rafmacalaba/datause-encoder-data huggingface dataset:
yh `(` the consumption measure of interest) and idiosyncratic error term εh. The βi captures the difference each module makes on the consumption measurement relative to the diary arm `.` Table 4 shows the main results. The initial outcome variable we focus on is annual levels of per capita food away from home spending (column 1 and shown visually in Figure 2). Next, in order to see whether results are driven by differences in the incidence of FAFH, we estimate on the probability that the household reported having zero food away from home spending (column 2). Lastly, we present the difference in the value of FAFH conditioned on reporting positive FAFH consumption (column 3). All regression coefficients should be interpreted as deviations away from our benchmark individual diary measure (T2). Under this framework, a positive (negative) coefficient implies that the food measurement in an arm is likely over (under) estimated.

_Table 4 Regression of annual consumption on treatment status. The dummy variable for treatment by T2, the diary arm, is the omitted category. Column (2) provides marginal effects reporting no food away from home on treatment groups, and Column (3) shows estimated food away from home spending among households that report positive FAFH. The mean reporting 0 FAFH in the diary (omitted) arm is 8.678%. All values are in 1000 VND._

||(1)<br>FAFH|(2)<br>Reported 0 FAFH|(3)<br>FAFH<br>(Conditional)|
|---|---|---|---|
|T1: 7-day one-line recall|-2,818***|0.19***|-1,649***|
||(548)|(0.03)|(617)|
|T3: 7-day individual recall|-1,847***|0.07**|-1,543***|
||(549)|(0.03)|(594)|
|T4: 7-day HH bounding/salience|-883|0.03|-713|
||(549)|(0.03)|(590)|
|Constant|8,527***||9,315***|
||(389)||(415)|
|Observations|1,904|1,904|1,622|
|R-squared|0.015|0.038|0.006|
|T1=T3|0.08|0.00|0.86|
|T1=T4|0.00|0.00|0.13|
|T3=T4|0.08|0.18|0.17|

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

17
_Rauch_ -<sup>_I_</sup> _i_ is the Rauch (1999) index of product differentiation taking three integer values.

> <sup>_I_</sup> _iRauch_ =1, 2 and 3 for homogeneous goods, reference-priced sectors, and differentiated goods, respectively;

-<sup>_t_</sup> _iMFN_ , _US_ is the US MFN tariff for 1993;

_US Mex_ - _RCAIi_  _RCAIi_  is the revealed comparative advantage (RCA) index for the US (Mexico) for good i averaged out over 1991-1993, the three-year period preceding the implementation of NAFTA<sup>13</sup> ;

- _X iUS_ , _RoW_  _X iUS_ , _World_  _X iUS_ , _Mex_  and _X iMex_ , _RoW_  _X iMex World_ ,  _X iMex US_ ,  are US and Mexico exports to the RoW (i.e. all countries other than US or Mexico) averaged out over 1991-1993;

- _D ji_ is a sectoral dummy that is equal to 1 if good i belongs to section j, and zero otherwise (for j=1...20).

Trade and tariff data was compiled from COMTRADE. I briefly describe the variables included in each vector.

# **_i) Trade Deflection (_** _TD_ **_)_**

What creates an opportunity for trade deflection is, above all, the fact that one of the FTA partners has a lower tariff to third parties. Therefore,  _iUS_ , _Mex_  _Max_  0, _tiMFN_ , _US_  _tiMFN_ , _Mex_  is a primary indicator of the propensity to trade deflection towards the US that may happen when MFN tariffs are higher in the US than in Mexico. A positive and significant coefficient would be evidence that more stringent RoO were negotiated in tariff lines where there were greater incentives for trade deflection towards the US. Since the MFN tariffs were greater in Mexico than in the US for the majority of lines, it is natural to incorporate<sup></sup> _iMex US_ ,  _Max_  0, _ti_<sup>_MFN_</sup> ,<sup>_Mex_</sup>  _tiMFN_ , _US_  in the model, which is the variable that symmetrically controls for

> country j to country (or group of countries) k.

12
integration of agricultural markets (e.g. Takayamna and Judge 1971, Ravallion 1986, Dercon 1995, Timmer 1986, Baulch 1997, Fafchamps 1996). But this work remains fragmentary in the sense that it does not examine, in a comprehensive manner, how rural areas interact with each other and with cities as a function of their geographical proximity to cities.

The purpose of this paper is to begin filling this lacuna by examining how Nepalese households fit into the local economy as a function of their proximity to urban centers. Nepal is a particularly suitable place to study spatial specialization given the extreme diversity of the country in terms of accessibility. At one end of the spectrum, Terai villages are a few hours away by truck from many towns and cities, while Nepalese villages tucked in the Himalaya are among the most difficult to access anywhere on earth. As already argued by Jacoby (2000), Nepal is the perfect place to examine the effect of geographical isolation on economic activity.

Using household data from the Living Standard Measurement Survey (LSMS) of 1995/6, we investigate geographical patterns of agricultural production, agricultural sales and purchases, and non-farm work. To control for differences in road quality, travel time is used as a measure of distance instead of mileage. City population and travel time are instrumented to control for possible endogeneity.

Our contribution is threefold. First, by using several different measures of economic activity and market participation, we provide a detailed and compresensive picture of the spatial division of labor in a poor country. Second, thanks to non-parametric methods, we obtain precise estimates of the distance at which various activities dominate. As a result, we can characterize the various forms that market specialization takes depending on location. Third, we show that spatial effects are large and that city size matters.

