rafmacalaba/datause-encoder-data huggingface dataset:
40 Wodon and S. Yitzhaki (2002), "Evaluating the Impact of Government Programs on Social Welfare: The Role of Targeting and the Allocation Rules Among Program Beneficiaries," _Public Finance Review_

# **Bibliography for corruption**

Ackerman, J. (2004), “Co-Governance for Accountability: Beyond "Exit" and "Voice"”, World Development, Vol. 32 ( 3); p. 447 Aidt, T. S. (2003), Economic Analysis of Corruption: A Survey”, _The Economic Journal_ , 113, F632-F652 Armstrong, M. and J.C. Rochet (1999), “Multidimensional screening: a user’s guide”, _European Economic Review_ , 43: 959-979 Bardhan and Mookherjee, (2000b), P. Bardhan and D. Mookherjee, Capture and governance at local and national levels. _American Economic Review_ **90** 2 (2000), pp. 135–139 Bardhan, P and D. Mookherjee (2003), “Decentralization and accountability in infrastructure in developing countries”, Boston University, mimeo Bardhan, P. (2004), “Governance Issues in Delivery of Public Services”, _Journal of African Economies_ , 13, AERC Supplement 1, ppil67-il82 Bardhan, P and D. Mookherjee (forthcoming), “Corruption and decentralization of infrastructure in developing countries”, _Economic Journal,_ available in draft at: ~ <u>http://www.econ.yale.edu/ egcenter/decdelinf.pdf</u> Boehm, F and Polanco (2003), “Corruption and privatization of infrastructure in developing countries”, Transparency International, working paper 1, available at <u>http://www.transparency.org/integrity_pact/resources/working_papers/dnld/wk1_boehm_polanco. pdf</u> Campos, J., A. Estache and L. Trujillo (2003), “Processes and accounting matter for regulators: Learning from Argentina’s railways privatization”, _Journal of Network Industries_ , Vol. 4, No1, 3-28 Celentani, M. J-J. Ganiuza and L.L. Peydro (2004), “Combating corruption in international business transactions”, Economica, 71, August., 417-448 Clarke, G. and C. Xhu (2004), “Privatization, Competition and Corruption: How characteristics of Bribe takers and Payers Affect Bribes to Utilities”, _Journal of Public Economics_ , Vol. ( 9-10), pp 2067-2097 Davies, J. (2004), “Corruption in Public Service Delivery: Experience from South Asia’s Water and Sanitation Sector”, _World Development_ , 32 (1), pp53-71 Ebrahim, A. (2003), “Accountability in practice: Mechanisms for NGOs », W _orld Development,_ vol. 31 **(** 5); p. 813 Estache, A., M. Rodriguez-Pardina, J.M Rodriguez and G. Sember (2003), “An Introduction to Financial and Economic Modeling for Utility Regulators”, World Bank., Policy Research Working Paper Estache, A. and E. Kouassi (2002), ”Sector Organization, Governance, and the Inefficiency of African Water Utilities', World Bank Research Working Paper 2890
# **5.1 The average effect of index insurance on production decisions**

We first present evidence on _τ_ , the average effect of index insurance on farmer production decisions. Estimates of _τ_ provide important indications on the effect of index insurance in contexts that are comparable to the current set of contexts.

Figure 1 shows the posteriors obtained from the Bayesian hierarchical model as well as the estimates from the pooling model for comparisons. We find evidence from the pooling model that farmers cultivate more land and invest more in productive inputs when they are offered access to index insurance. The average amount of cultivated land is +0.09 SD higher among farmers receiving insurance offers (p-value = 0.013). Farmers use also more seeds (+0.10 SD), more pesticides (+0.09 SD), and more fertilizers (+0.07 SD) when they receive insurance offers. The effect on the risk index is positive but much smaller and not statistically significant, suggesting that access to index insurance does not lead farmers to cultivate more risky crops, or that such effects are too peculiar to be captured by our generic risk index. The latter explanation is perhaps more plausible because individual studies report subtle effects on crop choices.<sup>35</sup> In addition, our risk index ignores the imperfect correlation between crops, and some farmers might increase the number of crops in their portfolio to reduce their exposure to risk. Looking at these aspects in a systematic way is beyond the scope of this paper but is a fruitful area for future research.

