rafmacalaba/datause-encoder-data huggingface dataset:
constructing regional averages, we further re-weight observations so that each country contributes its relative population share to the regional sample; for this purpose, we use population estimates for the year 2000.<sup>8</sup> We present a series of robustness tests that show that our findings are largely similar regardless of whether we present the weighted or unweighted regional averages.

# **III. Effect of child gender composition on fertility behavior**

_Region and country differential stopping behavior_ : Table 2 presents the first set of results, by region. The first column reports the coefficient on _bmn_ from Equation (1), while the second column reports the coefficient on _bfn_ . The third column reports the difference between these two values, while the fourth column reports the p-value of this difference. The last two columns, finally, report the mean number of children and the mean of the ratio of the ideal number of male to female children, as reported by mothers to the survey enumerators.<sup>9</sup> For each region, the top row presents the averages across family sizes, while the next five rows present the individual coefficients for family sizes 2-5 (where, as before, a family size of 2 corresponds to women who have already had 2 children). Although the results from all family sizes are included in the averages, we only report the size-specific coefficients for sizes 2-5 because the results for higher numbers of children are very noisy, and represent less than 5 percent of the total number of births. The sample is limited to women age 40+, who are most likely to have completed their fertility.

> In order to derive a Cambodia database, we pool the data from the two survey years and adjust the survey weights such that each survey contributes half of the weighted observations to the analysis. Pooling data across surveys allows us to increase the number of observations for each country and therefore increase the precision of our estimates.

> 8 In other words, if one country has twice the population of another in the same region, it will contribute twice the weighted observations to the analysis.

> 9 DHS surveys routinely ask mothers for their “ideal” number of children, separately for boys and girls. This subjective measure is often taken in the literature as an expression of son preference.

9
_Brazilian spatial labor dynamics Page 20_

**Table 7B,  Summary statistics, Non-metropolitan Brazil**

|**Variable**|**Obs**|**Mean**|**Std.Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Delta ln Wage (2000/1991)|3532|-0.0233|0.3097|-1.5242|0.8495|
|ln Teacher qualification in 1991|3532|1.1990|2.1519|-4.6052|4.2370|
|Years of Schooling in 1991|3532|3.6414|1.3197|0.4425|7.1707|
|Total rainfall|3,531|1,312.3340|436.0881|368.6636|3,361.7090|
|Government Accountability|3,532|0.9023|0.2969|0.0000|1.0000|
|Delta ln Employment (2000/1991)|3532|0.1231|0.2173|-0.6577|1.3689|
|lnTransport Cost to São Paulo in 1995|3531|7.1172|0.7733|4.1589|9.2603|
|ln Transport Cost to State Capital in<br>1995|3531|5.9358|0.6736|2.1972|8.6910|
|Delta ln Transferences (2000/1991)|3532|0.8316|0.3262|-1.1823|2.4240|
|Delta ln Market Potential (2000/1991)|3523|0.4053|0.1330|-0.4691|2.0949|
|ln Population in 1991|3,532|9.2629|0.8171|6.6214|12.9814|
|Dependency Ratio in 1991|3532|0.4840|0.0460|0.3738|0.6205|
|ln Transport Cost to São Paulo in 1968|3531|7.6962|0.7123|4.3694|9.6385|
|ln Transport Cost to State Capital in<br>1968|3531|6.3629|0.7037|3.1135|8.6910|
|ln Proportion of Elderly in 1991|3532|-2.2260|0.2566|-3.9145|-1.5332|
|Illiteracy Rate in 1991|3532|38.0138|17.5883|2.4800|87.0500|
|Urbanization Rate in 1991|3532|48.7978|20.6294|2.7639|100.0000|
|Population Density in 1991|3532|31.1089|33.0202|0.0896|522.2028|
|ln 5 to 15 over 15 to 55 years old Ratio|3,532|-0.7264|0.2329|-1.6218|-0.0354|
|Repetition Rate in 1991|3,532|4.2707|0.2125|2.3542|4.5756|
|ln Employment Rate in 1991|3532|-0.3722|0.1462|-1.2429|0.1601|
|Delta ln Education Market Potential<br>(2000/1991)|3516|0.9745|0.1732|0.0535|2.7704|
|Percentage of Good Soils|3531|0.7816|0.3179|0.0000|1.0002|
|Rainfall - First Principal Component|3531|0.0033|2.2334|-4.2315|6.8953|
|Rainfall - Third Principal Component|3531|0.0262|1.6087|-2.4997|9.2440|
|ln Wage in 1991|3532|7.9349|0.4619|6.6139|9.3168|
|Proportion of Natives in 1991|3532|0.6778|0.1760|0.0855|1.0134|
|Homicides per Capita in 1991|3530|0.0015|0.0010|0.0000|0.0090|
|Employment in Farming Activities in<br>1991|3532|0.1975|0.0743|0.0000|0.5437|
|Bank Dummy (1991)|3532|0.7087|0.4544|0.0000|1.0000|
||3532|9.2629|0.8171|6.6214|12.9814|
|Average Temperature|3531|22.4151|2.8196|13.8325|27.7363|
|Fertility Rate in 1991|3532|3.7759|1.2169|2.0100|8.6800|
Comprehensive research on the nexus between corruption and migration is in its infancy and conclusive evidence remains to-date very scarce. This paper speaks to this new body of research, which could be split into two blocks.

