rafmacalaba/datause-encoder-data huggingface dataset:
Our hope is that these data can be fruitfully used to further our understanding of how national economies innovate and learn, including how the structure of financing of R&D evolves over the course of development, and how innovation affects development outcomes. For example, by utilizing this database, Lederman and Maloney (2003) found that the social rates of return to R&D can be significantly higher in developing countries than in industrialized economies. But econometric analysis indicates that developing countries fail to raise their R&D effort due to financial underdevelopment, poor IPR protection, and other factors. Bosch et al. (2005) extended a literature on the link between R&D and patents that had previously been circumscribed to firm-level panel data (e.g., Hausman et al. 1984) that cannot assess the social returns of R&D at the national level, and utilized our database to estimate the impact of national R&D investment on patenting activity and investigated the determinants of the low patent elasticities with respect to R&D in various regions of the world. Surprisingly, there seem to be constant returns to scale in developed countries but slightly lower returns in developing countries. Furthermore, these authors found that factors such as the collaboration that exists between universities and the private sector, as well as the quality of the research institutions have a positive impact on patenting elasticities.<sup>6</sup> The data has also been used to conduct simulations of R&D-driven growth models with externalities (Klenow and Rodriguez-Clare 2004). Nevertheless, these modest contributions are admittedly the tip of iceberg of the research question on development and innovation that we can investigate with internationally comparable and consistent data from around the world. What is clear from the theoretical literature and from the empirical evidence, including the econometric evidence discussed herein, is that innovation should be placed at the top of the research agenda.

> 6 The data on the quality of research institutions and links between the private sector and universities used by Bosch et al. (2005) were drawn from various issues of the _Global Competitiveness Report_ . See data annex.

25
For enrollment, we first measure the number of pupils enrolled in _katchi –_ pre-primary – and Grade 1, according to school enrollment records at both baseline and endline. To measure the retention of students into upper grades, we also measure the number of pupils enrolled in the ensuing grades 2-5, using the same records.

For school infrastructure, we measure change in the number of classrooms, and whether a school has any of four key facilities that are important for a safe, secure and comfortable school environment: toilets, electricity, drinking water, and a boundary wall.<sup>31</sup> For availability of teachers, we first measure the average number of teachers registered at schools at endline. In addition, in recognition of the fact that the large number of one-teacher schools appears to be a central driver of school closures in Sindh, we also present the share of schools which have only one registered teacher. For teacher attendance, we measure the number of teachers present at schools during unannounced visits at baseline and endline<sup>32</sup> ; as well as the share of teachers absent during these visits, in comparison to the number registered at the school.

For learning outcomes, we measure IRT scores in Mathematics and English, based on the standardized tests described above.<sup>33</sup> We report findings from tests administered to children

> 31 Electricity, sanitation and water are particularly important given the prevalence of high temperatures in Pakistan, where temperatures during summer term periods can often reach upwards of 40°C, while a wall is important for student safety and to support student attendance (Andrabi et al., 2007).

> 32 Teacher presence and absence was measured at both surprise and announced visits; data from the unannounced visit were used except in 29 cases where the school was closed at the unannounced visit but open at the announced visit. The results are robust if restricted to the unannounced sample only.

> 33 The Sindhi test has a large number of open-ended questions. Only multiple-choice items were used to fit the 2plIRT model. Results of the multiple-choice section of Sindhi were largely consistent with the English and Math tests and are omitted for brevity.

