rafmacalaba/datause-encoder-data huggingface dataset:
|**HS**<br>**code**||||**Year**|||
|---|---|---|---|---|---|---|
|**2 digits**|**_Type of products_**|**2001**|**2002**|**2003**|**2004**|**2005**|
|||12|12|12|12|12|
|79||4.0|3.9|3.8|3.0|3.3|
||_Zinc and its products_|<br>_(1.4)_|<br>_(2.0)_|<br>_(2.2)_|<br>_(2.4)_|<br>_(2.3)_|
|||8<br>|8<br>|8<br>|8<br>|8<br>|
|80|_Tin and its products_|5.0|4.7|12.9|5.5|5.0|
|||_(0.0)_|_(0.8)_|_(9.1)_|_(1.4)_|_(0.0)_|
|81|_Non-ferrous metals, cermets and their_|78<br>12.3|78<br>10.7|78<br>10.9|78<br>11.0|78<br>11.1|
||_products_|_(5.0)_|_(5.9)_|_(5.7)_|_(6.0)_|_(6.0)_|
|||108|108|108|108|108|
|82|_TOOLS AND CUTLERY_|8.5|8.8|8.7|8.8|8.7|
|||_(6.2)_|_(6.2)_|_(6.1)_|_(6.1)_|_(6.1)_|
|||50<br>|50<br>|50<br>|50<br>|58<br>|
|83|_Oh lli d_|18.3|18.7|18.2|17.8|16.9|
||_ter metac proucts_|_(3.4)_|_(3.0)_|_(3.9)_|_(4.4)_|_(5.5)_|
|||1136|1150|1134|1168|1205|
|84||6.7|6.8|6.9|6.8|6.7|
||_MACHINERY/APPLIANCES_|_(4.2)_|_(4.2)_|_(4.2)_|_(4.5)_|_(4.4)_|
|||745|745|735|739|787|
|85|_Electrical equipment, audio- and video_|10.4|10.7|10.7|10.8|10.3|
||_equipment_|_(7.9)_|_(9.4)_|_(9.8)_|_(11.7)_|_(9.5)_|
|||41|47|44|44|44|
|86|_Railroad locomotives and cars_|3.7|3.2|3.2|3.1|2.9|
|||_(4.2)_|_(4.2)_|_(4.2)_|_(4.1)_|_(4.1)_|
|||249|283|266|266|272|
|87|_VEHICLES AND PARTS_|10.8|18.1|24.0|19.4|18.0|
|||_(7.4)_|_(44.9)_|_(74.4)_|_(41.9)_|_(38.8)_|
|||36|36|36|36|36|
|88||14.0|15.0|14.5|12.5|13.4|
||_Aircrafts_|_(7.8)_|_(7.1)_|_(7.4)_|_(8.4)_|_(8.0)_|
|89||39<br>10.9|39<br>10.5|39<br>10.7|39<br>10.2|39<br>9.6|
||_Boats, ships_|_(6.9)_|_(7.1)_|_(7.0)_|_(6.8)_|_(7.3)_|
|||320|320|317|317|321|
|90||7.5|7.6|7.7|7.8|7.7|
||_Optical equipment_|_(4.5)_|_(4.5)_|_(4.5)_|_(4.5)_|_(4.5)_|
|||63|63|63|63|65|
|91|_CLOCKS AND WATCHES_|27.3|27.7|28.3|27.1|27.7|
|||_(26.0)_|_(27.9)_|_(21.1)_|_(26.9)_|_(27.2)_|

53
―income-rich‖ but ―services-poor‖ household.<sup>43</sup> There are two policy instruments, a transfer payment and service provision. The economy is divided into geographic areas and a given area gets either the service or the transfer. We then calculate a composite index like the MPI based on survey data on incomes and access to services. There is bound to be a positive correlation between average income and service provision, but (nonetheless) some places have high income poverty but adequate services, while others have low income poverty but poor services. The policy maker then decides whether each area gets the transfer or the service. Plainly, the policy maker should not be using the aggregate MPI for this purpose, for then some income-poor but service-rich households will get even better services, while some income-rich but service poor households will get the transfer. The total impact on (multidimensional) poverty would be lower if one based the allocation on the MPI rather than the separate poverty measures—one for incomes and one for access to services. It is not the aggregate mashup index that we need for this purpose but its components. Indeed, for such policy applications we do not need the mashup index.

