rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2: Provincial government operating expenditure on health in relation to local GDP, 2001‐2006**

<!-- Start of picture text --> 450<br>Provincial government<br>400 operating expenditure on<br>health per capita (RMB)<br>350<br>300<br>250<br>200<br>150 2006<br>100<br>50<br>2001<br>0<br>1500 15000<br>Provincial per capita GDP (RMB, log scale)<br><!-- End of picture text --> Note: A comparable figure for 2000 is not available. A new government budget classification methodology was introduced in 2007. Operating expenditure on health was a pre‐2007 term, primarily including government subsidies to providers and excluding some health security programmes. From 2007, the definition of health spending was expanded. Source: China Statistical Yearbooks

**Figure 2A: Provincial government expenditure on health in relation to local GDP, 2007‐2008**

<!-- Start of picture text --> 900<br>Provincial<br>800 government<br>spending on<br>700 health per capita<br>(RMB)<br>600<br>500<br>2008<br>400<br>300<br>200<br>100<br>2007<br>0<br>5000 50000<br>Provincial per capita GDP (RMB, log scale)<br><!-- End of picture text --> Source: China Statistical Yearbooks 14
# **Figure S17. Share of providers who provide accurate diagnosis and treatment conditional on availability of equipment and drugs**

# A. Medical equipment

<!-- Start of picture text --> 40<br>30<br>20<br>10<br>0<br>Facilities without equipment Facilities with equipment<br>No condition One condition Both conditions<br>Share of providers<br><!-- End of picture text -->

**Note** : Mean number of conditions (0,1,2) diagnosed and treated correctly by the most knowledgeable provider in the facility conditional on the facility having access or not to a minimum set of functional medical equipment (thermometer, stethoscope, and sphygmomanometer). Two conditions: diarrhea and pneumonia. The estimates are (unweighted) mean outcomes across countries, with the country means calculated using country-specific sampling weights. The error bars represent the 95-percent confidence interval. Data are from clinical vignettes and visual inspections of storage facilities and consulting rooms from Kenya (2018), Madagascar (2016), Mozambique (2014), Nigeria (2013), Niger (2017), Senegal (2010), Sierra Leone (2018) Tanzania (2016), Togo (2014), and Uganda (2013).

# B. Antibiotics

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Facilities without antibiotics in stock Facilities with antibiotics in stock<br>Cannot manage condition Can manage condition<br>Share of providers<br><!-- End of picture text -->

**Note** : Share of the most knowledgeable provider in the facility that can provide correct diagnosis and treatment for pneumonia, conditional on the facility having access or not to antibiotics. The estimates are (unweighted) mean outcomes across countries, with the country means calculated using country-specific sampling weights. The error bars represent the 95-percent confidence interval. Data are from clinical vignettes and visual inspections of storage facilities and consulting rooms from Kenya (2018), Madagascar (2016), Mozambique (2014), Nigeria (2013), Niger (2017), Sierra Leone (2018) Tanzania (2016), Togo (2014), and Uganda (2013).

65
MNCH (section 3.2.1). We also looked at the trends in rate differences (section 3.2.2), rate ratios (Section 3.2.3), and concentration indexes and curves (section 3.2.4).<sup>11</sup>

# _3.2.1 Trends in MNCH for different groups_

We disaggregated the trends in MNCH outcomes by selected background characteristics: rich vs. poor or top 60% compared to bottom 40%, wealth quintiles, place of residence (urban and rural) and mother’s education. All the curves in each graph plot the values for all survey years and the 95% confidence intervals. In each graph, the vertical lines perpendicular to the years in the horizontal axis are 95% confidence intervals. Figures 1a through 1d present the results.

Figure 1a is on nutrition; Figure 1b - child mortality; Figure 1c- immunization; and Figure 1d is on modern contraceptive use by married women, ANC4+, and SBA. In all cases, outcome curves are downward and health services curves are upward sloping. This shows improvement in MNCH in all indicators on average. However, the distance between the curves and the slope of each curve in each graph shows that the performance has been different for different outcomes. For example, in Figure 1a, while the reduction in undernutrition occurred for both the bottom 40% and the top 60%, the curve for the top 60% is steeper than the bottom 40%. Similarly, the curve for the top 20% is steeper. Some four observations are noted from the disaggregated graphs in undernutrition rates over the period covered by the surveys. One is that the incomebased inequality is starker because the poor–rich differences are driven by the richest

> 11 Mother’s education is used in child health indicators, including stunting, wasting, underweight, IMR , UFMR, measles vaccination and  full immunization, while own education is used in maternal health services, including contraception,ANC4+, and SBA.

