rafmacalaba/datause-encoder-data huggingface dataset:
The estimation results for the Euro Area show a greater precision of the interest rate pass-through that the one estimated for the entire sample. The precision approaches one—that is, an almost complete pass-through. Interestingly, the Euro Area results reveal a substantial asymmetry of the pass-through. Therefore, for the Euro Area countries the study estimates may suffer from a significant publication selectivity bias. Regarding study-specific, macro-financial, and monetary policy factors, the results show a statistical significance of only three factors: market capitalization, the stock turnover ratio, and the dummy for rates on loans for other purposes. The significance of market capitalization and the dummy appear in all previous estimations regardless of the sample size and the estimation method. As for the stock turnover ratio, the effect seems to be rather small and statistically significant only at the 10% level. Nevertheless, it suggests that more actively-trading (liquid) stock markets can reduce the size of the pass-through.

For the non-EA countries, the estimation sample covers only about one-quarter of our original sample. Therefore, the regression estimation faces the problem of increased collinearity among variables, which could bias the results even after applying the GETS selection procedure. Nevertheless, the model still largely confirms the results of our previous estimations. We confirm significant effects of the dummy for rates on long-term loans, consumer loan rates, average rates, market capitalization, central bank independence (using the Garriga (2016) data series), and the inflation targeting regime. Several additional factors appear significant in this case. Namely, for the non-Euro Area countries, we find that using data in quarterly frequency reduces the size of the estimated pass-through. Further, using the monetary policy rate rather than the money market rate as the reference rate increases the size of the estimated pass-through. Looking at the macro-financial factors, two additional variables appear significant. One, the stock turnover ratio lowers the size of the pass-through—much more so than for the Euro Area subsample. This could suggest that active stock markets may serve as competing source of capital to finance both firms and banks alike. For this reason, changes in the central bank rate and money market rate might have a lower impact on funding costs of banks and firms—resulting in a lower interest rate pass-through. Two, increasing net foreign asset balances strengthen the interest rate passthrough. This estimated effect may imply that as more domestic savings are exported abroad (relative 25
# **4. Policy  Implications**

# **4.1 Why Worry About Informality?**

There are (at least) three reasons why informality is of concern in policy circles—it is associated with higher levels of poverty and vulnerability, lower levels of productivity, and lower contributions to fiscal revenue. Let us consider each of these in turn.

Figure 4 plots the share of informal jobs against poverty rates across countries. The line is upward sloping suggesting that a larger size of the informal sector is associated with higher poverty rates. The macro level association is confirmed by almost every micro level country specific study that looks at the association between poverty and informality. Most people who work in the informal sector are predominantly poor in income and in non-income dimensions. This is because informal jobs have low productivity, and certain groups, such as the young or women are over represented in the informal sector (Jutting and de Laglesia, 2009). In India, workers in the unorganized sector have a much higher incidence of poverty (20.5 %) than their counterparts in the organized sector (11.3 percent, NCEUS (2007)). Studies also find lower levels of achievement in health, education and other non-income achievements in the informal sector.

**Figure 4: Strong Association between Informality and Poverty**

<!-- Start of picture text --> Large informal sectors associated with high poverty<br>100 rates<br>90<br>India<br>80<br>Indonesia<br>Kenya<br>70 Philippines<br>Pakistan<br>Sri Lanka<br>60 Argentina<br>Thailand<br>50 Brazil South Africa<br>Mexico<br>40 Egypt Ethiopia<br>Turkey<br>30<br>0 10 20 30 40 50 60 70 80<br>% of Population Below Poverty ($1.25/day, PPP)<br>Source: OECD, 2009. World Development Indicators, 2010. ILO KILM.<br>Note: 48 countries with available data shown. Chart uses latest available data on informal share of employment<br>(1995-99 or 2000-07).<br>employment<br>informal employment share of non-agricultural<br><!-- End of picture text --> Certain caveats are in order. The links between working informally and being poor are not always simple. Recent empirical work (Chen 2006) has emphasized heterogeneity within the informal sector. On the one hand, not all jobs in the informal economy yield paltry incomes. Many in the informal economy, especially some self-employed, in fact earn more than unskilled or low-skilled workers in the formal economy. There is much innovation and many dynamic growth-oriented segments in the informal economy, some of which require considerable knowledge and skills. One of these is the fast-growing information and communications technology (ICT) sector in the large cities of India. There is also some evidence that 16
Using a very large database of OECD imports at the SITC4 level since 1963, we find that, up to the turn of the century, OECD markets have been diversifying their sources of supplies (geographically) at the product level. This is reflected in decreasing concentration indices and a rising number of export sources. However, the trend in concentration has reversed itself in recent years. We show that this trend reversal is entirely explained by the rising share of Chinese products in OECD imports, as concentration indices keep on decreasing monotonically when China is excluded. We also find that the pattern of import diversification at the product level is broadly consistent with a simple model where buyers screen suppliers for quality and toss them out when they under-perform. The model predicts that diversification happens by ―bouts‖, or temporary episodes, during which OECD buyers search for high-quality suppliers. Each diversification episode is followed by a phase of reconcentration on the best performers, until those fail (which happens stochastically), triggering new search phases. The model is a very simple, finite-horizon version of a classic two-arm bandit problem. It is close in spirit to Jaud (2011) who also uses a multi-arm bandit setup to explore the effect of tightening standards on purchase volumes.

