rafmacalaba/datause-encoder-data huggingface dataset:
RNGs, however, this can still happen quite easily.

The figure below shows two plots for the Stata `rnormal()` command, which generates normally-distributed pseudorandom numbers. In both cases, the seed is reset to a new value after each pair of draws.

Figure 1: Resetting the seed has important effects on `rnormal()` <!-- Start of picture text --> Seeds 1 through 5000, June 2007 − March 2011 Seeds 1 through 5000, April 2011 − present<br>−4 −2 0 2 4 −4 −2 0 2 4<br>Second draw from rnormal() after setting seed Second draw from rnormal() after setting seed<br>4<br>4<br>2<br>2<br>0<br>0<br>−2<br>−2<br>First draw from rnormal() after setting seed First draw from rnormal() after setting seed −4<br>−4<br><!-- End of picture text --> While no problem is evident in the right panel of Figure 1, resetting the seed between each pair of draws prior to April 2011 produced the unusual pattern on the left. The first and second draws often have the same sign, and cover the plane only in patches. This problem was brought to the attention of StataCorp in March, 2011, and the company acted swiftly; in a matter of days, a software update was released that remedied the problem. Though the Stata 10 documentation did not discuss the consequences of frequently resetting the seed, the current Stata documentation is quite explicit on this topic: it instructs the user, “Do not set the seed too often.” Figure 1 then arises from a mis-use of the software, albeit one that was not clearly described as such at the time. The questions remaining are why the problem arose, whether existing tests should have detected it, what it means for researchers, and what researchers and software developers and evaluators should do in the future.

# **2 The case of rnormal between 2007 and 2011**

Stata 10, released in June, 2007, was the first version of Stata to include the `rnormal()` command. The command uses a ziggurat-style algorithm based on that of Knuth, Marsaglia, and 3
Table 6: Are gender labor practices of FDI siblings different from those of other domestic firms?

|||<br>|<br>Dependent varia|<br>ble is firm-leve|<br>l:||
|---|---|---|---|---|---|---|
||number of<br>female<br>workers|number of<br>female<br>workers|number of<br>female<br>administrative<br>workers|number of<br>female<br>administrative<br>workers|<br>number of<br>female non-<br>administrative<br>workers|<br>number of<br>female non-<br>administrative<br>workers|
||(1)|(2)|(3)|(4)|(5)|(6)|
|FDI siblings dummy|101.9|43.00|1.919***|1.405**|102.4|38.49|
||(70.83)|(51.57)|(0.471)|(0.468)|(88.10)|(62.75)|
|Firm size (log of total sales)||200.0**||1.505***||212.0**|
|||(69.35)||(0.331)||(84.16)|
|Exporter dummy||-245.5||-1.380**||-237.3|
|||(139.3)||(0.480)||(173.1)|
|Other apparel dummy||-125.5||4.442**||-373.0|
|||(310.8)||(1.661)||(329.9)|
|Apparel woven dummy||54.87||0.907**||62.11|
|||(49.87)||(0.400)||(66.54)|
|EPZ dummy||503.0||-0.322||529.2|
|||(295.1)||(1.054)||(336.5)|
|Observations|293|290|248|245|239|236|
|R-squared|0.011|0.246|0.030|0.114|0.009|0.242|

Notes: robust standard errors in parentheses clustered by industry-location. ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively.

Table 7: Are gender labor practices of FDI partners different from those of other domestic firms?

|||<br>|<br>Dependent varia|<br>ble is firm-level|<br>:||
|---|---|---|---|---|---|---|
||numer o|numer o|numer o|numer o|numer o|numer o|
||<br>female<br>workers|<br>female<br>workers|<br>female<br>administrative<br>workers|<br> <br>female<br>administrative<br>workers|<br>female<br>production<br>workers|<br>female<br>production<br>workers|
||(1)|(2)|(3)|(4)|(5)|(6)|
|FDI partners dummy|-17.54|-67.89|1.602*|0.332|-18.80|-65.77|
||(35.14)|(29.29)|(0.600)|(0.280)|(34.48)|(29.09)|
|Firm size (log of total sales)||48.98**||1.527***||46.65**|
|||(8.949)||(0.101)||(9.216)|
|Exporter dummy||19.79||0.651*||10.85|
|||(13.16)||(0.242)||(15.68)|
|Other apparel dummy|||||||
|Apparel woven dummy|||||||
|EPZ dummy||-87.57||-2.470||-82.13|
|||(101.2)||(1.228)||(101.2)|
|Observations|113|112|117|116|106|105|
|R-squared|0.000|0.045|0.017|0.280|0.000|0.041|

