil price boom of the 2000s. More precisely, I estimate the following regression: _Armsit_ = _βDit_ 5 + _αi_ + _σt_ + _εit_ where _Armsit_ is the total imports of arms in country _i_ in year _t_ , and _Dit_ 5 is dummy equal to 1 in the year of the discovery and the five subsequent years. I use a five-year post-discovery period as this is less than the average delay between discovery and extraction (Arezki et al. (2017) puts it at 7 years.). Country fixed effects ( _αi_ ) pick up factors that vary little year-on-year such as governance, year fixed effects ( _σt_ ) pick up global factors such as arms races. As mentioned above, giant discoveries can be thought of as plausibly exogenous due to their unexpected nature (Arezki et al., 2017; Tsui, 2011; Lei and Michaels, 2014; Cavalcanti et al., 2019). This allows me to interpret the _β_ coefficient as a causal effect. I discuss the results in the next section.

# **3 RESULTS**

Baseline results are in Table 1. It shows the effect of giant discoveries on arms imports based on SIPRI data (columns 1-2), COMTRADE data (columns 3-4), and on the arms share of total imports or GDP, based on COMTRADE and World Bank data (columns 5-8). While odd columns give the results of the baseline regression as described in the previous section, even columns show how robust the results are to the inclusion of an additional control variable, i.e. the number of previous giant discoveries. I include this control as a country which experiences a first giant discovery might not react the same way as a country which had many discoveries in the past, as Angola for example (see Figure 1).

I find a positive and statistically significant effect of giant discoveries on arms imports, 12
supply side that emphasizes the role of inter-sectoral factor mobility in the determination of sectoral output. Product differentiation between imports and domestic goods, and imports by region of origin allows for two-way trade in each product category, depending on the ease of substitution between products from different regions.

At the macroeconomic level, each region's final demand is governed by a representative, regional household which allocates regional income across private consumption, government expenditure and savings according to a Cobb Douglas utility function. Land, labor, capital and the natural resources in a region are unchanged and fully employed. The returns to these factor inputs accrue to the households in the region in which they are employed. Global savings must equal global investment, which is allocated across regions in order to equate expected rates of return. The price of the global investment good is chosen as the numeraire.

The GTAP model is solved in order to determine the endogenous changes in output and trade flows as a result of the proposed trade policy changes. The model maintains all of the restrictions imposed by economic theory: changes in consumer demand add up to changes in total spending; each region's total exports equal total imports of these goods by other regions, less shipping costs; each region's income is determined by spending on its output and tax revenues.

We apply the model to an aggregated version of Version 4 GTAP data base (McDougall _et al.,_ 1998). It combines detailed bilateral trade, transportation and protection data accounting for inter-regional linkages among economies and input-output data bases accounting for inter-sectoral linkages within countries. The regional aggregation features the QUAD regions: the European Union, the United States, Canada, and Japan, SubSaharan Africa (excluding Southern African Customs Union (SACU) countries and Rest of Southern Africa; see footnote to Table 1), an aggregate region representing all other

> 12 See OECD (1997) and Hoekman, Ng and Olarreaga (2000) for detailed descriptions of the extent and structure of tariff preferences to developing countries in the QUAD.

