rafmacalaba/datause-encoder-data huggingface dataset:
39

- Chandrabose, A. S. (2015). Outgoing labour and its impact on the tea plantation sector in Sri Lanka. In _Proceedings of 5th International Symposium on Emerging Trends and Challenges in Multidisciplinary Research_ . South Eastern University of Sri Lanka.

- Costinot, A., D. Donaldson, and C. Smith (2016). Evolving comparative advantage and the impact of climate change in agricultural markets: Evidence from 1.7 million fields around the world. _Journal of Political Economy 124_ (1), 205–248.

- De Chaisemartin, C. and X. d’Haultfoeuille (2022). Difference-in-differences estimators of intertemporal treatment effects. NBER working paper 29873.

- Desmet, K. and E. Rossi-Hansberg (2015). On the spatial economic impact of global warming. _Journal of Urban Economics 88_ , 16–37.

- Dhingra, S., R. Freeman, and H. Huang (2023). The impact of non-tariff barriers on trade and welfare. _Economica 90_ , 140–177.

- Dissanayaka, N. and M. Thibbotuwana (2021). Sri Lanka’s Agri-food Trade: Structure, Opportunities, Challenges Impacts of COVID-19. Food Security Policy Research, Capacity, and Influence (PRCI) Research Paper 6, Michigan State University.

- Dong, J., X. Xiao, M. A. Menarguez, G. Zhang, Y. Qin, D. Thau, C. Biradar, and B. Moore III (2016). Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. _Remote Sensing of Environment 185_ , 142–154.

- Donovan, K. (2021). The equilibrium impact of agricultural risk on intermediate inputs and aggregate productivity. _The Review of Economic Studies 88_ (5), 2275–2307.

- Duflo, E., M. Kremer, and J. Robinson (2008). How high are rates of return to fertilizer? Evidence from field experiments in Kenya. _American Economic Review 98_ (2), 482–488.

- Duflo, E., M. Kremer, and J. Robinson (2011). Nudging farmers to use fertilizer: Theory and experimental evidence from Kenya. _American Economic Review 101_ (6), 2350–2390.

- Eckert, F. and M. Peters (2023). Spatial structural change. Working paper.

- Ederington, J. and M. Ruta (2016). Nontariff measures and the world trading system. In K. Bagwell and R. W. Staiger (Eds.), _Handbook of Commercial Policy_ , Chapter 5, pp. 211–277. Elsevier.

- Evenett, S. and J. Fritz (2020). The global trade alert database handbook.

- Fajgelbaum, P. and S. J. Redding (2022). Trade, structural transformation, and development: Evidence from argentina 1869–1914. _Journal of Political Economy 130_ (5), 1249–1318.

- Fan, T., M. Peters, and F. Zilibotti (2023). Growing Like India– The Unequal Effects of Service-led Growth. _Econometrica 91_ (4), 1457–1494.

FAO (2025). Employment indicators 2000–2023: July 2025 update.

- FAO and WFP (2022). _Special report - FAO/WFP crop and food security assessment mission (CFSAM) to the Democratic Socialist Republic of Sri Lanka_ . Rome: United Nations.

- FAO and WFP (2023). _Special report - FAO/WFP crop and food security assessment mission (CFSAM) to the Democratic Socialist Republic of Sri Lanka_ . Rome: United Nations.

- Farrokhi, F. and H. S. Pellegrina (2023). Trade, technology, and agricultural productivity. _Journal of Political Economy_ .
was conducted from September 2019 to March 2020 and included 601 firms. Three follow-up phone surveys focusing on a subset of questions (including about sales and employment) were conducted in June and July 2020 (June 2020 round), December 2020 to February 2021 (December 2020 round), and July to September 2021 (July 2021 round). The response rate was 96.1 percent in the June 2020 round and 95.1 percent in the other rounds. Questions about changes in firm sales used a ‘pre-pandemic’ level of the same month in 2019 (to address concerns about seasonality across the calendar year) and changes in employment (both permanent and temporary) were based on a ‘pre-pandemic’ level of February 2020.

