rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_4.2 Population and Economic Development._**

The Brantas basin is considered a strategic basin of Indonesia and East Java. Comprising 24.6% of East Java’s land area, it has 23% of the province’s forest land and 55.8% of its arable land (see Table 1). (Ramu 2004) Most arable land in the basin is used for productive farming (38%), while the remainder is used for forest, settlement, and non-agricultural activities. (Rusfandi 2000) The agricultural economy centers on paddy cultivation, nearly all of which is irrigated (Table 2). In 2000, the Brantas basin made up 32% of East Java’s total rice production and 5% of that of Indonesia. Aside from rice, important food and cash crops include maize, cassava, soybean, peanuts, tobacco, coffee, and sugarcane.

**Table 1:  Land Use Data (hectare)**

|**Location**|**Forest**|**Plantation**|**Agricultural**<br>**Land**|**Homestead**|**Others**|**Total**|
|---|---|---|---|---|---|---|
|East Java|1,346,500|859,600|1,157,100|564,960|864,038|4,792,198|
|Brantas Basin|309,400|31,400|645,800|170,100|23,300|1,180,000|

**Source:** Land Use Plan, East Java.

**Table 2: Production of Major Crops (1000 metric tons)**

|**Location**|**1970**|**1980**|**1990*
**Table 2: Mahalla Sample and Descriptive Statistics**

||First-|Stage Samp|le|Admin|Data|
|---|---|---|---|---|---|
|Province|Total<br>PSUs|Urban|Non-<br>Urban|Migrant<br>PopShare|Male Share<br>Migrant|
|Andijan|15|2|13|4.6%|85%|
|Bukhara|18|3|15|4.5%|84%|
|Jizzakh|8|1|7|2.8%|80%|
|Kashkadarya|19|2|17|2.0%|87%|
|Karakalpakstan|22|4|18|2.4%|79%|
|Navoi|10|2|8|1.7%|79%|
|Namangan|16|3|13|2.8%|85%|
|Samarkand|16|2|14|3.9%|84%|
|Syrdarya|5|1|4|1.9%|78%|
|Surkhandarya|10|1|9|3.8%|91%|
|Tashkent|15|4|11|1.7%|55%|
|City of Tashkent|14|14|0|1.8%|54%|
|Fergana|18|4|14|3.0%|86%|
|Khorezm|14|2|12|5.6%|80%|
|Total|200|45|155|3.1%|80%|

The administrative data collection approach was simple data entry from official mahalla records in selected PSUs. Each mahalla in Uzbekistan is legally obligated to maintain such records for every person and family residing in that location. These data cover the total population size
replace the dependent variable in our SDM in equation (3) by estimates of the Gini index of consumption inequality from the same NSS data.

Table 5 provides the results. Whether we use illuminated area or total light, or variable or fixed masks, we find no sign of significant effects on inequality. The effects of urban growth on rural poverty appear to be transmitted entirely via growth in mean rural consumption. This result does not differ between secondary towns or cities.

# **VI. Conclusions**

The scope for escaping rural poverty through urban economic growth has been a longstanding development issue, going back to the classic model of Lewis (1954). We have revisited this issue, focusing specifically on the question of whether cities or towns are better generators of rural-poverty reducing growth in India, using data on 59 regions observed four times from 1993/94 to 2011/12. The rural headcount poverty rate fell by half in this period and the poverty gap index fell by two-thirds. The pace of poverty reduction was spatially uneven. Using night lights as the indicator of urban economic activity, we find that the geographic expansion of urban areas is associated with lower rural poverty measures; these effects are consistently found using different specifications, while there is little apparent effect on the rural sector from the brightness of lights coming from urban areas. It is urban growth on the extensive rather than the intensive margin that matters for rural poverty reduction.

When the effects of big-city growth on rural poverty are compared to those of secondary towns, some consistent associations emerge, indicating that the growth of towns matters far more than cities to reducing rural poverty in India. This effect is close to inequality-neutral, in that it was not associated with higher, or lower, inequality within rural areas. In expectation, the poverty effect is transmitted through mean consumption.