We test the model‘s basic prediction by looking at the evolution of unit values during re-concentration episodes and at how the volatility of concentration indices varies across products types, taking the variation in unit values as a proxy for quality heterogeneity. We find, as predicted by the model, that re-concentration, when it happens, is associated with a _rise_ in unit values. That is, when buyers re-concentrate, they do so on higher-priced (and hence presumably higher-quality) suppliers rather than on the most price-competitive. We also find that concentration indices are more volatile, over time, for products whose quality (as proxied by unit values) is more heterogeneous across suppliers.

The paper is organized as follows. Section 2 analyses the overall trend in OECD import concentration. Section 3 and 4 set up a simple model of supplier screening in the presence of adverse selection. Section 5 explores empirically the model‘s implications for patterns of concentration and diversification.

4
# **RURAL Administrative Units**

**Figure A7: Standard Error as Percentage of point estimate: Rural Area Azerbaijan, FTG(0) Comparing survey based stratum-level estimates to census based administrative level estimates (896 administrative level)**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15 Stratum ratio (from survey)<br>10<br>5<br>0<br>0 200 400 600 800 1000<br>Ranking by (s.e./point estimate)<br>Percentage<br><!-- End of picture text -->

**Figure A8: Standard Error as Percentage of point estimate: Rural Area Azerbaijan, mean adult equivalent consumption Comparing survey based stratum-level estimates to census based administrative level estimates (896 administrative level)**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15 Stratum ratio (s.e./point estimate)<br>10<br>5<br>0<br>0 200 400 600 800 1000<br>Ranking by (s.e./point estimate)<br>Percentage<br><!-- End of picture text --> 44
Table 7: Care and Domestic Responsibilities - Correlation with Perceived Norms, conditional on Personal Beliefs

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Main<br>Caregiver:<br>Preschool<br>or School<br>Children|Hours<br>spend on<br>care<br>activities<br>(Main<br>Caregiver<br>Children)|Hours<br>spend on<br>household<br>chores<br>(Main<br>Caregiver<br>Children)|Main<br>Caregiver:<br>Preschool<br>or School<br>Children|Hours<br>spend on<br>care<br>activities<br>(Main<br>Caregiver<br>Children)|Hours<br>spend on<br>household<br>chores<br>(Main<br>Caregiver<br>Children)|
||**_Gend_**|**_er Equality _**|**_Norm_**|**_Femal_**|**_e Caregiver_**|**_Norm_**|
|Female|0.11***|2.21***|1.77***|0.05***|2.17***|1.13***|
||[0.03]|[0.58]|[0.21]|[0.01]|[0.33]|[0.11]|
|Personal Belief:Agree(No=0/Yes=1)|0.01|-0.21|0.07|0.03***|0.34*|0.09|
||[0.02]|[0.27]|[0.10]|[0.01]|[0.18]|[0.06]|
|Personal Belief:Agree*Female|-0.00|-0.31|-0.30|0.02*|0.49*|0.21**|
||[0.02]|[0.53]|[0.19]|[0.01]|[0.27]|[0.09]|
|Community Norms (0/1)|0.01|0.00|0.33**|-0.01|0.91***|0.39***|
||[0.02]|[0.38]|[0.13]|[0.02]|[0.30]|[0.11]|
|Community Norms (0/1)*Female|-0.04|1.41**|-0.39**|0.04*|0.46|-0.01|
||[0.03]|[0.56]|[0.19]|[0.02]|[0.49]|[0.17]|
|Constant|0.29***|4.24***|4.07***|0.27***|3.23***|3.49***|
||[0.07]|[0.99]|[0.51]|[0.05]|[0.85]|[0.35]|
|Observations|15506|6820|7162|21211|9180|9670|
|Adjusted R-squared|0.23|0.17|0.18|0.22|0.16|0.17|
|Mean Female|0.48|9.52|4.26|0.47|9.42|4.27|
|Mean Male|0.42|6.17|2.93|0.42|6.20|2.94|
|PersBelief+Fem(P-value)|0.80|0.26|0.16|0.00|0.00|0.00|
|ComNorms+Fem(P-value)|0.17|0.00|0.66|0.07|0.00|0.00|
|Mean VIF|3.65|4.79|4.62|2.60|3.80|3.65|
|Sample|ALL|ALL|ALL|ALL|ALL|ALL|
|CountryFE|YES|YES|YES|YES|YES|YES|
**<u>Table 1: Correlations between democracy and measures of economic institutions</u>**

