rafmacalaba/datause-encoder-data huggingface dataset:
Shelar, Hemlata, Gagandeep Kaur, Neha Heda, and Poorva Agrawal. 2020. “Named Entity Recognition Approaches and Their Comparison for Custom Ner Model.” _Science & Technology Libraries_ 39 (3): 324–37.

Smirnov, Anatoly A., and Irina V. Stukova. “Determinants of integration approach in the agrarian sphere development in contexts of transformation.” _Review of European studies_ 7, no. 8 (2015): 8.

World Bank. 2021. “World Development Report 2021: Data for Better Lives.” Washington DC: World Bank. https://wdr2021.worldbank.org/ Yu Tian. 2020. “THE PARTNER REPORT ON SUPPORT TO STATISTICS PRESS 2020.” PARIS21. https://paris21.org/sites/default/files/inline-files/PRESS2020%20Final.pdf.

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to examine localization using micro geographic data.<sup>3</sup> We start by calculating the Euclidian distance between every pair of manufacturing establishments. For _−_ manufacturing industry _M_ with _n_ establishments, this generates _n_ ( _n_ 1)/2 unique distances between establishments. We then calculate the frequency for each distance level and plot the corresponding density. We can represent the Euclidian distance between establishments _i_ and _j_ by _D_ ( _i_ , _j_ ), and define _δ_ ( _i_ , _j_ , _d_ ) such that _δ_ ( _i_ , _j_ , _d_ ) = 1 when _D_ ( _i_ , _j_ ) = _d_ and _δ_ ( _i_ , _j_ , _d_ ) = 0 otherwise. The un-smoothed distance density or _K_ -density is: For overall manufacturing, we find small distances between establishments. A third of all manufacturing firms are within 3.7 kilometers of each other; 45 percent are within 9 kilometers, and the 50<sup>th</sup> percentile is 41.5 kilometers. The distribution of overall distances is shown in figure 2. The distribution shows a kernel density of distances, which is plotted using the Gaussian kernel specification in STATA.

**Figure 2: K density of overall manufacturing in Uganda**

<!-- Start of picture text --> 0 50 100 150<br>Distance Km<br>.025<br>.02<br>.015<br>kdensity<br>.01<br>.005<br>0<br><!-- End of picture text --> We also looked at specific industries to examine the extent to which firms were clustered. For instance, for establishments in paper and printing (SIC 222), the 50<sup>th</sup> percentile of inter-establishment distance is 1.8 kilometers, signifying that the industry is

> 3 At this time, we have not compared the distribution of sector specific concentrations to a counterfactual random distribution of establishments, using all manufacturing locations as the universe of location possibilities. We will be providing this comparison in subsequent work.

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# **3.2 An effect on latrine ownership**

Is there evidence in the data, beyond the government’s claim to the World Bank researchers, that the program happened at all? As a first piece of evidence, in Ahmednagar, in the midline survey shortly after the intervention, respondents were 7.2 percentage points more likely to report a recent visit by a sanitation promoter in the treatment group than in the control group, compared with a 1.5 percentage point difference in Nanded and Nandurbar. This difference is, in general, only barely statistically significant. The exact two-sided _p_ -value differs in household-level regressions with standard errors clustered by village, regressions collapsed to 60 village-mean observations, and in a non-parametric Mann-Whitney-Wilcoxon test on the collapsed means (0.116, 0.099, and 0.066), although note that a one-sided test is arguably preferable to this two-sided test, and would be higher-powered.

More importantly, villages in the treatment group built more latrines. In the final survey round in August of 2005, treatment village household latrine coverage in Ahmednagar had increased by 8.2 percentage points more than for control households,<sup>15</sup> compared with a difference of -0.9 percentage points in Nanded and Nandurbar. The Ahmednagar difference has a two-sided _p_ -value of 0.073 and 0.072 in uncollapsed (clustered) and collapsed regressions, respectively, although curiously the corresponding figure is 0.22 in a collapsed Mann-Whitney-Wilcoxon test.