Notes: robust standard errors in parentheses clustered by industry-location. ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively.

In summary, the regression analysis above shows that FDI firms hire more female administrative workers. Domestic firms that are related to FDI firms directly as their customers 16
(83 percent vs. 34 percent). Although during the period covered in this study almost all households were connected to the electricity grid, as much as 70 percent of the population suffered from extensive shortages of electricity, especially during winter months. Electricity shortages in 2013 were estimated at about 14 percent of annual electricity demand and imposed economic losses estimated at US$200 million per year, or 3 percent of GDP.

The next section introduces the survey and automated data collection methods employed in this study. Section III describes the analytical approaches used, focusing on comparisons between the surveybased reporting and the unbiased automated monitoring system. Section IV describes and the results and provides interpretation of their significance for monitoring initiatives and policy making. Section V concludes.

# II – Data

Data from two sources are used in this study: data gathered from a monthly phone-based panel survey of households, and from an automated system of Smart Survey Boxes (SSBs). The survey data begin in May 2015. However, SSB monitoring began in November 2016. Thus, for the direct comparisons that follow, only survey data covering comparable periods is used.

## II.I – Listening to Tajikistan

The analyses in the following section use novel data collected by the authors in the L2T survey. Fieldwork proceeded in two stages. The first “baseline” stage included an extensive face-to-face interview with household members. Households were selected using a stratified two-stage clustered sample design based on the 2010 national census of Tajikistan. A total of 150 clusters were selected, with a probability of selection proportional to size. From these clusters, 3,000 households were selected to participate in the baseline survey. During the baseline interview, detailed information was gathered on household composition, asset ownership, and migration. A full household roster was collected (including age, gender, etc.), as well as a comprehensive consumption module that allows analysis in terms of monetary welfare groups in the country.

The baseline survey was completed in March 2015. Following completion of the face-to-face fieldwork, interviewers began regularly calling a randomly selected panel of between 800-1,200 households over the phone to conduct short interviews, following a set monthly schedule agreed to by the participating household. The questionnaire for these phone interviews was designed to monitor trends in subjective wellbeing, alongside measures of income, employment, service disruptions, and related indicators. Phone-based interviews began in May 2015, and survey months 10-25 are used in the analysis that follows, covering the period from October 2016 to January 2018. After excluding missing values and inconsistent entries, a total of 30,350 unique observations are available for analysis.

4
Policy Research Working Paper 8007

# **Abstract**

The emergence of global value chains has opened up new ways to achieve development and industrialization. However, new evidence shows that not all countries have gained from participating in global value chains, and that country-specific characteristics matter for economic upgrading in global value chains. This paper uses two panel data sets of developing and industrialized countries at the sectoral level to relate global value chain participation as a buyer and seller to domestic value added. These are combined with a wide range of policy measures at the country level that can play a role in economic upgrading through global value chains, by targeting global value chain integration or the quality and conditions of input and output factors.

First, the study finds that global value chain integration increases domestic value added, especially on the selling side, which holds across all income levels. Second, the results highlight the importance of policy for economic upgrading through global value chain integration. Although the study cannot claim causal evidence, all the assessed policy areas are consistently shown to mediate the effects of global value chains and magnify the gains for domestic value added. Third, a detailed analysis shows that several policy areas mediate the gains from global value chains more through integration as a seller. Finally, the study observes that many of the results are driven by high- and upper-middle-income countries.

