rafmacalaba/datause-encoder-data huggingface dataset:
and personal income, I find that foreign born are doing worse than native born from the same racial background, with the exception of Blacks. Among men, for labor force participation and employment, I also find that foreign born Blacks are doing better than native Blacks. The paper tests different possible explanations for that “reversal” of the advantage of natives on immigrants among Blacks. I consider citizenship, ability in English, age at and time since arrival in the US as well as neighborhood effects, but I conclude that none of these channels explain or modify the observed reversal.

While this reversal of the traditional native/foreign born advantage among Blacks is interesting to document in itself, it might also lead to some insights about the sources of racial differences, in particular the “Black-White” differences in the United States. For example, there is a debate on whether the “Black-White” gap is mainly driven by racial discrimination or by cultural factors. On the one hand, Bertrand and Mullainathan (2004) indicate that discrimination is still pervasive in the labor market since among similar resumes sent for job applications the ones with “Black” sounding names where less likely to get an interview and a job. On the other hand, recent work by Fryer and Levitt (2004b) study the potential role of cultural factors. Austen-Smith and Fryer (2005) and Fryer and Torelli (2005) also analyze the economics of “acting-white” in Black ghettos where studying and reading might be poorly perceived and education not rewarded.

Section II describes the data and the variables used. Section III contains the main results of the analysis, section IV tests whether that result is robust to composition effects and section V adds neighborhood effects to the analysis. Section VI discusses how the results could be interpreted and concludes.

# **II. Data**

The analysis uses the Integrated Public use Micro Data Series (IPUMS) data for the 2000 Census in the United States. These data have two main advantages for the purpose of this paper. First, they contain good information about immigration (foreign born status, number of years in the US, country of origin) and labor market outcomes. In addition, the 3
8 position of power within either the hierarchy of the village or the hierarchy of the lineage. The problem is that the land may get taken away from those with little influence when it is lying fallow. Because women rarely hold these positions of power, women’s land is not left fallow enough and is much less productive than men’s. This land gets degraded because women do not have the social status needed to hold on to it during the fallowing period. The key point for our argument is that the resulting decline in land productivity is a pure loss for society. The fact that other people do have status and can fallow their land as needed does not, in any way, compensate for the loss of productivity on the land of the powerless.

A separate study by the same authors (and in the same country) provides a second example of how unequal opportunities that arise from the interaction between poverty and imperfect or missing markets leads directly to inefficiency. In the forest-savannah in Southern Ghana, cocoa cultivation, receding for many years because of the swollen shoot disease, has been replaced by a cassava-maize intercrop. Recently, however, pineapple cultivation for export to Europe offered a new opportunity for farmers in this area. In 1997 and 1998 more than 200 households cultivating 1,070 plots in four clusters in this area were surveyed every six weeks for about two years. The survey results reveal that the profitability of pineapple production dominates that of the traditional intercrop (figure 4).<sup>8</sup> The average returns associated with switching from the traditional maize and cassava intercrops to pineapple is estimated to be in excess of 1,200 percent! Yet only 190 out of 1,070 plots were used for pineapple. When the authors asked farmers why they were not farming pineapple, the virtually unanimous response was: “I don’t have the money”.<sup>9</sup> While it is true that some heterogeneity in ability between those who have switched to pineapple and those who have not cannot be entirely ruled out, the authors conclude that the fixed costs involved in switching crops (combined with imperfect credit and insurance markets) prevent a large number of farmers from making a very profitable investment. Output and income levels in these areas is correspondingly below potential.

> 8 From Goldstein and Udry (1999), figure 4. 9 From Goldstein and Udry (1999), 38.
# **5.3 Nightlight**

We conduct a similar analysis on the nightlight data. As in other analyses of this kind, we take nightlight as a proxy for income. We report results from three regression specifications: (1) a cross-section specification; (2) a first-difference specification, which corresponds to model (8); and (3) a first difference specification (9) in which we allow _γk_ to be different for contracting and expanding gold production. All regressions include year dummies. They control for annual differences in sensor sensitivity, as well as for overall economic and infrastructure development. The mining data, as before, comes from formal gold mines. We assign to each EA the average measure of nightlight across its area. Standard errors are spatially clustered at the district level, as before.

