rafmacalaba/datause-encoder-data huggingface dataset:
|Other income source|0.02|0.02|0.05|0.06|0.05|
|---|---|---|---|---|---|
|Aid/transfer sent to relatives/friends in Haiti|0.14|0.17|0.25|0.26|0.29|
|Aid/transfer received from relatives/friends in Haiti|0.11|0.11|0.20|0.22|0.23|
|Aid/transfer received from relatives/friends abroad|0.14|0.12|0.15|0.21|0.34|

Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake.

# _(f) Damages and losses due to the earthquake_

Table 6 presents households damages and losses by pre-earthquake wealth quintile. Many households in the directly affected areas appear to have had their house damaged or destroyed (82.6% of all households). Concerning income sources, the richest households appear to have more experienced the death of one or more income earners (11.5%) compared to other groups (8.7% on average for all households). They have also experienced loss of savings more often. Compared to other households, the richest ones were more to lose: in February 2010, 86.5% experienced assets losses, compared to only 17.6% among the poorest. The main assets lost were a television, radio or fan among the richest; they consisted of a radio, cell phone and iron among the poorest. In June 2010, many of the richest households had recovered back to their pre-earthquake situation (16.0%), whereas the poorest households were more to lose. This feature of the dynamics of poverty may indicate the existence of a _poverty trap_ : the poorest households continue losing assets even after the disaster, whereas the richest households manage to recover their assets. From these figures, what is important to know yet is how the richest households have recovered, whereas the poorest have not. Is it actually the case that assistance might not have been allocated in an equal and unbiased way? Or, were the richest households more able to cope with the disaster?

**Table 6. Damages and losses due to the earthquake, by pre-earthquake quintile of wealth**

|Pre-<br>|<br>||Housing<br>||I<br>|ncome<br> <br>% Loss||
|---|---|---|---|---|---|---|---|
|earthquake<br>wealth<br>quintile|<br>Number of<br>households|% Not<br>damaged|% Damaged,<br>but still<br>usable|% Partially or<br>totally destroyed<br>and not usable|% Death of<br>income<br>earner|<br> <br>of<br>income<br>earner|% Lost<br>their<br>savings|
|Poorest|250|24.4|42.4|32.4|5.6|11.2|14.0|
|2|259|10.8|43.2|45.9|8.1|26.6|20.1|
|3|239|14.2|43.5|38.9|9.6|27.6|21.8|
|4|254|15.7|49.2|35.0|8.7|19.3|27.6|
|Richest|244|17.6|49.6|32.4|11.5|25.8|30.7|
|Total|1246|16.5|45.6|37.0|8.7|22.1|22.8|
|Pre-|Number of||Assets||Agric|ultural as|sets*|

17
# **II. Caribbean Countries in a Logistics Context: A Quick Benchmarking**

## **Key Messages**

- Caribbean states rank fairly well when compared to the rest of the developing world -and in particular compared to the island states of the Pacific-  in terms of port development, and time needed to process import and export transactions.

- The non-tariff costs of importing to and/or exporting from the Caribbean are very high when compared with similar countries. These elevated costs are driven to a large extent by excessive costs and inefficiencies in customs and document preparation.

- The main role of customs has primarily been to collect revenue, rather than facilitating trade. This conflicting role has led to increased costs for traders.

Caribbean states in general, and small Caribbean States in particular, have relatively well developed ports, particularly with respect to the rest of the developing world, and have improved their facilities over time (Figure 3). The rankings revealed by the quality index, together with the actual reporting of physical infrastructure and traffic, seem to indicate that Caribbean ports are relatively well equipped given their level of traffic.

**Figure 3: Port Quality Index: Small Caribbean States and Other Regions**

<!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>2007 2008 2009 2010 2011<br>Pacific Island Small States Latin America and the Caribbean<br>Small Caribbean States Sub-Saharan Africa<br>Europe and Central Asia East Asia and the Pacific<br>Index (base 7)<br><!-- End of picture text --> _Source: World Economic Forum (2012)_ . _The index measures business executives’ perception of their country’s port facilities. Data are from the World Economic Forum’s Executive Opinion Survey, conducted for 30 years in collaboration with 150 partner institutes._ _Note: 1=extremely undeveloped to 7=well developed and efficient by international standards._ When turning to service performance indicators, Caribbean countries, with the notable exception of Haiti and Antigua and Barbuda, compare fairly well with the island states of the Pacific in terms of the amount of _time_ needed to process import and export transactions. Nevertheless, their performance is well behind what is considered best practice, represented here by Singapore. Both for imports and exports, the continental Caribbean states take the longest time, and are therefore the least efficient, and closest to that 12
# **TABLE 3: REGRESSION-BASED EXPERIMENTAL ESTIMATES**