The survey samples of formal firms were largely based in urban areas, had 20–100 workers, and were mainly involved in retail, manufacturing (other than food), and other services (excluding agricultural inputs and equipment). The distribution of firms by location is Kitwe (100, 16.6 percent), Livingstone (105, 17.5 percent), Lusaka (294, 48.9 percent), and Ndola (102, 17.0 percent). By firm size, it is small (231, 38.4 percent), medium (250, 41.6 percent), and large (120, 20.0 percent)<sup>3</sup> . By primary activity, it is manufacturing (121, 33.9 percent), retail (123, 20.5 percent), and other services (194, 32.3 percent).

# **3.1.2 Tax administrative data**

There are three main types of tax in Zambia; corporate income tax (CIT), personal income tax (PIT), and VAT. CIT in Zambia is taxed in accordance with the principal activity of the business. The CIT rates range from 10 percent for incomes earned from agriculture to 40 percent for incomes in the banking and telecommunications sectors. All businesses with annual turnover exceeding ZAR800,000 (approximately US$45,000) are expected to register, file returns, and pay income tax. It is compulsory for some sectors, such as activities involving scientific and professional services, to register for CIT irrespective of the size of their annual sales/turnover.

There are few self-reported PIT taxpayers in Zambia, which means the responsibility to pay PIT largely falls on employers. In this form, PIT is a PAYE tax and is withheld at

> 3Small, medium, and large firms comprise 5–19, 20–99, and 100+ workers, respectively

11
<!-- Start of picture text --> (c) United Kingdom: Standard Deviation of (d) United Kingdom: Standard Deviation of<br>UK Real GDP Growth, % points GDP growth, % points<br>2.5<br>Oil price shocks 1.4<br>2 1.2<br>Global<br>Crisis<br>1.5 1<br>0.8<br>1<br>0.6<br>0.5<br>0.4<br>0<br>0.2<br>0<br>1957-1971 1972-1985 1986-2008 2009-2011(iii)<br>(e) Canada: Standard Deviation of Real GDP (f) Canada: Standard Deviation of GDP<br>Growth, % points growth, % points<br>1.8<br>1.2<br>1.6<br>1.4 Oil price shocks Global 1<br>Crisis<br>1.2<br>1 0.8<br>0.8<br>0.6 0.6<br>0.4<br>0.4<br>0.2<br>0<br>0.2<br>0<br>1963-1971 1972-1985 1986-2008 2009-2011(ii)<br>1957 Q1 Q4-1959 Q3-1962 Q2-1965 1968 Q1 1970 Q4 1973 Q3 Q2-1976 1979 Q1 1981 Q4 1984 Q3 1987 Q2 1990 Q1 1992 Q4 1995 Q3 1998 Q2 2001 Q1 2003 Q4 2006 Q3 2009 Q2<br>1963 Q1 Q3-1965 1968 Q1 Q3-1970 1973 Q1 Q3-1975 1978 Q1 1980 Q3 1983 Q1 1985 Q3 1988 Q1 1990 Q3 1993 Q1 1995 Q3 1998 Q1 2000 Q3 2003 Q1 2005 Q3 2008 Q1 2010 Q3<br><!-- End of picture text --> 13
Table A2: Balance tests

||Property<br>Value|Front<br>Meters|House<br>type|Past due<br>debt|Bill<br>amount|N Bills<br>paid 2019|Digital<br>payment|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|**_A. Women and_**|**_Men_**|||||||
|Treated|_−_0.00|_−_7.89|_−_0.00|_−_0.01|2.86|0.08|_−_0.01|
||(0.01)|(10.13)|(0.00)|(0.01)|(5.31)|(0.06)|(0.01)|
|Mean Control|13.65***|822.26***|0.92***|0.57***|386.25***|7.06***|0.38***|
||(0.01)|(6.88)|(0.00)|(0.00)|(3.66)|(0.04)|(0.00)|
|Observations|47,645|48,488|48,488|48,488|48,488|48,488|28,194|
|**_B. Women_**||||||||
|Treated|0.00|_−_11.87|_−_0.00|0.00|_−_0.20|0.02|_−_0.00|
||(0.01)|(13.14)|(0.00)|(0.01)|(7.32)|(0.09)|(0.01)|
|Mean Control|13.58***|779.42***|0.93***|0.56***|362.04***|7.02***|0.37***|
||(0.01)|(9.30)|(0.00)|(0.01)|(4.70)|(0.06)|(0.01)|
|Observations|17,766|18,089|18,089|18,089|18,089|18,089|10,416|
|**_C. Men_**||||||||
|Treated|_−_0.00|_−_5.86|_−_0.00|_−_0.01|4.49|0.11|_−_0.01|
||(0.01)|(11.27)|(0.00)|(0.01)|(6.60)|(0.07)|(0.01)|
|Mean Control|13.70***|847.87***|0.91***|0.57***|400.72***|7.08***|0.39***|
||(0.01)|(7.23)|(0.00)|(0.00)|(4.40)|(0.05)|(0.01)|
|Observations|29,879|30,399|30,399|30,399|30,399|30,399|17,778|