This effect of the program on latrine coverage is interesting in two further ways. First is that it is modest: an 8.2 percentage point increase left many people openly defecating. Indeed, we do not know exactly what the change in latrine _use_ was. However, it is comparable to the average change in latrine coverage due to the TSC throughout India that Spears (2012a) finds in the Indian government’s inflated official statistics. The fact that the program had an effect on height without resulting in universal latrine coverage suggests one of two possibilities. One is that here it was not the case, as is sometimes claimed in the policy 15If each of the 60 villages in Ahmednagar is omitted in turn, this point estimate ranges from 5.5 percentage points to 9.1 percentage points.

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# **4 Future Land Reallocations and the Decision to Migrate**

As a starting point in examining effects of land tenure insecurity on current migration decisions, we first examine results from the OLS and first‐differenced models in Table 6. Both models include the full set of individual, household and village‐level variables discussed in our estimation strategy. In the OLS specification, the coefficient on future land reallocation is ‐0.016 and statistically significant. Given that 16 percent of 16 to 50 year olds, on average, worked as migrants during the period, this amounts to a ten percent reduction in the probability of migrating. Gender‐ and age‐time trends carry expected signs and are consistent with other descriptive work, and in OLS models the probability of being a migrant is positively associated with educational attainment. An individual is also more likely to migrate if the working‐age share of household registered residents (exclusive of the individual himself, or herself) is greater, which is consistent with the likelihood that the marginal product of adult labor on household land will be lower in households with more family members.

The likely presence of important time‐varying unobservables is underscored by positive coefficients on village land per capita and the time‐varying village land gini index. If average land quality and off‐ farm opportunities were the same, after controlling for village fixed effects, we would expect to observe a negative sign village land per capita. In fact, land per capita may be increasing more rapidly, with more permanent migration or in less dynamic regions with fewer local opportunities. This unobserved difference across villages is consistent with a positive coefficient on village land per capita. With respect to the village land per capita gini, one might expect to see a negative sign if increases in land inequality predicted a future land reallocation. In fact, the considerable heterogeneity in governance of land and in ability to formally rent land to other households makes it likely that, in some villages, longer‐term increases in the village land per capita gini are associated with increases in security of land tenure. As the land per capita gini may reflect, in part, unmeasured perceptions of the security of tenure, it is not surprising to find a significant positive coefficient on this regressor in OLS models.

_Land Tenure Insecurity and Migration, Page 26_
sophisticated technologies (i.e., _MAXf,j ≥_ 3) even in such an advanced economy as South Korea.<sup>20</sup> To explore the cross-country differences in technology sophistication, the top panel of Figure 4 also plots the histogram of _MAXf,j_ in Senegal (left) and India (middle). There are stark cross-country differences in the distribution. The mean and variance increase uniformly with the level of development in the country (i.e. Senegal, India, South Korea). Additionally, the cross-establishment distribution of _MAXf,j_ is most skewed to the right in Senegal, and least in Korea.

The bottom panel in Figure 4 shifts the focus to the most widely used technology. In particular, it shows the histogram of _MOSTf,j_ in fabrication across food processing establishments for each country. By construction, the distribution of _MAXf,j_ stochastically dominates the distribution of _MOSTf,j_ , as _MAXf,j ≥ MOSTf,j_ . However, the distributions of MAX and MOST differ significantly. For example, in 65% of Indian food processing establishments the most sophisticated technology used in fabrication is ’machines controlled by operators.’ Yet, this technology is the most widely in only 35% of establishments.

The gap between MAX and MOST in this example motivates a deeper exploration of whether MAX and MOST are statistically distinct across a broad range of business functions and countries and, if that is the case, their relative importance in shaping the relationship between technology sophistication and productivity across establishments.

# **4 Technology Sophistication at the Business Function**

We use the FAT dataset to examine technology at the business function level. We explore two issues: the range of technologies used in each function and the comparison between the most widely used and the most sophisticated technology available in the business function. We then compare our findings with the predictions of state-of-the-art models of technology in production and draw conclusions about relevant extensions.

