rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2. Summary of evidence on social assistance and migration**

|**Mechanism**|**Impact on likelihood of**<br>**migration**|**Reference**|**Timeline**|**Evaluation method**|
|---|---|---|---|---|
|Implicitly<br>increasing<br>mobility|Increase (4-6 percentage<br>points)|Ardington et al<br>(2009)|2001-2004|Regression with individual fixed-<br>effects from two rounds of panel data|
||Increase (13- 25<br>percentage points)|Egglestone et<br>al. (2016)|2012|Regression Discontinuity (RD) design<br>based on eligibility of pension age<br>(60) (Higher magnitude of impact<br>probably due to smaller sample size)|
||Increase (0.32<br>percentage points)*|Cortina (2014)|1995-1997|Probit regression with instrumental<br>variable approach|
||Decrease (0.34<br>percentage points)|Howell (2017)|2012|Regression with instrumental variable<br>approach, although weak<br>identification could be a problem here|
|Conditioning<br>on mobility|Increase (20-23<br>percentage points)|Bryan et al<br>(2014)|July-December<br>2008|Regression with randomized control<br>trial design|
||Increase (40-55<br>percentage points)|Chetty et al<br>(2017)|1991-2012|Regression with randomized control<br>trial design|
|Implicitly<br>reducing<br>mobility|Decrease (8-11<br>percentage points)|Imbert and<br>Papp (2018)|2010|Regression with propensity score<br>matching and identification based on<br>variation in program implementation<br>across states and seasons|
||Decrease (2.6 percentage<br>points)|Neto (2008)|2004|Probit regression with propensity<br>score matching|
||Increase (0.7 percentage<br>points)*|Angelucci<br>(2015)|1997-1999|Regression with randomization design<br>(depending on timing of Progresa<br>allocation) based on panel data|
||Decrease (0.2 percentage<br>points)*|Stecklov<br>(2005)|1997-1999|Difference-in-Differences using two<br>rounds of data and using<br>randomization design (depending on<br>timing of Progresa allocation)|

* International migration

# **5. Discussion**

The compilation of impact evaluations sparks several reflections. One, for example, is the importance of diagnosing the motivations for migrating and possible bottlenecks. This is key for designing effective interventions. In Mexico, we observe that reasons for migration include both labor and non-labor factors, such as education and marriage. The latter was also noticed as a factor in India. In Bangladesh, there was no significant increase in migration from households assigned to the ‘information-only’ group – i.e., participants who were given information about jobs in urban areas, but not cash or credit grants. This suggests that lack of information was not the main reason that held back rural households from moving to urban areas. Instead, it was a combination of credit 12
foreign equity ownership share as a criterion reveal that during 1994-97, 42 percent of all manufacturing firms with more than 10 employees were involved in some kind of foreign partnership (Czech Statistical Office, 1998).

Firms with foreign partnerships tend to be significantly larger than firms that remain independent: the median level of total employment in FDI firns is 689, in firms with joint ventures the median number of employees is 578, as compared to 352 employees in the median firm without foreign links. Foreign affiliates or joint ventures also have higher levels of initial labor productivity, measured as sales per worker in 1991 (Figure 1). This suggests that foreign investors are attracted to firrns with above average performnance and size.

**Figure 1: Labor Productivity, 1991 (in 1,000 CK)**

<!-- Start of picture text --> 2 S0 .... . _~-..-- -.-------- ------- . .......... . . ........ .. .- --- . .... ................... .. . . ... ..... . . ... ....... ....... ... ....... . ..... .<br>200<br>iONO Foregn Partner<br>i 1 01111 i 1 : [tEt~~~~~~Jcint V-ntre<br>150<br>ioolI1 II 1!_<br>lnitial Prod-otivity<br><!-- End of picture text --> Average TFP growth perfornance of firms with FDI is also highest of the three groups, followed by firms with joint ventures and domestic enterprises (Figure 2). This may be a reflection of the better than average initial level of productivity perfornance, suggesting foreign investors choose the "best" firms as partners. In the statistical analysis we therefore correct for 10
It would additionally be useful to explore the extent to which selection/nonresponse bias might be mitigated in an online survey by varying the introductory language to the questionnaire and increasing the salience of other features that might affect survey participation. To try to gauge the potential effects of misreporting, future survey experiments might include a scenario question designed to prime the value of honesty or alter the questionnaire branding and introduction to vary the extent to which the survey is linked to national statistical processes versus international assistance planning.

Internet surveys may never replace in-person surveys as a gold-standard tool for data collection, but investigating questions like these could help us learn how to maximize the unique advantages they offer.