13
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32
|||**Province**<br>**Mass**|**level interv**<br>**media treat**|**ention**<br>**ment**|||**Commun**|**ity treatmen**|**t sample**|**District level i**|**ntervention**|<br>**School**|**component**|**sample**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Contro|l mean|Treatme|nt mean||Contro|l mean|Treatm|ent mean||Contr|ol mean|Treatm|ent mean||
||N|Avg.|N|Avg.|Difference|N|Avg.|N|Avg.|Difference|N|Avg.|N|Avg.|Difference|
|Average number of times child<br>was left at the charge of<br>another child|732|0.657|735|0.707|-0.05|732|0.657|786|0.641|0.016|717|1.024|729|1.06|-0.036|
|Average number of times was<br>child left alone|734|0.327|735|0.405|-0.078|734|0.327|786|0.314|0.013|717|0.211|729|0.258|-0.047|
|Percentage of children with<br>clean aspect|982|0.667|955|0.625|0.042|982|0.667|1027|0.689|-0.022|980|0.624|955|0.672|-0.048|
|Percentage of children with<br>dirty hands|979|0.501|953|0.518|-0.017|979|0.501|1020|0.429|0.072|976|0.513|951|0.449|0.064|
|Percentage of children with<br>dirty finger nails|973|0.597|948|0.584|0.013|973|0.597|1018|0.476|**0.121****|969|0.608|945|0.525|**0.083***|
|dirty face|981|0.414|955|0.452|-0.038|981|0.414|1029|0.331|**0.083***|979|0.441|955|0.365|0.076|
|clothes|976|0.411|955|0.439|-0.028|976|0.411|1028|0.371|0.04|980|0.476|954|0.399|0.077|
|Percentage of children with<br>dirty clothes|980|0.989|955|0.988|0.001|980|0.989|1030|0.993|-0.004|981|0.989|956|0.985|0.004|
|Pot-belly|979|0.153|944|0.144|0.009|979|0.153|1019|0.106|0.047|977|0.159|951|0.139|0.02|
|Percentage of children wearing<br>shoes (or shoes available)|986|0.853|956|0.844|0.009|986|0.853|1032|0.834|0.019|982|0.869|962|0.823|**0.046***|
|Percentage of children that<br>play with household objects|733|0.673|734|0.58|**0.093****|733|0.673|784|0.626|0.047|718|0.636|729|0.647|-0.011|
|Percentage of children that<br>play with toys|733|0.809|734|0.8|0.009|733|0.809|784|0.8|0.009|718|0.788|729|0.813|-0.025|
|Average number of children's<br>books or pictures|736|0.292|736|0.255|0.037|736|0.292|788|0.208|0.084|718|0.23|730|0.225|0.005|
|attended early education<br>programs|732|0.04|734|0.03|0.01|732|0.04|783|0.041|-0.001|718|0.033|726|0.032|0.001|
|Percentage of adults that read<br>books with child|731|0.274|733|0.225|0.049|731|0.274|784|0.241|0.033|717|0.225|729|0.263|-0.038|
|Percentage of adults that tell<br>stories to the child|732|0.265|731|0.197|**0.068****|732|0.265|784|0.227|0.038|717|0.247|729|0.254|-0.007|
|Percentage of adults that take<br>the child outside the home|733|0.943|734|0.913|0.03|733|0.943|784|0.926|0.017|718|0.911|729|0.918|-0.007|
|Percentage of adults that play||||||||||||||||
|<br>with the child|733|0.868|734|0.869|-0.001|733|0.868|784|0.857|0.011|718|0.831|729|0.842|-0.011|
|Average daily caring time<br>*p < 10Percentage, ** < 5Perc|1028<br>entage, *** <|5.038<br>1Percentage|988|4.924|0.114|1028|5.038|1062|5.704|-0.666|1015|4.858|1001|5.393|-0.535|

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45
Policy Research Working Paper 6802

# **Abstract**

This paper is the first to show that excess mortality among adult women can be partly explained by strong preference for male children, the same cultural norm widely known to cause excess mortality before birth or at young ages. Using pooled individual-level data for India, the paper compares the age structure and anemia status of women by the sex of their first-born and uncovers several new findings. First, the share of living women with a first-born girl is a decreasing function of the women’s age at the time of the survey. Second, while there are no systematic differences at the time of birth, women with a first-born girl are significantly more likely to develop anemia when young (under the age of 30) and these differences disappear for older women. Moreover, among those in the older age group, they appear to be significantly better off in terms of various predetermined characteristics. These findings are consistent with a selection effect in which maternal and adult mortality is higher for women with first-born girls, especially the poor and uneducated with limited access to health care and prenatal sex diagnostic technologies. To ensure the desired sex composition of children, these women resort to a fertility behavior medically known to increase their risk of death. The observed sex ratios for first births imply that 2.2–8.4 percent of women with first-born girls are ‘missing’ because of son preference between the ages of 30 and 49.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at amilazzo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 8873

# **Abstract**

Open data and open government data have recently attracted much attention as a means to innovate, add value, and improve outcomes in a variety of sectors, public and private. Although some of the benefits of open data initiatives have been assessed in the past, particularly their economic and financial returns, it is often more difficult to evaluate their social and political impacts. In the public sector, a murky theory of change has emerged that links the use of open government data with greater government accountability as well as improved service delivery in key sectors, including health and education, among others. In the absence of cross-country empirical research on this topic, this paper asks the following: Based on the evidence available, to what extent and for what reasons is the use of open government data associated with higher levels of accountability and improved service delivery in developing countries? To answer this question, the paper constructs a unique data set that operationalizes open government data, government accountability, service delivery, as well as other intervening and control variables. Relying on data from 25 countries in Sub-Saharan Africa, the paper finds a number of significant associations between open government data, accountability, and service delivery. However, the findings suggest differentiated effects of open government data across the health and education sectors, as well as with respect to service provision and service delivery outcomes. Although this early research has limitations and does not attempt to establish a purely causal relationship between the variables, it provides initial empirical support for claims about the efficacy of open government data for improving accountability and service delivery.

This paper is a product of the Governance Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at mjelenic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team