30
||max-min stringency|**0.40%**|**3.32%**|**-10.31%**|1.01%|**4.01%**|**0.18%**|**0.71%**|
|---|---|---|---|---|---|---|---|---|
||Post|**0.62%**|**0.64%**|-1.28%|**0.35%**|0.06%|**0.03%**|-0.54%|
|France|stringency_index|**3.4%**|**20.2%**|**-22.8%**|**53.9%**|**-10.0%**|**-0.5%**|**1.1%**|
||max-min stringency|**4.8%**|**28.6%**|**-32.3%**|**76.4%**|**-14.2%**|**-0.7%**|**1.5%**|
||Post|0.55%|**0.85%**|-0.44%|**0.95%**|**-2.86%**|-0.01%||
|Italy|stringency_index|-0.19%|**1.02%**|-0.43%|**0.78%**|**-2.28%**|**-0.16%**||
||max-min stringency|-0.25%|**1.37%**|-0.58%|**1.05%**|**-3.07%**|**-0.22%**||
||Post|-0.80%|0.95%|2.84%|0.17%|-2.97%|0.35%|-0.69%|
|Spain|stringency_index|0.78%|-0.33%|1.15%|0.76%|-2.65%|0.30%|-0.22%|
||max-min stringency|1.05%|-0.45%|1.55%|1.03%|-3.57%|0.40%|-0.30%|
||Post|**0.26%**||**-4.04%**|1.63%|**1.63%**|**0.08%**||
|Poland|stringency_index|-0.07%|-0.43%|**-1.52%**|0.82%|**0.97%**|**0.09%**||
||max-min stringency|-0.11%|-0.65%|**-2.29%**|1.23%|**1.45%**|**0.13%**||
||Post||**2.79%**|**-14.0%**|**5.22%**|**3.11%**|**0.27%**||
|Denmark|stringency_index||**2.36%**|**-8.22%**|-0.38%|**3.19%**|**0.36%**||
||max-min stringency||**3.16%**|**-10.98%**|-0.50%|**4.26%**|**0.48%**||
||Post|**1.94%**||**-0.98%**||**-1.03%**|||
|Norway|stringency_index|**0.83%**||-0.33%||**-0.44%**|||
||max-min stringency|**1.23%**||-0.49%||**-0.66%**|||
||Post|**-4.30%**|**1.58%**|**-0.08%**||||**2.80%**|
|Switzerland|stringency_index|**-2.62%**|0.36%|-0.02%||||**2.28%**|
||max-min stringency|**-3.76%**|0.51%|-0.03%||||**3.28%**|

Note: The first row shows the effect using the pandemic dummy “post”. In the second row, we calculate how the stringency index translates to an overall effect during the pandemic. We multiply the change in the average stringency index before and during the pandemic by the coefficient on the stringency index for that country or continent. In the third row, we multiply the coefficient on the stringency index by the difference between the maximum and minimum stringency index values. Significant coefficients are shown in boldface.

To compare the coefficients of different COVID-19 severity measurements, we calculate how the stringency index translates to an overall effect during the pandemic for each country. First, we multiply the change in the average stringency index between pre-pandemic and pandemic levels for each country by the coefficient on that country’s stringency index. This tells us the average impact of lockdowns and other measures on the generation profile, and the result is presented in the second row. We are also interested in the effect on generation when the public health measures are at their most stringent. To see this, we multiply the coefficient on the stringency index by the difference between the maximum and minimum stringency index value, which is presented in the third row. Each column reports the result for an energy source. For each 16
# **<u>Table 5.  Determinants of Government Facilitation</u>**

||**Loan help**|**Info facilitation**|
|---|---|---|
|Ln(firm age)|-0.028***|-0.042***|
||(0.009)|(0.012)|
|Ln(L)|0.052***|0.050***|
||(0.008)|(0.010)|
|State|-0.056*|0.054|
||(0.030)|(0.036)|
|Foreign|-0.051**|-0.035|
||(0.021)|(0.034)|
|Ln(GDP per capita)|-0.015|0.019|
||(0.031)|(0.057)|
|PS long tenure|-0.010|0.114***|
||(0.030)|(0.031)|
|Entertainment & travelling costs/sales, city-ind avg|0.778**|0.477|
||(0.307)|(0.626)|
|Property rights, city-ind avg|0.121|-0.079|
||(0.078)|(0.103)|
|Share of disputes through the court, city-ind avg|0.116<br>(0.097)|0.529**<br>(0.224)|
|Gov efficient services, city-ind avg|0.202***|0.192|
||(0.070)|(0.198)|
|Official ability, city-ind avg|0.153|-0.062|
||(0.138)|(0.219)|
|Official helping firms, city-ind avg|-0.090|-0.033|
||(0.128)|(0.216)|
|CEO schooling|0.006|0.000|
||(0.004)|(0.008)|
|CEO tenure|0.005*|-0.003|
||(0.003)|(0.003)|
|CEO ownership share|0.000|0.001*|
||(0.000)|(0.000)|
|Industry dummies|Yes|Yes|
|R squared|0.082|0.079|
|Number of observations|5,944|4,080|

Note. *, ** and *** represent statistical significance at the 10, 5 and 1 percent, respectively. White-heteroskedasticity-corrected standard errors clustered at the city level in parentheses.