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<!-- Start of picture text --> Change in home production, manufacturing, and services for different shock sizes<br>6% shock, Home<br>6 6% shock, Manu<br>6% shock, Serv<br>12% shock, Home<br>5<br>12% shock, Manu<br>12% shock, Serv<br>4 18% shock, Home<br>18% shock, Manu<br>18% shock, Serv<br>3 24% shock, Home<br>24% shock, Manu<br>24% shock, Serv<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>2020 2025 2030 2035<br> Year<br> Percentage change in participation<br><!-- End of picture text -->

**Figure 9:** Paths of aggregate U.S. participation for home production, manufacturing, and services across different sizes of the shock. Blue depicts a 6% shock, green 12%, orange 18%, and red 24%. A solid line depicts home production, a dashed line depicts manufacturing, and a line with circle markers depicts services.

A higher _γ_ makes the DNWR less likely to bind and decreases the unemployment generated by the shock. In addition, it leads to a smaller participation increase in home production, a larger increase in manufacturing, and a smaller fall in services. The manufacturing sector is particularly sensitive to the value of _γ_ (as it is to the size of the shock) because it is the sector where most of the unemployment occurs when the shock dissipates. If agents realize that a lot of unemployment will be generated, they will hesitate to go into manufacturing in the first place.

The amount of unemployment generated across values of _γ_ differs substantially. While for _γ_ = 4% unemployment reaches 0.32% at its peak, this peak can reach 1.3% if _γ_ = 3%, 24
- A plot and crop-level dataset in which units are uniquely identified by the crop name variable, plot unit ID, survey wave ID and season ID.

- A plot-level dataset in which units are uniquely identified by plot ID and season ID.

- A household-level dataset in which units are uniquely identified by household ID, survey wave ID and season ID.

- An individual-level dataset, in which units are uniquely identified by individual ID, survey wave ID and season ID.

<mark>All datasets include a household ID. Individual IDs as well as plot IDs are nested within household IDs (see Usage Notes). All datasets are provided as Stata data files, and code is publicly available as Stata do-files on GitHub</mark> (Bentze, 2024).

# <mark>4) Technical Validation</mark>

The LSMS-ISA surveys have undergone rigorous technical validation and methodological innovation throughout their lifetime. For example, different approaches to land area measurement and labor time use have been tested in the context rigorous field experiments, and their insights drawn upon to ensure state-of-the-art data collection methods ( see, for example, Carletto et al., 2013).

Moreover, processes prior to publishing the raw LSMS-ISA data, substantial efforts were carried out by the World Bank LSMS-ISA team to clean and correct errors stemming from data collection, editing and entry. Errors were often corrected at various stages of the fieldwork. In PAPI and CAFE interviews, Stata and CSPro software were used to identify potential errors such as out of range values and inconsistent skip patterns. When deemed necessary, paper questionnaires were reviewed, and data was revised. In some cases, data entry was performed multiple times to better identify data entry errors. The use of CAPI software such as _Survey Solutions_ allowed for error and inconsistency flags in the field, and for real-time data availability which enabled high-frequency quality checks.

The harmonized variables created for and included in the HP were also validated. This involved inspecting key summary statistics of each variable: mean, median, minimum, maximum, as well as number of missing values, to detect any problems or irregularities. In case of any suspicious values, these values were further inspected and corrected if necessary. Each correction is noted in the directories of variables (Supplementary Tables 1 to 5).

# <mark>5) Usage Notes</mark>

# _Using the datasets together_

Each dataset contains both unit and linking identifiers. They can be used to merge the four datasets which comprise the HP. Observations can be uniquely identified by combining identifiers with the survey _wave_ variable and the _season_ indicator. The latter distinguishes observations between the two seasons in Uganda. Household IDs are present in all datasets, and users can therefore perform one-to-many joins between the household dataset (using _wave_ and _household_id_obs_ as keys) and any other dataset.

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changes in the occupational composition of employment. The demand for professionals increased across sectors, at the same time that lower-skill occupations contracted as a share of total employment. As Gimpelson (2015) indicates, even within the lower-skill sector, workers moved from low-productivity to high-productivity activities, consistent with a process of labor upgrading.

High wages in the oil and gas industry and in the energy-services sector reflected the high commodity prices in international markets during the 2000s (before and after the 2009 recession). Wages in the financial sector make up the highest wages within the services industry, and the second highest of the overall economy. Within manufacturing, the average wages in heavy industry tend to be larger than those in light industry and machine construction, potentially reflecting higher average skills of workers. On the other side, both agriculture and public services (particularly health and education) make up the lowest remunerated sectors in the economy.

