rafmacalaba/datause-encoder-data huggingface dataset:
The initial business might start as a relocation of a low-end _maquila_ operation that had employed the temporary migrants in a more advanced region. Or it might start by using old machinery that was being replaced in the more advanced region. Here again, the goal should be to gain control over the operation so that the sources of demand and the products could be diversified away from dependence on being just a link in a value chain based elsewhere.

Once an operation is mastered, then the town should seek to diversify into related products where the same or similar knowledge and skills could be used. A temporary labor stint in a similar operation elsewhere might be helpful. Climbing this first rung on the ladder is more feasible if the town is relatively close<sup>37</sup> to a thriving city so that volume can grow and new diversified products will find a ready market.

# **Appendix 2: Picturing the Dynamics of Convergence and Divergence Picturing the Dynamics of Convergence**

A diagram using two labor demand curves can represent an equalizing dynamics between two regions ("rich" and "poor") that is driven by wage differentials.

<!-- Start of picture text --> rinch Dpo<br>Wrich<br>- Residents'i \<br>W* W m\<br>WMovexs' || Stayers' Gain - W<br>cWpoor<br>Mig lar<br>Lrich 4 + Lpoor<br><!-- End of picture text -->

Figure A3: "How World Migration Reduces Poverty" [Source: Figure 1 in Williamson 2003] The vertical axis on the right is the wage scale in the poor country and the labor Lpoor is represented on the horizontal axis as increasing right to left. Thus the demand curve for labor Dpoor in the poor country is represented "backwards" sloping "downward" right to left. The total labor between the two countries is the length of the horizontal axis. At the initial pre-migration equilibrium, the wage is Wrich in the rich country and **Wp,or** in the poor country. When the gate between the two labor reservoirs is opened, then the equalizing flow of labor from poor to rich country is the indicated migration that equalizes the wage rate in both countries at W*. By comparing the equilibrium wages before and after the migration, we can compute the "Residents' Loss" for the original workers in the rich country, the "Movers' Gain" for the migrants, and the "Stayers' Gain" for the workers who stay in the poor country.

> 37 As we see from Bangalore, "close" need not be interpreted in physical terms when the imported inputs and exported products are informational.

52
Table 3: OLS estimates on the number of exporters adding new product-market relationships

|variables|**(1)**<br>nber. add|**(2)**<br>nber. add|**(3)**<br>nber. add|**(4)**<br>nber. add|**(5)**<br>nber. add|**(6)**<br>nber. add|
|---|---|---|---|---|---|---|
|tariff cut|0.009*|0.009**|0.007*||||
||(0.005)|(0.004)|(0.004)||||
|tariff||||-0.007**|-0.007**|-0.005**|
|||||(0.003)|(0.003)|(0.003)|
|nber of exporters, same product (t-1)||0.003***|0.002***||0.005***|0.004***|
|||(0.000)|(0.000)||(0.001)|(0.001)|
|nber of exporters, same prod. & mkt. (t-1)||0.111***|0.110***||0.097***|0.096***|
|||(0.007)|(0.007)||(0.006)|(0.006)|
|ez||-0.078|-0.093||0.117|0.094|
|||(0.076)|(0.077)||(0.141)|(0.142)|
|DR import weighted tariff cut|||0.038***|||0.076**|
||||(0.015)|||(0.033)|
|comparative advantage|||0.161***|||0.359***|
||||(0.024)|||(0.055)|
|comparative advantage_x_tariff cut|||0.019***||||
||||(0.006)||||
|comparative advantage_x_tariffs||||||-0.009***<br>(0.003)|
|Observations|37,486|37,486|37,435|13,629|13,629|13,621|
|R-squared|0.487|0.627|0.629|0.480|0.603|0.606|