13
significant reduction was partially offset by the increase in jobs offering fewer hours per week and lower wages, or a combination of both.

The worsening of socio-economic conditions demands a government's rapid response. To do so, representative information on migrants and host communities is vital for evidence-based policy design. However, two main challenges are associated with conducting a representative survey of the host and migrant populations. Even though Ecuador has a reliable and updated sampling frame for the resident population, the total number of Venezuelans and their geographical location in the country were not available at the time of the survey.<sup>7</sup> The official data provide only the net flows of migrants who entered and exited the country through an official gate. However, no official estimates exist for those migrants who did not register their entry into the country to add up to the total Venezuelan migrant population. In 2019, Ecuador was in a weak position relative to its neighboring countries in the availability of such data. Peru gathered the National Census in 2017, and Colombia had collected not only the 2018 National Census but also a national migratory registry.

Unlike other Latin American host countries, Venezuelan migrants were expected to be spatially clustered in Ecuador throughout the corridor. For instance, while in Peru, over 84 percent of migrants are in Lima, in Ecuador, it is estimated that only around 60 percent of Venezuelan migrants live in five main cities – Quito, Guayaquil, Cuenca, Manta and Santo Domingo.<sup>8</sup> This phenomenon is likely standard in this type of population's movements, a fact that has significant implications on both the survey's intended analyses and its sampling design.

From an analytical standpoint, assessing the impact of immigrants on the host population depends on their spatial proximity and their relative density. From the sampling viewpoint, the clustering of immigrants would help to implement a design that has proven to be successful in conducting similar surveys elsewhere. For instance, such as those addressed to immigrants from the rest of the continent in South Africa (Plaza, Navarrete and Ratha, 2011); Syrian refugees and their hosts in Lebanon, Jordan and Iraqi Kurdistan (World Bank, 2018, and Aguilera, Krishnan, Muñoz, Russo, Sharma and Vishwanath, 2020); and Myanmar Rohingya in Bangladesh (World Bank, 2020 b).

The paper is structured as follows: section II describes the innovative use of Big Data from Telefonica de Ecuador to come up with a strategy for generating representative samples of host and migrant households for the Human Mobility and Host Community Survey (EPEC acronym in Spanish). Section III presents the implementation strategy. Section IV concludes by drawing general lessons from our experience in sampling migrant populations.

> 7 The Human Mobility and Host Communities Survey in Ecuador (Encuesta de personas en Movilidad y Comunidades de Acogida – EPEC acronym in Spanish).

> 8 World Bank (2020 a).

3
_(Table C.6 continued from previous page.)_

||(0.01)|(0.01)||
|---|---|---|---|
|Income ratio of spouses|0.77**|0.65||
||(0.36)|(0.39)||
|Marriage duration|-0.00|-0.01||
||(0.01)|(0.01)||
|Brought assets in marriage|0.04<br>(0.07)|0.08<br>(0.08)||
|Constant|0.82|0.12|0.98**|
||(0.70)|(0.66)|(0.46)|
|N|198|166|198|
|p-value of F-statistic|0.87|0.96|0.92|
|Missing indicators|Yes|No|No|
|Onlymarried|No|Yes|No|

Notes: Standard errors in parentheses. In column (1), to include the variables only defined for married women and include currently unmarried respondents, I follow two steps. First, I set these variables to zero if they are missing and, second, I include dummy variables in the regression that indicate whether the variables are missing. Column (2) does not make these adjustments and hence only includes married respondents. Column (3) includes only covariates defined for all respondents. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01

**Table C.7: Role model analysis: Joint orthogonality of observable characteristics for exposed and non-exposed groups**

||(1)|(2)|(3)|
|---|---|---|---|
|Age|0.01|0.02|0.00|
||(0.01)|(0.01)|(0.01)|
|Married|0.40**|0.00|0.15*|
||(0.17)|(.)|(0.09)|
|Household members|0.01|0.00|0.01|
||(0.02)|(0.02)|(0.02)|
|Migrant|-0.06|-0.05|-0.06|
||(0.05)|(0.05)|(0.05)|
|Education years|0.00|0.00|0.00|
|_(Continued on next page.)_||||

76
Deininger, K. and P. Olinto (2000). “Why Liberalization Alone Has Not Improved Agricultural Productivity in Zambia: The Role of Asset Ownership and Working Capital Constraints.” World Bank Working Paper No 2302.

