rafmacalaba/datause-encoder-data huggingface dataset:
account information, we obtain a PEMEX capital distortion of 50.8 percent per unit of capital plus inventories invested, in addition to a staggering share of all taxes of 30.6 percent (see Table 13).

# **_4.3.1 The private return to capital in PEMEX and the rest of the private sector_**

**In the next stage of the analysis we distinguish between two scenarios: one in which we disaggregate PEMEX and another in which we do not.** In Table 12, the income to private capital and stocks of capital are now disaggregated into PEMEX and non-PEMEX accounts, based on available PEMEX data. The net capital income (or net operating surplus) in the private sector is split between PEMEX (based on its accounts) and the residual to non-PEMEX entities in the private sector.

**Table 12 Estimation of the rates of return to capital in PEMEX and the remainder of** **<u>the private sector, 2003–2011</u>**

||Net income to<br>capital (or net<br>operating<br>surplus)<br>[million pesos]|Share of<br>net<br>capital<br>income<br>to private<br>|Net<br>capital<br>income<br>over GDP|Share<br>of<br>capital<br>assets|Private<br>capital + land<br>+ inventories<br>over GDP|Return<br>on total<br>capital|Share of<br>indirect<br>taxes in<br>product<br>markets|Gross<br>return on<br>total<br>capital|
|---|---|---|---|---|---|---|---|---|
|||sector|||||||
|**_PE_**|**_MEX_**||||||||
|2003|367,567|11.8%|4.9%|2.7%|0.11|43.5%|0.5%|44.0%|
|2004|455,201|11.9%|5.3%|2.8%|0.11|46.2%|0.6%|46.8%|
|2005|498,755|12.1%|5.4%|2.9%|0.12|44.6%|0.6%|45.2%|
|2006|581,348|11.9%|5.6%|3.0%|0.12|45.7%|0.6%|46.3%|
|2007|590,431|10.9%|5.2%|3.1%|0.13|41.0%|0.5%|41.5%|
|2008|571,112|9.6%|4.7%|3.3%|0.13|34.8%|0.4%|35.2%|
|2009|428,277|8.2%|3.6%|3.6%|0.16|22.5%|0.3%|22.8%|
|2010|546,456|8.9%|4.2%|3.9%|0.17|25.1%|0.4%|25.5%|
|2011|672,191|9.8%|4.7%|4.0%|0.17|27.9%|0.4%|28.3%|
|**Average**||||||**36.8%**||**37.3%**|
|<br>**_Non-_**|**_PEMEX_**||||||||
|2003|2,735,997|88.2%|36.2%|97.3%|4.09|8.9%|0.6%|9.4%|
|2004|3,357,537|88.1%|39.2%|97.2%|4.05|9.7%|0.6%|10.2%|
|2005|3,618,719|87.9%|39.1%|97.1%|4.03|9.7%|0.5%|10.2%|
|2006|4,308,619|88.1%|41.5%|97.0%|3.95|10.5%|0.6%|11.1%|
|2007|4,812,545|89.1%|42.5%|96.9%|3.93|10.8%|0.6%|11.4%|
|2008|5,360,974|90.4%|44.0%|96.7%|3.99|11.0%|0.6%|11.7%|
|2009|4,791,926|91.8%|40.1%|96.4%|4.30|9.3%|0.5%|9.9%|
|2010|5,565,340|91.1%|42.6%|96.1%|4.13|10.3%|0.6%|10.9%|
|2011|6,202,977|90.2%|43.1%|96.0%|4.04|10.7%|0.6%|11.3%|
|**Average **||||||**10.1%**||**10.7%**|

_Source:_ Authors’ calculations using PEMEX and SHCP data.

26
59 Council of Europe: Parliamentary Assembly, _Readmission agreements: a mechanism for returning irregular migrants_ , 16 March 2010, Doc. 12168, available at: http://www.refworld.org/docid/4bdadc1c3.html [accessed 10 August 2017] Cohen, S., & Hsieh, C. T. (2001). _Macroeconomic and labor market impact of Russian immigration in Israel_ . Bar‐Ilan Univ..

Cortes, P. (2008). The effect of low‐skilled immigration on US prices: evidence from CPI data. _Journal of political Economy_ , _116_ (3), 381‐422.