Despite the large earnings differentials, wages _between_ and _within_ industries tended to converge over the last years. The growth of real wages was faster in traditionally low-paying industries (agriculture, public health and public education), while the growth rate in the high-pay mining and finance sectors tended to decelerate. Results from a Shorrocks-Fields decomposition analysis confirm signs of intra-industry decreasing inequality (Gimpelson, 2015).

Industry information is not available prior to 2005, making it impossible to explore the role that the industry affiliation of workers may play in explaining changes in wage inequality over the last decade.<sup>14</sup> Additionally, a profound analysis on intra-industry dynamics is unfeasible, since the RLMS only contains industry data disaggregated into 17 different sectors. Thus, for the analysis performed (see details below), we only include occupational dummies for the 2002-2012 period. Industrial dummies are only considered when analyzing the sub-period 2007-2012.

# **3. Data**

The data comprise Round VI (1995) to Round XXI (2012) of the Russian Longitudinal Monitoring Survey (RLMS), a longitudinal survey of nationally representative household and individual data. This survey, jointly conducted by the Carolina Population Center at the

> 14 Industry information is also available for 2003 (Round XIII). However, data for this round has been excluded for the analysis for the sake of comparability in the industry classification over time.

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capital seems to be a priority and competition agencies recognize it is a necessary condition to improve performance, almost all responses indicated, when asked how much are they willing to pay for training courses, that “this agency does not have enough budget for training”.

2) Priority infrastructure sectors: the telecom and electricity sectors are a priority across all regions. Transport is also a priority in LAC.

3) Workload: competition agencies across countries and regions have different mandates. Some competition laws impose an ex-ante control of all mergers in the economy, while others set rules that lead the competition authority to review only a few mergers per year. This fact plus the lack of a strong relation between the size of the economy and the number of mergers per year allowed us to conclude that the workload of a competition agency is, to a great extent, determined by the competition laws.

4) Personnel: the average endowment of personnel in competition agencies varies significantly across regions. We defined and computed proxy productivity variables “mergers per lawyer (economist)” and “anticompetitive conduct cases per lawyer (economist)”. However, no clear conclusion can be obtained because a labor productivity variable for competition agencies cannot ignore the quality dimension, that is, the inherent varying complexity of mergers and anticompetitive conduct cases.

It must be highlighted that competition advocacy, an area where competition agencies are investing more time and financial resources, was explicitly not included in the questionnaire. Given that The International Competition Network conducted during 2002 a detailed questionnaire on this topic,<sup>16</sup> we considered it was not appropriate to ask competition agencies to respond to two questionnaires on the same topic. Provided competition advocacy implies a broader and more general role for competition agencies -as they intervene in areas like design of regulatory frameworks, trade liberalization, and state aid- it must be included in the design of competition policy courses.

> 16 Available at http://www.internationalcompetitionnetwork.org

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_Source:_ Simulations based on OPSM Survey, May 2002

# **SUMMARY**

5.36 In conclusion, the diverse range of simulations has served to illustrate the potential for substantially improving the distributional incidence of existing social policies based on some simple changes of design. It is important to emphasize that the multi-dimensional poverty proxy that has been used is purely illustrative, and that more effective targeting mechanisms may well exist. Furthermore, the simulations report the theoretical maximum performance of this poverty proxy, without taking into account the deficiencies that are likely to be introduced once any such scheme is operationalized. In this sense, the results represent an upper bound on the potential targeting performance of the poverty proxy that has been used.

5.37 In general, existing social policies present concentration coefficients that range from –0.15 to +0.15, whereas the simulated social tariff schemes based on the application of the multi-dimensional poverty proxy reach concentration coefficients of around –0.20, representing a significant improvement in some cases. However, the simulated connection subsidy schemes are by far the most successful in terms of targeting performance, with concentration coefficients in the range –0.30 to –0.40. In addition, these connection subsidy schemes present errors of inclusion and exclusion in the 2040% range compared with the 40-60% range for existing social policies. Similarly, the errors of exclusion with respect to the target population are reduced to the 15-45% range, compared with the 60-90% range for existing social policies. Moreover, the errors of exclusion with respect to the total population are reduced to the 30-70% range, compared with the 65-95% range for existing social policies. Of all the sectors considered, the gas sector is the one where the largest potential targeting gains could be achieved based on the alternatives simulated.