The first strand of literature is interested in the role of corruption as a factor explaining migration patterns. It finds that corruption has both direct and indirect effects. A high level of corruption in a country significantly discourages immigration, as it is associated with weaker and more volatile economic conditions and more job insecurity (Poprawe 2015). More corruption is also associated with higher emigration rates in sending countries, with a particularly strong effect for high-skill migrants (Morano Foadi 2006, Clausen et al. 2011, Ahmad and Arjumand 2015, Auer and Tjaden 2020). Multiple channels are behind this effect. For example, higher corruption levels have a negative impact on the quality of local institutions, which further leads to lower levels of trust from the public, weaker economic security, fewer job opportunities, and a lower quality of life. Corruption has a stronger impact on high-skill migrants because it erodes the meritocratic structure and it reduces the returns to education (Ariu and Squicciarini 2013, Dimant et al. 2013, Cooray and Schneider 2015).<sup>3</sup> The second strand of the literature analyzes the impact of migrants on the corruption levels in both the home country and in the destination country. By increasing the demand for political accountability and a higher quality of local institutions, migration lowers the overall level of corruption in the origin country (Batista and Vicente 2011, Abdih et al. 2012). For example, students obtaining tertiary education abroad and in more democratic countries can reduce corruption in the home country (Spilimbergo 2009, Beine and Sekkat 2013, Ferreras 2013). As for the impact on the destination country, immigration from corruption-ridden countries tends to boost corruption levels (Dimant et al. 2015). This is in contrast with general migration, which is found to have a positive impact on the institutional quality of the destination country, with reductions in overall corruption levels in countries with a high economic freedom (Clark et al. 2015, Bologna Pavlik et al. 2019).

To further investigate the role of corruption both as a pull factor and as a push factor – – of migration decisions, we use a broad array of subjective and objective corruption measures combined with bilateral migration flow data, exploiting both cross-sectional and time dimensions. Having access to a panel dataset on both bilateral migration flows and corruption levels is a contribution of this paper, and it improves on the most closely related studies that exist on this topic. In particular, both Dimant et al. (2013) and Cooray and Schneider (2015) consider net migration rates, without being able to disentangle whether corruption

> 3These channels are not limited to developing countries. For instance, Morano Foadi (2006) studies the role of corruption on migration flows in Italy.