Crisp, Jeff, and Katy Long. "Safe and Voluntary Refugee Repatriation: From Principle to Practice." _J. on Migration & Hum. Sec._ 4 (2016): 141.

Dadush, U. (2014). The effect of low‐skilled labor migration on the host economy. _World Bank KNOMAD Working Paper Series_ , (1).

Dadush, U. (2015). Diaspora, Development and Morocco. Policy Paper PP15/18, OCP Policy Center.

Dadush, U., & Niebuhr, M. (2016). _The economic impact of forced migration_ (No. 1605). OCP Policy Center.

Dadush, U., Demertzis, M., & Wolff, G. B. (2017). Europe’s role in North Africa: development, investment and migration. Bruegel Policy Contribution Issue n ̊ 10| 2017.

Devictor, X. (2016). _Forcibly Displaced: Toward a Development Approach Supporting Refugees, the Internally Displaced, and Their Hosts_ . World Bank.

Dustmann, C. (1996). Return migration: the European experience. _Economic policy_ , _11_ (22), 213‐250.

EU, “On a more effective return policy in the European Union – A renewed Action Plan” March, 2017 European Council, Returning Rejected Asylum Seekers: Challenges And Good Practices In Belgium **,** Dec 2016 Evans, W. N., & Fitzgerald, D. (2017). The Economic and Social Outcomes of Refugees in the United States: Evidence from the ACS (No. w23498). National Bureau of Economic Research.

ESI "The Refugee Crisis Through Statistics." European Stability Initiative, 30 Jan. 2017. Web. http://www.esiweb.org/pdf/ESI%20‐ %20The%20refugee%20crisis%20through%20statistics%20‐%2030%20Jan%202017.pdf
in the CDRs we use in this report.

|**TABLE I Available attri**|**butes in the CDR data set**|
|---|---|
|**Attribute category**|**Description**|
|CDR type|event type(e.g.,call,SMS)|
|time stamp|exact time a call or SMS wasplaced|
|subscriber identifiers|phone number,IMEI,IMSI|
|geographic location|latitude and longitude of cellular tower|
|other location attributes|cell–id,Location Area Code(LAC)|

# _Basic metadata about the data_

The CDRs we used in this work were collected in 2018 for the following months: January, February, June and July. For these four months, there are over 2 billion events. The total number of unique subscribers over this period was 6,631,273. In table II and figure 3, below, we provide background characteristics of the data set to enable the reader to understand the broader context of the data set. When working with CDRs, it is crucial to understand the calling characteristics of users, such as calling frequency, since this has direct impact on the data points you get for each user. In figure 3, we present users’ average number of calls per day over the 120 days. Most studies (for example, [21]) have shown that this variable has a long tail distribution–which means there are very few occurrences of large events (in this case, users who make many calls per day) and many occurrences of small events (users who make very few calls per day). We see the same pattern in these data: most users make 1–2 calls per day. This information will be used later to select active users to include in the analysis.

|**TABLE II Background attributes o**|**f the data set**|
|---|---|
|**Description**|**Attribute**|
|Total number of call events<sup>5</sup>|2,060,691,843|
|Average call eventsper month|589,000,000|
|Number of unique subscribers|6,631,273|
|Number of cellular towers|267|
|Mean distance between cell towers|600 meters|

> 5 In this case a call event represents both actual calls and SMS.

6
4 Chapter 1 16. When differences appear between the coefficients estimated on sample (a) and the amalgamated samples (a + b) or (a + c) we have to establish that the difference is significant (i.e., unlikely to be due to chance). We do this by testing for the equality of coefficients (the Chow test) at the .05 level of significance. Annex D presents the steps in the test.

# CoUinearity

17. Since we are seeking to explain ton-kilometers of freight in a cross-section of countries by two variables, area and GDP, it is necessary to ask whether the two exogenous variables are related: could either stand in for the other? In that case the regression results would be biased. One should expect to find multicollinearity if it were the case that (a) country area is correlated across the sample with country population, and (b) the variation in area across the sample is greater than the variation in GDP per capita.