This paper is a product of the Trade and Competitiveness Global Practice 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 authors may be contacted at dtaglioni@worldbank.org and dwinkler2@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 9917

# **Abstract**

Building upon the literature on contact theory, this paper explores the role of inter-group interaction in shaping social cohesion between refugees and host communities in East Africa. It draws upon first-hand quantitative (n=16,608) and qualitative data collected from refugees and nearby host communities in urban and camp-like contexts in Ethiopia, Kenya, and Uganda. Focusing on the Uganda data, OLS regressions reveal a positive and significant correlation between refugee-host interaction and the perception of hosts towards refugees. This association disappears when an instrumental variable (IV) approach is used to address endogeneity issues, except when only data from the urban context is used. The analysis of cross-country data highlights further differences in the types of interaction and perception that matter between urban and camp-like contexts. It also suggests that ethno-linguistic proximity between refugee and host populations is associated with more positive attitudes. In all contexts, an important part of attitude formation appears to take place at the intra-group level, within households and immediate neighbourhoods, independently of individual interaction with the out-group. The paper proposes a series of policy recommendations to improve refugee-host social cohesion, with different approaches required in urban and camp-like contexts.

This paper is a product of the Social Sustainability and Inclusion 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 authors may be contacted at alexander.betts@qeh.ox.ac.uk.

_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
percent of entrepreneurs report that unpredictable changes in rules and policies seriously affected their business. In the CEE, Latin America and Africa around 60 percent of entrepreneurs complained about this problem. In developed countries, South Asia, and Southeast Asia, only about 30 percent of respondents identified this as a problem for their business.

# _Question 2. Credibility of announcements_

This question tackles uncertainty about rulemaking from the angle of whether entrepreneurs believe that government will implement the changes that it announces. This question is most closely linked to the concept of credibility as it is used in the macroeconomic literature.

<!-- Start of picture text --> 70<br>2. Do you expect the<br>government to stick to 60<br>announced major policies?<br>(1) always C*<br>(2) mostly O<br>(3) frequently [i 40<br>(4) sometimes O<br>(5) seldom C0<br>(6) never B 1<br>10<br>World LDC DC SSEA MNA CEE LAO SSA CIS<br><!-- End of picture text --> <!-- Start of picture text --> Percentage of firms who ticked (4), (5), or (6) for question 2<br><!-- End of picture text --> Entrepreneurs in the Asian region have the most trust in government announcementseven more so than in developed countries-only 10 percent of entrepreneurs do _not_ think that government will stick to major announced policies. On the other extreme in the CIS, entrepreneurs are most cynical about new announcements; 70 percent of entrepreneurs do not believe them. Half of all surveyed businessmen in Latin America and the CEE give their governments much credibility.

# _Question 3. Infonnation_

A major factor for entrepreneurs taken by the surprise of new regulation or policy is that they were not informed in advance. This question addresses the informational aspect of predictability.

11
# **Table 2 -- Sectoral value-added (%, unless otherwise indicated)**

||||G|<br>DP|
|---|---|---|---|---|
||Capital (%)|Labor (%)|bnDram|% of total|
|**Business Services**|||||
|Transport via railways|46.0|54.0|12.0|0.4|
|Transport via pipelines|46.0|54.0|7.3|0.2|
|Air transport|46.0|54.0|42.0|1.4|
|Telecommunications|46.0|54.0|90.1|2.9|
|Insurance|66.3|33.7|0.9|0.0|
|Banking|66.3|33.7|70.9|2.4|
|**Dixit-Stiglitz Goods**|||||
|Mining and quarrying|47.9|52.1|51.6|1.7|
|Manufacturing|46.9|53.1|332.8|10.9|
|**Other goods and services**|||||
|Agriculture, hunting, forestry and fishing|75.6|24.4|575.2|19.2|
|Electricity, gas and water supply|38.5|61.5|122.7|4.1|
|Construction|56.0|44.0|865.5|29.0|
|Wholesale and retail trade and repair|61.6|38.4|363.0|12.0|
|Hotels and restaurants|25.2|74.8|10.6|0.3|
|Other land transport|46.0|54.0|36.1|1.2|
|Auxiliary transport activities|46.0|54.0|14.2|0.5|
|Post and courier activities|46.0|54.0|1.0|0.0|
|Real estate and professional services|85.7|14.3|107.2|3.5|
|Public administration and defence|7.6|92.4|90.7|3.0|
|Education|23.1|76.9|98.5|3.3|
|Health and social work|56.0|44.0|89.6|3.0|
|Other social andpersonal services|43.1|56.9|34.3|1.0|

40
for both men and women. In addition, there is room to improve the progressivity by making social spending on health more pro–poor.