Delgado, C. (1996). “Agricultural Transformation: The Key to Broad-Based Growth and Poverty Alleviation in Africa,” in B. Ndulu and N. van de Walle (eds.), Agenda for Africa’s Economic Renewal, New Brunswick, NJ: Transaction Publishers.

Dixit, A. and V. Norman (1980). _Theory of International Trade. A Dual, General Equilibrium Approach_ , Cambridge: Cambridge Economic Handbooks, 1980.

Fackler, P.L., and B.K. Goodwin (2002). “Spatial Price Analysis,” in B.L Gardner and G.C. Rausser (eds.) _Handbook of Agricultural Economics_ , Amsterdam: Elsevier Science.

Fafchamps, M. (1992). “Cash Crop Production, Food Price Volatility, and Rural Market Integration in the Third World,” _American Journal of Agricultural Economics_ 74(1) pp. 90–99.

Ferreira, F., A. Fruttero, P. Leite, and L. Lucchetti (2013). “Rising Food Prices and Household Welfare: Evidence from Brazil in 2008,” _Journal of Agricultural Economics_ 64(1) pp. 151-176.

Friedman, J. and J. Levinsohn (2002). “The Distributional Impacts of Indonesia’s Financial Crisis on Household Welfare: A ‘Rapid Response’ Methodology,” _World Bank Economic Review_ 16, pp. 397–423.

Goldberg, P. and M. Knetter (1997). “Goods Prices and Exchange Rates: What Have We Learned?,” _Journal of Economic Literature_ , vol. XXXV, pp. 1243–1272.

Goldberg, P. and N. Pavcnik (2004). “Trade, Inequality, and Poverty: What Do We Know? Evidence from Recent Trade Liberalization Episodes in Developing Countries,” _Brookings Trade Forum_ 2004, pp. 223–269.

Goldberg, P. and N. Pavcnik (2007). “Distributional Effects of Globalization in Developing Countries,” _Journal of Economic Literature_ March, 45(1), pp. 39–82.

30
it became illegal although some sales have been reported in some of the villages studied), and only constitute a small percentage overall. About 5 percent of plots are sharecropped, and about 10 percent of plots were allocated by the government as part of land reform.

**Table 1: Descriptive Statistics – plot level variables – selected villages from ERHS**

|Variable|Mean|Standard<br>Deviation|
|---|---|---|
|Plot size(ha)|0.232|0.436|
|Share of land allocated to coffee*|0.214|0.290|
|Share of land allocated to chat|0.056|0.194|
|Share of land allocated to eucalyptus|0.075|0.245|
|Land with at least some coffee (dummy=1)**|0.448|0.497|
|Chat land (dummy=1)|0.104|0.305|
|Eucalyptus land(dummy=1)|0.107|0.309|
|Plot inherited %|0.717|0.451|
|Plot purchased %|0.104|0.305|
|Plot allocated %|0.096|0.294|
|Plot sharecropped in/rented in%|0.045|0.207|
|Number ofyearsplot owned|31.667|15.711|
|Good soil fertility %|0.521|0.500|
|Medium soil fertility %|0.370|0.483|
|Poor soil fertility %|0.099|0.299|
|Flat plot %|0.620|0.485|
|Slopy plot %|0.327|0.469|
|Steep plot with ravines%|0.041|0.198|

Note: Data on 3364 plots. Pooled data from four rounds of data (1994, 1995, 1997, 1999). Data on ‘Numbers of years plot owned’ not available for 1995 round. *The shares of total farm land allocated to coffee, chat and eucalyptus (i.e., at the household level) are 0.22, 0.035 and 0.049, respectively. Average farm size (cropped area) is 0.70 Ha. **Some plots are intercropped and this has been taken into account for share of plots allocated with particular crops. So while 44.8 percent of plots have some coffee, the average share of each plot is only 21.4 percent.

