rafmacalaba/data-use-mentions-extended huggingface dataset:
Swait, J. R. (1994). “A Structural Equation Model of Latent Segmentation and Product Choice for Cross-Sectional Revealed Preference Choice Data.” _Journal of Retailing and Consumer Services_ 1: 77–89.

Taber, Christopher and Rune Vejlin (2016). “Estimation of a Roy/Search/Compensating Differential Model of the Labor Market.” NBER Working Paper No. 22439, July 2016.

- Tybout, James. R. (2000). “Manufacturing Firms in Developing Countries: How Well Do They Do, and Why?” _Journal of Economic Literature_ 38(1), 11-44.

Ubach, Cristina, Anthony Scott, Fiona French, Morag Awramenko, and Gillian Needham

- (2003). “What do hospital consultants value about their jobs? A discrete choice experiment.| _BMJ_ 326(7404): 1432 - 1438.

- Van Ommeren, Jos, Gerard J. van den Berg, and Cees Gorter (2000). “Estimating the marginal willingness to pay for commuting.” _Journal of Regional Science_ 40: 541– 563.

Wiswall, Matthew and Basit Zafar (2018). “Preference for the Workplace, Investment in Human Capital, and Gender.” _Quarterly Journal of Economics_ 133(1): 457-507.

36
methods

## 4.1 Data sources

We draw data from the three-wave, nationally representative RALS survey conducted by the Indaba Agricultural Policy Research Institute (IAPRI) in collaboration with the Ministry of Agriculture and the Central Statistical Office (CSO) of Zambia. The first round of RALS was conducted in May/June 2012, the second in June/July 2015, and the third in June/July 2019. This timing coincides with harvesting for the previous agricultural production season and the agricultural marketing season (from May year _t_ to April year _t+1_ ). The dekadal (10-day) rainfall data from the Climate Hazards Group Infrared Precipitation with Station database (CHIRPS) for the period 1981 to 2018 compliments these data, with the CHIRPS merged into the RALS data set using household geo-coordinates.<sup>9</sup> We use the dekad CHIRPS data to compute

> 9 CHIRPS is a quasi-global (50'S-50'N), gridded 0.05' resolution with satellite and observation-based precipitation estimates. It combines station based rainfall data with satellite data (Funk et al., 2014).

7
17

# **5.3 Simulation with Separate Urban/Rural Growth Rates**

Appendix Table A4 and Figure 6 show results from a simulation applying separate urban and rural growth rates, and the national population growth rate of 2.9%. The separate growth rates were calculated from the data in the _Economic Survey 2002_ by grouping changes in output by sector; the rural growth rate was calculated as the growth rate of the agricultural sector (monetary and nonmonetary), and the total growth of other sectors was taken as the urban growth rate.

The simulated 2001 headcount rates are lower across the board than in the national growth scenario, and in all cases the simulated values fall within the confidence intervals of the estimates from the 2000/01 HBS. While the overall pattern is broadly the same as in the first scenario, this simulation shows a much steeper rise and fall for poverty in Dar es Salaam, and a flatter poverty trajectory in rural areas and the nation as a whole.

**Figure 6**

<!-- Start of picture text --> Simulated changes in poverty<br>using separate urban and rural growth rates<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year<br>Mainland Tanzania Dar es Salaam<br>Other urban areas Rural areas<br>.45<br>.4<br>.35<br>.3<br>.25<br>Fraction below poverty line<br>.2<br><!-- End of picture text -->

# **5.4 Simulation with Alternative Urban/Rural Population Growth Rates**

The sectoral (urban/rural) GDP per capita growth rates employed in the previous section are the same as those displayed in Table 2. These were calculated from sectoral GDP figures, assuming a uniform national population growth rate of 2.9%. This population growth rate corresponds to the change in national population between the 1988
survey employing only recall interviews is suboptimal in our case study, we find no evidence that such survey would compromise the accuracy of policy conclusions about poverty and inequality compared to a diary survey. We also show that recall distributions are closer to the underlying distribution of usual consumption than in diaries. Thus, our findings provide the theoretical and empirical rationale for the choice of switching to recall modules in expenditure surveys.