Time, country and sectors fixed effects are used in all specifications, but are not reported. Errors are cluster by product. Robust standard errors in parentheses. Significance levels: *** p<0.01, ** p<0.05, * p<0.1 As for the information spillovers, both the number of exporters of a given product and the number of exporters of a given product in a given destination have a positive effect on the number of new trade relationships introduced by existing exporters. Also compared to the effect on new exporters, the effect of a cut on the import tariffs is positive for existing exporters. Exporter’s location seems not to have an effect on the number of new product-market relationships. Finally, there are more new trade relationships that are created in comparative advantage sectors. The interaction term between NRCA and the tariff cuts is statistically significant and positive, which indicates that the effect of a larger tariff cut is amplified when the product belongs to a comparative advantage sector. Similarly, the interaction term between NRCA and the tariff levels is also statistically significant and negative, also suggesting that the effect of a lower tariff is magnified for sectors where the DR has a comparative advantage.<sup>29</sup> 29 We also estimate equation 1 and 2 in their log-linear version (i.e. dependent variable in logs). The results remains very similar for tariffs and tariffs cuts, although not always significant. Another possibility would have been to employ a fixed effect poisson estimator. However, while controlling for the zeros, this model would have not allowed us to evaluate the variable ez (Cameron and Trivedi, 2005, chapter 23). The estimation is by maximum likelihood and the procedure is similar to the one in the fixed effect logit model and therefore time-invariant regressors can not be identified. This is possible in the case of the negative binomial model, but the latter is subject to stronger distributional assumptions. Despite this, we run the equations (1) and (2) using both types of estimators 18
input. Therefore a labor variable was constructed, using the total area cultivated and total number of labor-days spent on agricultural activities, with a distinction between family and hired labor. The land variable was hectares planted with maize, with some adjustment for land quality differences. These were both small within regions and between regions, which is the major reason for selecting these three surveys for the analysis. Seed, fertilizer and other variable inputs were reasonably well recorded. In cases where seed input was very low or zero, and still some output was reported, it was assumed that seed had been kept from previous years (subsequent inquiries showed this to indeed be the case). Most farms reported using hybrid seed, except for Lebowa where a majority of farms used traditional varieties. Output was maize production in metric tons.

Thus, the farmers in the sample are maize producers, some of whom follow the FSP recommendations and use fertilizer and hybrid seeds, while traditional farmers do not use modern inputs (although also belonging to the FSP). For both outputs and some inputs --land and family labor-- there is no reliable price information and much of the maize is not sold, but is consumed within the household.

Table 3 illustrates the differences in performance of the three regions in the sample. KaNgwane has the largest farms and the highest average output, while the farms in Venda produce far less output. This is partly because the farms in Venda are smaller, but also because yields are far lower. Lebowa has the highest average yields, followed by KaNgwane. An important point is that the production potential of these three areas --as defined by soil quality and rainfall in the 1990/91 production season-- was essentially the same.

**Table 3: Mean Output and Selected Inputs in Maize Production, 1991**

|Region|Output|Land|Yield|Seed/ha|Fert/ha|
|---|---|---|---|---|---|
||(kg)|ha|kglha|(kg)|(kg)|
|KaNgwane|4,317|3.93|986|18.91|161.1|
|Lebowa|2,360|2.13|1,399|9.12|143.6|
|Venda|320|1.15|273|11.08|112.3|

These basic statistics are useful, with yield being a partial productivity measure, considering the relationship of output to the input of land alone. On the other hand, measures of total factor productivity relate output to the aggregate of all inputs, giving a better indication of the overall efficiency of the system, and allowing the more advanced producers to be compared with the traditional farmers who use little in the way of modern inputs. Total factor productivity can be measured using the programming methodology discussed in the next sub-section.

17
encouraged in 368 4-digit manufacturing industries from 1990 to 2000. In the 2002 Catalogue, FDI was more encouraged in 112 4-digit industries compared to the 1997 Catalogue. We assign value 1 to all industries where FDI became more encouraged, and 0 to other industries. We choose to use the 2002 Catalogue instead of the 2004 Catalogue for FDI policy changes during 2000 to 2005 for two reasons: first, the deregulation in 2002 was much more significant than the 2002 to 2004 revision. Second, the 2004 Catalogue was only published at the end of 2004 and went into effect in 2005, the effect of the 2004 changes may not be reflected in our data yet.