Estimates from the Bayesian hierarchical model confirm that the effects of index insurance on productive investments are likely positive. The posterior mean of _τ_ is positive for all outcomes but the risk index, and similar to estimates from the pooling model. Farmers cultivate more land (+0.11 SD), and use more seeds (+0.11 SD), more pesticides (+0.06 SD), and more fertilizers (+0.08 SD). However, the distributions of the posterior suggest that effects are more uncertain than in the pooling model. All 95% posterior intervals now include zero, and, for some outcomes, there is a sizable probability of negative effects. The 95%-interval is [ _−_ 0.02 SD, +0.25 SD] for cultivated area, [ _−_ 0.09 SD, +0.09 SD] for the crop riskiness index, [ _−_ 0.05 _− −_ SD, +0.21 SD] for fertilizers, [ 0.02 SD, +0.15 SD] for pesticides, and [ 0.02 SD, +0.25 SD] for seeds. Results are broadly robust to using the automatic priors (Figure A1) and to excluding any single study (Figure A2).

> 35For example, in India, Cole et al. (2017) show that the provision of index insurance induces farmers to shift production towards cash crops. In Burkina Faso, Stoeffler et al. (2022) show that the provision of an index insurance contract designed for cotton farmers led them to cultivate more sesame (an attractive cash crop in this context because it requires lower input and time investments).

17
# **Table 2**

# **Pension Fund Managers and Group Affiliations**

This table reports assets under management (AUM) and monthly net flows for each pension fund manager. The table also reports the number of stocks that are in each PFA portfolio and those that are in the same business group.

|PFA<br>(|AUM<br>U$ billions)|Flows<br>(% of AUM)|No. of Stocks|Stock with<br>afliation|No. Obs.|
|---|---|---|---|---|---|
|Ing|6.67|2.35|43|2|1977|
|Colfondos|8.00|2.22|40|2|1777|
|Horizonte|9.27|2.02|42|1|1814|
|Porvenir|19.30|2.32|29|8|1639|
|Proteccion|13.77|2.32|41|17|2036|
|Skandia|2.53|3.60|29|0|1461|
|Total|59.54|2.28|57|30|10704|

**Table 3**

# **Summary Statistics**

Descriptive statistics for publicly traded companies. Mean, median and standard deviation of variables split by affiliated and non-affiliated companies. Variables are defined in Table 1.

|||Group-Aflia|ted||Non-afliat|ed|Mean Diference|
|---|---|---|---|---|---|---|---|
|Variable|_Mean_|_Median_|_SD_|_Mean_|_Median_|_SD_|_t −statistic_|
|Market Value (U$ billions)|1.678|1.716|0.747|2.427|2.398|0.723|-4.025|
|_CONC_|0.601|0.619|0.265|0.633|0.716|0.294|-1.90|
|_INS_|0.888|0.920|0.134|0.848|0.938|0.202|3.94|
|_FOREIGN_|0.130|0.020|0.228|0.303|0.052|0.379|-9.35|
|_GOV NT_|0.047|0.000|0.120|0.30|0.000|0.412|-14.06|
|_B/M_|1.223|0.755|1.284|1.292|0.681|2.244|-0.62|
|_SIZE_|15.655|15.896|1.250|14.427|14.313|1.66|14.05|
|_ROA_|0.022|0.014|0.035|0.042|0.030|0.067|-6.26|
|_LEV_|2.399|0.613|3.139|1.219|0.372|2.805|6.68|
|_SPREAD_|-3.294|-3.547|1.191|-3.090|-3.157|1.222|-2.49|
|_TURNOV ER_|-9.150|-8.659|2.230|-8.407|-8.347|1.980|-5.86|
|_ARET_|0.000|-1.263|8.025|0.000|-1.120|0.922|2.648|
|∆_H_|0.083|0.005|1.086|0.086|0.022|0.363|-0.27|
|_H_|0.041|0.029|0.033|0.013|0.013|0.006|42.58|