## **4.1 The Array of Technologies Used in the Business Function**

We begin our analysis of the vector of technologies used in a business function by counting the number of technologies from the grid that an establishment uses, denoted as _ANUMf,j_ . Table C.2 reports, for each function, the average _ANUMf,j_ across all establishments that conduct the function in-house. On average, establishments use two different technologies

> 20This is also true in other sectors with greater penetration of robots such as the automotive sector. Even if we weight establishments by size (e.g., employment or sales) only 56% of establishments in automotive fabrication in South Korea use robots or a more sophisticated technology in fabrication.

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**Table 4: Drought Impacts on Child Anthropometrics**

**OLS with Province  and Year of Survey Fixed Effects and Wealth Quintile Controls**

|**VARIABLES**|(1)<br>**Stunted**|(2)<br>**Underweight**|(3)<br>**Wasted**|(4)<br>**Stunted &**<br>**Underwt**|(5)<br>**Any**|
|---|---|---|---|---|---|
|Drought: Birth Season|0.06***|0.02***|-0.00|0.02***|0.06***|
||[7.64]|[3.40]|[-0.39]|[4.16]|[7.19]|
|Drought: Season after Birth|0.10***|0.01**|-0.02***|0.02***|0.08***|
||[10.66]|[2.07]|[-4.50]|[3.67]|[8.77]|
|Lean Season|0.03***|0.01***|-0.00|0.02***|0.03***|
||[5.05]|[2.80]|[-1.27]|[3.21]|[4.44]|
|Second Poorest|-0.03***|-0.03***|-0.01***|-0.03***|-0.04***|
||[-3.73]|[-4.21]|[-3.33]|[-3.90]|[-4.61]|
|Middle|-0.05***|-0.05***|-0.01**|-0.05***|-0.06***|
||[-5.02]|[-7.02]|[-2.44]|[-6.71]|[-5.84]|
|Second Richest|-0.10***|-0.08***|-0.02***|-0.07***|-0.11***|
||[-8.79]|[-9.61]|[-4.01]|[-9.76]|[-9.72]|
|Richest Quintile|-0.16***|-0.10***|-0.03***|-0.09***|-0.18***|
||[-8.81]|[-8.89]|[-4.30]|[-8.36]|[-9.67]|
|Constant|0.38***|0.12***|0.05***|0.10***|0.41***|
||[9.98]|[6.34]|[3.52]|[5.48]|[11.15]|
|Observations|40,977|40,961|40,870|40,977|40,977|
|R-squared|0.04|0.02|0.01|0.02|0.04|

Notes: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors are clustered at the DHS cluster level. All regresssions employ province and year of survey fixed effects. The weights used are person weights adjusted for the national populations. All regressions use data on rural households from 5 Southern African countries (Mozambique, Malawi, Tanzania, Zambia and Zimbabwe). All metrics are based on children being measured below -2 standard deviation thresholds. 41.0 percent of the children are stunted, 15.1 percent are underweight, 5.1 percent are wasted, 12.6 percent are stunted and underweight, and 45.2 percent are malnourished based on any of the three metrics.

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in wealth- or consumption-poor households, other factors such as the local health environment can play an important role in determining policy effectiveness.

We have focused on just one dimension of individual deprivation. Individual welfare clearly depends on more than nutritional status, and we cannot rule out the possibility that household-level data are more revealing for other non-nutrition dimensions. That said, undernutrition is an undeniably important dimension of individual poverty and it has long played a central role in the measurement of poverty using aggregate household data. This dimension of welfare is also emphasized by policy makers concerned with reducing both current and longer-term poverty. The mounting evidence on the longerterm costs of stunting in young children adds force to that emphasis.

A great deal has been learnt about the socioeconomic differentials in individual health and nutrition from micro data, typically using cross-tabulations or regressions. This knowledge is valuable. However, there is a risk that the differentials in mean attainments often found between rich and poor households lead policy makers to be overly optimistic about the scope for reaching vulnerable individuals using only household-level data. Standard poverty data make _ad hoc_ assumptions about equality within households. Persistent effects of intra-household inequality on health and nutrition may not be evident in these measures. Just how adequate household-level data are for the policy purpose of reaching vulnerable women and children has been unclear.