Second, our empirical investigation demonstrates the cost-effectiveness of eliciting _repeated measurements_ from the same household, which is in the spirit of Browning and Crossley (2009). More specifically, when diaries are the collection mode of choice – as in the Iraq IHSES – the inclusion of one additional module on recalled consumption for a subsample of interviewees will allow for learning about survey errors and disparities in well-being at relatively low cost. When a recall survey is chosen instead, resources should be allocated by statistical agencies to conduct follow-up diary interviews on a smaller sample of interviewees. Our methodology therefore offers actionable recommendations to design a protocol for harmonizing time series of poverty and inequality in those countries that consider a transition to new modes of data collection.

The remainder of the paper is organized as follows. The next section presents the general formulation of the problem. Section 3 describes the institutional background and presents the diary-recall experiment. Section 4 presents the conditions for nonparametric identification. Estimation is also discussed, along with possible threats to the validity in our research design. Section 5 presents our results on the nature and consequences of survey errors. Section 6 looks at the optimal combination of diary and recall interviews. Section 7 concludes the paper.

# **2 General Formulation of the Problem**

## **2.1 Quantity of Interest**

Suppose that the household has a target monetary amount of consumption over a period of _τ_ time units. _Consumption_ is equal to _acquisition_ over this period. Without loss of generality the time unit coincides with the interview period, which is one _week_ in our case study.

We define _Y_<sup
Monte Carlo evidence from Table 1, row 2B suggests the estimators in Equations 9-11 are extremely accurate: the average across Monte Carlo replications is extremely close to μμ ii εε the true values in the Monte Carlo of σσ� , σσ� , σσ� = 5%. Consequently, the μμ ii εε performance statistics (R and bias ) and average shrinkage coefficients (σσ = 1.5%, σσ = 1%, σσ and ) are very μμ ii ii similar to those when the variance components {RREE λλ̂ , } are known in Table 1, row B1. ψψ γγ μμ ii εε σσ σσ , σσ

**Endogeneity** The key empirical challenge in estimating the effect of leaders on growth is that “leader transitions are often non-random, and may in fact be driven by underlying economic conditions” (Jones and Olken 2005, p836). While we cannot address this issue through a quasiexperimental design like Jones and Olken (2005) for hundreds of leader estimates, we can show that the most plausible kind of endogeneity in the data does not affect the performance of our empirical methodology. The first step in this approach is to estimate a linear probability model of how growth affects the probability of a leader transition in the actual leader-growth panel data:

25 Wooldridge (2001, p260) advocates using the covariance between errors within the same unit as a measure of the individual effect. Chetty et al. (2014) and Kane and Staiger (2008) also estimate teacher effects via the covariance between average test scores in each class taught by the same teacher each year. An alternative approach used in previous versions of this paper (and in Stata’s _xtreg, re_ and _xtreg, sa_ commands) is to first estimate the between 2 −1 2 variance (in our context, the variance across leader growth averages = ∑ ), and then subtract the iid NNLL 2 error variance in Equation 9. However, we found that this method was upward biased ( oo LL ii=1
8

# **3. Data and descriptive evidence**

In this section we describe in great detail our data on workers’ remittances and current account reversals, also providing a wide range of descriptive evidence. The other variables used in the econometric analysis are listed and described in Table A1; some statistical evidence on the relationship between these variables and current account reversals is postponed to section 4, where it is useful to motivate our empirical specification.

Before digging into our data, a comment is needed. Our descriptive analysis closely follows the one by IMF (2005). However, this must not be considered a useless repetition for two reasons. Firstly, we believe that our way to look at the data has some value-added: in particular, we devote a special care in dealing with the issue of the changing sample size and composition over time, which very likely biases IMF’s analysis on the evolution of migrants’ transfers in the last decades. Secondly, since our analysis focuses on workers’ remittances, we need to be sure that the “good” properties, which characterize IMF (2005) sample of migrants’ transfers, characterize our more restricted sample of workers’ remittances, too.

According to the IMF Balance of Payments Manual Fifth Edition (BPM5, 1993), the inflows of funds that may be properly interpreted as transfers from migrants can be distinguished into three different categories: i) workers’ remittances, included among the current transfers in the current account; ii) compensation of employees, which is a component of income again in the current account; iii) migrants’ capital transfers, which instead belongs to the capital transfers in the capital account<sup>9</sup> .