We remind readers that here we have only studied this question for India, at its stage of economic development. The pattern we have uncovered may not hold at all stages of development
of the value of treestock.

The available information for constructing the treestock consists of FAO data on the area harvested by crops, production, and output prices. We begin by using the condition for long-run equilibrium, in which the cost of investment in the orchard equals the present value of the expected future income generated by the orchard. The income from the orchard is value of output less the costs of production. There are no published data on production costs; thus we construct our estimates under the assumption that production costs account for 80% of gross revenues.

We derive the yield (output per hectare) from the data on output and area. The yield depends on the age of the trees, but the necessary information for estimating the yield curve is not

> crop when they are harvested cannot be fixed assets.” In the most recent (2008) version of the SNA, the terminology changes from “cultivated assets” to “cultivated biological resources,” while the description remains similar.

7
# **3.1 Growth Consistent with Reasonable Time Paths of Growth Drivers**

Having calibrated the model, I now move to analyze the long‐term growth scenarios for the country. The first approach consists of choosing reasonable paths of drivers of growth and uses the model to solve for the resulting growth paths. Specifically, I assume the following time paths:

- **Growth rate of human capital per worker:** �� ��. �% **.** The historical trend in human capital index from the PWT 8.1 provides guidance regarding the reasonable path of human capital per worker going forward. The PWT 8.1 reports the index based on the Barro‐Lee method that takes into account both the years of schooling as well as returns on education. The PWT 8.1 data is available for the period 1990 – 2010. After reaching a peak during the second half of 1990s, the growth of human capital index has been on a downward trend. The average growth rate stood just below 1.4 percent during the period 2006 – 2010. It seems likely that the growth rate of human capital will continue its gradual decline with contribution from gains in primary education diminishing as it reaches absolute levels. As such, the human capital per worker growth rate is assumed to be 1.3 percent per annum going into the future.

<!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1991 ‐ 1995 1996 ‐ 2000 2001 ‐ 2005 2006 ‐ 2010<br>Source: PWT 8.1<br>Growth Rate of HC Index (%)<br><!-- End of picture text -->

- **Growth rate of TFP:** �� ��%. A TFP index for the country is calculated using the data and methodology listed in PWT 8
2021). Are growth mindset, mastery orientation, and grit promising for promoting achievement in the Global South? Psychometric evaluations among Indonesian adolescents.

Niederle, M. and Vesterlund, L. (2007). Do women shy away from competition? do men compete too much? _The Quarterly Journal of Economics_ , 122(3):1067–1101.

Prada, M. F., Rucci, G., and Urz ́ua, S. (2019). Training, soft skills and productivity: evidence from a Field Experiment in Retail. Technical report, IDB Working Paper Series.

Raven, J. C. (1936). Mental tests used in genetic, the performance of related indiviuals on tests mainly educative and mainly reproductive.

Reuben, E., Rey-Biel, P., Sapienza, P., and Zingales, L. (2012). The emergence of male leadership in competitive environments. _Journal of Economic Behavior & Organization_ , 83(1):111–117.

Reuben, E., Sapienza, P., and Zingales, L. (2014). How stereotypes impair women’s careers in science. _Proceedings of the National Academy of Sciences_ , 111(12):4403–4408.

Reuben, E., Wiswall, M., and Zafar, B. (2017). Preferences and biases in educational choices and labour market expectations: Shrinking the black box of gender. _The Economic Journal_ , 127(604):2153–2186.

Rodriguez-Segura, D. and Schueler, B. E. (2023). Assessors influence results: Evidence on enumerator effects and educational impact evaluations. _Journal of Development Economics_ , 163:103057.

26
scale of operation. When hit by adverse demand shocks, firms would optimally reduce their workforces. Firing inflexibility would delay or prevent such adjustments and, therefore, increase operating costs and reduce firm profitability. Anticipating this, firms may become reluctant to expand and, therefore, fail to capture the economy of scale otherwise possible. Indeed, Fallon (1987) and Fallon and Lucas (1993) find that strengthening labor regulation in India was associated with lower labor demand for large firms covered by the regulation, but not for small firms not covered by the regulations. Amin (2009a) find

> 12 For more discussion, see, for example, Stern (2002) and Xu (forthcoming).  There could be other important determinants; our selection is undoubtedly constrained by data availability.  The China and India Enterprise Surveys are both non-standard—that is, they do not use the common survey instruments though they follow the common guidelines.

