rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 6.  Survey Methods’ Differential Effects on Employment and Work Status by Prevalence of Informal Employment**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Wo|men|Young|Men|
||Employed|Working|Employed|Working|
|LOA|0.063*|0.077**|-0.012|-0.036|
||(0.033)|(0.033)|(0.062)|(0.059)|
||[0.059]|[0.017]|[0.843]|[0.569]|
|ESR|0.013|-0.003|0.077|0.053|
||(0.034)|(0.034)|(0.064)|(0.058)|
||[0.667]|[0.922]|[0.228]|[0.394]|
|LOA x Informal employment in the EA|0.008|0.024|0.048|0.082|
||(0.032)|(0.032)|(0.056)|(0.055)|
||[0.837]|[0.459]|[0.417]|[0.157]|
|ESR x Informal employment in the EA|0.048|0.052+|0.142**|0.100*|
||(0.034)|(0.034)|(0.062)|(0.056)|
||[0.145]|[0.108]|[0.026]|[0.078]|
|LOA + LOA x Informal employment in the EA|0.070|0.100**|0.036|0.046|
||(0.047)|(0.045)|(0.084)|(0.081)|
||[0.124]|[0.026]|[0.704]|[0.591]|
|ESR + ESR x Informal employment in the EA|0.060|0.049|0.220**|0.153*|
||(0.048)|(0.047)|(0.091)|(0.084)|
||[0.181]|[0.285]|[0.012]|[0.067]|
|Observations|1,361|1,361|301|301|
|Outcome(Control Group)Mean|0.436|0.537|0.620|0.717|

_Notes:_ This table presents the treatment heterogeneity of including the LOA module (LOA) or enforcing self-reporting (ESR) by the prevalence of employment in the informal sector. This prevalence measure is estimated as the share of individuals (women in Columns [1] and [2] and men in Columns [3] and [4]) living in the same EA as the respondent and employed in the informal sector. For our estimations, we use informal employment in SD. On average, 1 SD = 11 percentage points of informal employment (see the descriptive statistics and definitions of this indicator in Table A4). This variable is estimated using the standard labor module. We present ITT estimates from Equation (2), where is the prevalence measure in SD, and the estimations of each survey method’s total effect. The vector iihss includes the variable and the list of DD control variables selected using a double-LASSO procedure for each outcome (see Table A3 in the iihss iihss XX DD Appendix for the selected variables). All estimations include EA (stratification variable) fixed effects. Each column is a separate dependent variable as defined in Section 3. The control mean refers to the mean of the control group for each outcome. Standard errors clustered at the household level are presented in parentheses, and the randomization inference _p_ -values are shown in brackets. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1, + _p_ <0.130 33
POLICY RESEARCH WORKING PAPER 1417

# Summary findings

Uncertainty is inherent in the analysis of global warming issues. Not only is there considerable scientific unccrtainty about the magnitude of global warming, but even if that problem were resolved, there is uncertainty about what monetary value to assign to the costs and benefits of various policies to reduce global warming. And yet the influence of uncertainty in policymakers' decisions is ignored in most studies of the issue.

Baranzini, Chesney, and Morisset try to explicitly incorporate the effect of uncertainty on the choice of global warming abatement policies. The approach they develop draws on the emerging literature on investment

- undcr unccrtainty in particular, that on the optionvaluation approach.

Their numerical applications focus on Cline's (1992) analysis of global warming, but it may be applied to a range of global warming analyses.

First, they assess whether it is optimal to implement Cline's strategy of limiting global warming today, or whether it should be postponed, and for how long Then, they identify the optimal policy to be implemented today for different levels of uncertainty about the costs and benefits of policies to reduce global warming.

- This paper a product of the Country Operations Division, Latin America and the Caribbean, Country Department I-is part of a larger effort in the region to understand the role of uncertainty in environmental policy issues. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Celinda Dell, room Q7-106, extension 85148 (30 pages). February 1995.