Inherited land relative to land allocated or sharecropped may appear surprisingly high in the overall context of Ethiopia. In the full country-wide data set (of which the data in this paper are a sub-sample), inherited land only constitutes about a quarter of the land, while government allocated land is about 55 percent and sharecropped land is most of the remainder. These figures are not dissimilar to those found in other data sets (such as Deininger et al., 2003). Permanent crop areas and the South of the country in general had a substantially different land tenure system before land reform in 1975, and land reform allowed many households in the South to cultivate land they were farming at the time and had inherited from their families, while in the more 11
Policy ReseaRch WoRking PaPeR 4567

# **Abstract**

The paper uses repeated cross-sections of Bulgaria’s household survey data (1995, 1997, 2001, and 2003) and a comparable list of durable goods to investigate the dynamics and distribution of durable goods over time, including during the economic crisis of 1996-1997 and the subsequent period of relatively robust economic growth leading up to European Union membership. It examines the dynamics of the ownership of durable goods by wealth classes, geographic locations, and various ethnic groups, including the Roma. In the aggregate, there was convergence between the poorest and the richest classes in the ownership of durable goods between 1995 and 2003, with the poorest class making a significant gain between 2001 and 2003 after having lost some ground between 1995 and 2001. There was also convergence in the ownership of durable goods between urban and rural residents. However, there appear to be some diverging tendencies between Bulgarians and the minority ethnic groups, particularly in the ownership of relatively more expensive goods such as personal computers and cars.

This paper—a product of the Human Development Sector, Europe and Central Asia Region—is part of a larger effort in the department to enhance growth and reduce poverty. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lersado@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
**Table** 12t Governoent bonds **(as a percentage** of **assets)**

||1970|1975|1960|1985|1990*|
|---|---|---|---|---|---|
|UK|16|18|22|18|11|
|Us|7|9|14|22|20|
|**Germany**|9|6|13|20|17|
|Japan.|11|16|15|13|5|
|**Canada**|38|34|40|42|39|
|**Netherlands**|10|7|5|13|14|
|**Sweden**|12|17|24|30|22|
|Switzerland|-|-|-|-|-|
|Denmark|11|6|4|14|11|

1989 for **Canada** 0 Government-guaranteed bonds only. 1987 for Denmark Table 133 Private bonds **(as a percentage** of assets)

||1970|1975|1980|1985|1990*|
|---|---|---|---|---|---|
|UK|14|6|2|2|3|
|Us|38|33|26|19|16|
|Germany|10|13|11|12|8|
|Japan|-|-|-|-|_|
|Canada|1S|17|12|8|8|
|Netherlands|3|4|3|3|4|
|8weden|64|59|50|47|63|
|Switzerland|-|-|-|-|-|
|Denmark|61<br>,|66|59|52|56|

* 1989 for **Canada** 1987 for Denmark

28a
<!-- Start of picture text --> Urban manufacturing employment in India<br>g<br>7)<br>s<br>=<br>©<br>310<br>2<br>=<br>1990 1995 et 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Urban manufacturing employment in India<br>- “7<br>a Pa<br>2 a _----7-*<br>= 7<br>E ea<br>Lo “7<br>Ke) “7<br>a ae<br>5 -oT<br>e~<br>1990 1995 aeear 2005 2010<br>—+*— Formalsector ——®-—- Informal sector<br><!-- End of picture text --> <!-- Start of picture text --> Urban manufacturing employment in India<br>Oo<br>e<br>4<br>= “<br>E par TS c<br>oa<br>E o-7<br>Qe pean<br>uie _a— ~<br>1990 1995 2000 2005 2010<br>Year<br>-@--~ Formal tradables — ®— - Formal non-tradables<br>——@e-—- Informal tradables ——*— Informal non-tradables<br><!-- End of picture text --> <!-- Start of picture text --> Urban manufacturing firms in India<br>“<br>7 Z<br>a 7o<br>2re 7 oo<br>— Ys<br>=o<br>= miei ee<br>is} “7<br>th ie”<br>-4te--7-"”<br>1990 1995 2000 2005 2010<br>Year<br>“@-- Formal tradables — ®— - Formal non-tradables<br>—-—@ —- Informal tradables ——*——. Informal non-tradables<br><!-- End of picture text -->
<!-- Start of picture text --> T1A<br>T1B<br>T1C<br>T2A<br>T2B<br>T2C<br>−3.5 −3.0 −2.5<br><!-- End of picture text --> Notes: the figure shows the posterior distribution of parent engagement coefficients after wave 2. Greater values are associated with a higher probability of a successful engagement. The vertical bar marks the median of each posterior distribution. The shaded areas indicate the 95% credible intervals. A total of 8,000 posterior draws sampled from 4 independent Markov chains were used.

Figure 3: Posterior distributions of parent engagement coefficients.

arms (T1C, T2A, and T2C) have essentially zero posterior probability that they deliver the highest level of engagement. Arm T1A, parent-led reading with leveled exercises, has the second highest engagement rate of 7.43%, but has only a 5.24% probability optimal.