To help improve our knowledge about this constraint on policy, the paper has provided a comprehensive study for 30 countries in Sub-Saharan Africa. We find a reasonably robust householdwealth effect on individual undernutrition indicators for women and children. Nonetheless, on aggregating across the 30 countries studied here, about three-quarters of underweight women and undernourished children are not found in the poorest 20% of households when judged by the household wealth index in the Demographic and Health Surveys. A similar pattern is found in the available household surveys that allow a comparison of individual nutritional measures with an estimate of the household’s consumption per person, which is clearly the most widely used welfare metric in measuring poverty in developing countries. Adding other household variables—interpreted as either a re-weighting of the DHS wealth index or as supplementary variables—improves the performance of household data in this respect, but we still find that a large share of undernourished individuals are not among those predicted to be undernourished based on household variables. It is clear from this study that to have any hope of reaching undernourished women and children, policy interventions in this setting will either 22
# **Appendix Table I. 4: Effect of Reforms on Unorganized Sector Log (Real Output)**

||(1)|(2)<br>(3)|
|---|---|---|
||De-licensing|Trade Liberalization<br>FDI Liberalization|
|||_Other-sector Output Spillovers_|
|Horizontal Output Spillovers from|||
|Organized Sector|0.2433***|0.2295***<br>0.0722***|
||(0.0338)|(0.0263)<br>(0.0094)|
|Backward Output Spillovers from|||
|Organized Sector|1.1067**|3.3029***<br>1.2749***|
||(0.5242)|(0.5535)<br>(0.1888)|
|Forward Output Spillovers from|||
|Organized Sector|2.2269***|1.8672**<br>0.4256+|
||(0.7296)|(0.8313)<br>(0.2907)|
|Reforms|-35.1456***|0.0163<br>-20.2495***|
||(7.8722)|(0.0938)<br>(5.0166)|
|Reform*Horizontal Spillovers from|||
|Organized sector|-0.1364***|0.0018***<br>-0.0160|
||(0.0311)|(0.0003)<br>(0.0132)|
|Reform*Backward Spillovers from|||
|Organized sector|1.3535***|0.0046<br>0.1788|
||(0.3229)|(0.0038)<br>(0.1623)|
|Reform*Forward Spillovers from|||
|Organized sector|-1.1322***|0.0003<br>0.4035++|
||(0.3194)|(0.0044)<br>(0.2911)|
|||_Other-sector Productivity Spillovers_|
|Average TFP of Horizontal-linked|||
|Organized Plants|0.1156|0.1276<br>0.0659|
||(0.1538)|(0.1561)<br>(0.0623)|
|Average TFP of Backward-linked|||
|Organized Plants|0.2777|0.6205*<br>0.4485***|
||(0.3441)|(0.3437)<br>(0.1400)|
|Average TFP of Forward-linked|||
|Organized Plants|1.3048***|1.0213**<br>0.4978***|
||(0.3868)|(0.3989)<br>(0.1606)|
|||_Own-sector Output Spillovers_|
|Forward Output Spillovers from|||
|Unorganized Sector|-0.5289++|0.0910<br>-0.0596|
||(0.3695)|(0.4724)<br>(0.1290)|
|Backward Output Spillovers from|||
|Unorganized Sector|0.7383**|-0.0171<br>0.2540***|
||(0.2947)|(0.3036)<br>(0.0891)|
|Reform*Backward Spillovers from|||
|Unorganized sector|-0.3448++|-0.0068**<br>-0.0473|
||(0.2467)|(0.0030)<br>(0.1330)|
|Reform*Forward Spillovers from|||
|Unorganized sector|1.9076***|-0.0008<br>0.3408++|
||(0.2957)|(0.0038)<br>(0.2462)|
|Constant|-82.1772***|-123.1047***<br>-44.5242***|
||(17.4894)|(17.6378)<br>(7.1572)|
|Observations|5888|5888<br>5888|
|R-squared|0.1512|0.1406<br>0.1489|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. All specifications also include state-year fixed effects. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 57
# ACKNOWLEDGEMENTS

A number of persons provided us with important information for this work or assisted us in preparing it in other ways. The Estimates and Projections Section of the U.N. Population Division has been continually supportive in providing us with data, often prior to its publication in their own books. We are grateful to the section chief, Y. C. Yu, and to his continually accomodating staff, as well as to the head of the division, Jean-Claude Chasteland. Through the U.N. Population Division, we also received data from the Centro Latinoamericano de Demografia (Celade).

