rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 3 Demographic Change and Probability of Land Reallocation** (Deaths Per Hundred Versus Significant Land Reallocation)

<!-- Start of picture text --> 0 3 6 9 12<br>Number of Deaths (per 100 Households) within a Year<br>.6<br>.4<br>.2<br>0<br>Probability of Significant Land Reallocation in the Year<br><!-- End of picture text --> Source: RCRE Annual Village Surveys (1995-2003) and 2004 Rural Household Social Network, Labor Allocation and Land Use Survey, RCRE, Ministry of Agriculture..

_Land Tenure Insecurity and Migration, Page 40_
- 31 -

compared monthly rice prices implied by the CPI with those for a specific grade of rice over recent years. The results suggest that the recent rates of rice price increase for an approximately uniform quality of rice are higher than those implicit in the CPI. However the divergence has mainly occured since mid-1987, seemingly associated (for some unknown reason) with high rates of rice price increase as a result of that year's poor harvest. The implicit rice price in the CPI tracks well the monthly market price of a uniform quality of rice over the period of the present study.

Prices also vary between urban and rural areas. Past practice has been to make some assumption kbout the urban/rural cost-of-living differential, reflecting the fact that the prices for most goods (particularly food and housing) tend to be higher in urban areas. For example, on Indonesia's most populous island, Java, it has been estimated that average dwelling rents in 1981 were six times greater in urban areas than rural areas, while the price of the main food staple, rice, was on average 10 percent higher in urban areas (van de Walle, 1989a).

Observations of this sort have often led some past investigators to use substantially different poverty lines for urban and rural areas of Indonesia. This practice has important implications for sectoral rankings in terms of the headcount index of poverty (given by the proportion of the population below the poverty line). For example, the 66 percent differential in poverty lines between urban and rural areas for 1981 assumed by a past CBS study is sufficient to reverse the poverty ranking of sectors in terms of the headcount index (over that obtained at a zero differential).27
# **Appendix 1: Intrumenting equation for town population**

(dependent variable is the log of town population)

||**unit**|**coef.**|**t-stat**|
|---|---|---|---|
|Areaofdistrictinwhichtownislocated|log(squarekm)|-0.472|-1.38|
|Totalarableland area|log(ha)|-0.592|-1.18|
|Distancetonearestnavigablepartofriver|log(km)|-0.206|-1.04|
|Meanelevationofthedistrict|meters|-0.000|-0.04|
|Standarddeviation of districtelevation|meters|0.001|0.97|
|Mountainous terrain|yes=1|-1.218|-1.85|
|Intercept||21.034|3.63|
|Numberofobservations||34||
|R-squared||0.2705||

Source: distance to river, elevation, and area of district come the Nepal GIS data base. Arable land comes from the Statistical Yearbook of Nepal.

45
<!-- Start of picture text --> 8=I=I1I<br>1<br>I<br>2° 1<br>Fi28=28== I'i'ii<br>3s '<br>Fy55 !11<br>es H<br>i<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Year<br>——-- EU27 ———= China +9——— Indonesia<br>——— Japan =~" Korea ———— Malaysia<br><!-- End of picture text --> <!-- Start of picture text --> 8=I=I1I<br>1<br>I<br>2° 1<br>Fi28=28== I'i'ii<br>3s '<br>Fy55 !11<br>es H<br>i<br>i<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Year<br>——-- EU27 ———= China +9——— Indonesia<br>——— Japan =~" Korea ———— Malaysia<br>EU27 CHN, IDN, JPAN, KOR & MYS<br>to C-)° i! .=4no 2ad iIi<br>5 _ : |<br>ag 8 \ a \<br>zec52 ¢| sfecS= |1H<br>%%<br>Zo I £ & |<br>oN ' o — !<br>oO | o FS nee 1<br>a ee oe 1 a se<br>De seee o or<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Year Year<br>— Mineral Products Chemicals — — = = Wood& Pulp ———— Anima! Products = Mineral Products Chemicals<br>—-—— Precious Metals on Base Metals ————— Machinery — —-— — Wood& Pulp —-—— Precious Metals = Base Metals<br><!-- End of picture text -->
results (excluding the fixed effects and the constant) are presented in Table 5.