||**bq**|**corr**<br>**1984**|**polity4**|**bq**|**corr**<br>**1997**|**polity4**|
|---|---|---|---|---|---|---|
|**bq**|1|||1|||
|**corr**|0.8276|1||0.6971|1||
|**polity4**|0.6069|0.5634|1|0.4555|0.5783|1|

**<u>Table 2: Correlations between democracy and measures of economic institutions</u>**

||**avg_bq**<br>**Average**|**avg_corr **<br>**1984-1997**|**avg_polity4**|
|---|---|---|---|
|**avg_bq**|1|||
|**avg_corr**|0.829|1||
|**avg polity4**|0.5942|0.6002|1|

*Notes for Tables 1 and 2: bq= bureaucratic quality, corr= corruption, from ICRG; polity4 (from polity IV index, normalized 0-1)= democracy measure, avg= average.

7
||Nigerian 2006’s SAM||GTA|P database version 8.1|Aggregat|ed 21 GTAP sectors|
|---|---|---|---|---|---|---|
|No.<br>Code|<br>Description|No.|Code|Description|<br>No.<br>Code|<br>Description|
|||34|nmm|Mineralproductsnec|||
|||35|i_s|Ferrousmetals|||
|||36|nfm|Metalsnec|||
|||37|fmp|Metalproducts|||
|||38|mvh|Motor vehicles and parts|||
|||40|ele|Electronic equipment|||
|||41|ome|Machinery and equipment<br>nec|||
|||42|omf|Manufacturesnec|||
|48 croil|Refined oil|32|p_c|Petroleum, coalproducts|16<br>RefinedOil|Petroleum, coalproducts|
|44cemfc|Transportationand otherequipment|39|otn|Transport equipmentnec|17<br>TransEquip|Othertransport equipment|
|51<br>cutil|Electricity and water|43|ely|Electricity|18<br>Electricity|Electricity|
|||_45_|_wtr_|_Water_|||
|Zero sector||44|gdt|Gas manufacture,<br>distribution|||
|50 ccons|Building and construction|46|cns|Construction|||
|54 ctrad|Wholesale and retail trade|47|trd|Trade|19<br>TrdFinBusIns|Trade, financial and business<br>services|
|55 chotl|Hotel and restaurants||||||
|57<br>cbser|Financial institutions, Insurance,|52|ofi|Financialservicesnec|||
||Business service|53|isr|Insurance|||
|||54|obs|Business services nec|||
|58 crest|Realestate||||||
|53<br>cotra|Other transportation|48<br>49|Otp<br>wtp|Transport nec<br>Sea transport|20<br>TransComm|Transport and communications|
|||50|atp|Airtransport|||
|56 ccomm|Telecommunications, Post,<br>broadcasting|51|cmn|Communication|||
|62 coser|Private nonprofit organizations, Other<br>services|55|ros|Recreation and other<br>services|21<br>OServices|Other services|
|59 ceduc|Education|56|osg|PubAdmin/Defence/Health|||
|60 cheal|Health|||/ Education|||

33
- Bond, S. (2002), "Dynamic Panel Data Models: A Guide To Micro Data Methods  and Practice", _CEMMAP Working Paper CWP09/02_ .

- Bond S., and M. Soderbom (2005), “Adjustment Costs and the Identification of Cobb Douglas Production Functions”, _The Institute for Fiscal Studies, Working Paper, 05/04_ . Oxford.