Notes: This table shows balance regressions to test for differences in observable characteristics between the treatment and control groups. Each column corresponds to a separate regression (equation (3) in the text). The dependent variables in each column are: (1) the log of assessed property value; (2) the front meters of the property; (3) an indicator for the property being a house versus a house with a store; (4) an indicator for past due debt; (5) the amount paid in the bill corresponding to December 2019 (including zeroes); (6) the number of bills paid in 2019 (the maximum is 12); (7) for those who paid, whether they did so digitally. The rows _Mean Control_ display the constant of each regression, corresponding to the average of the dependent variable for accounts that did not receive a letter. Standard errors clustered by blocks are reported in parentheses. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01 C-11
where **0** is a block of zeroes; the number of columns equals the _V_<sup>ˆ</sup> _c_ from above, and the number of rows to the _V_<sup>ˆ</sup> _c_ next to it.

The _V_<sup>ˆ</sup> matrix is similar to the Ωmatrix from the ELL methodology.<sup>ˆ</sup> In the most current version of PovMap users no longer have the option to use the _GLS_ estimator originally offered by ELL (2003), and independently of how users choose to model the location effect, the manner in which the _GLS_ estimators are obtained is using the estimators from equations 16 and 17.

# **4 The Second Stage: Simulation**

The final goal of the process described up to this point is to simulate values for the variable of interest. In the Elbers, Lanjouw, and Lanjouw (2003) context, this entailed log welfare and poverty rates for specific locations using the census data. Monte Carlo simulation is used to obtain the expected welfare measures given the first stage model (ELL, 2003). This is done by applying the parameter and error estimates from the survey to the census data. The goal is to obtain a sufficient number of simulations in order to obtain reliable levels of welfare. The section begins by introducing Empirical Bayes prediction as is done by Van der Weide (2014).

## **4.1 Empirical Bayes Prediction Assuming Normality**

Along with the _GLS_ and Henderson’s Method III additions to the ELL approach mentioned before, Empirical Bayes (EB) prediction was also added. EB prediction makes use of the survey data in order to improve predictions on the location effect. Since EB makes explicit use of data from the survey, its use only improves predictions for areas that are included in the survey.

If we assume that _ηc_ and _ech_ (from equation 1) are normally distributed, then the distribution of _ηc_ conditional on _ec_ will also be normally distributed (Van der Weide, 2014). The distribution of the random location effect, _ηc_ , is obtained conditional on the observation specific residuals of the observations sampled in the location. Van der Weide (2014) indicates that the assumption of normality for both components of the residual is necessary to derive the distribution of the random location effects. In order to proceed, Van der Weide (2014) defines the following: 10
Income, and Civil Conflict in Colombia,” _The Review of Economics and Statistics_ , 90(2), 191–215.

- Aragón, F. M. and J. P. Rud (2013a): “Modern Industries, Pollution and Agricultural Productivity: Evidence from Mining in Ghana,” _International Growth Center Working Paper_ .

- Aragón, F. M. and J. P. Rud (2013b): “Natural Resources and Local Communities: Evidence from a Peruvian Gold Mine,” _American Economic Journal: Economic Policy_ , 5(2), 1–25.

- Auty, R. (2001): “The political economy of resource-driven growth,” _European Economic Review_ , 45(4), 839–846. (2007): “Natural resources, capital accumulation and the resource curse,” _Ecological_

- _Economics_ , 61(4), 627–634.