# **Conclusions**

The lesson to be drawn from all this is not to abandon mashup indicies. Composite indices derived from development-data mashups are often trying to attach a number to an important, but unobserved, concept, for which prevailing theories and measurement practices offer little guidance. And there are clear attractions to finding a way of collapsing a (potentially) large number of dimensions into one. Rather the main lessons are (first) that the current enthusiasm for new mashup indices needs to be balanced by clearer warnings for, and more critical scrutiny from, users, and (second) that some past mashup indices do not stand up well to such scrutiny.

While there is invariably a gap between the theoretical ideal and practical measurement, for past mashup indices the gap is huge. Greater clarity is needed on what exactly is being measured. And more attention needs to be given to the tradeoffs embodied in the index. In most cases the tradeoff is not even identified in the most relevant space for users to judge, and in cases where it can be derived from the data available it has been found to be questionable—implying,

> 43  Sufficient conditions are that there is declining marginal utility to both income and services and that the marginal utility of income (services) is non-decreasing in services (income).

30
PISA 2000. Because the PISA 2012 rescaling was applied to the PISA 2000 data before estimating the skills cost function (see above), the PISA 2000 skills cost function implies a different level of education quality and subsequently a different elasticity of skill supply and a different level of skills in the economy. Some countries saw increases in PISA scores between the 2000 and 2012 rounds, notably Poland, while others saw decline. In reality, the change in education quality between 2000 and 2012 would not be instantaneous and affect all workers simultaneously; rather, the PISA results provide a measure of the skills that a single-aged cohort of students possess. However, by estimating the implication of the change in the quality of education for the marginal effects of a carbon price, we provide a value, in terms of marginal effects, of the change in quality.

Table 10 presents the comparison of the marginal effects for each country between the education quality implied by 2012 and 2000 rounds of PISA. A value greater than one implies that the quality of education in 2012 results in a stronger marginal effect in the same direction and a value less than one implies a weaker marginal effect in the same direction. As before, statistical significance is indicated for a difference from one rather than zero.

Changes in education quality through changes in the elasticity of skill supply alone (Scenario 2a in Table 10) result, on average for the countries in the sample, in no change in the marginal effects of a carbon price. However, the marginal products are found to be both higher and lower depending on the country. For example, Poland’s PISA scores increased substantially between 2000 and 2012. The implied increase in the elasticity of skill supply results in a marginal increase in carbon prices having a slightly larger effect on emissions reduction, a lower effect on reducing output, and a stronger effect on skills supply. Another example is Italy. Italy experienced minor improvements in its PISA reading scores between 2000 and 2012. In our model this implies a slightly higher effect on reducing emissions, slightly lower effect on reducing output, and slightly higher effect on increasing skill supply, all statistically insignificant. However, this slight increase in PISA reading scores was accompanied by a change in the relationship in household wealth and scores resulting in carbon prices having a larger rather than smaller effect on increasing wage inequity. Table 10 also demonstrates cases when education quality decreased from 2000 to 2012. Overall, we see the predicted link between changes in education quality and equity and the resulting marginal effects of an increase in carbon prices.