Table 5: Gravity model estimation results

|**Dep. var. (ln)**|Total im|ports|Preferential|imports|
|---|---|---|---|---|
|**Variable**|**Coefcient**|**Std. Err.**|**Coefcient**|**Std. Err.**|
|Ym|1.216<sup>_∗∗_</sup>|0.408|1.104<sup>_∗∗_</sup>|0.411|
|Yx|-0.001|0.234|-0.168|0.258|
|dGDPpc|0.001|0.019|-0.011|0.020|
|GDPsim|0.099<sup>_∗∗_</sup>|0.016|0.154<sup>_∗∗_</sup>|0.018|
|dist|-1.276<sup>_∗∗_</sup>|0.033|-1.341<sup>_∗∗_</sup>|0.036|
|comlang|0.608<sup>_∗∗_</sup>|0.063|0.597<sup>_∗∗_</sup>|0.069|
|border|0.276<sup>_∗_</sup>|0.124|0.288<sup>_∗_</sup>|0.130|
|colony|0.473<sup>_∗∗_</sup>|0.139|0.642<sup>_∗∗_</sup>|0.155|
|PTA|0.762<sup>_∗∗_</sup>|0.077|0.864<sup>_∗∗_</sup>|0.080|
||N|13317|N|11846|
||R<sup>2</sup>|0.760|R<sup>2</sup>|0.751|
||F (233_,_13083)|257.047|F (232_,_11613)|203.591|
|Signifcance levels :|_†_ 10%<br>_∗_5%|_∗∗_1%|||

_Note:_ Preferential imports are defined as imports in tariff lines above 3 percent MFN.

_Total imports_ . We begin with the discussion of results for total imports. Due to the presence of heteroscedasticity in the data (Breusch-Pagan _χ_<sup>2</sup> (1)<sup>= 1253</sup><sup>_._44</sup><sup>_, p_= 0</sup><sup>_._00),themodelisestimated</sup> with Huber-White robust standard errors. The _R_<sup>2</sup> is high and the _F_ statistic is significant at the 1 percent level, indicating that the overall fit of the model is quite good. The residual distribution is approximately normal (see figure 4 in Appendix D), although formal tests reject normality of the errors.<sup>32</sup> Despite the violation of the normality assumption, the deviations from normality are small and are not likely to have a significant impact on the results (see Appendix D for a more detailed discussion). The fixed effects are significant as a group ( _F_ (224 _,_ 13083) = 20 _._ 41 _, p_ = 0 _._ 00)<sup>33</sup> and most of the model coefficients are significant, carry the expected sign, and take on values that are consistent with estimates reported in other gravity studies. Since the dependent variable is expressed in logs, the coefficient estimates should be interpreted as elasticities for the GDP, GDP

> 32The distribution is slightly skewed to the left and its tails are about one and a half times as thick as expected from a normal distribution.

> 33Furthermore, a Wald test shows that both importer and exporter fixed effects are significant as individual groups ( _F_ (54 _,_ 13083) = 23 _._ 63 _, p_ = 0 _._ 00 and _F_ (168 _,_ 13083) = 19 _._ 44 _, p_ = 0 _._ 00, respectively), indicating substantial cross-section variation in the data. On the other hand, the year fixed effects are not significant either individually or together ( _F_ (2 _,_ 13083) = 0 _._ 08 _, p_ = 0 _._ 92). This is likely due to the short time duration of the panel and, consequently, relatively little time-series variation in the variables.