Dependent Variable: Weekly Income from Work in New Zealand Dollars

||(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>IV|(5)<br>IV|(6)<br>IV|
|---|---|---|---|---|---|---|
|Ballot Success Dummy|90.634<br>(3.68)**|89.741<br>(3.71)**|87.390<br>(3.89)**||||
|Male Dummy||-29.070|-23.855||-33.104|-27.772|
|||(1.19)|(1.08)||(1.43)|(1.33)|
|Married Dummy||-4.493|24.535||-10.695|18.376|
|||(0.16)|(1.05)||(0.40)|(0.82)|
|Age Dummy||0.558|-0.886||0.987|-0.462|
|||(0.34)|(0.71)||(0.64)|(0.41)|
|Years of Education||13.427|4.605||12.034|3.274|
|||(2.03)*|(1.18)||(1.99)*|(0.91)|
|Born on Tongatapu Dummy||29.167|27.600||29.594|28.005|
|||(1.55)|(1.87)||(1.64)|(2.04)*|
|Height||1.281|0.381||1.249|0.353|
|||(1.96)|(0.92)||(2.04)*|(0.93)|
|Past income|||0.662<br>(6.98)**|||0.660<br>(7.31)**|
|Migration Dummy||||273.996<br>(4.46)**|281.050<br>(4.56)**|273.736<br>(4.99)**|
|Constant|104.051<br>(8.85)**|-297.878<br>(2.45)*|-60.422<br>(0.74)|104.051<br>(8.90)**|-285.011<br>(2.45)*|-48.595<br>(0.66)|
|First stage F-statistic on<br>instrument:||||66.53|61.88|61.51|
|Observations|197|191|190|197|191|190|
|R-squared|0.04|0.14|0.27||||
|Robust t statistics in parentheses<br>* significant at 5%; ** significant at 1%<br>Average first quarter 2005 exchange rat<br>comparing mean incomes|e of 1 Pa'a|nga = 0.72|9 NZ Dollar|s used for|||

43
**Table T2.CU: Intergenerational Persistence in Schooling in Urban China**

|||**LINEARCEF**||**QU**|**ADRATIC C**|**EF**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|
|Linear Coefft.|**0.29*****|**0.35*****|**0.061*****|**0.093****|**0.32*****|**0.22*****|
||(0.016)|(0.016)|(0.023)|(0.045)|(0.045)|(0.063)|
|Quadratic Coefft.||||**0.015*****|**0.0023**|**-0.012*****|
|||||(0.0029)|(0.0029)|(0.0041)|
|Intercept|**8.97*****|**8.51*****|**-0.46****|**9.33*****|**8.57*****|**-0.76*****|
||(0.15)|(0.15)|(0.21)|(0.18)|(0.18)|(0.25)|
|No. Observations|3192|3241|6433|3192|3241|6433|
|||**Estimate**<br>|**s of Mobility**|**fromQuadra**|**tic CEF**<br>||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|9.33|8.51||**_IGME0 _**|0.093|0.35|
|**_ES6 _**|10.43|10.61||**_IGME6 _**|0.268|0.35|
|**_ES9_**|11.38|11.66||**_IGME9 _**|0.354|0.35|
|**_ES16_**|14.66|14.11||**_IGME16_**|0.558|0.35|

Notes: (1) The data used are CFPS 2016 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
“she” in English. Some languages go even further, extending the gender distinction to inanimate nouns through a system of _grammatical gender_ . For example, languages such as Spanish and Italian partition all nouns — even inanimate objects — into distinct gender categories. This feature of language forces gender into every aspect of life: for a speaker of a _gender language_ , gender distinctions are salient in every thought and utterance; every object is either masculine or feminine because it is intrinsically linked to a word that carries a grammatical gender.