**Table 11: Summary of targeting performance for simulated alternative social** **<u>policies</u>**

||**Error**<br>**o**<br>**inclusion**|**Error of ex**<br>**f**<br>Target<br>population|**clusion**<br>Total<br>population|<br>**Concentration**<br>**coefficient**|
|---|---|---|---|---|
|Water status quo (provincial social tariffs)|44%|73%|76%|-0.15|
|Simulated alternatives|||||
|National discount on minimum unmeasured tariff<sup>*</sup>|43%|38%|56%|-0.15|
|National sewerage connection subsidy|42%|0%|60%|-0.16|
|National water connection subsidy|28%|0%|90%|-0.28|
|Electricity status quo (provincial social tariffs)|39%|93%|94%|-0.37|
|Simulated alternatives|||||
|National social tariff scheme (reallocation of Tariff<br>Compensation Fund)<sup>*</sup>|<br>41%|31%|36%|-0.21|
|National social tariff scheme (current allocation of Tariff<br>Compensation Fund)<sup>*</sup>|<br>41%|31%|36%|-0.24|
|Gas status quo (Patagonian subsidy scheme)|70%|64%|98%|+0.08|
|Simulated alternatives|||||

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SME firms in India are in general consistent with those from other countries, including developed countries (see Peterson and Rajan 1994 and Berger and Udell 1995 for evidence on small firms in the U.S.).

# **V.3 Ownership structure and corporate governance**

Our survey also sheds light on the organization, ownership pattern and corporate governance mechanisms in Indian firms. In about 85% of the SMEs surveyed, the largest share block belonged to the founder and his (all firms in our sample had male founders) family. Two companies (1%) had some foreign (including expatriate Indian) investment. The remaining 14% firms had their largest share block held by the founder (and his family) jointly with unrelated partners. About 70% of the businesses had unlimited liability. When asked how the owner planned to protect personal assets in case of business failure, 96% of the respondents preferred negotiating with debtors for an extension; 14% of these respondents also planned to file for personal bankruptcy.

In terms of corporate governance, about 37% of all units surveyed that had non-owner CEOs (or equivalent), indicated that the CEOs enjoyed “little discretion” in their business decisions and had to consult the owners for most decisions. The proportion of firms with CEOs with “no discretion” and “full discretion” were 13% and 8% respectively, while 43% indicated they had some to a lot of discretion and needed to consult the owners only in critical matters (the total exceeds 100% owing to multiple answers in some cases). Clearly there is not much separation between ownership and control in the typical SME environment, with the owner keeping a close watch over day-to-day functioning even with a hired CEO. When asked about the possibility of an outsider buying up a firm’s assets in case of bad management, 57% thought it was “very likely”, with 22% considering it “somewhat likely” and 21% “not likely.” 32
# **5 Conclusions**

The most used measures of welfare, mean income and the poverty headcount, are not distribution sensitive. Despite widespread agreement that inequality is important, the large menu of existing distribution sensitive welfare measures is rarely used in academic, policy, or public discourse. We argue that a main reason for this is their complexity and lack of intuitive units. To remedy this shortcoming, we have introduced a new family of distribution sensitive welfare, poverty and inequality measures that has a simple mathematical formulation and simple units. The key ingredient of these measures is the ratio of reference income z to individual individual incomes, z/yi, which captures the factor by which individual incomes must be multiplied to attain the reference level of income. This ratio formulation ensures that our measures satisfy all three definitions of distribution sensitivity in the literature. Our measures are based on averages of this ratio, giving a simple “average factor by which incomes must increase” interpretation to our measures. This simple average property also ensures that our measures are sub-group decomposable with population weights, further enhancing its ease of communication.

We demonstrate the empirical properties of our measures in the context of the world interpersonal income distribution over the period 1990-2019, as well as in selected country comparisons. Consistent with their strong distribution sensitivity, we find that our welfare and poverty measures decline more strongly than their non-distribution sensitive counterparts in a setting that has featured a substantial decline in global inequality. Finally, we exploit the simple structure and units of our new measure to propose a new “prosperity gap” measure that the World Bank could use to track progress towards its goal of promoting shared prosperity.

One area that requires further work to facilitate the implementation of these new measures is a more careful consideration of the treatment of low incomes in household surveys. As discussed in Section 3, by virtue of being based on z/yi, which makes them distribution sensitive, our measures also are sensitive to mismeasurement of very low or, at the extreme, zero incomes. In this paper, we bottom-coded income at $0.50 per day, at which we arrived by applying the consumption floor methodology outlined in Ravallion (2016) to estimate minimum consumption levels. Further work is needed on different approaches to address the issue of mismeasurement of income (or consumption) at the bottom of the distribution.

******************************

**_Contributors_** : _Sterck had the original idea for the welfare index, which aggregates individual contributions to the index in the form of_ z/yi _. Decerf, Kraay, Özler, and Sterck drafted Sections 1 and 2, which were revised by all authors. Jolliffe, Kraay, Lakner, and Yonzan drafted Sections 3 and 4, which were revised by all authors. All authors interpreted the data, contributed to writing the paper, and approved the final version._

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