32
**Table A3.CR:  Father's Education and Educational Expenditure on Children**

|||**RURAL**<br>**CFPS**<br>|**CHINA**<br>**2010**<br>||
|---|---|---|---|---|
||**(6-22) Yea**|**rs Children**|**(8-23) Yea**|**rs Children**|
|Father's Sch.|105.5|79.6|115.7|85.6|
||(12.8)***|(12.1)***|(15.0)***|(14.0)***|
||[15.3]***|[14.1]***|[17.5]***|[15.6]***|
||{18.7}***|{15.3}***|{20.7}***|{14.8}***|
|Father's Sch. * Daughter|-31.8|-31.2|-21.7|-21.7|
|Dummy|(17.3)*|(16.6)*|(20.9)|(20.0)|
||[18.9]*|[17.8]*|[21.6]|[19.8]|
||{21.0}|{16.9}*|{23.2}|{16.5}|
|Daughter Dummy|151.6|240.3|111.2|275.2|
||(103.1)|(100.6)**|(120.7)|(117.1)**|
||[116.9]|[110.3]**|[132.5]|[121.1]**|
||{147.9}|{126.6}*|{168.4}|{135.9}*|
|Intercept|648.0|1939.7|705.7|2173.6|
||(71.0)***|(117.9)***|(83.3)***|(132.7)***|
||[88.1]***|[171.4]***|[100.0]***|[186.5]***|
||{86.4}***|{229.6}***|{87.9}***|{246.5}***|
|No. of Children||-716.8||-838.0|
|||(49.7)***||(56.8)***|
|||[87.2]***||[100.1]***|
|||{123.2}***||{141.5}***|
|No. Observations|4326|4326|3632|3632|

Notes: (1) The data used are CFPS 2010. (2) Robust standard errors in parentheses; standard errors clustered at county-level in square brackets; standard errors clustered at province-level in braces; *** p<0.01, ** p<0.05, * p<0.1.
<!-- Start of picture text -->
><br>o<br>=aeanPy<br>zoc co=<br>—<br>2s<br>a<br>o==—aoJ<br><5)<br>£R==<br>=<br>=> => ae = Me = Ne<br>ee oe oe 2 a s 2<br>— oS se oe —S ee<br>Ghana Kenya Zambia<br>Poe Contract and not known to be on VC Eon Contract but not known to be on VC<br>He Contract but known to be on VC (on contract and known to be on VC<br>Source: Authors’ calculations based on survey responses.<br><!-- End of picture text -->

<!-- Start of picture text --> i—]<br>oa<br>an<br>=wD<br>a<br>S ooo<br>cc<br>=<br>=smy<br>J<br>fat<br>=<br>—ot<br>a<br>£R==<br>=<br>oa<br>a= os= oa= os= os= os=<br>a er aa er Er we?<br>3 = S oe = S oe mae<br>Ghana Kenya Zambia<br>FY Mo Contract and not known to be on WiC FY On Contract but not known to be on VC<br>FY Mo Contract but known to be on WC FY On contract and known to be on WC<br>Source: Authors' calculations based on survey responses<br><!-- End of picture text -->
employment.

**Distortions** We use the distortions from WBES in the same way as we did in the empirical section as a proxy of distortionary infrastructures. As mentioned previously, we sum up the percentage of sales lost due to poor infrastructure and services (losses due to power outage or surges from public grid, insufficient water supply, unavailable mainline telephone service, transport failures); crime (losses due to theft, robbery, vandalism or arson against the establishment); and corruption (informal payments to "get things done"). For a given country, we compute losses for each establishment and use the distribution of distortionary infrastructures across establishments in the simulation.

**Institutional Quality** We calibrate institutional quality _A_ using the measure of public policies effectiveness from the Worldwide Governance Indicators (WGI) as described above. Recall that this variable is defined as perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. We use the percentile distribution of policy effectiveness variable ranged from 0 to 1.<sup>10</sup> In the calibration, we approximate the detection probability using this index of public policies effectiveness. We argue that effective government in formulating and implementing public policies will also be effective in detecting illegal activities such as tax evasion. In the sensitivity analysis, we relax this assumption using an alternative measure of detection probability and show that the predictions of the paper remain the same and are not driven by this assumption.