27
|Study and Location|Definition of<br>Empowerment|Measurement<br>Concept|Data Sources|Focus|Conclusions|Implication|Scope|
|---|---|---|---|---|---|---|---|
|_implementation in_<br>_13 countries (South_<br>_Africa, Mexico,_<br>_Uruguay, Indonesia,_<br>_Pakistan, Belarus,_<br>_Croatia, Estonia,_<br>_Romania, Ukraine,_<br>_Canada, New_<br>_Zealand, and Wales)_|<br>(whom) to do<br>(what)|based on the<br>average of five<br>indicators scored<br>from 0 to 3: activity<br>and effectiveness of<br>civil society in<br>informing and<br>educating, building<br>capacity for<br>collective action,<br>empowering poor<br>people, empowering<br>women, and<br>building social<br>capital|community<br>surveys,<br>secondary data<br>and fact finding|the world|Society Index<br>with a larger<br>scoring matrix<br>and 69 indicators<br>encompassing<br>four dimensions<br>(structure,<br>external<br>environment,<br>values, and<br>impact of<br>activities)|forces, map of<br>civil society;<br>power relations in<br>society and<br>relations within<br>organizations)|relation to the impact of<br>activities of civil<br>society (opportunity<br>structure); indicators<br>related to aspects of<br>inclusion/participation<br>(under structure),<br>opportunity structure<br>(under environment and<br>values), and influence<br>(under impact). Social<br>domain.|
|Grootaert (2003)<br>_Albania, Armenia,_<br>_Azerbaijan,_<br>_Georgia, Kyrgyz_<br>_Republic,_<br>_Macedonia,_<br>_Moldova, Tajikistan,_<br>_and Uzbekistan_|Expanding<br>assets and<br>capabilities of<br>poor people to<br>participate in,<br>negotiate with,<br>influence,<br>control, and hold<br>accountable<br>institutions that<br>affect their lives|Average<br>empowerment score<br>based on 12 priority<br>indicators along five<br>elements: state<br>reform (government<br>effectiveness,<br>corruption<br>perceptions index,<br>incidence of illicit<br>payments); reform<br>of legal system (rule<br>of law, regulatory<br>quality, pro-poor<br>decentralization);<br>democracy (civil<br>liberties and<br>political freedoms,<br>voice and<br>accountability, civil<br>society strength);|International<br>databases<br>(governance<br>database,<br>Transparency<br>International,<br>WBES/BEEPS,<br>Freedom House,<br>UNDP, and<br>World<br>Development)|Non-income<br>dimensions of<br>poverty|Many indicators<br>are available from<br>international<br>databases; lack of<br>indicators of<br>decentralization<br>or social capital|Overview of<br>accessible<br>indicators<br>available at<br>different points in<br>time|National level.<br>Opportunity structure.<br>Political domain.|

36
Panel A in Table 1 reports results from simple regressions of potential yields of a number of ...eld crops on percentage of area under LDO leases and distance of an area from large urban centers. The potential yield data at sub-district level (i.e., DSD level) are derived from the IFPRI SPAM model for Sri Lanka. The potential yields are determined on the basis of soil quality, moisture level, rainfall and other land quality and climatic variables. The potential yields assume an ideal amount of labor to be applied irrespective of prevailing wages. The yield regressions also include a district level ...xed e¤ect to control for factors such as infrastructure, other services and demand conditions. As noted before, the district ...xed e¤ects also wipe o¤ any time invariant land or labor productivity di¤erences across districts. The results reported in Panel A of Table 1 show that, in a given district, yields are signi...cantly higher in DSDs with higher percentage of land under LDO restrictions for 11 out of 12 ...eld crops considered. For ‘other oilseeds’, the estimated coe¢ cient is positive but not estimated with precision. One might worry that the potential yield does not give us a complete picture regarding di¤erences in productivity as the LDO areas might have di¤erent levels of human capital (health and education), and as a result the actual (ex post) productivity of land might be systematically di¤erent. Panel B in Table 1 reports the estimates for actual yield which can be treated as a summary measure of all the di¤erent factors a¤ecting productivity including di¤erences in human capital and cultural norms regarding work across di¤erent DSDs. It is reassuring that the regression results for actual yield con...rm the conclusions based on potential yield in Panel A of Table 1. The evidence in Table 1 is thus very strong in favor of the conclusion that the LDO lands are more productive compared to the other lands in Sri Lanka and that any possible adverse e¤ect of low labor quality is clearly dominated by strong positive correlations between land productivity and incidence of LDO restrictions in a DSD.

The evidence in Table 1 is also consistent with the evidence on overall crop yields at the district level reported by the Statistical Abstracts of Sri Lanka. According to Statistical Abstract, 2009, paddy yields during the monsoon season in Mahaweli system H in the Dry zone is about 30 to 40 percent higher than average yield in Sri Lanka. Mahaweli annual reports also indicate signi...cant productivity advantage of settlement schemes for nearly all ...eld crops.<sup>17</sup> The striking productivity advantage of the LDO areas implies that the OLS estimate of the

> 17 The crop productivity statistics in Mahaweli area are posted in http://www.mahaweli.gov.lk/.