2
|**Table 11. Sector Import Intensities by F**|**ederal Okrug**<br>_Regional Impor_|_ts of the Sector a_|_s a Percent of R_|_egional Consum_|_ption of the Prod_|_uct_||
|---|---|---|---|---|---|---|---|
|**Sector**<sup>**b/**</sup>|Northwest|Central|South|Volga|Urals|Siberia|Far East|
|Railway transportation|0.3|0.4|0.4|0.4|0.4|0.3|0.4|
|Truck transportation|1.6|1.7|1.7|1.7|1.7|1.7|1.7|
|Pipelines transportation||||||||
|Maritime transportation||||||||
|Air transportation|2.8|2.8|2.8|2.8|2.8|2.8|2.8|
|Other transportation|1.1|1.2|1.1|1.1|1.2|1.1|1.2|
|Telecommunications|5.6|5.6|5.6|5.6|5.6|5.6|5.6|
|Science & science servicing|0.1|0.1|0.1|0.1|0.1|0.1|0.1|
|Financial services|1.4|1.4|1.4|1.4|1.4|1.4|1.4|
|Ferrous metallurgy|20.7|24.1|31.3|11.0|12.1|9.1|13.6|
|Non-ferrous metallurgy|18.2|21.2|25.2|11.4|11.5|8.7|10.0|
|Chemical & oil-chemical industry|37.0|45.7|21.2|20.4|19.7|36.3|30.2|
|Mechanical engineering & metal-working|50.6|57.6|22.6|17.2|15.7|11.5|24.4|
|Timber & woodworking & pulp & paper industry|28.7|32.2|16.3|3.3|3.5|2.3|6.0|
|Construction materials industry|17.6|18.7|8.5|8.4|8.3|15.6|11.3|
|Food industry|36.0|21.0|12.8|10.6|9.8|10.6|12.7|
|Other industries|5.4|5.4|5.4|5.4|5.4|5.4|5.4|
|Public services, culture and arts|1.3|1.3|1.3|1.3|1.3|1.3|1.3|
|Agriculture & forestry|3.5|3.5|3.5|3.5|3.5|3.5|3.5|
|Coalmining|7.0|7.0|7.0|7.0|7.0|7.0|7.0|
|Housing and communal services|7.9|7.9|7.9|7.9|7.9|7.9|7.9|
|Construction|7.6|7.6|7.6|7.6|7.6|7.6|7.6|
|Electric industry|0.6|0.6|0.6|0.6|0.6|0.6|0.6|
|Gas|1.5|1.5|1.5|1.5|1.5|1.5|1.5|
|Crude oil extraction|4.5|4.5|4.5|4.5|4.5|4.5|4.5|
|Oil refining and processing|8.1|8.1|8.1|8.1|8.1|8.1|8.1|
|Other goods-producing sectors|16.7|16.7|16.7|16.7|16.7|16.7|16.7|
|Post|1.9|1.9|1.9|1.9|1.9|1.9|1.9|
|Wholesale and retail trade|0.4|0.4|0.4|0.4|0.3|0.4|0.4|
|Textiles and apparel|62.1|62.1|62.1|62.1|62.1|62.1|62.1|

45
costs recover capacity, and the appropriate engagement with private and non‐governmental actors.

Overall Alm (2010) is quite pessimistic that theory has much to add to our understanding of the practice of urban infrastructure finance, or that it will tell us which approach is superior relative to others in specific contexts. He is also pessimistic about the extent to which local governments can finance urban infrastructure by themselves. And he concludes that a fundamental lack of data is an obstacle to effective research and learning in this area.

Nonetheless, he recommends further research to promote a better understanding on the six gaps summarized above and the related questions that they raise. Specifically, he suggests the following four avenues of research:

- <u>Data: Generate more and more reliable data on the various dimensions of</u> infrastructure finance and on the administrative capacity of local governments.

- <u>Project decisions: Explore the use of modern private project appraisal</u> methods in selecting urban infrastructure investments.

- <u>Institutional and political analysis: Include institutional and political factors</u> explicitly in the analysis of urban investment and finance decisions.