(ii) We then generate a FDI deregulation variable ∆FDI_policyit for all 3-digit industries in 2000 and 2005 by calculating the share of 4-digit industries that became more encouraged relative to the previous period over all 4-digit industries in each 3-digit industry. There are 162 3-digit manufacturing industries in our sample, FDI became more encouraged with varying degrees in 154 industries from 1990-2000, and in 63 industries from 2000-2005.

(iii) Next, we predict changes in foreign firms’ employment share ∆FDIit at the 3-digit industry level using FDI deregulation variable ∆FDI_policyit based on the simple regression below: We use the predicted FDI employment share growth ∆FDIit to calculate city level FDI shock, FDI_shockcpt based on equation (1).

In our baseline regression, we evaluate the effects of a positive FDI shock on city level structural transformation and school enrollment based on the reduced form regression below: ∆Ycpt = γFDI_shockcpt + θFDI_empsharecpt−1 + θYcpt−1 + Xcpt−1′ β+ αpt + εcpt (3) ∆Ycpt is the change in dependent variable, which can be the change in sectoral employment share, sectoral GDP share, high school enrollment or college enrollment in city c, province p and time t. The key variable of interest is ∆FDI_shockpct , the city level FDI inflow shock. FDI_empsharecpt−1 is foreign firms’ manufacturing employment share in t-1. We also control the initial level of FDI FDI_empsharecpt−1, initial city characteristics Xpct−1′ and initial level of the dependent variable Ycpt−1. Adding Ycpt−1 could induce endogeneity, but higher Ycpt−1 strongly predicts lower ∆Ycpt. As our dataset only has three periods, using the Arellano and Bond’s GMM technique to alleviate the endogeneity from controlling lagged dependent variable is not feasible. However, Monte Carlo simulation shows that while the bias of the coefficient on Ycpt−1 can be significant, the bias for the coefficients on other independent variables tend to be very minor (Judson and Owen 1996). Given that the key coefficient of interest γ is not 10
- Vial, J., Dufresne, J.‐L., Bony, S., 2013. On the interpretation of inter‐model spread in CMIP5 climate sensitivity estimates. Climate Dynamics 41, 3339‐3362.

- von Lampe, M., Willenbockel, D., Ahammad, H., Blanc, E., Cai, Y., Calvin, K., Fujimori, S., Hasegawa, T., Havlík, P., Heyhoe, E., Kyle, P., Lotze‐Campen, H., Mason D.'Croz, D., Nelson, G.C., Sands, R.D., Schmitz, C., Tabeau, A., Valin, H., van der Mensbrugghe, D., van Meijl, H., 2014. Why do global long‐term scenarios for agriculture differ? An overview of the AgMIP Global Economic Model Intercomparison. Agricultural Economics 45, 3‐20.

- Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., Schewe, J., 2013. The Inter‐Sectoral Impact Model Intercomparison Project (ISI–MIP): Project framework. Proceedings of the National Academy of Sciences.

- Williams, J.R., 1995. The EPIC model. In: Singh, V.P. (Ed.). Water Resources Publications, pp. 909‐1000.

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49
9 Ono pattern that seems to emerge with respect to out-of-pocket costs is that in countries where there are large mining and enterprise interests, such as in Swaziland, Zaire, Zambia and Zimbabwe, the percentages of out-of-pocket expenditures (at least for fo=al care) seem to be lower than in countries where such activity does tlot exist. What makes all of these estimates difficult though is the paucity and unreliability of the existing data on expenditures upon traditional care. For example, the out-of-pocket share for Burkina Faso, an extremely poor country, is 25.4 percent and it is 29.9 percent in Guinea, which seem to be low relative to the 56.9 percent in the Cote d'Ivoire. However, these percentages may simply reflect the fact that the Cote d'Ivoire has been a relatively large iLporter of pharmaceuticals (see Vogel 1989a) for which people willingly pay cash at the many private pharmacios located around the country, but principally in the more affluent ci_ies. Because of a lower per capita economic- base and lower rate of economic growth, people in Burkina Faso and in Guinea do not have the pharmaceuticals available for purchase, and therefore turn to tradittonal healers; these people may be spending as high a share out-of-pocket, or even higher, than the people in the Cote d'Ivoire, but this will not be reflected in the kind of data in Table 3.