- Brautigam, D. and S. Knack (2004), “Foreign Aid, Institutions, and Governance in Sub-Saharan Africa”, _Economic Development and Cultural Change_ , Vol. 52, 1, 255–85.

- Calderon, C. and L. Serven (2004), “The Effects of Infrastructure Development on Growth and Income Distribution”, _Central Bank of Chile working paper, #270_ .

- Celasun, O and J. Walliser, (2005), “Predictability of Budget Aid: Experiences in  Eight African Countries”, _Preliminary draft_ .

- Clague, C., P. Keefer, S. Knack S., M.Olson (1999), “Contract-Intensive Money:  Contract Enforcement, Property Rights, and Economic Performance”, _Journal of Economic Growth_ , Vol. 4, 185-211.

- Clemens, M., S. Radelet and R. Bhavnani, (2004), “Counting chickens when they  hatch:  the short-term effect of aid in growth”, Center _for Global Development WP No.44._

- Collier, P., and D. Dollar (1999), “Aid Allocation and Poverty Reduction”, _WBPRWP_

- Collier, P., and D. Dollar (2004), “Development effectiveness: What have we learned?”, _Economic Journal_ , Vol. 114, 244–271.

- Dalgaard, C., H. Hansen, F. Tarp (2004), “On the Empirics of Foreign Aid and Growth”, _The Economic Journal_ , Vol. 114, 191–216.

Djankov, S., J.G. Montalvo, and M. Reynal-Querol, “The curse of aid” _Mimeo, World Bank_ .

- Dollar, D., and V. Levin (2004), “The Increasing Selectivity of Foreign Aid, 1984-2002”, _World Bank Policy Research Working Paper._

- Easterly, W. (2003), “Can Foreign Aid Buy Growth?", Journal _of Economic Perspectives_ ,Vol.17,3, 23-48.

- Easterly, W., R Levine, and D. Roodman (2004), “New Data, New Doubts: A Comment on Burnside and Dollar’s- Aid, Policies, and Growth”, _The American Economic Review_ , forthcoming.

- Fielding, D. and G. Mavrotas (2005), “The Volatility of Aid”, _Mimeo_ , United Nation University, WIDER.

36
**<u>Table 10 Trade Orientation and Firm Evolution</u>**

||EmployGrowth|Sales Growth|ProyGrowth|Log (Proy)|Prod Innov|Proc Innov|Foreign Lic|QualityCert|
|---|---|---|---|---|---|---|---|---|
|Export/Import|0.037<br>(0.004)***|0.048<br>(0.008)***|0.028<br>(0.005)***|0.214<br>(0.042)***|0.193<br>(0.015)***|0.091<br>(0.020)***|0.122<br>(0.030)***|0.141<br>(0.022)***|
|Export Only|0.020|0.043|0.021|0.150|0.142|0.073|0.104|0.109|
||(0.005)***|(0.010)***|(0.007)***|(0.042)***|(0.022)***|(0.022)***|(0.044)**|(0.033)***|
|Import Only|0.015<br>(0.004)***|0.019<br>(0.006)***|0.009<br>(0.004)**|0.125<br>(0.029)***|0.132<br>(0.014)***|0.046<br>(0.020)**|0.092<br>(0.027)***|0.013<br>(0.018)|
|Foreign (≥%10)|0.015|0.025|0.027|0.170|‐0.020|‐0.057|0.113|0.049|
||(0.004)***|(0.007)***|(0.004)***|(0.026)***|(0.016)|(0.027)**|(0.026)***|(0.020)**|
|Log(Invest/Worker)|‐0.000|0.011|0.022|0.263|0.010|0.002|0.006|0.031|
||(0.001)|(0.002)***|(0.002)***|(0.011)***|(0.004)**|(0.005)|(0.004)|(0.005)***|
|NonProd Worker Share|‐0.012<br>(0.004)***|0.008<br>(0.007)|0.024<br>(0.005)***|0.261<br>(0.060)***|0.072<br>(0.024)***|‐0.046<br>(0.017)***|0.025<br>(0.014)*|0.076<br>(0.028)***|
|R&D Ind|0.019|0.014|0.003|0.147|0.141|0.189|0.042|0.080|
||(0.003)***|(0.006)**|(0.004)|(0.025)***|(0.018)***|(0.015)***|(0.014)***|(0.017)***|
|Training|0.023<br>(0.003)***|0.029<br>(0.005)***|0.012<br>(0.003)***|0.110<br>(0.020)***|0.107<br>(0.013)***|0.133<br>(0.018)***|0.055<br>(0.014)***|0.181<br>(0.015)***|
|Age|‐0.001|‐0.000|0.001|‐0.000|‐0.001|‐0.000|‐0.000|0.001|
||(0.000)***|(0.000)***|(0.000)***|(0.001)|(0.000)*|(0.000)|(0.000)|(0.000)**|
|Log(Labor)t-3|‐0.021<br>(0.001)***|||0.069<br>(0.009)***|0.009<br>(0.005)*|‐0.001<br>(0.007)|0.034<br>(0.006)***|0.073<br>(0.006)***|
|Log(Sales)t-3||‐0.014<br>(0.002)***|||||||
|Log(Productivity)t-3|||‐0.077<br>(0.003)***||||||
|Observations|8355|7193|7792|8827|9311|3622|3628|9189|
|R2/Pseudo R2|0.121|0.094|0.246|0.590|0.158|0.162|0.134|0.370|