- <u>Civil society organizations (CSOs): Explore the role of CSOs in urban</u> infrastructure service delivery.

# **External assistance and partnerships**<sup>**18**</sup>

Foreign assistance can help developing countries in creating an efficient and equitable urban development pathway, with funding for infrastructure investments and with technical advice and exchange of experience on technical and institutional aspects of the urban management process. Kharas et al. (2010) review what we know about two aspects external assistance: (a) the amounts and effectiveness of external aid for urban programs; and (b) the potential for external‐domestic partnerships to respond effectively to the urbanization challenge.

# 1. <u>Urban aid and aid effectiveness</u>

Figuring out how much aid goes to fund projects in cities turns out to be difficult, since readily available aid data do not permit a simple identification of urban aid. Kharas et al. (2010) have combed through the available data and with some clever assumptions have calculated that about US$ 1 billion per year or less than 1 percent of overall official development assistance (ODA, equal to about US$120 billion per year) goes to urban projects.<sup>19</sup> This is a quarter of what has been estimated is needed to meet the urban slum reduction target under the MDGs (US$4 billion) and

> 18 This section is based on Kharas et al. (2010)

> 19 Note that ODA numbers do not include the lending by the multilateral development banks (the World Bank and regional development banks) for urban projects in middle‐income countries. No estimate of how much this amounts to is available at this time.

19
8 the safety of oral contraceptives for women who are chronic carriers of the hepatitis B virus; the safety of different contraceptives for nursing mothers; the long-term safety and efticacy of the widely used opper-containing intrauterine devices; improving the safety of sterilization procedures; the safety of the NORPLANT contraceptive implant, and the complex problems posed by contraception and HIV infection.

15. New and Improved Methods. Research into new methods of fertility regulation has concentrated on the development of methods critically needed in the developing world--long-acting systemic methods, vaccines, male methods and post-ovulatory methods. Great progress has been made with two female methods which have been brought to the stage of large-scale introduction. These methods are a once-a-month injectable, and a steroidproducing vaginal ring. In fact, two once-a-month injectable preparations have been produced, and bo.h offer an alternative to the three-monthly injectables which, while proven to carry no major health risk, disrupt menstrual patterns in a way that makes them unacceptable to many women. A great deal of work has gone into the development of a birth-control vaccine, and early trials have proven so encouraging that a prototype vaccine is nearing the stage of trials with women volunteers. HRP has continued to assess the efficacy and safety of the new non-surgical approach to early pregnancy termination, involving the use of a combination reg;nen of an antiprogestin, mifepristone (often known as RU486), and a prostaglandin. Regulatory approval has been granted in France and Great Britain. Success with male methods is further away but some progress is being made. Promnising results from the first clinical trial of male hormonal contraception have recently been published. A non-surgical and potentially reversible method of male sterilization is being pursued.

16. Infertility. HRP has been one of the foremost agencies investigating the causes and incidence of infertility. It has already established that infertility is a condition that affects some 35 to 70 million mr.arried couples around the world, and that a principal cause, particularly in women, is infection as a result of a sexually transmitted disease. Other causes are mis.rriage and delivery in conditions of inadequate health care, and illegal abortion.