15
conceptual underpinning to estimate housing deficit focused on inadequacy of housing across and within countries. Therefore, the estimation of the housing deficit presented in this section frames the challenge faced by households in securing housing that is adequate. We present the estimates of housing deficit in terms of number of housing units based on the _AHI_ , average HH size, and the country’s current population. This presentation of the deficit as the number of housing units offers practical applicability for policy makers drafting housing policies or planning subsidy programs for lower-income segments as well as private sector companies along the housing value chain. We refer to this number as the adequate housing _stock deficit_ . This deficit can be tackled by not only building new housing units but also by expanding infrastructure networks to reach all segments of the population, by providing consistent services to all aspects of housing adequacy, as well as by upgrading or replacing inadequate housing. Although our data does not allow to fully distinguish these two aspects contributing to the overall stock deficit, we present an indicative estimate of the subset of the deficit that may require new or replacement buildings, as opposed to just upgrade and refurbishments. Based on the stock assessment, under assumptions of continuation of business-asusual policies and practices, and using projection of urbanization rates and population growth, we also estimate the _flow deficit_ , which is the projected housing deficit by 2030.

# **_5.1 Stock Deficit_**

Making use of the _AHI’s_ interpretation at the aggregate level as the share of housing that is inadequate, we estimate the current stock deficit, defined here as the difference between the projected number of housing units in a country and those units that are currently inadequate. We approximate the number of current housing stock by drawing on available data of countries rural and urban population as well average HH size in rural and urban areas.<sup>19</sup> Based on these estimations, we estimate a country’s current housing stock deficit as where _D_ is the current housing stock deficit for country _c, AHIu,c_ is the _urban_ Adequate Housing Index, _AHIr,c_ the _rural_ Adequate Housing Index, S _u,c_ the average urban household size, S _r,c_ the average rural household size, P _u,c_ the current urban population, and P _r,c_ the current rural population.

The overall housing deficit across emerging economies is denoted as the sum of the countries’ housing stock deficits (Table 4). Based on this calculation, we estimate that the housing deficit in emerging markets affects at least 268 million households or about 1.26 billion people. Across 64 emerging economies, about 26 percent of the current stock of housing units is inadequate.

> 19 We do not draw a distinction between number of households and number of housing units/dwellings. The latter data is available through HOFINET for only a small sample of countries. For these countries, the number of dwellings is approximated closely by the number of estimated households we use.
# **4  Livestock and climate change**

Estimates of temperature increases in the Fourth Assessment Report (AR4) are in the range 1.8 to 4°C in 2090-2099 relative to 1980-1999, depending on the scenario of future greenhouse gas emissions that is used to drive the climate models (IPCC, 2007). At the lower end of this range, global food production might actually increase but above this range would probably decrease (IPCC, 2007). However, broad trends will be overshadowed by local differences, as the impacts of climate change are likely to be highly spatially variable. Climate change will alter the regional distribution of hungry people, with particularly large negative effects in Sub-Saharan Africa. Smallholder and subsistence farmers, pastoralists and artisanal fisherfolk will suffer complex, localized impacts of climate change, due both to constrained adaptive capacity in many places and to the additional impacts of other climaterelated processes such as snow-pack decrease, particularly in the Indo-Gangetic Plain, and sea level rise (IPCC, 2007). Furthermore, changes in the frequency and severity of extreme climate events will have significant consequences for food production and food security; it is not only projected mean climate change that will have an impact. Increasing frequencies of heat stress, drought and flooding events are estimated to be likely, even though they cannot be modeled in any satisfactory way with current levels of understanding of climate systems, but these will have adverse effects on crop and livestock productivity over and above the impacts due to changes in mean variables alone (IPCC, 2007).

# **_4.1  Livestock's impact on climate_**

Total emissions of greenhouse gases (GHGs) from agriculture, including livestock, are estimated to be between 25 and 32%, depending on the source (US EPA, 2006; IPCC, 2007) and on the proportion of land conversion that is ascribed to livestock activities. Agricultural crops are estimated to account for 14% of this total. The convention for accounting for GHGs emitted by livestock followed by the IPCC does not account for respiration, because livestock are not considered a net source of CO2. Overall, the CO2 absorbed by the plants that are consumed by animals is considered to be approximately equivalent to that emitted by them. This is not always the case, but there is a considerable body of evidence to show that in general, grasslands do offset the CO2 emissions from livestock, and in places they may more than offset them (IPCC, 2001; Fisher et al., 1994). Steinfeld et al. (2006) estimated that overall, livestock activities contribute some 18% to total anthropogenic greenhouse gas 50
law to obey their husbands. The overlap between the Global Findex and Gallup data and the WBL database is for up to 90 developing countries.