36
Policy Research Working Paper 10598

# **Abstract**

This paper introduces a new disaggregated and harmonized dataset on public procurement and budget execution by Brazilian subnational entities, which currently covers half of Brazilian municipalities and spans the years 2003–21. This dataset provides key information that was previously unavailable from aggregate data, such as the identities of suppliers, details on purchases of goods and services, and granular information on the life cycle of each expenditure action. It then uses these data to provide new stylized facts about local public finance. First, it shows that about one-quarter of government purchases are locally procured and discusses implications for efficiency. Second, it demonstrates that close to 15 percent of payments exceed the 30-day threshold and that payment timeliness is systematically correlated with the income level of the municipality.

This paper is a product of the Development Impact Group, Development Economics. 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 authors may be contacted at tscot@worldbank.org.@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

_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
**Table 3.9: Change in Real Wages by Group**

||||||year endin|g the seco|nd quarter|
|---|---|---|---|---|---|---|---|
||year en|dingthe se|condquar|ter of...||of...||
||2007|2008|2009|2010|2008|2009|2010|
||Hourly wa<br>2010)|ge by char|acteristics|(pesos|inter annu<br>changes|al percenta|ge|
|**BY SEX**||||||||
|Female|34.8|34.5|33.4|32.1|-1.1|-3.0|-3.9|
|Male|37.1|36.0|34.7|32.9|-2.9|-3.6|-5.3|
|**BY EDUCATION LEVEL**||||||||
|Without instruction|20.6|20.0|20.5|19.3|-3.2|2.6|-5.5|
|Primary|25.7|25.5|24.8|23.6|-1.0|-2.5|-4.8|
|Secondary|29.0|28.1|27.3|26.0|-3.1|-3.1|-4.5|
|High school|39.4|38.7|37.2|35.0|-1.6|-3.8|-6.0|
|Professional|69.2|66.3|64.5|60.8|-4.2|-2.7|-5.7|
|**BY AGE GROUP**||||||||
|Less than 26|25.4|25.3|24.9|24.5|-0.4|-1.6|-1.7|
|From 26 to 35|35.2|34.3|33.6|32.2|-2.5|-2.2|-4.1|
|From 36 to 45|40.0|39.4|37.5|35.0|-1.6|-4.8|-6.5|
|From 46 to 55|42.9|41.3|39.3|37.2|-3.7|-4.9|-5.3|
|More than 55|36.9|35.5|34.7|33.9|-3.8|-2.0|-2.4|
|**BY GEOGRAPHIC**||||||||
|**LOCATION**||||||||
|Capital|36.4|33.3|33.3|32.8|-8.4|0.1|-1.7|
|Center region|33.1|31.1|30.4|29.2|-6.1|-2.3|-3.7|
|Center gulf region|35.0|34.5|33.6|32.5|-1.3|-2.6|-3.3|
|North central region|33.8|33.6|31.6|29.9|-0.4|-6.1|-5.3|
|North pacific region|42.9|42.7|41.6|40.1|-0.4|-2.5|-3.7|
|Central pacific region|35.2|34.8|33.3|32.4|-1.2|-4.4|-2.6|
|South pacific region|28.9|28.2|26.4|26.5|-2.5|-6.3|0.3|
|Peninsula region|35.4|34.3|33.2|31.4|-3.1|-3.2|-5.5|
|Border|40.4|40.4|39.0|35.6|-0.1|-3.4|-8.7|

**Source: Own calculations using data from ENOE.**

The distribution of wage changes by economic activity show some interesting patters. In 2009 average wages fell in all sectors but two: manufacturing and government services. Note that manufacturing was also the sector that endured the largest job losses, showing a clear pattern of adjustment to the crisis through quantities rather than prices. The case of government services is perhaps due to the fact that wages in the public sector are less sensitive to economic crisis, particularly if the crisis does not have budgetary implications such as fiscal consolidation.

28
# **6. Bibliography**

Ajibola, B. (2015). Constraints Faced by Women Vegetable Farmers in Kwara State, Nigeria and Its Agricultural Practices. _Jordan Journal of Agricultural Sciences 1_ , _11_ (4), 995–1005.

