rafmacalaba/datause-encoder-data huggingface dataset:
Thus, the variance of observed _n_ -period income changes is a linear function of _n_ , 2 where the slope coefficient is equal to σε _j_ . The insight that the random walk component in income implies a linearly increasing income dispersion over time is the basis of the estimation method used by several authors. For example, Carroll and 2 Samwick (1997) estimate σε by performing OLS regressions of the left-hand-side of (9) on _n_ . While the preceding example, with time-invariant parameters, serves to illustrate the intuition underlying the estimation procedure, it should be clear that our exercise is more general in the sense that it allows for arbitrary time-variation in the income risk parameters.

# _II.4. Estimation using ENEU data_

The preceding section provided a detailed description of a general econometric methodology that may be used to estimate time-variant income risk parameters given longitudinal data on individual incomes. We note here some additional issues that arise in applying this methodology to our data, with particular emphasis on the type of income risk accounted for by our estimation procedure.

In forming the sample analogs to the moment conditions (5), we can only use information on individuals who are present in a given manufacturing industry in both time periods _t_ and _t_ + _n_ . In doing so, we pick up shocks to workers who retain their jobs but experience income changes due to changes in their wage rates or the number of hours worked. Moreover, we also account for changes in income experienced by displaced workers who are re-employed in the same industry. In contrast, displaced workers who are reallocated to a different manufacturing industry are not taken into account.<sup>11</sup> However, the exclusion of such workers should not be expected to cause too great an under-estimation of our income risk parameters as the fraction of displaced manufacturing workers who make transition from one manufacturing sector to another is very small. In our data, on average less than 10 percent of displaced

> 11This allows us to circumvent the extremely difficult problem of assigning industries (and thus trade policy) to individuals who transit to different industries. Including individuals who make transitions to the service (non-tradables) sector by using the procedure of counting them as belonging to the manufacturing sector in which they are first observed does not result in any qualitative difference in our reported results.

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distribution and find that this modification has dramatic effects on the estimation results: the coefficient on lagged export participation in their performance equation becomes positive and significant.<sup>4</sup> Table 1 presents a selective overview of the methodology and results of learning-byexporting studies. The table focuses only on segments of the cited studies that apply the methods examined in this section to the study of learning-by-exporting. The numbers (1) and (2) in the second column correspond, respectively, to studies based on variants of Equations (1) and (2). In some cases, more than one method is used in the same paper. While the majority of studies use dummy variables to define the treatment group of exporters or to indicate whether the firm has exported in an earlier period, there are a few exceptions. Kraay (1999) and Castellani (2002) use export intensity, defined as the ratio of exports to sales. Interestingly, Castellani (2002) finds evidence of learning-by-exporting for Italian manufacturing firms when the dummy _D_ i0 is replaced by export intensity in the initial year. Fafchamps et al. (2002) use the number of years since the firm began exporting to identify learning-by-exporting for a cross-section of Moroccan exporters.<sup>5</sup> As mentioned in Section 1, we believe that the phenomenon of learning-by-exporting is related, like learning-by-doing, to the intensity of exposure to new challenging tasks. Therefore, an important aspect of our methodology is to capture learning-by-exporting using measures of export experience that convey not only whether or not the firm has participated in export markets in the

> 4 Van Biesebroeck (2004) proposes a third method of estimation of Equation (2) based on Olley and Pakes (1996). 5 Blalock and Gertler (2004) introduce export intensity and the number of years a plant has exported as robustness checks to their main regression.  Unfortunately, the main variable in those regressions is a dummy for contemporaneous exports, whose interpretation as learning-by-exporting is problematic due to the endogeneity of the export participation decision.