**Figure 8a: Progressivity and Pro-poorness of Public Spending, Men**

**Figure 8b: Progressivity and Pro-poorness of Public Spending, Women**

<!-- Start of picture text --> Health Health<br>Tertiary Tertiary<br>Secondary school Secondary school<br>Primary school Primary school<br>Education Education<br>In-kind transfers In-kind transfers<br>Wheat subsidy Wheat subsidy<br>Kerosene subsidy Kerosene subsidy<br>Indirect subsidies Indirect subsidies<br>Other transfers Other transfers<br>PSNP PSNP<br>Direct transfer Direct transfer<br>-0.2 0 0.2 0.4 0.6 0.8 1 -0.2 0 0.2 0.4 0.6 0.8<br>Progressive but not pro-poor Progressive but not pro-poor<br>Regressive and not pro-poor Regressive and not pro-poor<br>Progressive and pro-poor Progressive and pro-poor<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br><!-- End of picture text -->

# **4.3. Welfare Effects of Fiscal Policy**

# _Effect on Income Distribution_

Figure 9 presents income distributions by gender before and after fiscal actions. Income is concentrated in the lower end of the distribution; only 18 percent of Ethiopian households earn more than US$2.5 at purchasing power parity per day.

Figure 9 depicts no shift in income distribution when there is a move from market to final income for women and men. Government intervention through taxes and transfers raises the median income for women and men, except that for men the change in mean is negative. This implies that the intervention contributes more to upward income mobility for women than men. Such a differential income distribution shift illustrates that taxes and spending are affecting the welfare of individuals differently.