10
While poststratification typically adjusts based on demographic variables, propensity score adjustments often use demographic and webographic (lifestyle/attitudinal) questions. Webographic variables capture general attitudes or behaviors that are hypothesized to differ between the web sample and the general population.<sup>10</sup> <mark>An alternative to propensity score adjustment is matching. This technique utilizes a sample of cases representative of the population. This serves as the “target” sample. The aim of the matching exercise is to pair each case in the target sample with the most similar case from the online opt-in sample. When the closest match has been found for all of the cases in the target sample, any unmatched cases from the online opt-in Internet survey sample are discarded. At the end of the procedure, the matched cases from the Internet survey should be a set that closely resembles the target population.</mark> <mark>There are several ways to conduct the matching procedure. To perform the matching, the target sample and the online survey data can be combined and a selection model for the Internet survey can be implemented as before. A propensity score can be constructed as in the above section, and matching can be based on those. If there are many variables for selection, machine learning algorithms, such as random forest, can be employed. Random forest algorithms can measure the probability that each case belongs to either the target sample or the survey and produce a measure of the similarity between each case and every other case. The similarity measure can also be used for matching. Another approach is to match based on Mahalanobis distance (Diamond and Sekhon, 2005).</mark>

# **<mark>2.3. Review of Studies Applying the above Methods</mark>**

<mark>The selection of covariates for constructing propensity scores has been widely studied.</mark><sup>11</sup> <mark>Covariate selection is usually done in a variety of ways
projections have been addressed in this study using actual input data. This allows focusing on the predictive capacity of each variation of the proposed micro-simulation method. In addition, using the last available household data as a comparator is tricky because the comparison could potentially attribute specific outcomes to that projection when these outcomes could result from other unrelated factors occurring simultaneously.

Secondly, the simulation relies on behavioral models built on past data that reflect the pre-existing structure of the labor market and household incomes, plus the relationship of these factors and their relationships with demographics as they stood before the expected change. Consequently, the simulation assumes these structural relationships remain constant over the period projections are made. The further back the baseline year is from the present, the more questionable this assumption will likely be.

Thirdly, the model is limited in its ability to account for shifts in relative prices between different sectors of the economy because of external shocks. One such example is the general equilibrium effect of a change in the terms of trade between agriculture and other sectors. In the absence of a CGE model, it is nearly impossible to model changes in terms of trade between economic sectors explicitly.

A fourth consideration is that the model does not consider the geographic mobility of factors (labor or capital) across time. Thus, all individuals are assumed to remain in their place of origin, even as their labor force status changes or their employment sector alters. Usually, this assumption seems like an abstraction from the truth in a stable environment and would only matter when the results are disaggregated spatially or across rural and urban areas.<sup>17</sup> Fifthly, the simulation component of the model relies on random draw using a pseudorandom number generator in computing the allocations of individuals into labor status, as well as labor earnings of new workers and workers who are changing sectors. The model is configured such that the seed of this random number generator is the same over different runs, so generally, the results will be reproducible. However, small changes
<mark>Schonlau, M., Van Soest, A., Kapteyn, A., & Couper, M. (2009). Selection bias in web surveys and the use of propensity scores.</mark> _<mark>Sociological Methods & Research</mark>_ <mark>,</mark> _<mark>37</mark>_ <mark>(3), 291-318.</mark> <mark>Skitka, L. J., & Bauman, C. W. (2008). Moral conviction and political engagement.</mark> _<mark>Political Psychology</mark>_ <mark>,</mark> _<mark>29</mark>_ <mark>(1), 29-54.</mark> <mark>Smyk, M., Tyrowicz, J., & Van der Velde, L. (2021). A cautionary note on the reliability of the online survey data: the case of wage indicator.</mark> _<mark>Sociological Methods & Research</mark>_ <mark>,</mark> _<mark>50</mark>_ <mark>(1), 429464.</mark> <mark>Steinmetz, S., & Tijdens, K. (2009). Can weighting improve the representativeness of volunteer online panels? Insights from the German Wage indicator data.</mark> _<mark>C&M Newsletter</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>.</mark> <mark>Steinmetz, S., Bianchi, A., Tijdens, K., & Biffignandi, S. Improving web survey quality– Potentials and constraints of propensity score weighting.</mark> _<mark>Online Panel Research: A Data Quality Perspective. Wiley<
Figure 3: Political Influence Concentration Curve <!-- Start of picture text --> A<br>B<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Share of individuals from the top<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>Share of individuals from the bottom<br>.1<br>0<br><!-- End of picture text --> The capacity of a state to mobilize domestic resources for the eradication of poverty, therefore, is positively associated with the resource-based capacity of the state, as measured by _I_ ( _P_<sup>_δ_</sup> ), but negatively associated with the concentration of political influence, as measured by _I_ (Π). Given the same taxable capacity, in fact, any pro-poor redistribution scheme will be more difficult to be voted and implemented in a country where the political equilibrium is unbalanced in favor of rich elites.