- Barro, R. (2000): “Democracy and the Rule of Law,” in _Governing for prosperity_ , ed. by B. B. de Mesquita and H. Root, pp. 209–231. Yale University Press, New Haven, CT.

- Berman, N., M. Couttenier, D. Rohner and M. Thoenig (2014): “This Mine is Mine! How minerals fuel conflicts in Africa,” _CEPR Discussion Paper No. DP10089_ .

- Bigsten, A. and M. Söderbom (2006): “What Have We Learned from a Decade of Manufacturing Enterprise Surveys in Africa?,” _World Bank Research Observer_ , 21, 241–265.

- Black, D., T. McKinnish and S. Sanders (2005): “The Economic Impact Of The Coal Boom And Bust,” _The Economic Journal_ , 115, 449–476.

- Brückner, M. and A. Ciccone (2010): “International Commodity Prices, Growth and the Outbreak of Civil War in Sub-Saharan Africa,” _The Economic Journal_ , 120(544), 519–534.

- Brunnschweiler, C. and E. Bulte (2008a): “Linking Natural Resources to Slow Growth and More Conflict,” _Science_ , 320, 616–617.

- (2008b): “The Resource Curse Revisited and Revised: A Tale of Paradoxes and Red

- Herrings,” _Journal of Environmental Economics and Management_ , 55(3), 248–264.

- (2009): “Natural Resources and Violent Conflict: Resource Abundance, Dependence,

- and the Onset of Civil Wars,” _Oxford Economic Papers_ , 61(4), 651–674.

- Buhaug, H. and J. Rød (2006): “Local determinants of African civil wars, 1970-2001,” _Political Geography_ , 25(3), 315–335.

- Campbell, C. (1997): “Migrancy, Masculine Identities and AIDS: The Psychosocial Context of HIV Transmission on the South African Gold Mines,” _Social Science & Medicine_ , 45(2), 273–281.

- Caselli, F. and G. Michaels (2013): “Do Oil Windfalls Improve Living Standards? Evidence from Brazil,” _American Economic Journal: Applied Economics_ , 5(1), 208–238.

- Collier, P. (2010): _The Plundered Planet_ . Allen Lane, London.

- Collier, P. and A. Hoeffler (2004): “Greed and grievances in civil war,” _Oxford Economic Papers_ , 56(4), 563–595.

- (2005): “Resource Rents, Governance and Conflict,” _Journal of Conflict Resolution_ ,

- 49, 625–633.

26
Table 6: Effect of participation in NEP on parental practices and beliefs

||Obs.|NEP-B|NEP-I|P value for B=I|
|---|---|---|---|---|
|Parental Practices|||||
|Home Index|2545|0.464<br>(0.396)|0.659**†<br>(0.311)|0.532|
|PBC Affection|2545|0.234<br>(0.254)|0.358*<br>(0.199)|0.536|
|PBC Interaction|2545|0.082<br>(0.251)|0.059<br>(0.197)|0.907|
|PBC Negative discipline|2545|-0.262<br>(0.257)|-0.332*<br>(0.201)|0.731|
|PBC Positive discipline|2545|0.294<br>(0.262)|0.246<br>(0.205)|0.817|
|Parental Beliefs, Attitudes, Perceptio|ns||||
|Perceived Self-efficacy|2543|0.205<br>(0.256)|0.411**†<br>(0.202)|0.311|
|Perceived Parental Impact of own|2545|0.372|0.446**†|0.714|
|behavior on child development||(0.254)|(0.199)||
|Perceived Social Support – Family|2545|-0.423<br>(0.263)|-0.033<br>(0.206)|0.059|
|Perceived Social Support – Friends|2545|0.389<br>(0.255)|0.367*<br>(0.200)|0.912|
|Perceived Social Support - Others|2545|-0.075<br>(0.257)|0.042<br>(0.201)|0.560|
|Democratic style|2545|0.222|0.205|0.936|
|||(0.264)|(0.207)||
|Authoritarian style|2545|0.140|-0.084|0.277|
|||(0.262)|(0.205)||
|Permissive style|2545|-0.350|-0.112|0.249|
|||(0.262)|(0.206)||
|Elicited Age High Investment|1487|-0.262|-0.248|0.947|
|Home Scenario||(0.256)|(0.179)||
|Elicited Age Low Investment|1486|-0.305|-0.387*|0.736|
|Home Scenario||(0.295)|(0.205)||
|Psychological Well Being|||||
|Parental Stress|2545|0.238|-0.013|0.211|
|||(0.255)|(0.200)||
|Depression|2545|0.201|0.153|0.812|
|||(0.255)|(0.200)||