Coefficient estimates _γk_ are again reported graphically, together with their 95% confidence interval. In Figure 15 we limit our analysis to the years 2000 and 2010. This is done so as to obtain results as comparable as possible to those reported so far. As before, mining variables represent average mining output in the preceding decade. From the upper-left corner graph, we see that, across Ghana, locations with more mining activity have more nightlight, but only in the immediate proximity ( _<_ 10km) of a mine. From the first-difference regressions we see that an increase in local mining activity is also associated with an increase in nightlight. The opposite is true when local mining activity decreases: we find that nightlight falls.

We also carry out a yearly analysis, using a long panel over 19 years on the EA level on annual nightlights and local gold mining output. Since we observe data at a relatively high frequency, we are concerned with serial correlation in local shocks that might influence both mining and nightlights. A prime example would be annual local fluctuations in electricity supply. 13 Furthermore, nightlights themselves might be serially correlated. To address these issues, we estimate a dynamic panel version of our basic model: We again take the first difference to purge out any possible correlation of time-invariant local

> 13Ghana obtains most of its electricity from hydro power plants at the Akosombo dam of Lake Volta. Electricity supply therefore partly depends on rainfall conditions. Shortages of electricity, and power cuts due to rationing, are common even in the largest cities.

23
# **Annex figure 7. Employee compensation in Australia**

<!-- Start of picture text --> Compensation of Employees (% of GDP, left scale)<br>versus Real GDP Growth (% right scale)<br>58% . . 9<br>higher avg. real GDP growth<br>56% lower COE<br>54% A<br>52%<br>50%<br>48% lower avg. rei GOP<br>44% -3<br>1949/50 1954/55 1959/60 1964/65 1969/70 1974/75 1979/80 1984/85 1989/90<br>-Compensation of Employees as percent of GDP(l) GDP(E) Annual Growth Rate (RHS)<br><!-- End of picture text --> The long-term average in compensation of employees in Australia is 51 percent of GDP. This value is still lower than any COE level in the US during the past 50 years.

# **Annex figure 8. Employee compensation in the United States:**

<!-- Start of picture text --> Compensation of Employees (% of GDP, left scale)<br>versus Real GDP Growth (%, right scale)<br>62- 4 higher avg. real 1 0<br>1 GDP<br>61 1 growth 8<br>60-1 I<br>569 -<br>/ 0 ~~~~~~~lower avg. real GDP<br>55- g9rowth -2<br>' H\/ 0 ~~~~~~higher COE<br>1952 1957 1962 1967 1972 1977 1982 1987 1992<br>_ Share of Compensation of Employees .. k --GDP growth,,Real<br>in percent of GOP<br>Source: NIPA<br><!-- End of picture text --> 54
# **1. Introduction**

The number of randomized experiments being conducted in economics has exploded over the past decade, especially in development economics. The vast majority of these studies use only a single baseline and single follow-up survey, or a single follow-up with no baseline. This has been the case in the early randomized experiments looking at education (e.g. Glewwe et al, 2004) and health outcomes (e.g. Miguel and Kremer, 2004), and has remained true as experiments have expanded to consider other interventions and outcomes, such as recent high-profile experiments in microfinance (Karlan and Zinman, 2010; Banerjee et al, 2010). In the rare cases where multiple post-treatment survey waves have been conducted, they have been typically taken relatively far apart in time, with the goal of examining whether the treatment effect differs in the short-term and medium-term. For example, Banerjee et al. (2007) examine impacts of educational interventions at one year and two year horizons. Indeed, so much is the paradigm of baseline plus follow-up accepted that the excellent toolkit for randomization of Duflo et al. (2008) does not discuss at all the possibility of doing more than one pre-treatment or posttreatment round of surveying, let alone the choice of how many such rounds.