The share of private insurance in the total ranges from zero _ 16 of the 23 countries to 15.0 and 16.5 percent in Senegal and Zimbabwe. The estimate on private insurance expenditures in Zimbabwe is fairly firm, because the National Association of Medical Aid Societies (NAMAS, the approximate equivalent of the Blue Cross/Blue Shield Association in the U.S.) keeps good statistics. The large share of private insurance for Senegal (15 percent) may simply be an artifact of the large expatriate community (mostly French) living
AIDS, and nutrition, among others. These surveys are conducted every few years in a large number of countries, so that it is possible to track similar indicators over time. Out of the 30 countries in Africa that have at least one publicly available DHS survey conducted since 1990, 22 countries are covered by at least two DHS surveys data points between 1990 and 2005. A few other countries – Togo, Central African Republic, Comoros, Congo-Brazzaville, Gabon, and Lesotho only have one data point during the period. In countries such as the Democratic Republic of Congo (DRC), Lesotho, and Sudan where data is not available at all or only one data-point is available, Multi-indicator Cluster Surveys (MICS) has been used as a substitute (Annex 1). The resulting database is called AICD DHS/MICS Survey Database, 2007 and is referred to throughout this paper.

Based on a review of the DHS surveys implemented in Africa since 1990, analysis on the following sub-sectors is possible - water supply, sanitation, lighting and cooking fuels, rubbish disposal, and landline and cell phones. The DHS are undertaken in phases and there have been five phases since 1990. New questions are added in each phase and questions posed in each phase are relatively harmonized across surveys. Questions on water supply, sanitation, and cooking fuels are available since 1990 while some questions on electricity, rubbish disposal, and cell phones are more recent. Only five countries in the sample include questions on cell phone use, which makes it difficult to track improvements over time for cell phone use. The limited coverage of infrastructure modules in the DHS is not surprising as the objective of the surveys is to gather information on infrastructure that has direct relevance to health and nutrition – such as water, sanitation, and cooking fuel.

To conduct a distributional analysis of access, a household welfare measure to establish a correlation between access to infrastructure and well-being needs to be constructed. The DHS does not collect any income or expenditure information on households. Therefore, we construct instead an index of wealth or assets, which is known to be highly correlated to income and consumption in surveys where this information is available. The number of asset variables available for the construction of the asset index varies across countries and time periods. Thus, an asset index that would be completely consistent for all countries and years would lead to not using substantial data on wealth that is available for some countries, or some time periods only. This is why, following Diallo and Wodon (2007a), the asset index is constructed using principal components analysis based on the maximum amount of information available on assets for each 5
over time. I also attempted to use administrative data on enrollment and repetition levels, but discarded these as unreliable. Administrative data on enrollment in various grade levels is collected in Peru from the school headmiiasters themselves. This creates a serious incentive problem, as headmasters inflate enrollmenit figures to receive larger transfers. The last time that the Ministry of Education figLires could be checked, by comparing them with the figures from the 1993 population census, there were estimated to be almost one million "ghost" students in the statistics supplied by the lheadmasters themselves. Moreover, these (highly unreliable) figures are available for only three years-l 993, 1998, and 1999. The administrative enrollment and repetition figures for other years in the 1980's and 1990's are based on imputations which statisticians at the Ministry of EdLucation tlhemselves do using the LSMS surveys.