Note: Each line reports estimates from a separate regression. All regressions control for health center’s fixed effects. Significance levels: *p<=10%, **p<=5% testing individual hypotheses, †p<=10% testing multiple hypotheses.

21
NUES/MES panel allows further adjustment for imputed return on the value of capital (tools, inventories, and location if owned), and hours worked by unpaid workers (table **3).10** The results would appear to invert the conventional view of the relation between formal and informal sectors, regardless of the definition chosen: movement into formal salaried employment is associated with a significant _decline_ in remuneration, except from informal salaried employment; movement from formal salaried employment always leads to a significant _increase._ However, in the absence of information on the value of benefits, compensation for risk, the value of independence, or in-kind payments or implicit training costs, the magnitude of the distortion-free differential cannot be known ex-ante, and neither these, nor any previous reported sectoral differentials are reliable measures of segmentation. Further, though table 3 suggests that those reporting voluntary moves into self-employment do far better than those moving involuntarily, we cannot necessarily interpret asymmetries in differentials in tables 2a and b as evidence that there is a larger component of voluntary movement into the formal salaried sector: Involuntary separations due to formal sector firm closures imply the loss of well paying jobs and a large differential, while the analogous failure of a micro-enterprise may imply low earnings prior to transition and a smaller differential.

However, the differentials among informal sectors are less affected by unobservables, such as the loss of formal sector benefits, and are suggestive of similarities and differences among them.

> **10** Despite choosing the NUES sample to straddle the MES sample, predictably, the raw differentials differ somewhat. For the critical salaried formal salaried/self-employed transition they are extremely close. For the other cases, although we are dealing with the same relative orders of magnitude, it is less easy to consider these simply as adjustments to the NiJES results reported above. The formal/self-employed differential is slightly, although not significantly higher (11,5 vs 9,4) and the contract/self-employed differential is also broadly similar (10,-i vs 7, 8) with a substtial but insignificant difference in the median. The differential from informal/ salaried employment issubstantially higher (33,25 vs 19,16) and significant different.

7
- 10 -

18. For the spatial weighting matrix, inverse distances between two locations _i_ and _j_ are used. The distance is calculated using the Euclidean distance between the two locations. The intuition is that two locations are more closely related to each other, if they are located closely. This follows Tobler’s first law of geography: “everything is related to everything else, but near things are more related than distant things (Tobler 1970).”

# **IV. DATA**

19. Our unit of analysis is fokontany, which is the fourth-level of administrative boundaries, following regions, districts, and communes. In Greater Antananarivo, there are 556 fokontany in 4 districts. From the analysis, 8 observations are excluded, of which some data are missing. The summary statistics are shown in ( **Table 1** ). The average land prices were collected at each fokontany in 2018. The prices vary substantially between MGA 110 and MGA 1.3 million, with an average of about MGA 64,000 or US$18 per m<sup>2</sup> . Travel time is estimated using spatial software based on the road network data with road conditions and bus traffic attached (see **Figure 7** above). It takes 42 minutes on average, but from some fokontany, it may take more than an hour and a half.

20. As discussed, the JAI is calculated based on the size of job opportunities at each destination and the travel time between the origin and the destination. To identify and quantify the size of job opportunities, satellite imagery was used. The following areas were considered: Administrative Zone; Commerciale Zone; Industrial Zone; Cultural Facilities; Hotel Buildings; Market Area; and Office Buildings. In total, 1,094 land parcels were identified ( **Figure 11** ). The size of each parcel of land was measured as a proxy of the number of job opportunities.<sup>2</sup> It differs from 0.007 ha to 37 ha.