In contrast, the clinical trials literature has noted the potential advantages of taking repeated measures of outcomes of interest not just to study the time course of treatment effects, but to obtain more precise estimates of effects around particular endpoints (Frison and Pocock, 1992). Vickers (2003) argues that the number of repeat measures should be a key design choice in conducting experiments, and concludes that the benefit of such additional measures is of greatest value when the autocorrelation of measures is low, such as with episodic conditions like headaches. Such a description would certainly seem to fit key economic outcomes like business profits, and incomes and consumption of the poor. As the recent work by Collins et al. (2009) makes abundantly clear, one of the key difficulties of living on $2 a day is that people don’t receive $2 every day, but rather a highly irregular stream of income. Measuring microenterprise income, in particular, can be difficult, with the resulting data typically having large heterogeneity in reported profits among reasonably similarly sized firms (de Mel et al, 2009). While some of this likely reflects measurement error, Fafchamps et al. (2010a) report that Ghanaian microenterprise owners confirm 85 percent or more of changes in profits above 150 percent or 2
longer is the time interval between the base survey and the target survey, the more likely that this assumption can be violated.

Another concern with survey-to-survey imputation, or more accurately speaking, acrosssurvey imputation methods, is that the variables used in the imputation in both the base survey and the target survey should have the same distribution. This seemingly rather innocuous condition appears often taken for granted in many studies, but if it is not satisfied, it may result in severely biased estimates (Dang _et al._ , 2017b)<sup>16</sup> . The intuition is rather straightforward: variables such as household size or labor force participation may be defined differently in a household consumption survey and a labor force survey, and the data can be collected accordingly in different ways.<sup>17</sup> Consequently, this condition should be carefully checked, and appropriate adjustments (e.g., standardizing the variables) should be done before imputation is implemented on surveys of a different design.

# **V. Disaggregated Poverty Estimates at the Sub-National Level** _Motivation_

In most household consumption surveys, consumption data are rarely available at a disaggregated level (such as the state or province level) due to the typically limited sample size of household surveys. However, there exists a strong demand to produce poverty estimates at more disaggregated levels for various purposes such as social transfer targeting or budget allocation. For example, statistical agencies such as the U.S. Census Bureau routinely implement this task to

> 16 Dang _et al._ (2017b) also offer a simple method to standardize the variables from the two different surveys. This study also provides more discussion on another related issue of selecting variables in estimating the consumption model as in Equation (1).

> 17 The inconsistency between different surveys is well documented in studies using data from richer countries. For example, Abraham _et al._ (2013) examine the differences between employment data between the U.S. Current Population Surveys and employer-reported administrative data. See also Angrist and Krueger (1999) for a related review of comparability and other data issues with a focus on labor force surveys.

17
# **5.2 Rural model**

Table 3 presents the rural models for Morocco (for both years; with and without additional variables). As with the urban model, the _IruX* variables denote interactions between selected independent variables and regional dummy variables.

Also here we find that the estimated model coefficients are largely coherent. For the variables that are shared by the rural and urban models, the signs of the coefficients generally match. Let us highlight some aspects that differentiate the rural model from the urban model: (a) employment in agriculture, transport and commerce all enter the regression positively (sectors that are found to be less significant in urban Morocco), (b) returns to education are lower in rural compared to urban Morocco, as is to be expected.