Robust standard errors clustered by country, industry, and year are in parentheses. In the regressions, I control for 2‐digit industry, survey year, and country fixed effects. Coefficients for the probit regressions show the marginal effects at their mean values. *** p<0.01, ** p<0.05, * p<0.1.

22
|Table V(b)<br>|||
|---|---|---|
|Effect of Elections on Cap<br>[t-statistics in pare|ital Spen<br>nthesis]|dinga|
||[1]|[2]|
|Independent Variables:|||
|1 yr before Elections|-0.71|-1.30|
||[-0.36]|[-0.70]|
|2 yrs before Elections|-1.33|-1.75|
||[-0.78]|[-1.02]|
|3 yrs before Elections|-2.43|-3.00|
||[-1.40]|[-1.66]|
|4 yrs before Elections|-3.58|-4.07|
||[-1.87]|[-2.12]|
|State domestic product [SDP]||0.002|
|||[0.51]|
|Share of agriculture in SDP||8.34|
|||[0.90]|
|Proportion of rural population||-103.58|
|||[-2.12]|
|Number of Observations|469|407|
|R-sq|0.65|0.66|

a. Capital outlays and SDP are in 1973 rupees per capita. Include state and year effects, and reported with robust std. errors.

44
||Average<br>change<br>in sales<br>(%)|Average<br>change<br>in sales<br>(%)|Average<br>change<br>in sales<br>(%)|Average<br>change<br>in sales<br>(%)|Average<br>change<br>in sales<br>(%)|
|---|---|---|---|---|---|
|2 quantile|3.77|4.15|2.44|4.26|3.42|
||(3.79)|(3.79)|(3.87)|(4.79)|(3.81)|
|3 quantile|10.16**|11.13***|8.97**|11.70**|9.91**|
||(4.30)|(4.25)|(4.32)|(5.28)|(4.29)|
|4 quantile|9.62**|10.74**|8.30*|12.17**|9.75**|
||(4.64)|(4.68)|(4.73)|(5.61)|(4.67)|
|5 quantile|11.72**|12.46**|11.15**|14.74**|11.42**|
||(5.10)|(5.08)|(5.10)|(6.09)|(5.12)|
|Increased use of digital platforms|6.27***<br>(2.36)|||||
|New investment in digital solutions||6.93**||||
|||(3.41)||||
|More work from home|||11.49***|||
||||(3.97)|||
|More online sales||||16.20***||
|||||(4.31)||
|Increased use of digital tech|||||6.16***|
||||||(2.26)|
|Medium|2.08|1.73|1.16|2.01|2.22|
||(2.69)|(2.68)|(2.74)|(3.13)|(2.66)|
|Large|0.62|1.11|1.02|-1.26|1.47|
||(3.36)|(3.48)|(3.53)|(4.06)|(3.40)|
|Age 6 to 10|-4.04|-4.40|-2.84|-0.30|-4.65|
||(3.32)|(3.34)|(3.40)|(3.89)|(3.31)|
|Age 11 to 15|-1.82|-2.30|-0.31|0.90|-2.57|
||(3.66)|(3.69)|(3.77)|(4.19)|(3.67)|
|Age 16+|-0.78|-1.37|0.34|2.31|-1.26|
||(3.69)|(3.72)|(3.81)|(4.35)|(3.72)|
|Export|3.64|3.28|3.43|1.91|2.99|
||(3.01)|(2.99)|(3.07)|(3.70)|(2.99)|
|Foreign owned|11.41**|11.14**|8.13*|6.92|11.07**|
||(4.65)|(4.64)|(4.75)|(5.67)|(4.67)|
|Change in mobility|4.91***|4.89***|4.97***|3.82**|4.91***|
||(1.49)|(1.52)|(1.53)|(1.83)|(1.48)|
|Work experience|-0.01|-0.01|0.01|0.09|-0.01|
||(0.12)|(0.12)|(0.12)|(0.15)|(0.12)|
|Post-graduate degree|2.07|2.55|3.25|5.35|2.14|
||(3.71)|(3.69)|(3.85)|(4.41)|(3.67)|
|Studying abroad|-7.92**|-8.10**|-6.24|-5.49|-8.10**|
||(3.75)|(3.73)|(3.84)|(4.81)|(3.76)|
|Experience in large business|2.93|2.64|2.44|2.90|2.47|
||(2.80)|(2.78)|(2.87)|(3.28)|(2.79)|
|Management Z-score|0.34|0.53|0.34|-1.47|0.53|
||(1.86)|(1.87)|(1.92)|(2.26)|(1.87)|
|Observations|885|891|853|605|892|
|R-squared|0.21|0.21|0.21|0.24|0.21|