_Th e Workiog Policy Resarch Paper_ P _Sncr dinata the fin&gs of_ **_uk_** X **_prog_** _to_ **_erag the c of i*ea abwt_** _dtvelopmext issulesAn ob*tiueof the series is toge_ **_t_** _hermdingsoutqukkciy, cuen if thc presmtiom are kss tbanfilypolWshd. ne |pa pers amry the names of the authos and should be used axd cied acrwrdngl. The frtigs, bapdaim od mdsso are the aud;=x' owx and sdm not be au&c d to the World Bank, its ExcYtive Board of Dirers, or axy of its mknbr aoitries._

Produced by the Policy Rescarch Dissemination Center
liberalization<sup>9</sup> although generally improved allocative efficiency may outweigh this loss. The corresponding figures resulting from services liberalization for the U.S. and India are $1.4 billion and $1.2 billion (Columns 3-4).

When goods and services liberalizations are combined (baseline scenario), the United States and India gain by $3.7 billion and $1.4 billion respectively (about 0.03 percent and 0.1 percent of initial U.S. and Indian GDP respectively) (Columns 5-6). When India extends the removal of barriers on an MFN basis (Columns 7-8), India’s welfare expands dramatically to $13.5 billion (about 1.0 percent of its initial GDP) as its much larger allocative efficiency gains far surpass its deteriorating TOT effects. The strategy of lowering external barriers in the aftermath of an FTA is documented both in ASEAN (Calvo-Pardo, Freund, and Ornelas 2011) and in Latin America (Estevadeordal, Freund and Ornelas 2008). For instance, Estevadeordal et al. (2008) find strong evidence that regional agreements induced a faster decline in external tariffs in Latin America and conclude that free trade areas are likely to be building blocks in the region.

Experiments in the last four columns of table 6 separate the baseline scenario into two parts, _i.e._ , the effects from the US lowering its protection against India’s exports (Column 9-10) and from India’s doing so against U.S. exports (Columns 11-12). The results reveal that both countries lose if they only lower their own barriers without obtaining reciprocal commitments, as gains resulting from doing so appear to be reaped by their partners. Our finding suggests that FTA models which focus only on the import side are misleading, and highlights the importance of negotiating concessions by FTA partner countries.

> 9 This result is partly attributable to the fact that India’s initial AVE protection of 9.9 percent against the U.S. goods (table 3b) is lower than the rates India applies to its other trading partners. Thus, as a result of the discriminatory trade liberalization, India would expect a large tariff revenue loss from the other partners.