In the model, the cost of tax evasion for an establishment is assumed to be a convex function, with a convexity parameter _θ >_ 2. This assumption guarantees that the marginal cost of tax evasion is positive and increasing with the share of sales not reported for tax purposes. The convexity parameter _θ_ is calibrated to 3. Sensitivity analysis shows that the findings are

> 10To the best of our knowledge, the proportional tax rate on sales is not publicly available for each country in the sample. Calibrating institutional quality using the data allows us minimizing errors arising from a not accurate calibration of the proportional tax rate on sales _τ_<sup>_c_</sup> for each country. The proportional tax rate on sales _τ_<sup>_c_</sup> is therefore normalized to 0 in the quantitative model.

21
23 share of manufacturing exports, which is not surprising given the growing importance of Chinese manufacturing exports in total merchandise exports over the period. The falling share of imports of parts and components reflects the substitution of domestic inputs for foreign inputs by Chinese firms, a finding that is corroborated by evidence of increasing domestic value added in Chinese firms (Kee and Tang, 2014).<sup>17</sup> The reduced responsiveness of manufacturing trade with respect to income for the United States mirrors in some ways developments in China. In the 1990s, as US firms increasingly relocated production stages outside national borders, trade tended to respond more to changes in income as variations in domestic demand were increasingly met by imports. In recent years, the international fragmentation process seems to have leveled off. Figure 9 provides preliminary evidence. While merchandise imports grew consistently in the United States since the 1980s, the share of manufacturing imports in merchandise imports (and in national GDP) declined since the early 2000s. In fact, US manufacturing imports as a share of GDP have been stable at around 8 percent since the turn of the century, after nearly doubling in the preceding decade-and-a-half. Interestingly, Chinese manufacturing imports as a share of GDP rose from 10 percent at the beginning of the long 1990s to almost 30 percent in the early 2000s and have sharply declined since then.

In order to analyze the impact of global supply chains more systematically, we estimate the long-run elasticities of value added trade with respect to income and contrast these estimates with our results for the (gross) trade elasticities.<sup>18</sup> The varying gap between the two provides information on the extent to which the changing pace of supply chain expansion is behind the 17 These changes do not mean that China is turning its back on globalization. As discussed in Kee and Tang (2014), the enhanced availability of inputs domestically is in part linked to growing foreign direct investment in these industries. Moreover, there may be a geographical dimension to these changes, with China’s coastal regions beginning to source relatively more from the Chinese interior, because transport and communication costs have declined more sharply with the interior than with the rest of the world. Trade integration may now be taking the form of greater internal trade than international trade, which is captured by official statistics.

18 Data on world domestic value added and foreign value added in gross exports from the OECD-WTO dataset are only available starting from 1995 and for selected years. Here we use a time series that was created by Duval et al. (2014) by interpolating the OECD-WTO data. Further details can be found in the technical appendix of Duval et al. (2014).
those services. Other households may be able to pay for the services, but may live too far from the electric line or water pipe to be able to connect to it.

In this paper, using DHS data for a large sample of African countries, we have relied on two different methods for decomposing the lack of coverage observed in urban areas into three components: pure demand-side problems, pure supply-side problems, and mixed demand and supply-side problems. The results obtained with the statistical method suggest that for Africa as a whole, demand-side problems are prominent on average. But the results obtained from the sounder econometric method suggest that for piped water, lack of supply appears to be the main issue, and for electricity, supply-side problems loom as large as demand-side problems. At the country level, whether one is confronted mostly with demand- or supply-side problems depends in large part on the underlying access rate to the services at the neighborhood level.