**2** HRP's ow'n research on RU-486, a substance which has attracted some controversy, accounts for 1.8% of the total HRP budget.
<!-- Start of picture text --> there is a distribution around the conditional mean. For example, even if all conditional<br>means are positive, some individuals may still be hurt by the program. This idiosyncratic het-<br>group effects to inform future targeting of a program may backfire if the impact on the mar-<br>ginal participant is very different from the average treatment effect among the current set of<br>participants, even within each subgroup. Smith (2015) provides the following example. Sup-<br>the cost of participating in the program is 5, only the “big responders” will choose to partici-<br>pate and the program will appear more effective for women than men. However, the impact<br>on the marginal male participant (4) is larger than the impact on the marginal female partic-<br>We can attempt to overcome the above issue by estimating a model of participation<br>of this participation model. The methods in this section can<br>age treatment effect at a specific value of the unobservable component of the participation<br>equation:<br>This is a conditional a erage rea e se described below, but 0 e<br>conditioning variable, , is not observed. Thus, estimation is more complex. Under the a -<br>sumptions of Heckman and Vytlacil, it can be estimated as the derivative of the outcome,<br>with respect to the ex genous variation in the propensity to participate. Brinch, Mogst<br>discuss the relation to LATE.<br>Statistical Inference and Power Calculations<br>Introduction<br>Parts 3 and 4 discuss identification and estimation of treatment effects. However, meaning-<br>ful policy evaluation must also account for the precision of point estimates. This part surveys<br>some tools for formal hypothesis tests and power calculations in DIA: Section 6.2 discusses<br>statistical inference for DIA. It focuses on repeated testing and functional hypotheses. Sec-<br>tion 6.3 surveys tests of a particular distributional hypothesis: whether individual responses<br>to treatment were heterogeneous. Section 6.4 considers the choice of sample size for RCTs.<br>It aims to help researchers design RCTs to address distributional hypotheses.<br>These three sections draw on approximations to the finite-sample behavior of estimators<br>of treatment effects. Inference and power calculations would ideally use the exact distribu-<br>tion of the estimator in question for each sample size. However, finite-sample results are on-<br><!-- End of picture text -->
**Table 5: Explaining intangible capital**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Rule of law|3,000|2,819|308|37|354|188|
||(434)***|(433)***|(383)|(388)|(385)|(380)|
|Years|11,025||36,568||2,480||
|schooling|(2,817)***||(7,877)***||(13,893)||
|_h_||46,178||124,450||92,899|
|||(10,280)***||(24,078)***||(27,590)***|
|li|-107,214<br>(28,644)***|-92,272<br>(29,568)***|||||
|lmi|-153,354<br>(22,928)***|-141,295<br>(23,375)***|||||
|umi|-163,090<br>(18,925)***|-153,567<br>(19,352)***|||||
|dy2000|||||12,954<br>(4,264)***|6,470<br>(2,337)***|
|dy2005|||||25,356<br>(8,588)***|10,013<br>(4,137)**|
|Constant|-22,502<br>(42,663)|-90,246<br>(50,148)*|||||
|Country FE|No|No|Yes|Yes|Yes|Yes|
|(F-test p-value)|||0.00|0.00|0.00|0.00|
|Time FE<br>(F-test p-value)|No|No|No|No|Yes<br>0.01|Yes<br>0.02|
|Observations|315|309|315|309|315|309|
|Number of id|115|112|115|112|115|112|
|R-squared<sup>a</sup>|0.77|0.78|0.21|0.30|0.25|0.31|

Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. a. R-squared in fixed-effects regressions corresponds to within-R-squared.

27
**Table 8: Change in net revenue for each AOGCM climate prediction (US$/farm)**

|**Variable**|**Impact per**<br>**small farm**<br>**($/farm)**|**% of livestock**<br>**income**|**Impact per**<br>**large farm**<br>**($/farm)**|**% of livestock**<br>**income**|
|---|---|---|---|---|
|**2020**|||||
|**Change _CCC**|151|24%|-290|-9%|
|**Change _CCSR**|259|42%|-317|-10%|
|**Change _PCM**|-26|-4%|-545|-17%|
|**2060**|||||
|**Change _CCC**|332|53%|-639|-20%|
|**Change _CCSR**|180|29%|-750|-24%|
|**Change _PCM**|40|6%|-709|-23%|
|**2100**|||||
|**Change _CCC**|720|116%|-722|-23%|
|**Change _CCSR**|945|152%|2|0%|
|**Change _PCM**|194|31%|-842|-27%|

_Note:_ Small farms have a baseline income of $623 per farm. Large farms have a baseline income of $3142 per farm.