28
Table S.7: Impacts on children’s educational achievement - English: heterogeneity <u>(OLS)</u>

|Dependent variable:||||English:|GPA z-sco|res||||
|---|---|---|---|---|---|---|---|---|---|
|_X_:|Age|Education|Male|Household<br>size|No. of<br>male<br>members|Head<br>age|Head<br>education|Head<br>male|Land<br>size|
||(1a)|(1b)|(1c)|(1d)|(1e)|(1f)|(1g)|(1h)|(1i)|
|**December in 2013**||||||||||
|All products|-1.398**|-0.320|-0.076|0.118|0.145|0.437|-0.072|-0.019|-0.070|
||(0.580)|(0.245)|(0.075)|(0.243)|(0.151)|(0.340)|(0.065)|(0.215)|(0.064)|
|One product|-0.923|-0.322|-0.099|0.197|0.059|-0.105|-0.132*|-0.327|-0.099|
||(0.564)|(0.228)|(0.097)|(0.337)|(0.160)|(0.330)|(0.076)|(0.223)|(0.074)|
|All products _× X_|0.109**|0.058|0.051|-0.035|-0.083|-0.012|0.013|-0.040|0.001|
||(0.045)|(0.047)|(0.157)|(0.044)|(0.062)|(0.008)|(0.031)|(0.231)|(0.002)|
|One product _× X_|0.064|0.041|-0.088|-0.069|-0.082|-0.001|-0.004|0.208|-0.003|
||(0.047)|(0.053)|(0.142)|(0.069)|(0.066)|(0.008)|(0.035)|(0.241)|(0.005)|
|R-squared|0.258|0.254|0.254|0.254|0.255|0.255|0.254|0.254|0.255|
|No. of obs.|850|850|850|850|850|850|850|850|850|
||(2a)|(2b)|(2c)|(2d)|(2e)|(2f)|(2g)|(2h)|(2i)|
|**December in 2014**||||||||||
|All products|-0.327|0.099|0.014|-0.424|0.015|-0.099|-0.079|-0.344|-0.044|
||(0.563)|(0.294)|(0.081)|(0.366)|(0.197)|(0.311)|(0.094)|(0.352)|(0.089)|
|One product|0.557|0.398|0.034|0.070|0.183|0.255|0.026|-0.244|0.056|
||(0.646)|(0.274)|(0.104)|(0.331)|(0.175)|(0.377)|(0.091)|(0.279)|(0.100)|
|All products _× X_|0.024|-0.030|-0.115|0.078|-0.020|0.002|0.035|0.339|0.001|
||(0.045)|(0.062)|(0.178)|(0.065)|(0.069)|(0.007)|(0.034)|(0.340)|(0.002)|
|One product _× X_|-0.041|-0.078|0.022|-0.006|-0.060|-0.005|0.010|0.313|-0.001|
||(0.050)|(0.055)|(0.169)|(0.068)|(0.072)|(0.009)|(0.036)|(0.280)|(0.004)|
|R-squared|0.100|0.100|0.099|0.101|0.099|0.099|0.100|0.101|0.099|
|No. of obs.|846|846|846|846|846|846|846|846|846|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

52
|I.INTRODUCTION**.....................................................**<br>**2**|
|---|
|II.GENERAL DATA ISSUES AND THE BANK'S EXPERIENCE .....................................................<br>3|
|III. IDEAL DATA VS. EXISTING DATA .....................................................<br>6|
|A. The Firm .....................................................<br>7|
|B. Human Capital .....................................................<br>9|
|C. Technology ....................................................<br>10|
|D. Market Structure .....................................................<br>11|
|E. Transaction Environment ....................................................<br>14|
|F. The Role of the State ....................................................<br>16|
|G. Beyond the firm: "The micro-foundations of macroeconomics" ........................ 19|
|IV. QUESTIONING QUESTIONS ....................................................<br>24|
|General Issues ....................................................<br>24|
|Response scales ....................................................<br>26|
|The "Don't Know" Problem ....................................................<br>28|
|Filters and Branching ....................................................<br>29|
|Order Effects ....................................................<br>31|
|Specific and general questions ....................................................<br>31|
|Recent and less-recent behavior ....................................................<br>32|
|Within-question order effects ....................................................<br>33|
|Sensitive Questions and Truth-Revealing Mechanisms ........................................ 33|
|V.CONCLUSIONS<br>....................................................<br>35|

# **I. INTRODUCTION**

Industrialized nations make rigorous data-collection the foundation of their policy analysis. The paucity of data on firms in developing countries, however, makes policy analysis in those countries more difficult, The World Bank has increasingly focused on firmn-level surveys to help build a data foundation in developing countries and transition economies. The most extensive firn surveys implemented by the World Bank include the Regional Program on Enterprise Development (RPED) survey in 8 African countries, the Industrial Competitiveness Study (ICS) in East Asia, a series of surveys in transition countries, and many surveys on small and medium enterprise (SME) issues in South Asia and South America (see Table 1 for a partial list of World Bank firm-level surveys). This paper takes stock of recent World Bank firm surveys, discussing what we have learned from them and how we could more consistently and efficiently gather data for policy analysis.