Does grammatical gender shape (non-grammatical) gender norms? Does it impact women’s participation in economic life? Writing nearly 100 years ago, Benjamin Lee Whorf argued that the existence of linguistic gender categories likely made other gender divisions appear more natural (Whorf 2011[1956]b), though he did not provide any empirical evidence that this was the case. However, recent work by social scientists supports his claim. For example, seemingly arbitrary grammatical gender distinctions do influence our subconscious thoughts, imbuing inanimate nouns with masculine or feminine attributes (e.g. strength or beauty) in line with their assigned grammatical category (Boroditsky, Schmidt, and Phillips 2003). P ́erez and Tavits (forthcoming) show that Estonian/Russian bilinguals are more supportive of gender equality when interviewed in (non-gender) Estonian than in (gender) Russian. In the economic realm, one recent study of immigrants to the United States shows that those who grew up speaking a gender language are more likely to divide household tasks along gender lines (Hicks, Santacreu-Vasut, and Shoham 2015) while another demonstrates that female labor supply is lower among immigrants who speak a gender language at home (Gay, Hicks, Santacreu-Vasut, and Shoham forthcoming).

We provide new evidence to support the hypothesis that grammatical gender shapes views of women’s role in society and directly impacts women’s labor force participation. To do this, we construct a data set characterizing the grammatical gender structure of 4,336 living languages, expanding the number of languages for which systematic data on grammatical gender is available by almost a factor of ten. We draw on a range of data sources including language textbooks, historical records, academic work by linguists, and 3
28 Figure 4: Trade- and Welfare-Reduction Indexes, 19 African focus countries, Asia and Latin America, covered tradables, 1960–64 to 2000–04<sup>a</sup> (percent) <!-- Start of picture text --> (a) TRI<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>40<br>20<br>0<br>1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>African focus countries Asia Latin America<br>(b) WRI<br>80<br>60<br>40<br>20<br>0<br>1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>African focus countries Asia Latin America<br><!-- End of picture text --> Source: Modified from Lloyd, Croser and Anderson (2009) using Anderson and Croser (2009), which is based on NRA and CTE data in Anderson and Valenzuela (2008) a. 1960–64 is 1961–64 for Sub-Saharan African countries.
the survey is restricted, making it difficult to deploy pseudo-panel techniques that rely on micro data from repeated cross sections. In yet other cases, particular for older datasets such as historical income tax records, the micro data may simply no longer exist, and therefore cannot be analyzed to measure mobility directly. In these cases, researchers may only have access to summary statistics on the distribution of income such as the mean and the Gini coefficient, or alternatively grouped data on the proportion of individuals in different income brackets, but not the record-level data from the entire micro dataset itself.

This paper proposes and implements a method for approximating individual-level income distribution dynamics in these situations where only aggregate summary statistics on the distribution of income are available. Specifically, we consider a situation in which the researcher only has access to time series data on the mean of the income distribution, as well as some measure of dispersion of income across individuals, such as the Gini coefficient or the income share of a particular subgroup. We assume that the underlying surveys are sufficiently comparable that the observed aggregate moments characterize the same population across the successive surveys.

Our key identifying assumption is that individual incomes follow an autoregressive lognormal process with individual fixed effects. Under this data generating process, the joint distribution of individual incomes in any two periods is fully characterized by the mean and standard deviation of log incomes in the two periods (which can be inferred from the observed aggregate moments of the income distribution), and a mobility parameter characterizing the covariance of individual incomes between the two periods (that we cannot observe directly in the aggregate data). This mobility parameter depends on two underlying parameters of the data generating process: the autoregressive coefficient on log income, and the cross-sectional variance of the individual effects. We show that the limited information embodied in the evolution over time of the observed aggregate moments is sufficient to allow us to estimate the autoregressive coefficient on log income as well as to place bounds on the variance of the individual effects. This in turn permits us to place bounds on the extent of mobility in the income distribution, even though we do not observe income dynamics at the individual level.