29
_Brazilian spatial labor dynamics Page 21_

**Table 7C. Summary statistics, Non-metropolitan Brazil without North Region**

|**Variable**|**Obs**|**Mean**|**Std.Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Delta ln Wage (2000/1991)|3300|0.0000|0.2901|-1.3942|0.8495|
|ln Teacher qualification in 1991|3300|1.3156|2.1187|-4.6052|4.2370|
|Years of Schooling in 1991|3300|3.6858|1.3320|0.4746|7.1707|
|Total rainfall|3299|1251.7990|367.9959|368.6636|2532.2020|
|Government Accountability|3300|0.9124|0.2827|0.0000|1.0000|
|Delta ln Employment (2000/1991)|3300|0.1103|0.2035|-0.6577|1.2464|
|lnTransport Cost to São Paulo in 1995|3299|7.0422|0.7355|4.1589|8.3051|
|ln Transport Cost to State Capital in<br>1995|3299|5.8691|0.6163|2.1972|7.7619|
|Delta ln Transferences (2000/1991)|3300|0.8448|0.3073|-0.2986|2.3336|
|Delta ln Market Potential (2000/1991)|3299|0.4101|0.1179|-0.2664|1.5442|
|ln Population in 1991|3300|9.2337|0.8054|6.6214|11.9770|
|Dependency Ratio in 1991|3300|0.4807|0.0447|0.3738|0.6205|
|ln Transport Cost to São Paulo in 1968|3299|7.6219|0.6676|4.3694|8.7367|
|ln Transport Cost to State Capital in<br>1968|3299|6.2990|0.6601|3.1135|7.9077|
|ln Proportion of Elderly in 1991|3300|-2.1936|0.2204|-3.6893|-1.5332|
|Illiteracy Rate in 1991|3300|37.7150|17.8150|2.4800|81.4600|
|Urbanization Rate in 1991|3300|49.2604|20.7435|2.7639|100.0000|
|Population Density in 1991|3300|32.7412|33.3495|0.2065|522.2028|
|ln 5 to 15 over 15 to 55 years old Ratio|3300|-0.7464|0.2243|-1.6218|-0.1870|
|Repetition Rate in 1991|3300|4.2817|0.1976|2.6575|4.5756|
|ln Employment Rate in 1991|3300|-0.3625|0.1406|-1.2429|0.1601|
|Delta ln Education Market Potential<br>(2000/1991)|3297|0.9707|0.1575|0.1650|1.6027|
|Percentage of Good Soils|3299|0.7881|0.3192|0.0000|1.0002|
|Rainfall - First Principal Component|3299|-0.0130|2.1967|-4.2315|6.8953|
|Rainfall - Third Principal Component|3299|-0.1705|1.3696|-2.4997|5.4633|
|ln Wage in 1991|3300|7.9233|0.4684|6.6139|9.3168|
|Proportion of Natives in 1991|3300|0.6765|0.1694|0.0855|1.0134|
|Homicides per Capita in 1991|3299|0.0015|0.0010|0.0000|0.0090|
|Employment in Farming Activities in<br>1991|3300|0.1995|0.0752|0.0000|0.5437|
|Bank Dummy (1991)|3300|0.7197|0.4492|0.0000|1.0000|
|Average Temperature|3299|22.1518|2.7223|13.8325|27.7363|
|Fertility Rate in 1991|3300|3.6513|1.1051|2.0100|7.9600|
A major factor that could influence income (revenue) from production diversity is climate variability. The extensive literature on climate variability and agricultural production has established a strong relationship between climate and crop yields (Tao et al. 2009; Rowhani et al., 2011). Rowhani et al. (2011) examined the relationship between seasonal climate and crop yields in Tanzania and found that both intra and inter seasonal changes in temperature and precipitation influence cereal (maize, sorghum and rice) yields in Tanzania. They found that seasonal temperature increases have the most important impact on yields. Tao et al (2009) found that major crop yields were significantly related to growing season climate in the main production regions of China, and that growing season temperature had a generally significant warming trend. Using a panel dataset, Schlenker and Lobell (2010) examine the impact of changes in temperature and precipitation on crop yields of five main staple crops (maize, sorghum, millet, groundnuts and cassava) in sub-Saharan Africa and find that temperature changes have a much stronger impact on yields than precipitation changes<sup>2</sup> . Hatfield et al. (2008) establish that degree days, the number of days extreme temperatures affect optimal plant growth, have been found to be correlated with reduced yields and agricultural income. For this reason, the paper uses degree day and rainfall deviations as a source of exogenous variation in agricultural production. Daily temperature data from 1981-2010 and daily rainfall data from 2000-2010 was extrapolated from the Surface Meteorology and Solar Energy version 6.0 developed by the Atmospheric Sciences Data Center at NASA<sup>3</sup> and georeferenced to the GHS-Panel. Historical averages for the number of degree days (1981-2009) and rainfall (2000-2009) during the planting season (April-June) were calculated for each household. The deviations from historical planting season degree day and rainfall averages were then calculated for the 2010 planting season.

9
**Table 2. Comparison of data on labor income in the national accounts, tax data, and EU-SILC.**

||**2020 National accounts**|**2020 Tax admin data**|**2021**<br>**EU-SILC**<br>**income year)**<sup>**5**</sup>|**(2020**|
|---|---|---|---|---|
|**Coverage**|Covers both employees<br>and<br>self-employed,<br>includes social security<br>contributions paid by the<br>employer|Covers only employees|Covers both emp<br>and self-employe|loyees<br>d|
|**Aggregate**<br>**compensation**<br>**of**<br>**group (employees and**<br>**or self-employees)**|87.0 B. € (annual)|67.0 B. € (annual)<br>(only employees, lack of<br>self-employed<sup>6</sup>)|75.8<br>B.<br>€<br>employees<br>and<br>employed)<br>74.5 B € (employe|(both<br> <br>self-<br>es)|