The last two columns transform the posterior estimates into an average probability of successful engagement for each arm, _θ_<sup> ̄</sup><sup>_k_</sup> , and report the expected policy regret based on the probability of engagement, the objective of interest (see section 4). This statistic shows that implementing T1B would lead to an expected loss in terms of the probability of a successful call of only 0.02 percentage points. For the other treatment arms, the loss ranges between 0.99pp and 4.49pp. These expected losses are equivalent to less than 1%, 12%, and 53% of the highest estimated success probability in arm T1B (of 8.40%).

In order to look more into parents’ decision to answer the biweekly IVR calls, we also analyze the extensive margin of engagement. Appendix C.4 shows estimates for the probability of any successful engagement (i.e., whether the recipient started the reading exercises in any of the calls received) and the probability of answering the phone at least once. Tables A.6 and A.7 report the coefficient estimates and the corresponding treatment arm averages. The arms had nearly identical initial response rates: in five arms at least one call was answered with 84.1%-86.6% probability, and the response rate was only slightly lower in T2A (81.7%). The share of phone numbers with at least one successful engagement varies somewhat more across arms, and is particularly low in T2C, where the rate is only about half of what it is in other arms. However, T1A, T1B and T2B have nearly identical engagement probabilities. It is instructive to also compare the 29
Fig 3 SALES REVENUES 1991.1 - 1992.3 by TOTrPt pCver ( 1991 .1 prices) <!-- Start of picture text --> 151<br>14<br>13<br>10-<br>7-<br>6<br>5<br>4-<br>3-<br>2<br>91.1 91.2 91.3. 91.4 92.1 92.2 92.3<br>0 manopolIt + oliopolIt O competitUe<br><!-- End of picture text -->

# FIRM TOTAL PRC.-'ITS **1991.11992.3**

<!-- Start of picture text --> by rmaeot pewr (1991.1 prices)<br>5<br>45<br>4<br>3.5<br>3<br>A 2.5<br>2<br>1.5<br>0.5 I<br>91.1 91.2 91.3 91.4 92.1 92.2 92.3<br>a monopolist + elluopelwst competitive<br><!-- End of picture text -->
of the sub-district (upazila/thana) where a community is located to major urban cities. We estimated travel time to two main centers of economic activities in the country: the capital city Dhaka (population more than 10 million) and the port city of Chittagong (population more than 4 million). We also estimated trave time to the nearest city of 100 thousand or more population.<sup>7</sup> Access to sanitation is measured by a set of variables indicating the proportion of households in a community with access to di¤erent types of sanitation o¤ering di¤ering degrees of protection.<sup>8</sup> Access to safe drinking water is also measured in a similar way. While the regressions show statistically signi...cant correlations between LRPCE and di¤erent access variables with expected signs, only access to sanitation appears to have an in‡uence on spatial disparity comparable to access to phone or electricity. Summary statistics in table A.1 indicate considerable improvement in access to sanitation<sup>9</sup> as the percentage of households using open ...elds or temporary earthen structures as toilets fell from 44 percent in 2000 to 23 percent in 2010. In contrast, more than 95 percent of households in Bangladesh had access to safer drinking water mostly from tubewells by 2000. Similarly our measure of distance has no time variation due to lack of access to data on new road construction and improvement.

Panel G of Table 3 reports the estimates of spatial disparity indices when all infrastructure and services variables are introduced simultaneously in the estimation. The results show that infrastructure variables do account for a large share of neighborhood correlations. In the pooled sample, infrastructure and services variables account for 59 and 42 percent of spatial disparities in 2000 and 2010 respectively. In the urban sample, their contribution to spatial disparity is even larger; 71 percent in 2000 and 56 percent in 2010. In the rural sample, these variables account

> 7 Note that these travel time estimates do not fully re‡ect the actual travel time from our neighborhoods/ communities to urban centers and hence should be taken as rough indicator of actual distances.

> 8 The HIES surveys for both years collected information on types of toilets/latrines used by the households. The surveys made distinction among six di¤erent types of latrines with sanitary latrine being one of them. Rest of the categories indicate di¤erent degrees of sanitation a¤orded by the latrines – from cemented water-seal latrines to open ...elds (no protection).

> 9 The use of complete sanitary toilets increased only slightly over the decade though there has been considerable increase in the use of toilets that provide some measure of sanitary protection.

19