**Table 3 – Rural Model**

||**Wit**|**hout Addit**|**ional Assets**||**W**|**ith Additio**|**nal Assets**||
|---|---|---|---|---|---|---|---|---|
||**2001**||**2007**||**2001**||**2007**||
|Domain R2|0.269|***|0.010||0.319|***|0.105||
|Domain R3|0.060||-0.143||0.127|*|-0.073||
|Domain R4|0.224|***|-0.086||0.239|***|0.008||
|Hhld size|-0.092|***|-0.162|***|-0.115|***|-0.197|***|
|Hhld size^2|0.003|***|0.007|***|0.004|***|0.008|***|
|Married (head)|0.109|***|0.181|***|0.088|***|0.147|***|
|Primary (head)|0.066|***|0.055|**|0.054|***|0.029||
|Secondary (head)|0.147|**|0.236|***|0.079||0.219|***|
|Tertiary (head)|0.449|***|0.271|**|0.405|***|0.133||
|Unemployed (count)|-0.420|***|0.422|*|-0.389|**|0.468|**|
|Selfemployed (count)|0.122|***|-0.137|*|0.156|***|-0.112||
|Employer (count)|1.841|***|1.519|***|1.614|***|1.329|***|
|Agriculture (count)|0.170|***|0.212|***|0.182|***|0.253|***|
|Transport (count)|0.704|***|0.829|***|0.550|***|0.587|***|
|Commerce (count)|0.604|***|0.556|***|0.449|***|0.483|***|
|Public (head)|0.354|***|0.283|***|0.276|***|0.184|**|
|Waged (head)|-0.115|***|-0.166|***|-0.104|***|-0.146|***|
|Waged (count)|0.365|***|0.393|***|0.301|***|0.418|***|
|Primary 1 (count)|0.170|***|0.167|***|0.092|***|0.094|**|
|Primary 2 (count)|0.542|***|0.480|***|0.371|***|0.322|***|
|Secondary (count)|0.849|***|0.599|***|0.695|***|0.348|***|
|Tertiary (count)|1.145|***|0.723|***|0.950|***|0.631|***|
|Rooms per cap|0.533|***|0.286|***|0.418|***|0.162|**|
|Rooms per cap^2|-0.070|***|-0.016||-0.051|***|-0.002||
|Electricity|0.206|***|0.236|***|0.087|***|0.054|**|
|Sewage|0.041||0.493||-0.006||0.418||
|Drinking water|0.063|***|0.079||0.046|**|0.063||

16
simply apply the contract terms in the table to calculate what average payouts would have been in past seasons, if the contract had been available (see Giné et al., 2007, for details). Historical daily rainfall data is available from 1970-2006 for the Andhra Pradesh contracts, and from 19652003 for the Gujarat contracts. These data are not available for three Andhra Pradesh stations, where payouts are based on automated rain gauges, or for Anand in Gujarat.

Calculated expected payouts range from 33% to 57% of premiums, with an average of 46%. Consistent with the generally higher price of financial services in developing countries, these levels are below those of U.S. auto and homeowner insurance contracts, where the payout ratios average 65-75%.<sup>5</sup> Giné et al., (2007) also show that the distribution of insurance returns on ICICI Lombard rainfall insurance contracts is highly skewed. Policies produce a positive return in only 11% of phases. The maximum return, observed in about 1% of phases, is 900%.

In Gujarat, sufficient rain fell in 2006 and 2007 that no payout was triggered. In Andhra Pradesh, every policy paid out at least once between 2004 and 2006. Some payouts were quite modest (Rs. 40 in 2006 for the Atmakur policy), while others were large (Rs. 1,796 in 2004 near Narayanpet). Using administrative data for all policies sold by BASIX in Andhra Pradesh from 2003 to 2009, Giné et al. (forthcoming) find an average ratio of total insurance payouts to total premiums of 138%. The difference between this figure and our historical estimated return may reflect unusual shocks such as the severe drought of 2009, or structural changes such as greater monsoon volatility (B.N. Goswami et al., 2003). Given the limited history of existing rainfall data and the skewness of the insurance return distribution, however, statistical tests of structural change are not likely to be powerful.

In part A of the Online Appendix we simulate a simple model of insurance demand to investigate more formally whether the insurance is potentially valuable to households at the prices offered, in the absence of non-price frictions such as liquidity constraints or limited trust. This model is calibrated to match the payout ratio and distributional features of ICICI Lombard contracts, in which payouts are realized on only around 10% of phases, but with high maximum returns. We assume a conservative level of 40% for the payout-to-premium ratio, and consider a range of assumptions about basis risk. Results suggest that the insurance product is valuable at

> 5 US insurance premiums data were generously provided by David Cummins of Temple University, based on the 2007 Best’s Aggregates and Averages. The ratio of aggregate claims to premiums is 76.2% for private passenger auto liability insurance, 68.4% for private passenger auto physical damage, and 64.7% for homeowners insurance. The ratio for earthquake insurance is much lower, 20.4%, but this may reflect the relatively small number of recent earthquake events.