Our sample, entirely drawn from the IMF Balance of Payments Statistics database, includes all available country-data on the three items above from 1976 to 2003<sup>10</sup> . After dropping

> 9 <u>Workers’ remittances</u> cover current transfers by migrants (defined
of government support. While many firm characteristics are significantly correlated with having received government support in Round 1 or 2, many of these correlations turn insignificant when we unbundle government support into Round 1 versus Round 2 support. Large firms, and those with foreign ownership are more likely to have received support in both rounds, possibly because these firms have stronger political connections. Also, having a credit or loan, and having a website are characteristics that are positively correlated with receiving government support in both rounds. This may be because firms with an existing financial relationship and online presence may be better placed to apply for and receive government support electronically where this option is available.<sup>8</sup>

> 8 We also find inconsistent or insignificant associations between government support and firm characteristics when we consider a multivariate OLS regression controlling for the same firm characteristics and country fixed effects. These results differ from our earlier findings reported in Demirguc-Kunt, Bruhn, and Singer (forthcoming) where we show in a different sample of government support data—corresponding to Round 1 in 11 countries and Round 2 in 13 countries—that more productive firms were less likely to receive any COVID-19 government support.

5
Figure 1 continued Attainment profiles for ages 15 to 19, by economic group: Eastern and Southern Africa continued <!-- Start of picture text --> Zambia 1996-97 Zimbabwe 1994<br>084 < > 0.8 N i<br>O 0,6<br>t 04 a 404<br>02 - 02<br>0 0<br>1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9<br>Grade Grade<br><!-- End of picture text --> Attainment profiles for ages 15 to 19, by economic group: East Asia and Pacific <!-- Start of picture text --> Indonesia 1991 I Indonesia 1994 Philippines 1993<br>08- 0.8 08.<br>0.6 0.6 0.6<br>a., 04 c_ 0.4 a.0.4<br>02 0.2 _ 0.2<br>0 0 I~~~~~ 0<br>1 2 3 4 5 6 7 8 9 1 2 3 4 5 67 89 I 1 2 3 4 5 6 7 8 9<br>Grade Grade Grade<br>* Poorest * Middle & Richest<br><!-- End of picture text --> 16
context. Thus a decline in capital due to tighter credit constraint would mean a larger negative impact of land restrictions on employment probability in manufacturing than services. The evidence discussed above suggests the opposite, probability of employment in services declines by a larger magnitude than that in manufacturing. Direct evidence on outmigration from villages also supports the conclusion that migration costs is the mechanism mediating the effects of land restrictions. Taking advantage of the recently released migration data at the district level from the 2012 population census, we plot outmigration rates against proportion of land under restrictions in Figure 1. It is clear from Figure 1 that propensity of outmigration is lower in areas with higher proportion of land under restrictions.

Taken together, all of the different pieces of evidence above suggest strongly that the negative effects of 24
28