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**Table 2.3  Pharmaceutical Products Manufacturing Growth in Singapore, 1960-2001**

|Year|Output<br>$mn|No. Workers|Value Added<br>$mn|Fixed Asset<br>$mn|Val.Add/<br>Labor<br>$’000|Val.Add/<br>Output<br>%|Capital/<br>Labor<br>$’000|
|---|---|---|---|---|---|---|---|
|1990|1,020.8|1,664|809.1|na|486.2|79.3|na|
|1995|1,339.0|1,855|1,083.5|583.7|584.1|80.9|314.66|
|2000|4,839.1|1,928|2,998.4|858.6|1,555.2|62.0|445.31|
|2001|5,134.2|2,375|2,796.8|2,085.3|1,177.6|54.5|878.01|
||||Average Per|Annum Growth<br>|Rate (%)|||
|1990-2001|15.8|3.3|11.9|23.6<sup>1</sup>|8.4||18.7<sup>1</sup>|
|||Percentage of T<br>|otal Manufacturing<br>|||||
|1990|1.4|0.5|3.7|na||||
|2001|3.7|0.7|8.8|4.3||||

1 Calculated for 1995-2001 _Note_ : Data for 1990 is from SSIC 35220 (Medicinal & pharmaceutical products), and from SSIC 242 (Pharmaceutical products) for all other years _Sources_ : Economic Development Board, Report on the Census for Industrial Production, various years; Economic Development Board, Report on the Census of Manufacturing Activities, various years

**Table 2.4  Growth Trend of Private Sector R&D Expenditure in Singapore, 1981-99**

|Year|Total Private Sector R&D<br>Expenditure (S$mn)|Private R&D Expenditure in Electronics<br>& IT Industries<sup>1</sup>(S$mn)|%|Private Sector R&D<br>Expenditure in Life<br>Sciences (S$mn)|%|
|---|---|---|---|---|---|
|1981|44.2|27.5|62.2|na|na|
|1984|106.7|53.4|50.0|na|na|
|1987|225.6|147.1|65.2|na|na|
|1990|309.5|177.6|57.4|na|na|
|1991|442.0|273.9|62.0|na|na|
|1992|577.5|324.2|56.1|na|na|
|1993|618.9|338.1|54.6|24.8|4.0|
|1994|736.2|385.2|52.3|38.6|5.2|
|1995|881.4|475.3|53.9|34.4|3.9|
|1996|1133.4|706.9|62.4|37.9|3.3|
|1997|1314.5|760.3|57.8|58.3|4.4|
|1998|1536.1|882.6|57.5|63.8|4.2|
|1999|1670.9|933.0|55.8|89.7|5.4|
|2000|1866.0|1182.8|63.4|83.5|4.5|
|2001|2045.0|1292.2|63.2|113.6|5.6|

1Figures from 1993 onwards exclude electrical products but include IT and Communications. _Source_ : _National Survey of R&D in Singapore_ (various years), Agency for Science, Technology and Research.

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**<mark>Table 4.2:   Average Per-Capita Household Income of the Extreme Poor, Selected</mark>** **<u><mark>Years 1995-2001 (R$ of Sep/01)</mark></u>**

|**1995**|**1998**|**2001**|
|---|---|---|
|Brazil<br>41.28|40.69|39.22|
|Northeast<br>43.10|42.85|40.15|
|Alagoas<br>44.38|43.35|40.50|
|**Bahia**<br>**43.37**|**43.06**|**39.93**|
|Ceará<br>43.15|43.29|40.37|
|Maranhão<br>37.64|39.91|40.20|
|Piauí<br>37.76|38.33|36.42|
|Pernambuco<br>45.71|43.37|40.13|
|Paraíba<br>43.09|44.53|41.61|
|Rio Grande do Norte<br>45.15|45.30|41.19|
|_Note:_Poverty Line in 2001: R$80,92.<br>_Source:_Authors calculations based on PNADs|1995-2001.||

Household heads with no income increased during 1992-2001 (Table 4.3). In 2001, 9 percent of household heads had no income - up 1.3 percentage points since 1992, although the household heads had attained more education.