Table B4: Regressions of sales on digitalization and quantiles of tech sophistication. _∗∗∗p <_ 0 _._ 01 _, ∗∗ p <_ 0 _._ 05 _, ∗p <_ 0 _._ 1 Robust standard errors in parentheses.

33
# **Dynamic Effects of Free Trade** Areas

Besides the static effects of trade creation and trade diversion, the creation of customs unions or FTAs could give rise to dynamic effects such as economies of scale, external economies, technical change, and increased investment. Dynamic effects include any effect that might increase the growth rate of economies participating in a customs union or FTA. The existing literature identifies numerous dynamic effects which affect different factor inputs, although there is relatively little consensus on the theoretical validity or empirical importance of these different linkages.'<sup>5</sup> Creation of a customs union or FTA leads to a larger internal market, permitting a greater degree of specialization, and leading ultimately to a reduction in costs. This cost reduction can result from fuller utilization of plant capacity, learning by doing, development of a pool of skilled labor and management, and so forth. Other dynamic effects include external economies, technical change, and change in investment. Of these effects, externalities in the form of technology spillovers are particularly important. According to Gunter and Meldrum (1993), technology spillovers take place when advancements in knowledge made by one economic unit are adopted by other economic units. The creation of customs unions increases the prevalence of technology spillover as trade barriers are reduced. Spillovers can also occur as union members adopt uniform standards which bring together experts from member countries. Spillovers could decrease the cost of innovations; increase an industry's competitiveness; and increase both the technology gap and the difference in average costs between members and non-members of the customs unions.

Related dynamic effects involve the possibility of increased technology transfer to developing countries as an externality associated with export-led growth. For instance, de Melo and Robinson (1992) suggest that these externality effects are an important factor behind the superior performance of countries

> 1 Dynamic effects are summarized by Gunter and Meldrum (1993) with information from Baldwin (1989), El-Agraa (1989), and Gunter (1989).

_Lewis, Robinson, and Wang 25_
# **8. Conclusion**

Without greater certainty about the development status of LGBTI people and a rigorous comparison of their welfare to the population at large, it is more difficult to make the case for effective policies and programs – and to know whether any interventions are effective.

Rigorous, quantitative data on LGBTI people are scare, even in developed countries, and even on the most basic issues – such as the size of the population. Surveying LGBTI people has challenges, not least defining the population and the unwillingness of many LGBTI people to reveal themselves to interviewers due to stigma. Lessons can be learned from experience with other ‘hard-to-survey’ populations, where issues of definition, stigma, and access also apply. Through consultation, cultural sensitivity and training it is possible to collect good quality data.

Probability sampling presents a particularly challenging issue. As the LGBTI population is relatively small, a general population survey would have to be very large (and costly) to pick up large enough samples of the L, G, B, T and I subpopulations (even if the issues with getting people to reveal their identities can be overcome). Experimental studies and big data can help triangulate information on some issues and address some of the constraints of population surveys.

We encourage survey experts, data analysts and LGBTI development professionals and the LGBTI community to work together to collect better data and keep focus particularly on issues of definition, sensitive interview protocols and collecting data that can be readily compared to non-LGBTI populations.

21