In order to assess the usefulness of these bounds, we use record-level data from the PSID that permits us to directly measure mobility using micro-level panel data. We then discard all of the information in the record-level data, and use only the two time-series on the mean and standard deviation of log income to retrieve bounds on mobility based on aggregate data. We show that these bounds contain the point estimates obtained using the record-level data, and moreover are reasonably tight. Encouraged by these findings, we apply our methodology to two large cross-country datasets. The first 3
outcomes. This contingency management approach has been most widely adopted in the substance abuse field, e.g. providing rewards to substance abuse treatment patients if they are negative on random drug tests; such programs have been found in developed country contexts to be feasible, acceptable, and effective (Stitzer, 2006).

In this paper we discuss a variant of the above incentive programs, using outcome-based incentives adapted to the sexual domain (possibly within the context of a larger CCT program). After reviewing traditional HIV prevention strategies as well as what is known from prior literature on incentives in the types of contexts discussed above (drawn partly from Medlin and de Walque, 2008), we discuss the theoretical pathways by which incentive programs could work to reduce risky sexual behavior. We then turn to a specific example in detail, elaborating on one such project in Tanzania (the RESPECT Project, of which the authors are the principal investigators) which has implemented a novel randomized trial of outcome-based incentives to reduce risky sexual behavior.<sup>1</sup> RESPECT was a one-year intervention in which participants were tested for curable sexually transmitted infections (STIs) at four-month intervals, and received cash rewards if STI negative. The results from the impact evaluation of this intervention are reported in de Walque et al, 2012. Figure 1 presents the basic randomized design of the RESPECT study‘s 2399 participants across two treatments arms (with different levels of cash rewards) and a control arm. The balance of the paper focuses on lessons learned from the RESPECT trial and implications for future such efforts.

# **Traditional Approaches to HIV Prevention**

The social, economic, and human costs of the AIDS epidemic in Sub-Saharan Africa are staggering (Eiss and Glass, 2007), and innovative solutions to stem the tide of the epidemic are desperately needed. Globally, an estimated 33 million people were living with HIV in 2009. That same year, an estimated 1.8 million people lost their lives to AIDS, and an estimated 2.6 million became newly infected. The global epicenter of

> 1 We are only aware of one other study that has attempted to similarly incentive outcomes associated with risky sexual behaviors.  Kohler and Thornton (2011) report on a trial in Malawi in which HIV negative individuals were randomized to cash reward levels to be paid one year later conditional on remaining HIV negative.  In contrast to the RESPECT study which found significant reductions in STIs, the Malawi study found null results on HIV conversion (the main measured outcome), although there was low power to detect HIV changes in that study.

3
34

**Table 4. Global Welfare Reduction Indexes, Aggregate, and Due to Production and Consumption Sides of the Economy, and to Exportables and Import-competing Subsectors, 2000-04 (percent)**

||**Aggregate**<br>**WRI**|**WRI,**<br>**production**|**WRI,**<br>**consumption**|**WRI,**<br>**exportable**<br>**goods**|**WRI,**<br>**import-**<br>**competing**<br>**goods**|
|---|---|---|---|---|---|
|**Grains and tubers**|**62**|**61**|**63**|**16**|**96**|
|Rice|141|139|142|20|215|
|Wheat|20|17|22|9|26|
|Maize|20|20|19|17|26|
|Cassava|10|10|9|10|0|
|Barley|33|31|35|10|85|
|Sorghum|39|39|38|35|30|
|<br>Millet|31|7|43|31|0|
|Oat|31|41|14|25|28|
|**Oilseeds**|**24**|**28**|**20**|**14**|**44**|
|Soybean|25|29|19|14|51|
|Groundnut|43|43|43|32|48|
|Palmoil|15|10|18|16|13|
|Rapeseed|26|29|22|2|47|
|Sunflower|19|21|16|22|8|
|Sesame|38|41|35|38|0|
|**Tropical crops**|**55**|**55**|**55**|**33**|**86**|
|<br>Sugar|87|87|87|47|95|
|Cotton|45|45|45|47|24|
|Coconut|12|12|12|12|0|
|Coffee|15|15|15|15|0|
|Rubber|11|13|8|11|0|
|Tea|30|29|32|30|0|
|Cocoa|38|39|36|38|0|
|**Livestock products**|**50**|**49**|**50**|**16**|**66**|
|<br>Pigmeat|28|27|28|7|40|
|Milk|73|76|69|56|75|
|Beef|68|63|73|21|82|
|Poultry|45|44|47|13|76|
|<br>Egg|26|25|27|16|36|
|<br>Sheepmeat|31|30|31|22|36|
|<br>Wool|6|8|4|6|22|
|**All of the above**<br>**28 commodities**|**52**|**51**|**52**|**17**|**73**|