_Source: Own based on Eurostat,_<sup>_7_</sup> _tax data, and the EU-SILC._

**Since this method of estimating tax compliance requires merging multiple data sources, proper methods for statistical matching must also be assessed, given the data availability and the country context; data fusion techniques receive increasing interest from researchers and statistical agencies (Donatiello et al., 2016a; Lamarche et al., 2020).** When common identifiers are available, research can apply the record linkage. For example, Lopez-Luzuriaga (2023) recently applied record linkage to match data on consumption from the electronic billing system with income tax data in Ecuador and investigate the frequency of envelope payments in the country. When common identifiers are unavailable, researchers rely on statistical matching (Yam & King, 2020; Lewa et al., 2021). Statistical matching allows for the synthetic data source to be derived based on data from various sources that do not contain the same unit or common identifier. The matching relies on common characteristics available in both data

# 5 Gross labor income.

6 According to the EU-SILC the share of self-employed in the economically active population is approximately 16%. The vast majority of self-employed are farmers. The vast majority of self-employed do not employ workers.

> 7 Compensation of employees: htps://ec.europa.eu/eurostat/databrowser/view/tec00013/default/table?lang=en

14
Table 7.

OLS and 2SLS Estimation of Equation 4

||Dependent Variable: Logarithm of|
|---|---|
||Sales per Labor in 1997|
||OLS<br>IV|
|Constant|2.28***<br>2.50***|
||(9.06)<br>(8.15)|
|_Private Ownership_|0.091<br>0.96***|
||(1.56)<br>(3.22)|
|_Ln(Sales/Labor,99 5) _|0.59***<br>0.57***|
||(32.9)<br>(25.1)|
|Total Assets|0.000<br>0.000|
||(0.81)<br>(0.39)|
|Subsidies in 1996|-0.00001**<br>-0.00000|
||(-2.12)<br>(-1.38)|
|DIummy97|0.025<br>0.03|
||(0.31)<br>(0.33)|
|Market Concentration|-0.24<br>0.03|
||(-0.89)<br>(0.09)|
|Industry dummies included|Yes<br>Yes|
|Adjusted R-sq.|0.66<br>0.59|
|Number of Observations|618<br>618|

T-statistics in parenthesis. * ** and * indicate significance at 1, 5 and 10 percent confidence levels respectively.

37
Table 2 Impact of Major Hurricanes on GDP per capita (SS ≥ 1)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Linear<br>Regression|Linear<br>Regression<br>correcting for<br>Serial & Spatial<br>Autocorrelation|Panel Data<br>with Controls<sup>1/</sup>|Panel Data<br>correcting for<br>Dynamic<br>Models|
|MSWS|‐0.00274|‐0.00861*|‐0.00839**|‐0.00894**|
|Standard Error|(0.00434)|(0.00469)|(0.00349)|(0.00399)|
|R‐squared|0.314|0.415|0.076|n.a.|
|IHD|‐0.0126*|‐0.0148**|‐0.0148***|‐0.0160***|
|Standard Error|(0.00540)|(0.00642)|(0.00320)|(0.00375)|
|R‐squared|0.365|0.458|0.157|n.a.|
|WEI|‐0.00498|‐0.00994**|‐0.00983***|‐0.0105***|
|Standard Error|(0.00569)|(0.00447)|(0.00330)|(0.00394)|
|R‐squared|0.319|0.423|0.093|n.a.|

*** p<0.01, ** p<0.05, * p<0.1

> <sup>1/</sup> Investment, population growth, openness, log of GDP per capita. All for t‐1.

Table 3 shows the results for events with a SS scale equal or greater than 3. As expected, effects are similar but with higher coefficients as evidence of non‐linear impacts: stronger hurricanes result in devastating damages. We cannot investigate further since the number of events for storms with SS ≥ 3 are limited (only four events).<sup>22</sup> Other controls considered include the number of events per year, the SS scale, and the inverse of the translation velocity; however, none of these proved significant.