2
|**Division**|**District**|**Stratum**|**Quarter 1**|**Quarter 2**|**Quarter 3**|**Quarter 4**|**Total**|
|---|---|---|---|---|---|---|---|
|55|94|18|4|3|4|4|15|
|55|94|17|5|6|5|5|21|

Table 9 shows the final allocation of PSUs across the 20 sampling strata. The four main CCs each have less than 36 PSUs allocated as these areas have only one sub-stratum. This means that the final sample might not provide reliable poverty and welfare statistics at the division level for urban, rural, and CCs, but the sample will provide reliable poverty and welfare estimates at the district level. This illustrates the trade-offs that household survey sampling experts must assess when designing a sample for a survey with multiple objectives. For the HIES 2016/17, BBS decided to prioritize the production of reliable annual district-level poverty and welfare statistics over annual division-level poverty and welfare statistics for rural, urban, and CC. This decision stems from the fact that district-level statistics correspond to clear administrative policy making units, while division-level statistics do not.

Table 10 shows the final allocation of PSUs for urban and rural areas.<sup>21</sup> The share of urban PSUs in the HIES 2016/17 sample is slightly higher than 30 percent, while only 23 percent of Bangladesh’s population lives in cities, according to the 2011 Census of Population and Housing. This is explained by the manual adjustments conducted during the allocation of PSUs. As described above, we purposely designed the survey to oversample urban PSUs to help BBS study the relationship between living standards and rapid urbanization.

> 21 BBS has traditionally published urban and rural, but not City Corporation, welfare statistics. Therefore, we only report in this paper the allocation of PSUs across urban and rural areas.

25
overlaps with official SSC estimates of migration for that period. Given the rare nature of migration in many populations, a common method of measuring domestic migration flows in the literature is to average over a 5-year window for an individual (rather than focusing solely on the previous year). For Uzbekistan, this crude rate of migration<sup>7</sup> between provinces over the 5-year window preceding baseline data collection in 2018 was approximately 1.24 percent of the population. This estimate is difficult to benchmark against rates in other countries due to differences in the definition, size, and population density of administrative units. However, by way of rough comparison, Bell and Muhidin (2009) find no case in their sample of 28 countries with five-year internal migration rates below 2 percent for the administrative territory equivalent of Uzbekistan’s provinces. A later update from Bell and Charles-Edwards (2013) – covering 70 countries – also finds no instances with migration rates as low as Uzbekistan’s when using territories analogous in size to Uzbekistan’s provinces (Figure 4). In only for a handful of cases when focusing on large macro-regions (much larger than provinces) do the authors find 5-year migration rates below 2 percent, including for Indonesia, pre-reform China, and Portugal.<sup>8</sup> Although low rates of domestic migration are generally more common in CIS countries (Seitz, 2018; OECD, 2017), in none of the cases for which data are available does the rate of domestic migration fall below 1 percent of the population per year.

> 7 Several alternative measures of domestic migration rates adjust for population size and geography of administrate units to increase international comparability.

> 8 Migration rates in each of these cases were higher than Uzbekistan when comparing similar-sized territories (3.4 percent in China, 2.9 percent in Indonesia, and 2.9 percent in Portugal.) Also notable is that migration rates have since risen post-reform in China to 6.7 percent, and that Indonesia’s island geography leaves it a relatively peculiar case.

14
Policy Research Working Paper 7156

# **Abstract**

This paper builds a tractable partial equilibrium model to help explain the role of trade preferences given to developing countries, as well as the efficacy of various subsidy policies. The model allows for firm level heterogeneity in demand and productivity and lets the mass of firms that enter be endogenous. Trade preferences given by one country have positive spillovers on exports to others in this model. Preferences given by the European Union to Bangladesh in an industry raise profits, resulting in entry, and some of these firms also export to the United States. The parameters of the model are estimated using cross sectional customs data on Bangladeshi exports of apparel to the United States and European Union. Counterfactual experiments regarding the effects of reducing costs, both fixed and marginal, or of trade preferences offered by an importing country are performed. The counterfactuals show that reducing fixed costs at various levels has very different effects and suggest that such reductions are more effective in promoting exports when applied at later stages when firms are more committed to production. A subsidy of 1.5 million dollars to industry entry costs raises exports by only 0.4 dollars for every dollar spent, but when applied to fixed costs of production, it raises exports by $25 per dollar spent.