18. We started investigating the question by looking at the largest countries (in terms of unadjusted area) within our data set: USSR, China, USA, Canada, Australia and India. The smallest of those, India, is not quite 4 times as large in area as the next biggest, Paldstan. India has a very large population but a very low income per head. Canada and Australia, while highincome countries, have relatively small populations and hence relatively small GDP (Canada's is about 70 percent of India's). That leaves the USA with large area, large population and high income per head; and the (former) USSR with the largest area, a population larger than that of the USA and with an income per head that may be a small fraction of USA incomes but a large multiple of India's, and China, with an area somewhat greater than the USA. The latest ICP estimate of China's GDP per capita (1988) seems unduly high at $2,472. But even after scaling this down by 30 percent, China's total GDP would amount to $1.9 trillion, second only to that of the USA.

19. We investigated the correlation between area and GDP in our various samples:

- (a) In our sample of 18 developed countries, the correlation coefficient (r) between area and GDP dropped precipitously when the USA was removed from the sample: from .94 to .22.

- (b) We then formed a sample of developed and socialist countries. With both the USA and the USSR in the sample, the simple correlation coefficient between area and GDP was .52. Removing only the USA, it rose to .63; removing only the USSR, it went up to .95. The two therefore operate as counterweights: the area of the USSR is 2.4 times that of the USA, but the USA (according to our numbers) has 3.8 times the GDP. While a correlation coefficient of .52 does not contain a major threat of bias in the estimated coefficients, we wanted to stay on the side of caution, and therefore removed both countries from the sample. The effect was to lower r to .27, an innocuous level.
the informal sector so that they remain under the radar and avoid compliance with such oppressive laws. Furthermore, the provision of childcare may play an important role. If such provisions are not available, women may opt for more flexible arrangements in the informal sector instead of the more time intensive demands of starting a formal business. Evidence has shown that women find the informal sector attractive as it offers an easy balance between market and homecare activities (Maloney, 2004). Thus, the overall impact of legal discrimination may be a greater presence of women in the informal sector. Of course, many of the factors described above could lead women to abandon economic activities altogether. Thus, a negative relationship between informality and legal discrimination is not certain and is in need of empirical verification.

The informality that we analyze in the current study is of a particular type. The Enterprise Surveys only consider formal (i.e., registered) firms and, thus, when discussing informality in this context, we consider only those informal firms that eventually managed to become formal. In relation to the entire informal economy, these are likely to be a subset of high-performance informal firms; however, the question of whether they perform at the same level as their counterparts who were formal from the outset is an empirical one. Indeed, the relationship between whether or not a firm started informally and the performance of the business at the time of the survey is under-explored. Ayyagari et al. (2020) find that a firm’s size when it begins operations is significant in explaining size and growth over its lifecycle, but do not make any statements whether formal status of the firm at birth matters. There are a number of ways the formal-informal status of firms at birth can have subsequent effects on performance. For one, the rare firms that transitioned from the informal to the formal sector may be extremely productive to have made that transition. And, thus, they may exhibit greater performance after formalizing (Williams et al., 2017; Williams and Kedir, 2017). On the other hand, starting off informally may entail huge opportunity costs in terms of accessing finance and growing at scale. Therefore, by the time a firm formalizes, there may be lost potential that can never be regained over time. Under this scenario, the firm may have lower performance than peers that started formally. Ultimately, the relationship between informal origins and subsequent performance remains an empirical question. This is especially true when it pertains to the gender of a firm’s owner. There is acknowledgement that women’s engagement in the informal sector is diverse consisting of necessity entrepreneurs engaging in informal activities as a means of survival and opportunity entrepreneurs that are looking to scale up and formalize but are held back (Maloney, 2004; Spring 2009; Williams and Gurtoo, 2011). In this study the firms we explore that began in the informal sector are likely to be opportunity entrepreneurs given that they eventually scaled up sufficiently to 4
# **1. Introduction**

Governments have grappled with policies to improve women’s job market prospects. Of late, the spotlight has turned to paternity leave. Two articles in the media ( _Fortune_ , February 2015;<sup>1</sup> _The New York Times_ , November 2014<sup>2</sup> ) highlight the potential benefits of paternity leave, including better job prospects for women. However, the discussion on paternity leave in the informal and formal literature is restricted to developed countries. Do paternity leave laws exist in developing economies? If so, can we uncover any relationship with employment outcomes?