|**Table 4.3: Household Heads with**|**No Income**|**, 1992 and 2001**|
|---|---|---|
|**Bahia**|**1992**|**2001**|
|total household heads (a)|2,859,318|3,592,317|
|no income head (b)|221,109|322,625|
|(b) / (a)|7.7%|9.0%|
|average age|38.6|37.0|
|averageyears of education|2.9|4.3|
|**Northeast**|||
|total household heads (a)|9,961,578|13,125,390|
|no income head (b)|755,077|1,249,656|
|(b) / (a)|7.6%|9.5%|
|average age|37.2|36.6|
|averageyears of education|3.2|4.1|
|**Brazil**|||
|total household heads (a)|24,189,737|49,425,994|
|no income head (b)|1,620,561|4,016,608|
|(b) / (a)|6.7%|8.1%|
|average age|38.3|38.0|
|_Source:_Author’s calculations based on PNAD 1992 a<br>averageyears of education|nd 2001.<br>3.8|5.1|

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Alderman and Heady, 2018). Given the rapid growth faltering in the first two years of a child’s life and the fact that measures of ECD are available only for children 36-59 months old, this study investigates not only whether there is a causal relationship in the full sample of children 0 to 59 months of age, but also whether there are any structural differences in the causal relation between maternal education and child stature separately in the sample of younger (0-35 months) and older (36-59 months) children.

A useful feature of the fourth round of MICS is the inclusion of 10 caregiver-reported questions with binary (0,1) responses aiming to measure the developmental status of children 36-59 months (or 3-5 years of age) within four domains. Specifically, the four domains include: (i) **literacy-numeracy** : children are identified as being developmentally on track _if they can do at least two_ of the following: identify/name at least 10 letters of the alphabet; read at least four simple, popular words; and/or know the name and recognize the symbols of all numbers from 1 to 10; (ii) **physical development** : If the child can pick up a small object with two fingers, like a stick or rock from the ground, and/or the mother/caregiver does not indicate that the child is sometimes too sick to play, then the child is regarded as being developmentally on track in the physical domain; (iii) **learning** : If the child follows simple directions on how to do something correctly and/or when given something to do, is able to do it independently, then the child is considered to be developmentally on track in the learning domain; and (iv) **social-emotional** : The child is considered developmentally on track if two of the following are true: the child gets along well with other children; the child does not kick, bite or hit other children; and the child does not get distracted easily.

According to UNICEF a child is developmentally on track if he or she is on track in at least three of the four domains (literacy-numeracy, physical, social-emotional and approaches to learning) (Loizillon et al., 2017).<sup>3</sup> This implies that affirmative responses to (any) 7 or more of the 10 questions are sufficient to classify a child as being on track in development. In this paper, an alternative composite score for early childhood development is the standardized Early Childhood Developmental Index (or ECDI), which is generated by simply summing the number of positive responses in any of the 10 questions without taking into consideration the domain of the questions (total score ranging from 0 to 10) and then normalizing to a z-score (mean of 0 and standard deviation of 1). This is a more sensitive, though less scientifically-based, measure of child development as it simply compares the developmental status of any given child to the developmental status of the average child in the sample.

> 3 The 10 items were determined through multi-country field tests, validity and reliability studies, and consultations with experts. For more details on the UNICEF method for constructing the ECDI see Loizillon et al. (2017).

6
retain the most recent survey data for each country. Our results are also robust to retaining data for the year with the largest number of firms surveyed.

While the sampling unit in ES is the establishment,<sup>15</sup> we restrict out analysis to single establishment firms that comprise over 85% of the sample.<sup>16</sup> Hence we use the terms firm and establishment interchangeably. We drop missing observations on size and age, drop observations where the date the firm began operations is before 1800 and obvious data errors (such as one observation where date the firm began operations is reported to be 2205). This leaves us with a final sample of 44,696 firms across 118 countries.<sup>17</sup> The key variables used in our analysis are the following:

**_Firm Size_** is the number of permanent, full-time employees in the firm where permanent, full-time employees are defined as all paid employees that are contracted for a term of one or more fiscal years and/or have a guaranteed renewal of their employment contract and that work 8 or more hours per day. The survey asks firms to report the employment numbers at the end of the last fiscal year.

**_Size at Birth_** is the size of the firm when it first started operations. **_Size Ratio_** is the ratio of current firm size to the size of the firm when it first started operations.