# <u>Figure 2: Mean per capita out-of-pocket expenditure and dispersion</u>

<!-- Start of picture text --> Per Capita Out−of−Pocket Expenditure on Health, 2011 PPP Dollars Dispersion in Health Expenditures (Coefficient of Variation)<br>$500+ 6–13<br>$250–$500 4–6<br>$100–$250 3–4<br>$50–$100 2–3<br>$10–$50 2–2<br>-$10 1–2<br>no data no data<br><!-- End of picture text --> <!-- Start of picture text --> Dispersion in Health Expenditures (Q90/Q50)<br><!-- End of picture text --> <!-- Start of picture text --> 21–91<br>12–21<br>9–12<br>7–9<br>5–7<br>3–5<br>no data<br>IBRD 44307 | APRIL 2019<br><!-- End of picture text -->
Note that the commonly available household surveys in developing countries include a random sample of parents (household head and spouse), and thus the estimate of the standard deviation of parental schooling is likely to be unbiased. We can put together the relations in inequality (4), and equations (8) and (9) to derive the following approximate relation: Now observe that _√δ > δ_ , because _δ ∈_ (0 _,_ 1), and as a result, the bias represented by the right hand side of approximation (10) is much smaller than the bias in approximation (4). To give a sense of the magnitudes, _δ_ = 0 _._ 9 implies a value of _√δ_ = 0 _._ 949 , and _δ_ = 0 _._ 8 implies _√δ_ = 0 _._ 90. Thus the IGC estimates from coresident sample suffer from much less bias when compared to the most widely used measure of intergenerational persistence: IGRC. If the bias in IGRC is 10 percent, the corresponding bias in IGC is half of that (5 percent), and when the IGRC estimate is biased downward by 20 percent, the corresponding bias in IGC is about 10 percent. An important implication of the above results is that, at high levels of coresidency rates ( _δ_ closer to 1), the difference between the biases in IGRC and IGC will be smaller, which is consistent with the evidence that in India the differences in biases between IGRC and IGC is much smaller when compared to that in Bangladesh. The actual biases estimated in the data, however, also reflect sampling variability.<sup>25</sup> From relations (4) and (10) above, we get the following approximate results on the slope of the bias in IGRC and IGC estimate with respect to the coresidency rate: The results in equation (11) above are important for cross-country and over time (for 25Our evidence that in many cases, the
financially more developed countries exhibit significantly more insurance activity. For example, the ratio of insurance assets to GDP is only 3.5% in the group with low ratios of credit to GDP, versus 31.5% in the group with developed credit markets. However, the results are weaker and even puzzling for the private bond variable.

These tests produce suggestive evidence and provide insights into how conditions affect insurance activities, but they could be driven by confounding factors. In particular, the test results could be driven by the fact that some of the grouping variables are correlated with general economic development, thus leading to erroneous conclusions about environmental factors that drive insurance market development. To address these concerns we turn to regression analysis next.

# _Life premium regression results_

We consecutively present the regression results for each of the three dependent insurance variables. The regressions aim to explore the robustness of the group comparison tests by assessing the individual and joint significance of the factors identified in section 3: economic, demographic, social/cultural, and institutional/market structure factors. Given their overall structural relevance for the sector, we include a core set of both economic and demographic variables in all regressions as baseline controls: economic development, population size and density, and inflation (LGDPPC, LPOP, LPOP_DENSITY, INFLATION). We employ pooled OLS regressions on annual country-level data for the period 2000-08. We substitute sample averages for each year in the dataset for factors for which only a few observations per country are available: LLIFE_EXP, LINCOME20, and LCONTRIB_RATE. Thus, these variables are time-invariant. We report robust standard errors in two variations: unclustered, and clustered at the country level. Year-fixed effects and a constant were estimated but are not reported.

Table 5 displays the results for life insurance. The dependent variable is log of life insurance premiums to GDP (LLIFE). Depending on the explanatory variables included in the models, the sample size varies from 181 (56 countries) to 767 (86 countries) annual countrylevel observations.

16
3 shows, for rural areas, the main driver for the almost 7 percentage point reduction in poverty was the increase in consumption of food and non-food items, while higher asset ownership and improvement in dwelling characteristics played a minor role. In urban areas, consumption of food and non-food items contributed to a lesser extent to reduce poverty. However, the higher household consumption could not compensate for the increase in average household size, nor the deterioration of dwelling conditions. Further validating the urban results, we see that no change in an individual component of the model would alter the net poverty increase.

**Figure 3: Decomposition of changes in welfare using Urban and Rural models**

<!-- Start of picture text --> 6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>Rural Urban<br>Change in poverty percentage point<br>Assets<br>Consumption Household characteristics Regional dummies Dwelling characteristics Model: Constant Model: Error Net poverty change (p.p.)<br><!-- End of picture text -->

**Note** : Results use a Shapley decomposition on the different components of the prediction of the welfare vector. The different categories include the variables of the urban and rural model independently as shown in Table 3. Model error is the average error across the multiple imputations.