The formal empirical literature suggests two mechanisms for the impact of paternity leave on employment of men and women. First, employer attitudes may change. That is, if a particular position requires investment in job training, employers may discriminate against women, anticipating frequent absence due to child birth and child rearing activities. However, if paternity leave allows men to spend more time outside employment, assuming considerable uptake of paternity leave, this may limit discrimination against women. Second, Becker (1985) indicated that significant changes in the household’s time allocation can be achieved by minor changes in initial conditions. Therefore, changing the allocation of labor via paternity leave could change the trajectory of women’s time spent on market activities. Empirical evidence on the impact of paternity leave on women’s employment is mixed. For example, while Amarson and Mitra find a positive impact in Iceland, other studies find no such impact (see for example, Cools et al. 2015, Rege and Solli 2013, Ekberg et al. 2013 and Mansdotter et al. 2007).

The present study extends the above literature to the case of developing countries. Using a cross-section of firm-level survey data for private firms in 53 developing countries, the study finds

> 1 <u>http://fortune.com/2015/02/01/paternity-leave/</u>

> 2 <u>http://www.nytimes.com/2014/11/09/upshot/paternity-leave-the-rewards-and-the-remainingstigma.html?_r=0</u>

2
<!-- Start of picture text --> Identification<br><!-- End of picture text --> <!-- Start of picture text --> Screening<br><!-- End of picture text --> <!-- Start of picture text --> Eligibility<br><!-- End of picture text --> <!-- Start of picture text --> Included<br><!-- End of picture text --> <!-- Start of picture text --> 4,294 records identified through<br>search of databases<br><!-- End of picture text --> <!-- Start of picture text --> 20 records identified through other<br>sources<br><!-- End of picture text --> <!-- Start of picture text --> 4,272 records excluded<br><!-- End of picture text --> <!-- Start of picture text --> All records screened<br><!-- End of picture text --> <!-- Start of picture text --> 42 full texts assessed for eligibility<br><!-- End of picture text --> <!-- Start of picture text --> 18 full texts excluded<br><!-- End of picture text --> <!-- Start of picture text --> 23 studies (26 programs) included<br><!-- End of picture text --> _Figure 1._ Search process and results 37
We now consider the gender dimension of some of these results. In general, male-headed households outperform female-headed households along all dimensions considered here, (note that the comparison is between females or males in the private sector and their nearest neighbors, regardless of whether male or female). We report only the most striking differences. Table 5 and table 6 show differences in yields and with respect to the use of fertilizer between male and female-headed households. It appears that men have yields that are much higher than women’s. Yet, the former also use significantly less fertilizer than the latter. Could there be a gender bias in the private sector’s extension services?

Table 6. Gender Differences in the Use of Fertilizer

||Gauss|Epanechnikov|Quartic|Uniform|Cosinus|
|---|---|---|---|---|---|
|Female|84.74|112.11|103.15|106.77|110.41|
|Male|-52.80|-114.45|-125.27|-97.54|-116.46|
|All|-19.34|-58.59|-68.94|-47.16|-60.52|

Source: Authors’ calculations

# **Looking Beyond the Tea Sector**

Up to now, we have focused our attention on tea households, comparing outcomes for those engaged in the private sector with outcomes observed in the public sector. The development of the tea sector is a key element of the agricultural policy in support of the poverty reduction strategy in Rwanda. A recent analysis of the 2001 household survey by Dabalen et al. (2004) reveals that agriculture remains the principal source of earnings for the poor and that non-poor households are more likely than poor households to have earnings from non-farm activities. In this perspective, we analyze the available data to determine whether, other things being equal, tea households are better off than households who earn their living mostly from non-tea activities. We proceed in a manner that is entirely analogous to the way we compared outcomes within the tea sector. We use a set of observable characteristics to attenuate some of the bias due to such characteristics.

17
# What Drives Local Food Prices? Evidence from the Tanzanian Maize Market<sup>_∗_</sup>

**John Baffes**<sup>_†_</sup> **Varun Kshirsagar**<sup>_‡_</sup> **Donald Mitchell**<sup>§</sup>

**JEL CLASSIFICATION** : E31, O13, Q02, Q13, Q18

**KEY WORDS** : Export ban, Food prices, Weather anomalies, Price transmission, Tanzania

> _∗_ This paper was funded by the USAID Tanzania SERA Policy Project and was prepared by Booz Allen Hamilton and the World Bank’s Development Prospects Group. We would like to thank Karen Brooks, Gary Eilerts, David M. Johnson, Charles Knoeber, John McIntire, Kateryna G. Schroeder and Sergiy Zorya for comments and suggestions on earlier drafts, Rachel Weaving for editorial services, Aneth Kayombo for data assistance, and Alex Mkindi for valuable insights on the Tanzanian maize market.