Because we have been dealing in this paper with data from a large number of countries, we have only tried to provide a broad snapshot of the issues. The method used here could easily be refined in order to be applied for policy work with more depth for any given country. For example, one could check the robustness of the econometric simulations to alternative estimation techniques, or alternative specifications of the regressions. One could also rely on census data in order to obtain estimates of demand as opposed to supply-side problems for smaller geographic areas. The results obtained from survey or census data could also be combined with additional information from willingness to pay studies, or focus group discussions. Data from household surveys with information on the service cuts imposed on households for non-payment of their utility bills would also provide additional information in order to assess the magnitude of supply as opposed to demand-side problems. Finally, changes over time in the estimates obtained with repeated cross-sections of data would also be very useful to assess how the mix of demand and supply-side issues evolves when, for example, neighborhood access is being improved.
**<u>Table 2  Economic and Demographic Indicators by Urban/Rural and By Regions 2004</u>**

|||**Hanoi**|**Red River**<br>**Delta***<sup>**1**</sup>|**Northeast**|**Northwest**|**North**<br>**Central**<br>**Coast**|**South**<br>**Central**<br>**Coast**|**Central**<br>**Highlands**|**HCMC**|**Southeast***<sup>**2**</sup>|**Mekong**<br>**River**<br>**Delta**|**Total**<br>**Vietnam**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Expenditure***<sup>**3**</sup>|Rural|387|323|281|213|253|298|277|569|410|350|**317**|
|**(1,000 dong)**|Urban|915|586|543|509|443|556|476|946|571|511|**662**|
||**Total**|**710**|**359**|**330**|**252**|**279**|**371**|**332**|**898**|**458**|**382**|**405**|
|**Land holding***<sup>**4**</sup>|Rural|.041|.069|.195|.316|.120|.134|.258|.042|.213|.176|**.152**|
|**(hectare)**|Urban|.008|.020|.040|.046|.022|.020|.138|.004|.063|.065|**.036**|
||**Total**|**.021**|**.062**|**.166**|**.280**|**.107**|**.102**|**.225**|**.009**|**.169**|**.154**|**.122**|
|**Minority***<sup>**5**</sup>|Rural|.0|1.0|46.7|89.2|11.9|7.0|40.6|1.5|8.1|6.5|**15.8**|
|**(%)**|Urban|.0|.6|14.5|23.3|.6|.5|9.7|.6|.7|3.6|**3.1**|
||**Total**|**.0**|**.9**|**40.8**|**80.5**|**10.4**|**5.2**|**32.0**|**.7**|**5.9**|**5.9**|**12.5**|
|**Education***<sup>**6**</sup>|Rural|8.3|7.8|7.0|4.9|7.5|6.4|5.5|7.2|6.2|5.3|**6.6**|
|**(years)**|Urban|10.7|9.4|9.9|8.9|9.5|8.6|8.3|8.7|7.9|6.9|**8.7**|
||**Total**|**9.8**|**8.1**|**7.5**|**5.5**|**7.8**|**7.1**|**6.4**|**8.5**|**6.7**|**5.6**|**7.2**|
|**State employment**<sup>**7**</sup>|Rural|18.7|6.5|6.1|3.9|5.0|6.9|5.4|11.6|7.4|4.3|**6.0**|
|**(%)**|Urban|41.9|24.9|33.4|38.6|32.0|22.7|14.0|18.1|17.0|16.5|**23.2**|
||**Total**|**32.7**|**8.8**|**10.7**|**7.9**|**8.2**|**11.2**|**7.6**|**17.2**|**10.2**|**6.6**|**10.1**|
|**Foreign employment**<sup>**8**</sup>|Rural|2.9|.5|.1|.0|.1|.4|.2|10.7|11.1|.3|**1.3**|
|**(%)**|Urban|3.1|2.9|1.9|.9|.2|2.5|.6|11.5|11.6|.4|**4.9**|
||**Total**|**3.0**|**.8**|**.4**|**.1**|**.1**|**1.0**|**.3**|**11.4**|**11.3**|**.4**|**2.2**|

Sources: Author’s calculation using the VHLSS 2004 and the _Enterprise Survey_ data 2004.