Alam, M. M., Cropper, M., Herrera Dappe, M., & Suri, P. (2021). _Closing the Gap: Gender, Transport, and Employment in Mumbai_ [Working Paper]. World Bank. https://doi.org/10.1596/1813-9450-9569 Alam, M. M., Kurshitashvili, N., Dominguez Gonzalez, K., Gonzalez Carvajal, K., & Baruah, B. (2022). _Is a Mile for One a Mile for All?_ World Bank. https://doi.org/10.1596/37354 Aloul, S., Naffa, R., & Mansour, M. (2018). _Gender in public transportation a perspective of women users of public transportation._ SADAQA, Friedrich-Ebert-Stiftung.

Anand, A., & Tiwari, G. (2006). A Gendered Perspective of the Shelter–Transport–Livelihood Link: The Case of Poor Women in Delhi. _Transport Reviews_ , _26_ (1), 63–80. https://doi.org/10.1080/01441640500175615 Barzin, S., Avner, P., Rentschler, J., & O’Clery, N. (2022). _Where Are All the Jobs ?: A Machine Learning Approach for High Resolution Urban Employment Prediction in Developing Countries_ [Working Paper]. World Bank. https://doi.org/10.1596/1813-9450-9979 Bastiaanssen, J., Johnson, D., & Lucas, K. (2020). Does transport help people to gain employment? A systematic review and meta-analysis of the empirical evidence. _Transport Reviews_ , _40_ (5), 607–628. https://doi.org/10.1080/01441647.2020.1747569 Bastiaanssen, J., Johnson, D., & Lucas, K. (2022). Does better job accessibility help people gain employment? The role of public transport in Great Britain. _Urban Studies_ , _59_ (2), 301–322. https://doi.org/10.1177/00420980211012635 Blumenberg, E. (2004). En-gendering Effective Planning: Spatial Mismatch, Low-Income Women, and Transportation Policy. _Journal of the American Planning Association_ , _70_ (3), 269–281. https://doi.org/10.1080/01944360408976378 38
Since we do not have any demographic or socio-economic profile of those who refused to participate in the survey it is difficult to say whether this phenomenon is related to life satisfaction of those who answered. As documented in the LiTS report, the highest refusal rates occurred in those countries which have most exposure to public opinion polling on a regular basis, such as Lithuania, Croatia, Latvia, Hungary, Poland and Slovenia. We control for these countries using a binary variable, but coefficient remains insignificant.

# **6. Conclusion**

Despite the huge growth of happiness or life-satisfaction literature especially since 2000, evidence of the experiences of transition countries is limited to a small group of countries. This study certainly bridges this gap by considering a representative sample population of all transition countries.

