rafmacalaba/data-use-mentions-extended huggingface dataset:
module in terms of definitions/thresholds, sequencing of questions, and the training of statistical agents.<sup>23</sup> Our paper has presented a number of potential measures for local level inequality, diversity, and group inequality. Future qualitative work might gain a better understanding of group dynamics at the local level, and how these could be mapped in local indicators (for example from the Population Census), and then mapped across datasets. Similar efforts should be made in devising simple but effective proxies for the effectiveness of local institutions that might help mediate conflict. We have argued that quantitative work is at this stage only effective in identifying underlying associations with conflict. Qualitative work can help in identifying pathways and directions of causality. In a mixed quantitative-qualitative framework (Rao and Woolcock 2003), future refinements of the quantitative evidence and methodological frameworks could lead to better hypothesis testing and the derivation of potential policy findings. This paper is but an initial presentation of the results and implications of the 2003 PODES on conflict. We hope that it will stimulate further debates and consideration, both of the causes of 23 First, the definitions – of conflict and its types – should be clearer in the instrument. At present, it is unclear what exactly is meant by conflict. For example rephrasing the guidance for the question about whether conflict existed in the village, refining its parameters, perhaps by setting a minimum threshold for incidents that should be included, would better ensure consistency of reporting. For example, respondents could be asked “has there been a conflict in the past year in this village in which at least one person was killed or injured, or in which there was damage of property or goods?” This would help ensure that there is a common understanding of conflict amongst respondents, and would hence limit biased under-reporting, giving us more confidence in the resulting data. More accurate descriptors for conflict and crime types would also help in this respect. Second, the ordering of questions might help with the incentives question
t∗ is the predicted level of imports by sector s, defined as follows: ′ and φs,t is the rate of growth of sector s imports of other low- and middle-income countries from the United States between year t0 and year t. Note that since our GVC participation measures draw on Mexican firm-level data (for more details, see section 2.3), we are unable to create similar instruments for them.

> 5 For example, Autor, Dorn, and Hanson (2013) estimate the impacts of import competition from China on local US labor markets by combining the initial local distribution of employment across sectors and nationwide changes in imports from China by sector. They instrument this variable using changes in Chinese imports by other high-income countries.

9
of organized-sector manufacturing activity in 283 Indian districts from 2000 to 2009 from the Annual Survey of Industries (ASI). Explanatory variables are indicators for distance from the GQ network that was upgraded starting in 2001. Estimations consider the effects relative to districts more than 50 km from the GQ network. Column headers list dependent variables. Young plants are those less than four years old. Labor productivity is total output per employee in district, and TFP is weighted average of Sivadasan (2009) approach to Olley-Pakes estimations of establishment-level productivity with repeated cross-section data. Outcome variables are winsorized at their 1% and 99% levels, and entry variables are coded at the 1% level where no entry is observed to maintain a consistent sample. Estimations report standard errors, have 283 observations, control for the level of district activity in 2000, and weight observations by log total district population in 2001. Initial district conditions include variables for national highway access, state highway access, and broad-gauge railroad access and district-level measures from 2000 Census of log total population, age profile, female-male sex ratio, population share in urban areas, population share in scheduled castes or tribes, literacy rates, and an index of within-district infrastructure. Estimations include state fixed effects as well.

Vie _= LicaVYrc=6 3 Bee * (0,1) years. to GQ completion,H * (0,1)GQDist;; gesohm + 9; +O + Ese
_25th quantile _<br>Uncondi|_50th quantile _<br>tionalQuantile R|_75th quantile_<br>egression|
|---|---|---|---|---|
|Log Real Value of Monthly Per Capita|-0.425***|-0.242***|-0.433***|-0.619***|
|Food Consumption|[0.0364]|[0.0437]|[0.0447]|[0.0571]|
|Lo Dail Per Caita Calorie Intake|-0.184***|-0.0866***|-0.187***|-0.279***|
|g y p|[0.0244]|[0.0290]|[0.0268]|[0.0340]|
|Lo Food Consumtion Score|-0.189***|-0.246***|-0.173***|-0.158***|
|g p|[0.0270]|[0.0508]|[0.0305]|[0.0319]|

Note: Coefficients and standard errors are from separate, population weighted regressions with dependent variable listed in the first column. Total observations: 20,483. OLS standard errors are corrected for clustering and stratification, UQR standard errors are clustered bootstrap (with replacement) estimates. *, **, and *** denote significance at 10%, 5%, and 1%, respectively.

29
village, and reach about 53 households per village. In both cases, fewer than 2% of SHGs had bank accounts, were credit-linked, had a prioritization plan, and had received two tranches of money.