**_Labor Productivity_** is the logarithm of the ratio of total annual sales (in USD) to number of permanent, full time employees. While the ES report annual sales in local currency units

> 15 In the Enterprise Surveys, the establishment is defined as a physical location where business is carried out and where industrial operations take place or services are provided. In addition, an establishment must make its own financial decisions, have its own financial statements separate from those of the firm, and have its own management and control over its payroll.

> 16 When we focus on multi-establishment firms, we find that they have similar size-age profiles as singleestablishment firms. The average 40+ multi-establishment firm is 4.5 times larger than the average multiestablishment firm that is <5 years of age.

> 17 The Enterprise Survey database was downloaded in Dec 2012.

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Figure 4: Marginal - Information (Jan ’11 survey).

<!-- Start of picture text --> 0 1<br>Information<br>.5<br>.4<br>Probability<br>.3<br>.2<br><!-- End of picture text --> Figure 5: Marginal - Delivery (Jan ’11 survey).

<!-- Start of picture text --> 0 1<br>Recipient<br>.5<br>.4<br>Probability .3<br>.2<br><!-- End of picture text --> Figure 6: Marginal - Partisanship (Jan ’11 survey).

<!-- Start of picture text --> 0 1<br>Partisanship<br>.5<br>.4<br>Probability .3<br>.2<br><!-- End of picture text --> 25
|experts = 18)|Switzerland (2)|25.0||
|---|---|---|---|
||United Kingdom (5)|27.4||
||France (3)|44.3||
||Greece (2)|10.5||
|Southern Europe|Italy (4)|15.0|26,800|
|(total number of<br>t 12|Portugal (1)|20.0|(23,400)|
|expers = )|Spain (2)|26.0||
||Croatia (2)|3.2||
|Eastern Europe|Czech Republic (2)|42.5|11,600|
|(total number of<br>8|Hungary (1)|4.0|(15,800)|
|experts = )|Poland (2)|25.0||
||Romania (1)|30.0||

*”Round 2” figure: no figure was given for Round 1

All participating experts except five had the nationality of the country in which they lived and worked. Some of the latter group were asked to state their estimate for the WTP for the country of origin and in which they had conducted studies (if there were few valuation practitioners in their country), or state the WTP for the country they now lived in if that country had few valuation studies. All respondents were also asked to assess the mean and median WTP for all European households; the measure we are aiming for. The Delphi CV survey instruments for Round 1 and 2, including the CV scenario description, are reproduced in appendices 1 and 2, respectively.

As there were only a handful of participating experts from any one country (1-5 ; see table 1), we only reproduce the experts ́ guesstimate of mean and median WTP /household/year, related to a hypothetical survey of a random sample of European households, in their own country, and in all of Europe. Tables 2-3 report the main results from Round 1 of this expert survey for Plan A, and Plan B; respectively. The tables report mean, median, standard deviation and lowest and highest amount stated as experts’ “best guesses” for mean and median household WTP per year; in their own country, and in Europe as a whole.

We see that experts’ mean expressed WTP in terms of their assessed mean WTP of preserving the current Amazon Rainforest among their own national population (Scenario A: 85 of original forest % protected) is about 34 euros, higher than the about 29 euros for Scenario B (75 % of 8
not all of them had received the program, we can use a difference in difference approach with household fixed effects. As the household panel goes back to 1982, we can also test for parallel pre-treatment trends in key outcome variables between treatment and control group. Second, data on labor supply and wages at individual level allow us to explore heterogeneity by gender and initial land ownership. Finally, we estimate a production function and use administrative data on the intensity of MNREGS implementation for a subset of villages to go beyond average treatment effects and make inferences on potential transmission channels.

With respect to labor markets, we find that in the short term MNREGs contributed to wage growth and employment diversification, especially for females and those with limited land endowments. Based on our estimates, the program increased wages by 10 points and reduced hired agricultural labor demand by 8.3 days, mainly in the pre-harvest period, a decrease that was more than outweighed by more intensive use of family labor (by a total of 18 days). The program triggered more intensive use of machinery (by 5.5 points), irrigation (by 7.9 points), and fertilizer (by 11.7 points) and contributed to an increase in the number of crops grown (by 0.17 in the first and 0.41 in the third season). Together with a shift from rice and wheat towards high value crops for small farmers, this points towards program-induced diversification of income sources and productivity growth.