# **4 Data**

In this paper, we use the World Bank _PovcalNet_ database, in the form put together by Dykstra, Dykstra, and Sandefur (2014). _PovcalNet_ is a computational tool developed by the World Bank to allow researchers to replicate the official poverty estimates, as well as develop new estimates under different assumptions, namely, different poverty lines and different grouping of countries.<sup>4</sup>

> 4The tool is accessible online, where the user selects the country, the year for the analysis (among the available years for each country) and a poverty line. _PovcalNet_ then returns the headcount index for the selected poverty line.

13
as a “country,” for ease of exposition since the antidumping trade policy instrument is administered by the EU/EEC jointly and not by each individual member country.

3 For recent surveys on various aspects of the economics research literature on antidumping, see Blonigen and Prusa (2004) and Nelson (forthcoming). See also the collection of published articles reprinted in Nelson and Vandenbussche (forthcoming).

4 Exceptions include a handful of empirical studies which have used a cross-country data set collected by researchers at the World Trade Organization (WTO), which is a data set limited to antidumping case initiations and with information on products that is typically categorized only at the industry (2-digit) level, e.g., reported in Miranda, Ruiz and Torres (1998) with updates. Examples of papers that have used this data include Prusa and Skeath (2002, 2005) and Feinberg and Reynolds (forthcoming) which focus on retaliation threats. Related papers that motivate some of this emphasis on cross-country linkages also include Blonigen and Bown (2003) and Bown (2005). There is also some emerging research from other user countries which exploits more detailed data that has been collected on an individual basis - an example is the Mexican use of antidumping examined by Niels and Francois (forthcoming).
’ and managers’ human capital plays a particularly important role in explaining differences in firms’ productivity. However, Acemoglu et al. (2014) argue that country fixed effects are not sufficient to eliminate omitted variable biases, and that the measure of institutions used by Gennaioli et al. (2013) misses meaningful subnational variation in institutional quality.

Another study exploiting within-country heterogeneity is Ciccone and Papaioannou (2009). The paper adopts a sectoral perspective to test whether human capital favours technology adoption and growth. The authors use data for 37 manufacturing industries across 40 countries, and show that countries with higher levels of education in 1980 saw faster growth in human-capital-intensive industries (relative to other industries) in the following decades, a period characterised by the rapid introduction of skilled labor augmenting technologies. They interpret this result as evidence that high educational attainment was instrumental in

> 3The instruments used by Glaeser et al. (2004) are a dummy for French legal origin and either settler mortality or population density in 1500. As pointed out by Acemoglu et al. (2014), the rationale behind this empirical strategy is not clearly discussed, and it is even unclear whether the two instruments have distinct explanatory power when considered jointly.

7
between the two.<sup>15</sup> We present estimates including and excluding provincial dummies, and with or without Aceh. A range of factors, including propensity to report, may vary at the provincial level.