> 22 Due to this reason and the lack of power, we hypothesize that results for column (2) in Table 3 are not significant (MSWS and WEI).

13
46

# Annex Table 3: Main Products in Intra-Baltic Trade in **1992** **_(%)_**

|||Ex|ports to:|||Imp|orts from:||
|---|---|---|---|---|---|---|---|---|
||Latvia||Lithuania||Latvia||Lithuania||
|Estonia a/|||||||||
||Electricity<br>Chemicals<br>Fertilizer|75<br>8<br>2|Cotton<br>Paper<br>Nuclear react.|16<br>10<br>7|Cotton<br>Sugar<br>Machinery|10<br>9<br>7|Oil<br>Paper<br>Cotton|58<br>5<br>4|
|Shareintctal<br>trade|11%||2%||2%||3%||
||Estonia||Latvia||Estonia||Latvia||
|Lithuania b/|||||||||
||Petroleum<br>Chemical -<br>Textiles|40<br>10<br>9|Petroleum<br>Gas<br>Elec.Machinery|40<br>15<br>7|Perfuimes<br>Chemicals<br>Dye|12<br>12<br>12|Textiles<br>Electricity<br>Transp.equip.|17<br>15<br>10|
|Share in total<br>trade|2%||5%||1%||2%||
|a HS classific|ation (2 digit).||||||||

_b/_ SITC classification (2 digit).

Source: Estonian Ministry of Trade, Comtrade. Data for Latvia not available.
Fogel, Robert W. (2004), _The Escape from Hunger and Premature Death, 1700-2100:_ _Europe, America, and the Third World_ . Cambridge: Cambridge University Press. Glaeser, Edward, José Scheinkman and Andrei Shleifer (2003), “The Injustice of Inequality,” _Journal of Monetary Economics_ 50: 139-65.

Hoffman, Philip T., David Jacks, Patricia Levin, and Peter H. Lindert (2005), “Sketching the Rise of Real Inequality in Early Modern Europe.” In Robert C. Allen, Tommy Bengtsson, and Martin Dribe (eds.), _Living Standards in the Past: New Perspectives on Well-Being in Asia and Europe,_ Oxford: Oxford University Press, pp. 131-72.

Jackson, R.V. (1994), “Inequality of Incomes and Lifespans in England since 1688,” _Economic History Review_ 47 (August): 508-24.

Kakwani, Nanak (1980), _Income, inequality and poverty: methods of estimations and_ _policy applicati_ ons, Oxford and Washington, D.C., Oxford University Press and World Bank.

Kuznets, Simon (1955), “Economic Growth and Income Inequality,” _American Economic Review_ 45 (March): 1-28.

Kuznets, Simon (1976), “Demographic Aspects of the Size Distribution of Income: An Exploratory Essay,” _Economic Development and Cultural Change_ 25 (October): 1-94.

Leigh, Andrew and Pierre van der Eng (2006), “Top Incomes in Indonesia, 1920-2004,” unpublished, Australian National University (February 11).

Lindert, Peter H. and Jeffrey G. Williamson (1982), “Revising England's Social Tables, 1688-1812,” _Explorations in Economic History_ 19 (October): 385-408.

Maddison, Angus (1998), _Chinese Economic Performance in the Long Run_ , Paris: OECD 32
10