16
||SYS<br>SYS<br>GMM<br>GMM<br>(7)<br>(8)<br>0.744***<br>0.713***<br>(0.0542)<br>(0.0561)<br>0.175*<br>0.214**<br>(0.0901)<br>(0.0900)<br>0.169**<br>0.215***<br>(0.0754)<br>(0.0770)<br>-0.000487<br>0.00234<br>(0.00476)<br>(0.00413)<br>-0.178<br>(0.176)<br>1.491*<br>(0.851)<br>-0.861***<br>(0.190)<br>1.982***<br>(0.566)<br>yes<br>yes<br>0.000<br>0.000<br>0.0765<br>0.0997<br>0.896<br>0.846<br>372<br>372<br>74<br>74<br>87<br>83<br>le is an unbalanced sample<br>ns. The values reported for<br> are instrumented for using<br>column (4) shows the same<br>ruments, the system GMM|
|---|---|
|x (EFW)|SYS<br>GMM<br>(6)<br>0.741***<br>(0.0585)<br>0.158**<br>(0.0762)<br>0.238***<br>(0.0878)<br>0.000695<br>(0.00507)<br>0.0666*<br>(0.0365)<br>yes<br>0.000<br>0.0841<br>0.543<br>372<br>74<br>76<br>ator. The samp<br>erial correlatio<br>rmined. They <br> In particular, <br>on to these inst|
|World Inde|SYS<br>GMM<br>(5)<br>0.788***<br>(0.0415)<br>-0.00642<br>(0.134)<br>0.0996<br>(0.0989)<br>-0.00381<br>(0.00541)<br>0.0182*<br>(0.0103)<br>yes<br>0.000<br>0.0472<br>0.716<br>357<br>73<br>76<br>m GMM estim<br>Bond test for s<br>ted as pre-dete<br> other columns. <br>heck. In additi<br>eriod.|
|m of the|SYS<br>GMM<br>(4)<br>0.760***<br>(0.0610)<br>0.173**<br>(0.0805)<br>0.201**<br>(0.0825)<br>0.000484<br>(0.00527)<br>yes<br>0.000<br>0.0754<br>0.308<br>372<br>74<br>62<br>ne step syste<br>s of Arellano-<br>ables are trea<br>ags in all the <br> robustness c<br> lagged one p|
|Table 4: Dependent Variable: Economic Freedo|Pooled<br>F.E.<br>SYS<br>OLS<br>OLS<br>GMM<br>(1)<br>(2)<br>(3)<br>EFWt�5<br>0.760***<br>0.456***<br>0.759***<br>(0.0323)<br>(0.0559)<br>(0.0615)<br>Human capitalt�5<br>0.201***<br>0.0997<br>0.167**<br>(0.0604)<br>(0.203)<br>(0.0767)<br>Total emigration ratet�5<br>0.158***<br>0.382<br>0.203***<br>(0.0432)<br>(0.325)<br>(0.0779)<br>Log populationt�5<br>0.00215<br>-0.0238<br>0.00221<br>(0.00175)<br>(0.0590)<br>(0.00494)<br>Log GDP per capitat�5<br>Share tertiary ed. migrantst�5<br>Human capitalt�5*Soc. dummy<br>Total emigration ratet�5*Soc. dummy<br>Human capitalt�5*Soc. dummy*d90<br>Total emigration ratet�5*Soc. dummy*d90<br>Time dummies<br>yes<br>yes<br>yes<br>R-squared<br>0.708<br>0.579<br>AR(1) test<br>0.000<br>AR(2) test<br>0.0711<br>Hansen J test<br>0.391<br>Observations<br>372<br>372<br>372<br>N. countries<br>74<br>74<br>N. instr.<br>74<br>*** p<0.01, ** p<0.05, * p<0.1. Robust standard errors clustered by country in parentheses. O<br>comprising data at ...ve year interval between 1980 and 2005. AR(1) and AR(2) are the p-value<br>the Hansen J test are the p-values for the null hypothesis of instrument validity. All the vari<br>their own ...rst to third lags in columns 3. They are instrumented for their own ...rst to second l<br>speci...cation as column (3), but now reducing the number of instruments till the second lags for<br>also uses as instruments for the level equations the explanatory variables in the ...rst di¤erences|

20
- 4 -

for measuring transport connectivity in different ways, while avoiding potential endogeneity caused by infrastructure placement. The primary data come from the Demographic and Health Surveys (DHS) carried out in Mozambique at two different points of time: 2011 and 2018. During this period, the government invested heavily in the road network, presumably resulting in significant improvement in connectivity. The paper exploits such variations observed over time to examine how people changed their behavior when they have a fever. To mitigate the potential bias caused by unobservables, household-, facility- and locationspecific characteristics are controlled.

The following sections are organized as follows: Section II describes some country background in the health and transport sector. Section III develops a methodology and summarizes our data. Section IV presents the main estimation results and discusses policy implications. Then, Section V concludes.

# **II. Country context**

In Mozambique, various health indicators have been improved in recent years. Life expectancy increased by nearly 9 years since 2001, from 48.8 to 57.6. Infant mortality, expressed as the number deaths per thousand live births, fell from 99.1 in 2003 to 68.1 in 2011 (World Bank, 2020). The share of children under 5 years old who slept under an insecticide-treated net (ITN) increased from 35 percent in 2011 to 73 percent in 2018 (DHS 2019). The number of malaria deaths declined from over 3,000 in 2014 to about 1,000 in 2018 ( **Figure 1** ).

Still, malaria remains one of the most important challenges to the country. Mozambique has one of the highest malaria death rates in the world. Importantly, while the number of deaths significantly declined, the total number of malaria cases reported has not reduced. According to the WHO statistics, the number of confirmed cases increased from 3.4 million in 2010 to 5.5 million in 2015 and 9.3 million in 2018. While antimalarials only cost a few U.S. cents to a few dollars at most, malaria treatment costs can be considerable. Though no comparable
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