World Bank country economists and population specialists in the operating divisions have also provided us with recent population estimates, bringing to our attention government reports we might not otherwise see. Among them, we wish to particularly acknowledge Althea Hill, who has in addition provided us with important analyses of her own.

This exercise was carried out using a personal computer program, PROJPC, written by Kenneth Hill of Johns Hopkins University. For this year's work, Hill undertook various revisions and refinements of the program that have speeded up the process.

As was its predecessor, the post-reorganization Population, Health and Nutrition Division has been hospitable to this work. Dean T. Jamison, the division chief during most of the period of this exercise, showed more interest in and enthusiasm for these figures than any of us would have predicted. His successor, Dr. Anthony R. Measham, has continued in the same supportive mode.

We also wish to acknowledge the interest of the Bank's International Economics Socioeconomic Data Division (IECSE), which not only consumes our numbers and incorporates them into the databases it maintains and publications it distributes but also helps coordinate communications with country economists.

Useful comments on the introduction were provided by Dr. Fred Sai and Susan H. Cochrane. In producing the manuscript, Ella Hornsby was responsible not only for typing and some data entry but also for coordinating the paper flow.

My T. Vu Eduard Bos Rodolfo A. Bulatao v
To address this, we also included in our instrument a survey experiment designed to quantify experimenter demand effects in the responses of village officials. This experiment provided a randomized priming treatment that made more salient the ongoing data collection effort; the message emphasized either (i) that data collection was part of a research collaboration with the Indonesian Ministry of Home Affairs or (ii) that data collection was simultaneously ongoing with citizens residing in the same village. The randomization was conducted at the village level. Treatment assignment in this survey experiment is uncorrelated with turnover in equation (1): the RD point estimate is _τ_ =-0.093 (robust SE: 0.131, _p_ =0.372). Nonetheless, we control for this treatment assignment in all our specifications. We report the takeaways from this survey experiment in a companion paper, and, in general, we find limited effects of our priming intervention on a wide range of bureaucrat-level outcomes and attitudes.

# **7 Conclusion**

This paper studies Indonesian villages as laboratories of local democracy. We use electoral turnovers— instances in which an incumbent leader failed to secure reelection in the most recent village election—as natural experiments that disrupt the status quo in local governance. These turnovers typically bring new leaders to power with a mandate to improve governance and development outcomes. Because village bureaucracies serve as the key link between citizens and frontline service delivery, they are a central instrument through which such political change may affect everyday bureaucratic performance.

We show that turnover in local elections reshapes the bureaucracy, most notably through staff reshuffling and a reduction in the presence of bureaucrats with nepotistic ties. Bureaucrats serving under new leaders report higher morale and exert greater effort: they interact more frequently with citizens, become better informed about constituents’ priorities, and are more likely to take actions aligned with those priorities. Improvements in the flow of policy-relevant information between officials and citizens translate into meaningful improvements in public service provision, visible in both administrative records and citizen reports of service access and quality, as well as village-level actions taken to improve local services. These gains reach even individuals who are socially distant from the village administration, and they are especially pronounced in villages where new leaders weaken entrenched nepotistic networks.

More broadly, our findings point to the importance of local accountability in shaping how democracy functions in practice. At a time when democratic institutions face growing skepticism in many settings, our results suggest that regular elections can help improve the quality of local governance by making bureaucracies more responsive and service delivery more effective. In that sense, the ability of elections to periodically shift power—even at very local levels—may be one important channel through which democracy delivers tangible benefits to citizens.

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