**Table 4:** Emlovees in the Formal Sector<sup>by Citizenship, June 1964-1984 (a)</sup>

||Total Employees|Zambians|Non Zambians|Non Zambians<br>as % of Total|
|---|---|---|---|---|
|1964|264,100|232,000|32,100|12.2|
|1965|301,600|267,760|33,840|11.2|
|1966|313,360|284,480|28,880|9.2|
|1967|303,730|274,650|29,080|9.6|
|1968|319,730|290,8'J0|28,930|9.0|
|1969|321,460|291,640|29,820|9.3|
|1970|337,750|310,320|27,430|8.1|
|1971|358,350|331,670|26,680|7.4|
|1972|364,740|338,750|25,990|7.1|
|1973|377,640|342,990|34,650|9.2|
|1974|386,270|351,190|35,080|9.1|
|1975|398,840|365,330|33,510|8.4|
|1976|379,400|346,310|33,090|8.7|
|1977|372,630|345,320|27,310|7.3|
|1978|369,310|344,480|24,830|6.7|
|1979|371,670|348,290|23,380|6.3|
|1980|381,490|360,980|20,510|5.4|
|1981|373,720|355,220|18,500|5.0|
|1982|367,510|350,050|17,460|4.8|
|1983|363,800|348,270|25,530|4.3|
|1984|365,190|351,190|14,020|3.8|

Notes: Prior to 1972, data are available for Africans and non-Africans on:y. These are shown in the relevant citizenship columns. Data for 198;-84 are preliminary estimates.

Source: GRZ 1985, Table **6.**
child mortality—it will be possible to generate baseline data using the data from the 3-year survey, since such data are inherently retrospective.

The MV protocol does acknowledge “that the absence of pure baseline data from comparison villages is a limitation.” But this is insufficient treatment of a very important issue. The lack of broad baseline data on comparison villages is one more reason why differences between treated and comparison villages cannot be scientifically attributed to the intervention.

The protocol describes the problem and asserts that “[f]or many indicators, in the absence of new interventions it would be unlikely that substantive year on year changes would be witnessed, such as malaria prevalence, institutional delivery rates or child nutrition.” The implicit argument is that if we assume that little changes without interventions, data for the comparison villages from the three-year point can be interpreted as roughly equivalent to baseline data. The protocol also notes that the “presence of external interventions in comparison sites will be monitored throughout the evaluation to better understand the nature of secular changes taking place during the project period.” There are a number of problems with this argument. First, changes may occur for hard-toidentify reasons that have nothing to do with local interventions. Economic growth, weather variation, changes in remittance flows, broad improvements in public administration, and national information campaigns are examples of factors that could change conditions in the comparison villages but not be identified as “external interventions” by the MVP monitoring teams. Second, while changes over a single year for many indicators may typically be negligible for many indicators, over the relevant period of three years changes are more likely to be substantial. Third, if the MVP monitoring did find identify interventions, it would not be possible to credibly adjust for them in the evaluation. Instead, the existence of external interventions which may have changed conditions in the comparison villages would only make it clearer why it is essential to have baseline data at those sites.<sup>22</sup>

> 22 In describing the details of how outcomes at the MV sites will be compared to those from the comparison villages, the protocol says that “[f]or variables where no data on baseline status exist, rural sub-national data will be imputed.” It is not clear how this imputation will work, and imputed baseline figures cannot substitute for true baseline data.

22
# **6. Conclusion**

Due to an increasing interest in measuring intra-generational mobility, and since panel datasets are rarely available or have many limitations, there has been a growing literature that studies mobility using cross-sectional data that do not follow individuals over time. Synthetic panel is the most recent technique and allows estimating the unobserved welfare level in the first round of cross sectional data for all households interviewed in the second round. A non-parametric version of the technique yields lower and upper bound mobility estimates depending on the assumptions made about the correlation between the first and second round error terms in the underlying regressions. On the other hand, a parametric version of the method produces a point estimate of poverty mobility.

By taking a weighted average of the estimated residuals in the underlying models, this paper proposes a non-parametric adaptation of the parametric point estimate of mobility. Unlike the parametric technique, this adaptation does not require any assumption regarding the distribution of the error terms in the underlying welfare models. The method is validated by producing welfare mobility estimates out of cross-sectional data and by comparing them with actual mobility from panel datasets in three LAC countries: Chile, Nicaragua, and Peru. Results suggest that the method performs well; most estimates are close to the actual panel mobility. Moreover, these results confirm that the method yields reliable estimates of household welfare growth, apart from the join probabilities of poverty dynamics. Finally, the paper also suggests that the methodology can be easily applied to regional analysis, since it does not require a large number of explanatory variables in the underlying welfare model estimations.

14