Construction of variables: **In** tlis paper, I consider the impact of the 1988-92 crisis on three outcomes: School attendanice, employment, and the mean years of completed schooling. I define attendance based oii two LLIuestiolns in the LSMS: All household members aged 6 or older are first asked whether they are "currently attending school or studying something", and those who answer "no" are then asked wlhether tlley "attended school or studied something in the last 12 months".<sup>19</sup> The variable I construLct for attendance takes the value of one for all individuals who answer "yes" to either one of these questions. Note that it is important to take into account _both_ these questions, as a large lractioni of households in the 1985/86 LSMS were surveyed during the summer vacation montlhs of December though March, so that attendance rates based on only the first question in this suLrvey would appear to be unreasonably low. I also construct a variable for the total number of years of sclhooling completed based on questions about age and attainment in the surveys.

The measure of employiienlt used in this paper is based on separate questions asked of all household members aged 6 or older. These questions first ask respondents whether they worked "as an employee for a business, corporation, government, a boss, or another individual", and second, whether they worked "fob- tlhemselves" ("a cuenta propia", or self employed), "as an unpaid family member, or on the (fainily) farm". The reference period for both questions is the last week; follow-up questionis wvith longer reference periods (12 months) are asked of those who

> '9 This measure of "attendance" does not take into account the number of times children are actually going to school-a shortcoming of the data. Using the term "enrollment" would also be somewhat inaccurate, however, as children may be formally enrolled in school, but not attend. The Spanish word used in the questions in the surveys _("asistir_ al colegio") is closer to attendance than it is to enrollment (the translation for enrollment would be "estar _inscri/o_ en el colegio"). See Paxson and Schady 1999 for a similar construction of variables.

19
and Seber, 1996, p.108 for a similar approach). Call this set _Ai_ . For most households, _Ai_ is simply a circle with radius _r_ centered at household _i_ . (For households near the boundary of the study region, the circle may be cut off a bit, but we ignore this issue for the moment.) Then the probability of selection of household _i_ is one minus the probability that no point in the area surrounding that household is ever selected, across all _c_ selections (based on Särndal et al 1992, p.50).

However, due to the stratification used in this study, the probabilities of selection of the interviewed households are more complex. Because the strata are quite co-mingled (see Figure 2) a circle drawn around a point selected in one stratum could extend outside of the boundaries of that stratum and include land in another stratum. For example, consider a household that lies in stratum 2 near the boundary of strata 1 and 2, as shown in the left panel of Figure 4. Household _x_ can be selected if points inside stratum 2 are selected but also if points inside stratum 1 are selected. In terms of the notation developed above, the selection region for household _x_ , _Ax_ , contains land in both stratum 1 and stratum 2: this region is indicated with shading in the right panel. See figure 4 in the appendix for an example.

This issue with stratum boundaries is not trivial. In our study, all land in the town stratum (stratum 1) is within five kilometers of the low probability stratum (stratum 5) and thus was also selectable from that stratum. In fact, more than 90 percent of all land area in strata 1, 2, 3, and 4 falls within the selectable range of points in stratum 5, due to the very large radius of stratum 5. Thus, even though each household itself lies in only one stratum, many households were selectable from more than one stratum. The probability of selection of a household _i_ , _πi_ , is equal to the probability that the points in _Ai_ that lie in stratum 1 were selected, plus the probability that points in _Ai_ that lie in stratum 2 were selected, and so on for the _H_ strata. Define _πi,h_ as the probability that household _i_ is selected from stratum _h_ . The overall probability of selection of household _i_ is then: where the terms after the first adjust for overlapping probabilities. However, because most households are selectable from only one or two strata, many of these terms are zero and the probabilities simplify a good deal.

Let _Sh_ be the land within stratum _h_ , and let Ai ⋃Sh be the land in the selection region of household _i_ that lies within stratum _h_ . Let |Sh| and |Ai ⋃Sh| be the areas of these two sets of land. Then the constituent terms in equation 1, the probability that household _i_ was selected from within stratum _h,_ are each:

that is, one minus the probability that none of the land in stratum _h_ that is within the selectable range of household _i_ is selected, across all _ch_ selections in stratum _h_ . The GIS tools allow us to calculate the areas 7