Second, we use data from the OECD Gender, Institutions and Development (GID) database to test whether certain manifestations of gender norms affect the use of financial services. GID indicators attempt to measure the actual situation of women.<sup>14</sup> We use the following two continuous variables measuring (i) the prevalence of early marriage and (ii) violence against women.<sup>15</sup> These variables range from zero to one, with higher values indicating higher prevalence, that is, a more restrictive environment for women. OECD GID data are available for 104 mostly developing countries; the database does not cover high income countries or countries in Eastern Europe. The overlap between the Global Findex and Gallup data and the OECD GID database is up to 86 developing countries.

Table 2 presents summary statistics of these variables. In general, men and women are treated equally by the law in about 80 percent of the countries in our sample for any one of the questions we consider, with the exception that in 11 percent of countries, women are required by law to obey their husbands. The default marital property regime is separation in 35 percent of countries. On average, 21 percent of women enter into an early marriage, and the violence against women variable falls slightly to the right (56 percent) of the midpoint of the no violence to violence spectrum. Appendix B provides more detailed variable descriptions and sources.

# **4. Empirical Methodology**

We next turn to multivariate regression analysis to explore whether gender differences in the use of financial services shown in summary statistics are robust after controlling for

> 14 For details on methodology of OECD GID-DB see http://genderindex.org/sites/default/files/GID_variables.pdf. 15 The OECD GID database contains a total of 12 indicators. However, the other variables show little if any variation across countries and are therefore not considered.

16
Guam, as reported in Clemens et al. (2019).<sup>15</sup> We take this to be the most plausible range in Table 1. Nonetheless, we consider both lower and higher values, given the uncertainty about these costs, and the interest in seeing how this impacts the results.

Introducing migration costs greatly reduces the equilibrium price of WPs (comparing column 1 with other columns). Without any cost of migration, the price is $27,000 with almost 54 million workers participating in the market. Since more Mexicans can afford to purchase the WP, driving the price up, this scenario brings the largest gains to the host country, with net wage gains of over 8% of U.S. GDP, and a poverty rate falling to 8% (from 12.3%). Simply adding a 10% remittance levy brings the price of a WP down by $4,500 (Scenario 2). In the most plausible inner range of parameter values (scenarios 4-6), we find equilibrium prices of the WPs in the range of $18-19,000 (depending on the tax rate on WPs), with around 28-33 million market participants, well outside the aforementioned range of estimates for the count of illegal migrants of 11-22 million. The gain in earnings (earnings of migrants less forgone earnings of natives) is 6-7% of U.S. GDP, though it should be noted that this is not the net gain as one would have to deduct the earnings gain from current migrants. Given that the market-based policy would tend to attract more skilled migrants, a net gain can be expected. The policy brings the poverty rate among citizens of the U.S. down to a little under 10%. (The Appendix shows the impact on poverty for a wide range of possible poverty lines.) Higher tax rates yield higher revenue from that tax, but lower revenue from taxes on the migrants’ earnings; on balance, higher tax rates yield higher total revenue (Table 1). The impacts on the GDP share and the poverty rate are small. Adding a 10 percentage point allowance for the extra cost of living in the U.S. (beyond what PPP rates allow for) brings the price down more substantially, by around $2-3,000. As expected, a higher remittance levy reduces the equilibrium price of a WP, though the effect is small after the first increment (from 0 to 10%). This also slightly reduces both the GDP share and the poverty impact.

> 15  Notice that we express migration costs as a share of the US earnings while Clemens et al. write them as a share of earnings at the origin. Following Clemens et al., let the earnings differential (US relative to Mexico) needed to compensate for the migration cost be for . Then the migration cost as a share of US earnings is .  The values for of 0.3-0.5 found by Clemens et al. based on wage differentials between Puerto Rico/Guam and the U.S. mainland imply that tigration costs as a share of U.S. earnings of 0.23-0.33. On also allowing for out-of-1 + ππ ππ> 0 ππ/(1 + pocket travel costs and rounding to the same order of accuracy as Clemens et al., the implied range for the total of ππ) ππ� the extra cost of living and the remittance levy in our analysis is 0.2-0.3, as a share of U.S. earnings.

11