Next, we examine outcomes reported by SHG women using data from an “Intermediate Outcome Survey” conducted by the Government of Rajasthan for 302 women from 150 SHGs from 6 blocks of 4 districts in October 2014. The sample included 102 women in Resource Blocks (i.e., external facilitators) and 200 women in Intensive Blocks (i.e., internal facilitators). The survey asked them about their SHG participation, financial transactions and participation in other community activities. The survey is admittedly imperfect for measuring program impact because of its small size, its lack of a comparable baseline and possible Hawthorne effects,<sup>29</sup> but our focus is on comparing differences between the two types of blocks. We have already established that the two types of blocks were similar prior to the introduction of RRLP. Therefore, even if women participating in the survey were screened or pre-selected based on their good standing in the program, and may have provided answers that would resonate positively with the enumerators, both types of blocks would be similarly affected by these limitations. Therefore we can, cautiously, infer differences about the program’s implementation in the two types of blocks from the differences in their response.

To test the hypothesis that differences in the style of facilitation led to differences in women’s benefits from the RRLP program, we run the following regression model: Where yibis a measure of an outcome of SHG participant _i_ in block _b_ , EXTERNALb is a dummy variable that takes value 1 if the SHGs in the block are being facilitated by external facilitators, Xiis a vector of controls for woman i (this includes age of her SHG, her self-reported preprogram income and whether or not she had an income source prior to the program), γb is a block fixed-effect and εibis the error
Power in Africa: Comparative Lessons in Authority and Control_ , Vol. 149, Princeton University Press, 2014.

- **Idler, A.** , “The Logic of Illicit Flows in Armed Conflict,” _World Politics_ , 2020, _72_ (3), 335–376.

- **Jimenez-Ayora, Pablo and Mehmet A. Uluba ̧so ̆glu** , “What underlies weak states? The role of terrain ruggedness,” _European Journal of Political Economy_ , 2015, _39_ , 167– 183.

- **Juan, Alexander De and Jan H. Pierskalla** , “Manpower to coerce and co-opt—State capacity and political violence in southern Sudan 2006–2010,” _Conflict Management and Peace Science_ , 2014, _32_ (2), 175–199.

- **Kivu Security Tracker** , “The Landscape of Armed Groups in Eastern Congo,” Kivu Security Tracker 2021. Available online: URL to be added.

- **Kleinschroth, Fritz, Nadine Laporte, William F Laurance, Scott J Goetz, and Jaboury Ghazoul** , “Road Expansion and Persistence in Forests of the Congo Basin,” _Nature Sustainability_ , 2019, _2_ (7), 628–634.

- **Kraay, Aart** , “How Large is the Government Spending Multiplier? Evidence from World Bank Lending,” _The Quarterly Journal of Economics_ , 2012, _127_ (2), 829–887.

31
A of Figure 9, where the statistic _ν_ represents the Kullback-Leibler distance (KLD) from the consumption distribution and _ν_ ( _FY ∗_ [ _y_<sup>_∗_</sup> ]) = 0. The continuous line here shows the value of (8) at different values of _p_ . The value corresponding to _p_ = 0 is the KLD from a survey with only recall questions. The panel conveys two important messages. First, as more resources are allocated to diaries, the KLD improves until it reaches the value _p_ = 0 _._ 42. In particular, Appendix Figure A.1 shows that weights _p_ ( _y_<sup>_∗_</sup> ) at the value _p_ = 0 _._ 42 are such that households with large values of _Y_<sup>_∗_</sup> should always be assigned to diaries. The second important message is that the KLD is larger at _p_ = 1 than at _p_ = 0. This implies that, in our case study, the data distribution obtained from a fully fledged recall survey has better properties than one obtained from diaries alone.

Distributions obtained from diary data yield worse measurements of poverty and inequality. This can be seen in the remaining panels of Figure 9, where the continuous lines represent alternative definitions for the statistic _ν_ ( _FY_ [ _y_ ]): the Gini coefficient (Panel B), the standard deviation of logged consumption (Panel C), and the share of consumption for households in the bottom decile (Panel D). The horizontal lines in panels represent the value of the true statistic _ν_ ( _FY ∗_ [ _y_<sup>_∗_</sup> ]). Diaries yield more disperse consumption distributions, as shown in Panel B and Panel C. Moreover, the Gini coefficient and the standard deviation computed from a recall survey are closer to their true values than one would obtain by using only diaries. It is also clear that allocating more resources
& Torrats-Espinosa, 2017).