To explore if this can be attributed to productive assets or resource transfers through MNREGS, we use, for a subset of villages, the amount of days spent on all and irrigation-specific MNREGS works. The elasticity of agricultural output with respect to the former is, at an estimated 7.1 to 10.3 percentage points, larger and more significant than the estimated coefficient on MNREGS time spent on irrigation-related works. We take this as suggestive of short-term productivity impacts of the program being largely due to resource transfers rather than improvements in local productive infrastructure and discuss ways in which potential heterogeneity and longer-term impacts in this respect could be explored.

The paper is structured as follows: Section two describes program characteristics and methodologies used to evaluate it, presents studies that assessed its impacts, and uses this to pose the empirical questions to be answered here and the strategy for doing so. Section three describes data, presents descriptive statistics, and discusses the identification strategy as well as its justification in terms of pre-program parallel trends between treatment and control group. Section four presents and discusses estimated impacts of MNREGS -overall and by gender and farm size- on wage levels and labor supply and agricultural labor demand; input demand, technology choice, and crop diversification; and productivity of agricultural production. Section five concludes by drawing out implications for future research and policy.

# **2. Program characteristics and evidence of impacts**

3
quality of that person's wardrobe. Once we have measured how much variety there is (on average) in each person's wardrobe, we assume that everybody has exactly the same wardrobe and predict how much inequality we would find solely due to idiosyncratic choices. To the extent that measured inequality is greater than this predicted amount of inequality, it must be the case that not everybody's wardrobe is equal. Moreover, one can calculate the amount of inequality that must exist in the quality of wardrobes.

**Figure 2: Measured versus Underlying Gini Coefficients**

<!-- Start of picture text --> Russia Russia Poland Poland<br>Income Gini Expenditure Gini Income Gini Expenditure Gini<br>0.50 0 50 4 i 00<br>t E 0.45 l t | | 0.4j 5 0 .45 _045<br>0.40 0.40 0.40 0.40<br>0.35 'a 0.35 0.35 _E 0.35<br>0 30 0 30 0.30 003<br>*) 0.25 025 O 02502<br>ou 0.20 0.20 uo02000<br>0.15 x 0.15 0.15 0..<br>0.10 0.10 0.10 0.10<br>0.05 0.05 0.05 0.05<br>0.00 0.00 0.0.00<br><!-- End of picture text -->

Note: Data refer to 1995 for :ussia and 1994 for Poland. Measured Ginis include transitory variation including possible measurement error while underlying Ginis are purged of any transitory variation. Standard errors on the measured and underlying Ginis are 0.02 or less for Russia and 0.01 or less for Poland. Differences between measured and underlying Ginis are highly statistically significant in all cases.

We apply this methodology to the income and expenditure distribution in Russia and Poland. For the purpose of living standards, we define any component of income or expenditure that is expected to disappear within 12 months as transitory. These transitory components are the economic equivalent of the idiosyncratic choice of what to wear on any given day. The underlying component is the component of income or expenditure that is expected to last at least 12 months (perhaps longer, but this cannot be inferred from the data). One can interpret this component as the income or expenditure that you Our own calculations, using the Tarki Panel for 1994-1997, yielded a 11.3% drop in Hungary's Gini for income per equivalent adult when income was averaged over 4 years. The equivalence scale equals household size raised to the power of 0.75.

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<!-- Start of picture text --> wb Unit Value and Quality over time for Korea for Car Sector<br>=<br>2 8<br>7 =<br>e_ | [i |<br>2548 ~ lew |<br>£z 5-782av 15 i<br>B ig" Ie i<br>=25°2 w |g204>tejE{2 "71648—Eke /7é/<br>-o@ r’ 4<br>\ \ “<br>© Ce- 2 \/ SvS/N I 1\ VNC LON a<br>1960 1980 2000 2020<br>Year<br>—— Quality — Unit Value<br>——-—-—~ Market Share<br>sector:SITC4=7321; pwt9.0<br><!-- End of picture text -->