Source: Authors’ calculations based on NRA and CTE estimates in Anderson and Valenzuela (2008).
<!-- Start of picture text --> effecti eness is only implicit in most double-sampling studies. _ bringing the prediction esti-<br>mator to a general GMM context and explicitly discussing optimal cost-effecti e estimation of<br>the population mean, we hope to expand the scope of possible applications of double-sampling.<br>recent example where double-sampling (without optimization) is explored as an option to<br>reduce the costs of po erty measurement in Bangladesh can be found in Ahmed et al. (2013).<br>2 Prediction estimator<br>Suppose that the potentially limited outcome ariable Y; for subject 7 is related to the continuous<br>state ariable y; by Y; = h(y;), where h is a function of y;. Further, the state ariable y; is related<br>toa ector of co ariates x; and a disturbance term u; by y; = q(2;, ui; O), where O is a _ ector<br>of parameters. e assume for no that the state ariable y; is unobservable. — e define the<br>expected alue of Y; gi en 2; by g(z;,0) = E,[Y;|x;], where 0 is a kK ector of identifiable<br>model parameters with kK < dim(Q) and g is assumed differentiable with respect to 6. _e also<br>define ¢; = Y; — g(2;, 0) and make the following assumptions about x; and u;<br>Assumption 1 The pair (x;, u;) is independently and identically distributed across i.<br>This assumption is relaxed in Section 6.<br>Assumption 2 The variables x; and u; are independent for all 1.<br>Notice that the parameter of interest in this study is 1 = E[Y;] = E,[g(x;,@)] and not O<br>or §. The standard ay to estimate ju is to take the sample mean Y = N-!S> ; Yi, where N<br>denotes the sample size. Howe er, Y is not the most efficient estimator in general. Using a<br>weighted least-square estimator 6” for 6, Matlof has shown that the sample<br>mean fi'”"5 = N-! S~, g(a;, 65) of predicted alues is asymptotically no less efficient than<br>Y.<br>It is not obvious whether the results of Matlof extend to other estimators. Therefore,<br>we first extend Matlof to a general case where predictions are made using a consistent<br>and asymptotically normal estimator for #. o this end, we make the following assumption:<br><!-- End of picture text -->
an employment visa to bring their spouse and dependent children, but not to immediately bring their parents or adult siblings. The United States also allows for parents to accompany the migrant, but not adult children or siblings. Consequently, the impacts on household structure and on other outcomes for the families of those may be quite similar in many other migrant-sending countries to what we observe among the Tongans left behind when family members emigrate to New Zealand.

In the next section we review relevant literature on the impact of emigration on source areas and discuss channels through which emigration may affect household members left behind. Section 3 provides background on the immigration program we examine, and Section 4 describes the data from the Pacific Island-New Zealand Migration Study (PINZMS) and our estimation methods. The impacts on household level outcomes are presented in Section 5 and on individual outcomes in Section 6. Section 7 discusses multiple hypothesis testing, while Section 8 concludes.

# **2. Previous Literature**

# **2.1. Channels and Impacts**

The most studied impact of migration on household members left behind has been the impact of remittances received. There are a variety of reasons that migrants send remittances, including altruism towards those left behind, exchange for a variety of services provided by the remaining family members (such as caring for property or other relatives), repayment of loans made to finance migration or education, and insurance and strategic motives (Rapoport and Docquier, 2006). These remittances directly contribute to household income, allowing households to purchase more assets, and buy more normal goods, including education and health inputs.<sup>3</sup> They can also relax liquidity constraints, enabling greater household investment in businesses and children’s education, and enable households to better mitigate the impact of domestic shocks.

If migration purely resulted in an exogenous increase in income for the remaining household members, the sign of the expected impact on many outcomes of interest would be easily determined. However, migration can also have a number of other impacts on the sending household. Most obviously, an absent migrant earns no domestic wage and provides no time inputs into household production. These effects may counteract the effect of remittances received, so for example, households have less time to spend educating children,

> 3 Remittances may also be received in the form of durable assets, directly increasing household asset stocks.

4