The paper is organized as follows. Section II describes the data set. Section III discusses the empirical methods. Section IV goes over the empirical results. Section V offers some robustness exercises and additional results. Finally, section VI concludes with final remarks.

# **II Data**

I use census data obtained from IPUMS International (Integrated Public Use Microdata Series, IPUMS, 2019), hosted at the University of Minnesota Population Center, which reports harmonized representative samples (typically 10%) of full census micro data sets for a large number of countries.

**Countries.** I use individual records, retrieved from 21 national censuses from 11 countries: Brazil, Colombia, Cuba, Ecuador, El Salvador, Guatemala, Jamaica, Mexico, Panama, Trinidad and Tobago, and Uruguay (see Table A1 in the Appendix for the details about years and the fraction of the data available by census sample). There is a much longer list of censuses available for Latin America and the Caribbean that can be used to estimate educational intergenerational mobility (see Munoz, 2021a), however, the main constraint that limits the number of samples being used in this study is the availability of the information necessary (which is detailed below) to analyze within-country migration as it is required in the empirical strategy.

**Education.** The data set contains a variable reporting educational attainment that is re-coded by IPUMS to capture educational attainment in terms of the level of schooling completed without necessarily reflecting any particular country’s definition of the various levels of schooling in terms of terminology or number of years of schooling. It contains four categories: 1) Less than primary completed, 2) primary completed, 3) secondary completed, and 4) university completed. This variable applies, to the extent possible, the United Nations standard of six years of primary schooling, three years of lower secondary schooling, and

4
# _Relationship to Factors Influencing Development_

In Table 3 we have shown correlation coefficients for several factors that may influence, or may be influenced by, governance quality: per capita PPP income, GDP growth, and military spending. The strong positive correlation between income and governance quality supports the casual observations drawn above. The negative correlation of military spending with governance quality is not statistically strong but it does suggest that it may be governments that are being defended rather than countries. The positive colrelation between the ten year economic growth rate and governance quality supports the argument that the institutional focus of government is an important determinant in economic development. Also, since the highest income countries have generally not had the highest growth rates over the last decade, the positive correlation between higher growth and better governance suggests that good governance improves economic performance rather than vice-versa.

**Table 3: Governance Index Correlation Coefficients**

||Governance<br>Index|Income|Military (as %<br>govt spending)|Annual Growth<br>(85-94)|
|---|---|---|---|---|
|Governance|1.00|0.89|-0.16|0.25|
|||(0.00)|(0.22)|(0.03)|
|Income||1.00|-0.11|0.26|
||||(0.42)|(0.03)|
|Mi:litary|||1.00|0.10<br>(0.47)|
|Annual Growth||||1.00|

_Sou'rces:_ Income and Growth from World Bank (1996b), Military and Subnational Expenditures from International Monetary Fund (1995)

# III:. **Quality of Governance and Fiscal Decentralization**

During the past half a century, developing countries have, in general, followed a
“loss participation” = – 20% * min(0; 100% – 80% – 17%) = – 20% * min(0; 3%) = 0 If on the other hand, LR = 125%, Ceded LR = 120%, RI commission = 10%, there will be some non-zero loss participation: “Loss participation” = – 20% * min(0; 100% – 120% – 10%) = – 20% * min(0; –30%) = 6% Please note that with this definition, the reinsurer’s loss participation is always positive or zero.

When modeling the risk exposure for this contract, we differentiate between basic losses below certain threshold and individual large losses above this threshold. Whereas basic losses are modeled on the annual aggregate basis, large losses are modeled individually through a combination of loss frequency and loss severity probabilistic assumptions. For the sake of simplicity, we assume that there are no loss development patterns, i.e. losses are fully settled within one year. Hence, loss discounting effects do not play any role.

Let’s assume that from the analysis of loss and premium statistics from the previous years, we can derive the following assumptions:

# <u>Basic losses</u>

- Losses below the threshold of € 3m

- Distribution assumption: ultimate LR follows a lognormal distribution

- Expected ultimate basis loss ratio = 80%

- Standard deviation of the ultimate basic loss ratio = 12%

# <u>Individual large losses</u>

- Losses above the threshold of € 3m

- Distribution assumptions: number of losses p.a. (loss frequency) follows a Poisson distribution, loss severity follows a truncated Pareto distribution

- Expected number of individual large losses above € 3m (Poisson Lambda) = 0.5

- Shape parameter of Pareto Distribution (Pareto Alpha) = 2.4

- Lower threshold of Pareto distribution = € 3m, upper threshold = € 100m

To
>American Economic Review</mark>_ <mark>, 104 (7): 2210-37.</mark> Ashraf, N., N. Bau, N. Nunn, and A. Voena (2020a). “Bride Price and Female Education”, Journal of Political Economy, vol. 128.