This paper is a product of the Trade and International Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hlkee@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
formal democratic institutions, primary school enrollment rates, and agricultural productivity (which may reflect the benefits of public investment in rural areas). None of these variables has any robust relationship to either growth or to income share of the poor. Social spending as a share of total spending has a negative relationship to income share of the poor that is close to statistical significance. That finding reminds us that public social spending is not necessarily well targeted to the poor.<sup>4</sup> The simple correlations between all of these variables and income share of the poor, in both levels and differences, are shown in Figures 2, 4, and 5. Those simple correlations reflect what we find in multivariate analysis: it is not easy to find any robust relationships between institutions and policies, on the one hand, and income share of the poor, on the other.

To summarize, we find that contrary to popular myths, standard pro-growth macroeconomic policies are good for the poor as they raise mean incomes with no systematic adverse effect on the distribution of income. In fact, there is weak evidence that macro stability, proxied by stabilization from high inflation and a reduction in government consumption, increases income of the poor more than mean income as they tend to increase the income share of the poorest. Other policies such as good rule of law, financial development, and openness to trade benefit the poor and the rest of the economy equally. On the other hand, we find no evidence that formal democratic institutions or a large degree of government spending on social services generally affect income of the poor. Finally, the growth-poverty relationship has not changed over time, does not vary during crises, and is generally the same in rich countries and poor ones. In the remainder of this paper we provide details on how these results are obtained.

This is not to say that growth is _all_ that is needed to improve the lives of the poor. Rather, we simply emphasize that growth generally does benefit the poor as much as

> 3This result is consistent with existing evidence in smaller samples. Agenor (1998) finds an adverse effect of inflation on the poverty rate, using a cross-section of 38 countries. Easterly and Fischer (2000) show that the poor are more likely to rate inflation as a top national concem, using survey data on 31869 households in 38 countries. Datt and Ravallion (1999) find evidence that inflation is a significant determinant of poverty using data for Indian states.

9
ILO LFS pilot study also found that in some contexts words like “business” were interpreted mostly as formal enterprises, and so respondents (often women) would be less likely to report their work as a business if they were self-employed in informal settings and/or had unpaid supporting roles in the family farm or business.

Women in contributing family roles are also considered employed, since it contributes to household profit, but are also often less likely to be identified (or self-identify) in this form of employment. The ILO LFS pilot study found that women in contributing family work often tend to identify themselves first as engaged only in domestic work, because of social and cultural norms that diminish the economic value of contributing family work, and since this work is also often conducted with domestic activity. As a result, follow-up or “recovery” questions were needed to hone in on this as well as other economic activity (also see Benes and Walsh, 2018a; Data2X, 2017). Women respondents in the cognitive testing stage, for example, often said they helped with stocking, storing, and other activities on the farm, but they had to be prompted specifically to report these activities. Figure 3 also shows, based on recent analysis of the ILO pilot data (Koolwal, 2018), that recovery questions on contributing family work re-classifies substantial shares of men and women — with the greatest effects for rural women — back into employment.

Figure 3. ILO LFS pilot countries: share of men and women re-classified as employed, through recovery questions on contributing family work <!-- Start of picture text --> Rural Urban<br><!-- End of picture text --> <u>Notes: Similar patterns observed for recovery questions on paid work in agriculture, and to a lesser extent on small/secondary jobs.</u> Source: Koolwal (2018), based on ILO LFS pilot study data accessed at ILO.

Careful attention to wording choice, adapted to local context, is therefore important in the framing of employment questions. A recent study by Comblon and Robilliard (2017) using data from more than 40 surveys and censuses conducted in Cameroon, Mali and Senegal between 1976 and 2012 also discusses how women’s reporting of employment is more sensitive to questionnaire wording and design, owing to the effects of social norms/interpretation of wording, and the specific types of work women in low-income contexts tend to be involved in. Most socioeconomic surveys also do not distinguish between running and supporting a family business in their labor modules — within the LSMS, the 2016-17 Malawi and 2013-14 Uganda surveys do ask separate questions on whether respondents “run a business of any size for 9