33
The variance decomposition analysis helps understand what drives the differences across countries regarding TFP growth. It does not, however, indicate what the most important or relevant drivers of TFP growth are for specific countries. For this, we would need to know the country-specific gaps in each determinant of TFP. We turn to this issue in section 6, on simulations and scenario analysis. Before, however, we need to obtain a reasonable estimate of the effect of the overall index on TFP growth, which we attempt next.

**_Figure 6. Variance decomposition of TFP growth rate corresponding to the determinant subcomponent indexes by decade for all, OECD, and developing countries, controlling for initial TFP and time effects_**

<!-- Start of picture text --> World<br>1985-1994 1994-2004 2004-2014<br>Innovation 0.06 0.05 0.09<br>Education 0.13 0.45 0.36<br>Market efficiency 0.14 0.13 0.18<br>Infrastructure 0.19 0.12 0.17<br>Institutions 0.48 0.25 0.19<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>OECD<br>1985-1994 1994-2004 2004-2014<br>Innovation 0.16 0.09 0.21<br>Education 0.11 0.20 0.16<br>Market efficiency 0.31 0.18 0.45<br>Infrastructure 0.18 0.37 0.08<br>Institutions 0.24 0.16 0.11<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Developing<br>1985-1994 1994-2004 2004-2014<br>Innovation 0.02 0.03 0.07<br>Education 0.13 0.47 0.49<br>Market efficiency 0.08 0.17 0.15<br>Infrastructure 0.20 0.12 0.14<br>Institutions 0.56 0.22 0.15<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br><!-- End of picture text --> Source: Authors' calculation. Note: The OECD group includes high-income countries that have been members of OECD for more than 40 years.

25
# **1 Introduction**

According to the Food and Agriculture Organization of the United Nations, over 60 percent of the world’s population depends on the agricultural sector for their livelihoods and it provides a key source of income and employment for the poor and vulnerable people (FAO, 2013; FAO, 2019). Yet, economic statistics of the agricultural sector are frequently produced at a national or lower administrative level and may not adequately capture the local variation. Furthermore, a spatial mismatch may exist between the geographic unit of interest like the natural area of a river and the administrative area.

Detailed agricultural data are critical to examining a wide range of agricultural issues including technology and land use (e.g. Bella and Irwin, 2002; Luijten, 2003; Samberg et al., 2016; Staal et al., 2002), exposure to natural hazards (e.g. Murthy, Laxman, and Sai, 2015) and patterns of and productivity of economic development (e.g. Elhorst and Strijker, 2003; Gollin, Lagakos, and Waugh, 2014; G. C. Nelson, 2002; Reddy and Dutta, 2018). Carr ̃ao, Naumann, and Barbosa (2016) examine the exposure of people and economic activity to drought using measures of physical elements (e.g. cropland and livestock). Rentschler and Salhab (2020) find that low and middle-income countries have 89% of global flood exposed population and poor people account for almost 600 million, who are directly exposed to the risk of intense flooding. Vesco et al. (2021) examine linkages between climate variability and agricultural production as well as conflict. They find that climate variability contributes to an increase in the spatial concentration of agricultural production within countries. Furthermore, in countries with a high share of agricultural employment in the national workforce, they find this combined effect increases the likelihood of conflict onset. To better target rural development strategies for economic growth and poverty reduction, as well as conserve the natural resource base for long-term sustainable development, we need to accurately delineate the spatial distribution of agricultural resources and production activities (Wood et al., 1999).

One method to partially address spatial mismatch between administrative and other geographic units such as natural hazards uses the gridded (raster) data format by providing an intermediate and consistent unit for disaggregation and aggregation (e.g. UNISDR, 2011). Data-disaggregation methods can use detailed data to inform estimates of aggregated data from large areas at the local level (e.g. see review in Pratesi et al., 2015). Several spatial data products from global models are available to estimate population at a local level (see review in Leyk et al., 2019).

Previous evidence-based risk analyses take advantage of global data of hazards to estimate exposure of population and economic activity (e.g. Gunasekera et al., 2018; Rashmin 3