Our findings confirm some standard findings in the literature. For example, they support existing evidence of higher life satisfaction with lower social distance, younger population, higher education, higher expectations, values and attitude about life, financial stability, and better health. However, some of our results add new nuances to the literature. We find the strongest negative effect of relative income on life satisfaction when the reference income is the median income at the country level. Moreover, we show that past events related to economic distress or labor shock lower life satisfaction and persist for a long time. Apart from historical events, we also control for current emotional state as inferred by a third party observant and demonstrate that current emotional state matters as average reported satisfaction is lower for those who were reluctant to answer during the interview. We also note that the effect of relative income on life satisfaction varies across income groups, with the poorest group exhibiting the strongest negative association. Finally, we provide evidence of sub-regional variation within the 27 transition countries. Additional sensitivity analysis did not change the key findings. The results on the links between relative income and life satisfaction remain consistent under 22
|P040102|1997|1997||2 Closed|Colombia|
|---|---|---|---|---|---|
|P040102|1997|1998||3 Closed|Colombia|
|P040102|1997|1999||6 Closed|Colombia|
|P040102|1997|2000|5/22/2000|8 Closed|Colombia|
|P040102|1997|2001|6/12/2001|10 Closed|Colombia|
|P040102|1997|2002|6/27/2002|13 Closed|Colombia|
|P040102|1997|2003|5/29/2003|16 Closed|Colombia|
|P040102|1997|2004|6/10/2004|20 Closed|Colombia|
|P040102|1997|2005|6/10/2004|20 Closed|Colombia|
|P040102|1997|2019|5/7/2019|21 Closed|Colombia|
|P040106|1998|1998||0 Closed|Ecuador|
|P040106|1998|1999||2 Closed|Ecuador|
|P040106|1998|2000|6/20/2000|4 Closed|Ecuador|
|P040106|1998|2001|6/29/2001|6 Closed|Ecuador|
|P040106|1998|2002|5/30/2002|8 Closed|Ecuador|
|P040106|1998|2003|5/30/2003|10 Closed|Ecuador|
|P040107|1998|1998||2 Closed|Peru|
|P040107|1998|1999||3 Closed|Peru|
|P040108|1999|1999||1 Closed|Trinidad and Tobago|
|P040108|1999|2000|5/19/2000|4 Closed|Trinidad and Tobago|
|P040108|1999|2001|6/11/2001|6 Closed|Trinidad and Tobago|
|P040108|1999|2002|4/29/2002|8 Closed|Trinidad and Tobago|
|P040108|1999|2003|4/30/2003<br>|10 Closed|Trinidad and Tobago|
|P040108|1999|2004|6/4/2004|12 Closed|Trinidad and Tobago|
|P040108|1999|2005|4/26/2005|14 Closed|Trinidad and Tobago|
|P040108|1999|2006|11/27/2005|15 Closed|Trinidad and Tobago|
|P040108|1999|2006|6/30/2006|16 Closed|Trinidad and Tobago|
|P040108|1999|2007|9/29/2006|17 Closed|Trinidad and Tobago|
|P040109|2001|2001|6/18/2001|1 Closed|Colombia|
|P040109|2001|2002|6/7/2002|3 Closed|Colombia|
|P040109|2001|2003|6/19/2003|6 Closed|Colombia|
|P040109|2001|2004|6/1/2004|8 Closed|Colombia|
|P040109|2001|2005|4/23/2005|10 Closed|Colombia|
|P040109|2001|2006|9/13/2005|11 Closed|Colombia|
|P040109|2001|2007|7/28/2006|12 Closed|Colombia|
|P040109|2001|2007|12/28/2006|13 Closed|Colombia|
|P040109|2001|2007|6/25/2007|14 Closed|Colombia|
|P040109|2001|2008|12/20/2007|15 Closed|Colombia|
|P040109|2001|2008|6/25/2008|16 Closed|Colombia|
|P040109|2001|2009|12/18/2008|17 Closed|Colombia|
|P040109|2001|2009|6/30/2009|18 Closed|Colombia|
|P040109|2001|2010|12/20/2009|19 Closed|Colombia|
|P040110|1998|1998||2 Closed|Bolivia|
|P040110|1998|1999||5 Closed|Bolivia|
|P040110|1998|2000|6/27/2000|7 Closed|Bolivia|
|P040110|1998|2001|6/20/2001|9 Closed|Bolivia|
|P040110|1998|2002|5/30/2002|12 Closed|Bolivia|
marginal product of the owner‘s effort in farming relative to other uses of labor time, thus generating a substitution toward non-farm activities. This is a distortion to intersectoral allocation, in the sense that marginal products of labor become unequal between activities. In principle, such an inefficiency could be avoided if the family is well informed about other (nonfarm) income sources, so allowing sharing rules based on total income.

Finally, it is worth underlining that land inheritance can be accompanied by learning within kinship groups, a source of specific human capital that may play an important role in the welfare gains from inheritance. It is widely believed that traditional farming practices in developing countries are characterized by a high degree of farm-specific knowledge, accumulated through experience farming the same land. Rosenzweig and Wolpin (1985) have emphasized the role of family-specific information in explaining inter-generational and intrahousehold land transfers, including inheritance; they find support for the claim that specificknowledge about the family farm entails that land is kept within the family rather than being sold. This could also explain why land markets are often thin or non-existent and also why the extended family is so common in underdeveloped rural economies, given that the older generation will have accumulated greater knowledge about the family farm. As long as the extended family can share knowledge there will be little economic loss at the death of the head of the household, though one can imagine circumstances (including unanticipated deaths) when that is not the case.

The upshot of these observations is that inheritance can have ambiguous effects on welfare and economic activities. The rest of this paper will address these issues empirically using an unusual data set for Senegal.

# **3. Setting and data**

More than half of Senegal‘s population is rural—57% out of a population of about 12.5 million inhabitants in 2009—although the contribution of agriculture to GDP amounts to only 18%.<sup>11</sup> Like other African countries, Senegal has seen considerable population urbanization; at the time of independence in 1960 rural areas were the home of 77% of the population.

> 11 This is less than for its neighboring countries where the share of the rural population is typically about two thirds (apart for Mauritania which, due to deserts over a large area, has only a third of its population in rural areas) and the share of agriculture in GDP hardly ever below 25%, reaching a third in Mali and even 60% in GuineaBissau.

7