**Source:** IE-LFS 2019-20 and AWMS 2023 (R3).

# Discussion of economic trends in Afghanistan and their effect on poverty levels

Before socioeconomic data stopped being collected, close to half of the population in Afghanistan was living below the national poverty line. As shown in Figure 4, while the national poverty rate in 2019-20 was lower than the 55 percent estimated in 2016-17,<sup>14</sup> poverty trends were showing diverging patterns in urban and rural areas. In urban areas, the observed increase in
.7|
|Total||6.7|

Source: Authors’ calculations based on TLMPS 2014, ELMPS 2018 Notes: Restricted to participants in the project type.

Market livestock work tends to be particularly underreported (55% of additional market employment was not reported as 3-month market employment in Tunisia and 39% in Egypt). There were meaningful differences only in Egypt in detecting market employment by livestock size, with those with only small/medium livestock particularly under-reported (48% of additional market employment was not reported as 3-month market employment, compared to 26% for large livestock).

Differences by crop land area are only available in Egypt. While 17% of additional market employment was not reported as 3-month market employment for those participating in market 14
This combined approach of (1) PSW, (2) maxentropy/raking, and (3) post-stratification preserves the target variable distributions within each region (cell), meanwhile ensuring that the cross-region (cell) household shares are identical to the reference survey.

# **III. Experimental studies on how reweighting procedures and SWIFT poverty projections affect poverty estimates**

This section conducts a series of experiments to examine how reweighting procedures and SWIFT modeling affect poverty estimates and other statistics.

First, we create artificially biased subsamples by selecting phone owners from the following reference surveys: Rwanda Integrated Household Living Conditions Survey (EICV) 5, Saint Lucia Household Budget Survey (HBS) 2016, and Uganda Refugee and Host Communities Household Survey (URHS) 2018. These surveys were selected because they are representative of the population of interest in each context and were used as the reference surveys for the pilot of the high-frequency phone surveys implemented by the World Bank (see more details in Yoshida et al., 2022b). Second, we estimate poverty rates among these biased subsamples by applying each of the following methods: 1) selected reweighting procedures alone among the actual consumption data in the phone survey (without SWIFT), 2) SWIFT poverty projection models trained with the reference data,<sup>7</sup> and 3) combinations of SWIFT and reweighting procedures. Finally, we compare each generated poverty rate with the actual poverty rates estimated using the consumption data in the reference survey. A smaller difference suggests better performance of the method in adjusting for the bias in poverty estimates produced using phone owner samples.

We separately examine the following reweighting procedures: (i) original weights; (ii) PSW proposed by Lee (2006), (iii) inverse propensity score approach, and (iv) PSW (Lee 2006) with non-PSW adjustments (maxentropy, raking and/or post-stratification).

Second, we conduct the experiment above using a sample of Ethiopia HFPS7, which was collected based on the ESS4 sample.
sions (only labor market conditions at the time an individual graduated); therefore, the regression coefficients should be interpreted as the effect of graduating in a recession, say, on the employment trajectory _given how labor market conditions tend to evolve after recessions_ (rather than the pure effect of one year of recession at graduation followed by normal conditions) (Oreopoulos et al. (2012)).

We first use as our independent variable the aggregate employment rates that vary at the governorate x year level (“regional model”). Because this measure is calculated using LFS data spanning 2006-2019, it is only available for cohorts that graduated in those years. To complement these results we consider other outcomes that are available from 1990 onwards but vary only by year (“national model”). In the national model, the identification exploits differences in the career profiles exhibited by each subsequent cohort. It cannot be ruled out that a spurious correlation arises where differences between cohorts in, say, social norms, happen to be correlated with the evolution of labor market conditions.

# **4 Results**

We present results from estimating equation 1 on the sample described in section 2 in tables 4 through 9 and appendix tables A1 through A6. The tables report the coefficients that multiply the interaction between measures of labor market condition at labor market entry and years of potential experience. Coefficients on the other control variables such as the sets of dummies, are available from the authors upon request. Our main set of results exploit regional variation in employment rates (“regional model”) between 2006 and 2019. We assess their robustness by considering a broader set of measures of macroeconomic conditions over a longer time period (1990-2019) that vary only at the national level (“national model”).

## **4.1 Regional model**

Tables 4 and 5 present results using regional employment rates as our independent variable. The individual employment outcomes of interest are aggregated by cells defined by 12