> 2 This was found to be a good proxy in our case: The measured land area is highly correlated with the number of registered businesses in the same area. Of course, some businesses may employ more people than others even though they occupy the same size of land. In general, however, there is strong correlation between them.
# **Table 10: Costs of producing and harvesting a jatropha crop ($/ha)**

|Years1-5 (total)|Year6 and onwards|Total 1-6|
|---|---|---|
|Establishment and tending the crop|||
|Labor<sup>a</sup><br>67|26|93|
|Fertilizer<br>187|125|312|
|Seed<br>4|0|4|
|Plough Hire<br>11|0|11|
|Subtotal<br>269|151|420|
|Harvesting the crop<sup>b</sup>|||
|Yield<sup>c</sup><br>9.25|7.5|16.75|
|Collect<br>26|21|47|
|De-coat<br>26|21|47|
|Shell<br>21|17|38|
|Subtotal<br>72|59|131|
|**Total**<br>**342**|**209**|**551**|
|**Costper ton of fruit**<br>**37**|**28**|**33**|

Source: Openshaw (2000), actualized to 2008 dollars.<sup>a</sup> The labor costs used by this author were $1 a day ($1.22 actualized to 2008).<sup>b</sup> Labor accounts for 95% of the harvesting costs.<sup>c</sup> Air dry tons.

More recently Francis, Edinger, and Becker (2005) placed the present value of life cycle costs at $1,459/ha. These authors seem to have only included minimal (if any) inputs other than labor.<sup>17</sup> The seeds yield is assumed to stabilize at 1.8 t/ha after the fifth year, with a 28% oil content, leading to a jatropha oil productivity of about 504 kg/ha. Including seed crushing, the feedstock costs were estimated at $407.8 per ton of jatropha oil ($442 per ton in 2008 terms).

Perhaps the most detailed estimates were recently provided by Kukrika (2008) for the case of India (see Table 11). This study calculates costs on a per year basis for a project that lasts 10 years. Many assumptions are behind these estimates. Yield levels, which are highly uncertain, are by far the most important assumption driving the results according to the author.<sup>18</sup>

> 17 These labor costs were offset from years 5 and onwards by a $109 per hectare income derived from vegetable intercropping.

> 18 Yields are assumed at 1kg per tree for the first harvest year (fourth of the crop), increasing to 3 by year 8. There are 667 trees per acre. The oil content is assumed at 25%.

22
they eventually lead to multilateral free trade is to encourage them to have an open membership. Any country wishing to accept the obligations of the bloc membership should be allowed the privileges offered by it. Is such a bloc in East Asia feasible and desirable?

Without going into the desirability issue, let me simply note that the feasibility of an open but discriminatory bloc is highly questionable. Internally, one must confront all the problems noted in the previous sub-section. Externally, it may appear that the United s tates will be more tolerant of a bloc which is willing to accept it as a member. But the reality is otherwise. The United States is not ready yet to join in a free trade area with East Asia and without that, from its perspective, de facto there is no difference between an open and a closed bloc.25 This brings us to the remaining option: nondiscriminatory regionalism whereby countries in the region will pursue a GATT type liberalization.26 The key element distinguishing this approach from a regional bloc will be nondiscriminatory nature of liberalization. The countries in the region will come together at a common forum and, very much in the spirit of the various GATT rounds, negotiate reductions in trade barriers. Any concessions made by a country to another will be extended automatically to all GATT mnembers. In the following, I offer a detailed discussion of the positive as well as negative side of this approach.

25The U.S. position that it will neither join East Asia in an FTA nor allow the latter to form one on its own and also pursue NAFTA is obviously devoid of any internal logic. But this is the reality which East Asia cannot ignore.

26To my knowledge, a proposal to this effect has been made for the first time in a recent World Bank (1993) report. Petri (1992) offers a similar proposal but makes the United States, Australia and New Zealand a part of the overall scheme. because intraregional trade among these countries and East Asia is so intense, the economic case for this proposal can be hardly disputed. But the same factors which make an open trading bloc a la Bhagwati and Cooper politically infeasible also cast a serious doubt on the workability of this proposal.

36