Notes: *<sup>1</sup> The region excludes Hanoi; *<sup>2</sup> the region excludes HCMC; *<sup>3</sup> the per capita average household monthly expenditure; *<sup>4</sup> land holding is defined as the average per capita area owned by the household members including farming, forestry land and water surface area for aquaculture, residence land and garden, and pond next to housing land; *<sup>5</sup> the minority is defined to represent all ethnic groups other than the Kinh majority and Hoa (Chinese); *<sup>6</sup> the average education years attained by those who are 15 and over; *<sup>7</sup> the proportion of workers employed by state sector relative to total workers **;**<sup>8</sup> the proportion of workers employed by foreign sector relative to total workers.
# THE DYNAMICS OF POVERTY: WHY SOME PEOPLE ESCAPE FROM POVERTY AND OTHERS DON'T An African Case Study

## **I. Introduction**

Empirical investigations of poverty in developing countries have tended to focus on the incidence of poverty at a particular point in time. This is largely dictated by the available data source, usually a household income or expenditure survey, which provides a snapshot picture of household welfare and poverty (at most over a one-year reference period). Studies which have looked at changes in incidence and depth of poverty over time, compare most often two snapshots (e.g., Glewwe and Hall, 1992; Grootaert, 1993; Mukui, 1994; World Bank, 1995). In many cases, such studies have been able to discover important socio-economic or regional patterns in incidence of poverty and in changes over time, which have guided policy makers in designing and targeting poverty alleviation policies.

However, all such studies must beg the question whether the observed trends in poverty pertain to the same or different poor people. In other words, if poverty incidence is observed to increase, it is not known whether this is due to new poor having joined the existing poor, or whether it is the net outcome of a dynamic process whereby some people escape poverty and others--a larger number than the net increase--have become poor. Yet, this distinction is of crucial policy importance. The chronically poor may be in need of programs to enhance their endowments of human and physical capital, or, in the case of poverty due to disability or old age, be in need of permanently targeted transfers. In contrast, the temporarily poor may be best helped with programs which complement their own resources and help them to "bridge" the period that they are poor.

Making the distinction between chronic and temporary poor requires information on the duration of poverty. In practice, this needs to come from panel surveys, which follow the same households over time. Panel surveys exist in a number of developed countries but are rare in the developing world, especially in Africa. A recent study based on the U.S. Panel
The remainder of this study is organized as follows. In the next section, we describe the data sets, while Section III outlines the damage function and wind field model employed. Section IV states our econometric specification and provides econometric results. Section V uses the estimates from Section IV to derive monetary values of the economic impact. The last section concludes.

# **Section II: Data**

# _II.A Nightlights Data_

The nightlights data consist of the monthly composites of the United States Air Force Defense Meteorological Satellite Program-Operational Linescan System (DMSPOLS). The source satellites have a 101-minute sun-synchronous near-polar orbit at approximately 830km above the Earth’s surface and provide coverage across the globe twice a day, during 20h30 and 22h local time. The raw data are processed to remove cloud obscured pixels and other sources of transient light, and are normalized to range between 0 and 63. Here we use the monthly composites provided by NASA for satellites F10, F12, F14, F15, F16, and F18. These provide information on the average stable monthly nightlight intensity as well as the number of cloud-free days from which these averages are calculated. A summary of the coverage and missing monthly composites is provided in Table 1. In order to derive unique monthly values for overlapping satellite observations, we calculate simple averages across satellites for each pixel. One may want to note, however, that Ishizawa et al (2017) in a similar analysis for set of Central American countries found that alternatively using cloud weighted averages or the newest images produced qualitatively similar results.

6