# **_Hypothesis: Poverty is associated with increased conflict._** Our estimates focused on

both absolute consumption poverty levels at the district level (from the SUSENAS), how poor a locality was relative to its respective district (PODES), and recent trends in locality poverty.<sup>16</sup> District poverty levels appeared to increase the likelihood of conflict (Table 8, (3) & (4)), although this was no longer the case when provincial dummies were added for the provinces (Eq., 1-2) or when we added Aceh to the sample. This suggests the province specific dynamics of district poverty incidence may matter. In one of our qualitative case study provinces, East Java, poorer districts actually seemed to be associated with less conflict (Eq. 7). Although relative poverty did not appear to be significantly associated with conflict, _changes_ in poverty were significant in rural areas.

**_Hypothesis: Inequality is associated with increased conflict._** Although the SUSENAS allows us to calculate a measure of household consumption inequality, the indicator is only

> 15 The qualitative work in the previous section focused on rural areas ( _desa)_ in East Java and NTT.  In contrast, for example, the work of Varshney on communal conflict in India focuses on cities.

16 We created the Poverty Gap index of poverty (Foster Greer Thorbecke) using per capita consumption as a welfare measure. The poverty gap is based on the average distance that the poor’s consumption falls below the poverty line. We defined village-based poverty from the PODES, based on the Indonesian family planning board’s (BKKBN) census of poor households. The indicators are whether a household has an earth floor, whether household members have separate clothing for
of family farms. There is an over-representation of female-headed households among the poorest urban quintile compared to male-headed households, and an under-representation in the highest urban quintile despite this group being the recipient of most of the household transfers of remittances from abroad<sup>12</sup> (Figure A. 4 in the Appendix). Almost 20 percent of the urban female-headed households fall into the poorest urban quintile versus 14 percent of male-headed households.

Results from the COVID-19 HFPS show that three months into the pandemic, 63 percent of the interviewed households reported having experienced a decrease in income from their main source of income. Non-agricultural business income is the main source of income reported by 39 percent of the households interviewed in the COVID-19 HFPS; followed by agricultural income, paid employment, and domestic remittances (Figure 6). There are no statistically significant differences between the proportion of total male and female-headed households reporting decreased income (Table 6, Column 1). However,

> 11 These characteristics are not unique to female headed households in Chad. See Milazzo and van de Walle (2015) for more on female headed households in Africa.

> 12 The share of remittance in income is very low in Chad. For comparison, Chad and Senegal have similar proportion of migrants relative to their total population, but the share of remittances in income in Senegal is 27 percent while the corresponding share in Chad is only 5 percent.

12
82.27|35.05|851|79.44|37.71|-3.87|-9.59|1.86|
|HH FIB log10 MPN/100mL|1098|1.76|0.53|851|1.72|0.58|-0.06|-0.15|0.04|
|Weight for age|1223|-1.17|1.16|919|-1.14|1.16|0.06|-0.04|0.15|
|Weight for length|1224|0.14|1.07|918|0.22|1.03|0.10|-0.02|0.22|
|Life satisfaction & self-esteem index|1843|0.00|1.00|1435|0.03|0.98|0.01|-0.12|0.14|
|Life satisfaction (WVS)|1843|4.87|3.20|1435|5.04|2.89|0.20|-0.10|0.49|
|Feel I am person of worth (Rosenberg)|1843|2.25|0.90|1435|2.19|0.90|-0.03|-0.13|0.07|
|Feel that I have good qualities (Rosenberg)|1843|2.42|0.74|1435|2.36|0.75|-0.02|-0.10|0.05|
|Inclined to feel I am a failure (Rosenberg)|1843|1.43|1.11|1435|1.49|1.05|-0.04|-0.16|0.08|
|Able to do things as well as other people (Rosenberg)|1843|
exhibit considerable heterogeneity in their preference for both private taps and mini-grids. The poor tend to value private taps significantly less than other households.

# **5. Concluding Remarks**

In this paper we used conjoint analysis to research the preferences of the poor and non-poor regarding alternative WSS programs. The data from the Sri Lanka study demonstrates how conjoint analysis can provide information to aid in the design and evaluation of WSS programs. Findings from our research in Sri Lanka suggest that consumers are interested in attributes other than costs and that demand for services will be reflected in their provision; that poor and non-poor households place similar values on water 16