> _†_ Senior Economist at the World Bank. Email: jbaffes@worldbank.org

> _‡_ Independent consultant. Email: varun.kshirsagar@gmail.com

> §Senior Advisor and former Chief-of-Party of the Tanzania SERA Policy Project. Email: don.mitchell09@gmail.com
- **Gawande, Kishore, and Usree Bandyopadhyay.** 2000. “Is Protection for Sale? Evidence on the Grossman-Helpman Theory of Endogenous Protection.” _Review of Economics and Statistics_ , 82(1): 139–152.

- **Gawande, Kishore, Pravin Krishna, and Michael J. Robbins.** 2006. “Foreign Lobbies and U.S. Trade Policy.” _Review of Economics and Statistics_ , 88(3): 563–571.

- **Goldberg, Pinelopi K., and Giovanni Maggi.** 1999. “Protection for Sale: An Empirical Investigation.” _American Economic Review_ , 89(5): 1135–1155.

- **Grossman, Gene M., and Elhanan Helpman.** 1994. “Protection for Sale.” _American Economic Review_ , 84(4): 833–850.

- **Grossman, Gene M., and Elhanan Helpman.** 1995 _a_ . “The Politics of Free Trade Agreements.” _American Economic Review_ , 85(4): 667–690.

- **Grossman, Gene M., and Elhanan Helpman.** 1995 _b_ . “Trade Wars and Trade Talks.” _Journal of Political Economy_ , 103(4): 675–708.

- **Hoekman, Bernard.** 2014. _Supply Chains, Mega-Regionals and Multilateralism A Road Map for the WTO._ London:CEPR Press.

- **Johnson, Harry G.** 1951-1952. “Optimum Welfare and Maximum Revenue Tariffs.” _Review of Economic Studies_ , 19(1): 28–35.

- **Johnson, Robert C., and Guillermo Noguera.** 2012. “Accounting for Intermediates: Production Sharing and Trade in Value Added.” _Journal of International Economics_ , 82(2): 224–236.

- **Johnson, Robert C., and Guillermo Noguera.** 2014. “A Portrait of Trade in Value Added over Four Decades.” Unpublished Manuscript, Dartmouth College.

- **King, Gary, and Langche Zeng.** 2001. “Logistic Regression in Rare Events Data.” _Political Analysis_ , 9(2): 137–163.

- **Kleinbergen, Frank, and Richard Paap.** 2006. “Generalized Reduced Rank Tests using the Singular Value Decomposition.” _Journal of Econometrics_ , 133(1): 97–126.

- **Koopman, Robert, Zhi Wang, and Shang-Jin Wei.** 2014. “Tracing Value-Added and Double Counting in Gross Exports.” _American Economic Review_ , 104(2): 459–494.

- **Lim ̃ao, Nuno.** 2006. “Preferential Trade Agreements as Stunbling Blockks for Multilateral Trade Liberalization: Evidence from the United States.” _American Economic Review_ , 96(3): 896–914.

- **Los, Bart, Marcel P. Timmer, and Gaaitzen J. de Vries.** 2015. “How Global are Global Value Chains? A New Approach to Measure International Fragmentation.” _Journal of Regional Science_ , 55(1): 66–92.

35
# **The Effects of Conflict on Fertility in Rwanda**

Kati Schindler<sup>a</sup> * and Tilman Brück<sup>b</sup>

- a German Institute for Economic Research (DIW Berlin) and Households in Conflict Network (HiCN)

- b German Institute for Economic Research (DIW Berlin), Humboldt University of Berlin, and Households in Conflict Network (HiCN)

- corresponding author, DIW Berlin, Mohrenstr. 58, 10117 Berlin, Germany, kschindler@diw.de

**Keywords** : conflict, demography, fertility, gender, genocide, Rwanda **JEL codes** : J13, O12

## **Acknowledgements**

We are grateful for helpful comments from Damien de Walque, Paul Francis, Kathleen Jennings, Adam Lederer, Marinela Leone, Tony Muhumuza, Amber Peterman, Susan Steiner, Håvard Strand, Mareijke Verpoorten, Philip Verwimp, and Marc Vothknecht. The study was funded by the World Bank, with generous funding from the Government of Norway. All errors are our own.