7
provinces and districts of Vietnam. Then, using the disaggregated data on poverty indexes, we simulate the effect of cash transfers on poverty under different targeting scenarios.

It is found that there is a large variation in poverty between districts and provinces. Mountainous and highland districts have high poverty incidence and severity in 2009. Rural and ethnic minority people account for a large proportion of the poor. There is a strong relationship between inequality and poverty: areas with low inequality are also more likely to have low poverty rates. The spatial distribution of the poor population has changed significantly during the period 1999-2009. In 1999, the poor were highly concentrated in delta regions such as Red River Delta and Mekong River Delta, partly because of the high population density in these areas. Since 1999, the number of the poor people has decreased remarkably in delta regions, but much less markedly in the Northern mountains and central highlands. While the distribution of poor people seems more evenly distributed across the country in 2009, areas with high poverty rates are more concentrated in Mountainous areas than they were a decade ago. Most provinces and districts experienced poverty reduction during 1999-2009, but the extent of poverty decline has been greatest in areas with average poverty levels in 1999. Provinces and districts with high initial poverty achieved smaller reductions in poverty.

We show that poverty rates have become more concentrated in Vietnam between 1999 and 2009. This phenomenon is likely not unique to Vietnam and may be widespread in developing countries. Economic activity and growth are becoming more spatially concentrated, due to agglomeration benefits related to network, technology and human capital externalities. This suggests that spatially targeted re-distribution policy executed alongside the development of high-potential areas may offer one means for a country to maintain growth while also maintaining an equitable distribution of wealth. As poverty becomes more spatially concentrated, the effectiveness of geographic targeting is likely to improve.

We take advantage of the high resolution available in our poverty maps to show that the more finely defined the beneficiary populations, the greater the gains from geographic targeting of anti-poverty resources over a uniform lump-sum transfer. For instance, in 2009 district level targeting is found to result in a 58 percentage point reduction in poverty severity 30
12 commodities constitute suitable adult goods for Ethiopia. For these reasons, I shall repeat the estimation with a sample with child age restricted to 12 or less here. Second, Ethiopia has a very large percentage of female-headed households, FHH henceforth, see Desta al et. (2006). Some studies suggest household budget under mother’s rather than father’s control improves child welfare. I shall also examine child gender bias with a sample of (nuclear) FHHs.

# **4-** **_Results_**

I shall discuss the results of this study in three separate parts along the lines discussed in the last section.

# **4.1** **_-_ Main Results**

The first column of table 4 sums up the main finding of this study with a sample of nuclear household with at least one (biological) child of the household head and spouse, and no restriction on the survey adopted child age definition of <15, that is, the estimated effects of _ng /nc_ from (1) and (2) on the budget share of tobacco, coffee, adult clothing, and adult personal services (I shall return to other columns of this table below). The detailed estimates appear, separately for each adult good, in table 5.

The first notable feature of table 4 (1<sup>st</sup> column) is that the reported estimates are _all positive_ , i.e. the expected direction for bias against girls, two (adult services and adult clothing) are significant at 1%, the other two at 5%. Therefore, all four groups of adult goods, in various degrees, suggest the presence of child gender bias in Ethiopian consumption patterns. As to the size of gender effects on consumption, note that the gender estimates appear small, but they are broadly similar to the size of the gender effects indicating discrimination against boys reported by Deaton (1987, LSMS, Table 5) for Cote D’Ivoire for two of the commodities also common to our study, namely adult clothing and tobacco. Moreover, although these effects are small, so are the budget shares of adult goods relative to which these effects should be measured. However, the child gender ratio for girls in (1) and (2) varies between 0-1, and its household-level unit change is 1 over number of children. 1 over the average number of children in the sample is the average value of this unit change, i.e. 1/3.26=0.307 for the sample of table 5. Hence, the percentage change in the budget share of each good, as a result of a unit