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Austrian, K., E. Soler-Hampejsek, J. Behrman, J. Digitale, N. Jackson Hachonda, M. Bweupe, and P. Hewett (2020): “The impact of the Adolescent Girls Empowerment Program (AGEP) on short and long term social, economic, education and fertility outcomes: a cluster randomized controlled trial in Zambia,” BMC Public Health.

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Baird, S., Chirwa, E., de Hoop, J., and Özler, B. (2016), “Girl Power: Cash Transfers and Adolescent Welfare. Evidence from a Cluster-Randomized Experiment in Malawi,” in S. Edwards, S. Johnson, D. Weil (editors), African Successes, Volume II: Human Capital, University of Chicago Press. NBER Working Paper 19479.

25
(0.0666)|
|Constant|1.523***|-12.44***|-12.30***|0.237***|-0.836**|-0.832**|
||(0.0701)|(1.522)|(1.537)|(0.0780)|(0.349)|(0.338)|
|Observations|1,579|1,574|1,574|601|601|601|
|_R_<sup>2</sup>|0.024|0.483|0.484|0.046|0.120|0.121|
|Number of frm|223|223|223|152|152|152|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|

**_Source:_** Author’s analysis based on data from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. The data on tariffs comes from the _Centre d’Estudes Prospectives et d’Informations Internationales_ (CEPII).

**_Note:_** Table report main results of specification 10. Columns (1) - (3) report results related to the product market while Columns (4) - (6) that of labor market. Robust standard errors clustered at three digit industry level in parentheses in all columns. _∗∗∗_ Significant at 1%. _∗∗_ Significant at 5%. _∗_ Significant at 10%.

Firm-level productive efficiency, _ωit_ , is positive and significant, indicating
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Evans, David and Edward Miguel. 2007. “Orphans and Schooling in Africa: A Longitudinal Analysis,” _Demography_ 44(1): 35-57.

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collection, NPS 2014-2015. The Tanzania example, chosen as its surveys were used by the studies reviewed above, is emblematic of the limitations and internal inconsistencies involving migration data in most surveys of the LSMS-ISA collection.

Prompted by the reply featuring migration as a coping strategy in the Tanzania surveys, we carried out a screening of all the questions and answers potentially related to the climate-migration nexus that are currently available in the entire LSMS-ISA collection. The detailed outcome of this screening is reported in Table A.1 in the Appendix. The migration-as-coping-strategy option is actually present in the shock questionnaires of many LSMS-ISA surveys in other countries. Interestingly, across the whole the LSMSISA project, the most explicit questionnaire reference to the climate-migration nexus can be found in the Uganda collection, in rounds 2008-2009, 2009-2010, 2010-2011, 2013-14, and 2015-2016 of the panel, where the following question appears: 29
analysis uses the 2015 data to proxy for educational attainment in 2020.

**Education of migrants** : While comprehensive data on the educational attainment of migrants is absent, the analysis turns to data from the United States as a case of migration to high-income destinations. The data on educational attainment come from IPUMS USA, which provides easy access to the harmonized public use microdata samples of the decennial census, as well as the US Census Bureau American Community Survey (ACS) for various years (Ruggles et al. 2022). This study uses the largest available microdata samples for each of the census years: 1960 (5% sample), 1970 (1% state form 1 sample), 1980 (5% state sample), 1990 (5% state sample), and 2000 (5% sample). It uses the ACS one-year samples for years 2010 and 2019, which provide surveys a 1-in-100 national random sample of the population.<sup>10</sup>

IPUMS USA provides harmonized variables across all available samples. This analysis uses harmonized detailed data on birthplace to identify migrants and their country of birth. Migrants with unidentified country of birth are dropped from the analysis. Once migrants are identified, educational attainment is straightforward. Adult (ages 15+) migrants are classified into three groups: low-educated individuals with primary or lower level of education; medium-educated individuals with some secondary education; and high-educated individuals with at least one year of college education. Likewise, working migrants are classified into three groups based on the occupational earnings score provided with the IPUMS USA data: low-earnings occupations are in the bottom third of earning score; mid-earnings occupations are in the middle third; and high-earnings occupations are in the top third.

# 2.2 Empirical strategy

The empirical strategy closely follows the non-parametric estimation outlined in Clemens (2020). That is, the following equation is estimated:

> 7 See <u>https://www.rug.nl/ggdc/productivity/pwt/ for more