Regarding spatial specialization, our results largely confirm the von Thilnen hypothesis of concentric circles around cities that vary according to transport costs (e.g. Henderson 1988, 3
Table 4: Definitions and Summary Statistics for Additional Variables Used in Table 5, Grade 5 Philippine Science Achievement

|Variable Name|Definition|Mean|S.D.|
|---|---|---|---|
|INSERV|Total days of inservice on science<br>teaching over past 12 months|3.23|1.51|
|SCTCHASS|Member of science teachers association<br>(1-yes; 2-no)|1.38|0.49|
|READGEN|Frequency of reading academic journals<br>or periodicals related to teaching in<br>general (1-weekly; 2-occasionally;<br>3-rarely or never)|1.57|0.55|
|READSCI|Frequency of reading journals or<br>periodicals on science (1-weekly;<br>2-occasinnally;<br>3-rarely or never)|1.62|0.58|

35
_25_ _Table 13. /Coefficients for the Variable "Per Capita Level of Total Federal Transfers ", in thousands of Rbl. Independent variables: shares of corresponding expenditures items in total expenditures. Controlledfor the level ofpre-transfer budget revenues. Significant results only._

||1992|1993|1994|1995|
|---|---|---|---|---|
|National Economy (total)|-3.80|-3.75|--|--|
|Housing|n.a.|--|--|--|
|Agriculture|n.a.|-1.32|-0.781|-2.00|
|Transportation|n.a.|-1.38|-0.440|-1.77|
|Other (93/94)/Industry (95)|n.a.|-1.40|--|--|
|Food|--|--|--|n.a.|
|Total Investments|--|-1.82|-1.000|-2.96|
|Budget Loans|--|--|0.712|5.84|

The negative sign for the corresponding coefficients should be interpreted to mean that the share of transfers spent on subsidies is smaller than the share of own regional revenues spent on these purposes. Federal transfers are partially spent on subsidization and therefore they expand the overall amount of regional expenditures on subsidies (positive regression coefficients in Table 11). However, when recipients allocate transfers between various expenditure categories, they spend relatively less on subsidies than when they spend their own budget money (pre-transfer revenues).

At the same time, the quantitative effect of such a negative correlation is relatively weak. In 1993, federal transfers of Rbl 1000 per capita would reduce the share on total regional expenditures spent on national economy by less than 4%. The actual average federal transfer in 1993 amounted to Rbl 220 per capita, i.e. it could decrease the share by less than 1% in a situation where the average region spent 44% of its expenditures on national economy.

# VI. **Other Determinants of Regional Subsidy Spending**

This section provides further analysis of subsidy determinants using multivariate regressions. In what follows, we expand the two-factor regressions reflected in Tables 9-11 to explore the impact of various demand and supply factors listed in section III. Tables 14-21 show results only for significant variables.

# VI. 1. National Economy

Multivariate regressions for each year as described in Table 14 below tell a story very similar to those in Table 9. Few demand factors prove to be significant in the different years under analysis; in all years by far the most significant explanatory variables remain pre-transfer regional revenue and federal transfer revenue. Moreover, demand, or need, factors (such as per
# **_c. Economic migrants_**

Data on skill mobility between countries and regions are difficult to obtain for SSA, as in other parts of the world. However, this can be proxied using data on international economic migrants although such data do not dissociate skilled from unskilled economic migrants. An economic migrant can be defined as a person who leaves his or her place of origin to seek better livelihood in another place (for a short or long term). In other words, economic migrants search for better job opportunities in places where they can earn better income compared to their place of origin. These migrants include both skilled and unskilled workers who move from one place to another purely for economic reasons. Since data on international economic migrants are available for most countries in the world, we use net migration-to-population ratio as a proxy for labor mobility. This is defined as: A positive ratio for a given country means that more people (i.e. workers) are coming than leaving the country for work. In addition, a positive ratio means that more people join the domestic workforce since economic migrants add to the workforce of host countries. It is however important to note that economic migrants are different from refugees or asylum seekers and their worker statuses depend on host countries’ laws and openness to foreign workers. In this regard, economic migrants can work either legally or illegally in formal or informal sectors of host countries. Consequently, their skills can be incorporated in GVC activities. Thus, we expect the net migration ratio to have a positive and significant impact on countries’ participation in GVCs.

# **3.2 Methodology: Model specifications and estimation technique**

We use a gravity model to estimate the effects of skills and migration on the participation of African countries in GVCs. Since the pioneering work by Tinbergen (1962), gravity models have become an important tool used in explaining trade flows. For example, McCallum (1995) used gravity-type equations to understand the impact of borders on trade patterns and to show the importance of gravity models as a framework to estimate the effect of trade integration policies.

14
# **S14 What land cover type does urban replace?**

The Globcover data shows 6,956 300-meter pixels that transitioned to Urban at some point from 1992 to 2018. Table S17 shows the frequency of land cover classes that transitioned to urban areas. Cropland is the most frequent land cover class that transitioned to urban; considering all cropland classes (which includes (1) cropland, rainfed, (2) mosaic cropland ( _>_ 50%), (3) Cropland, rainfed - Herbaceous cover, and (4) Cropland, irrigated or post-flooding), cropland makes up 52.6% of landcover that transitioned to urban during the study period.

**Table S17:** Summary Statistics of Dependent Variables

|Land Cover Class|N|Proportion|
|---|---|---|
|Cropland, rainfed|1825|0.262|
|Mosaic cropland (_>_50%); natural vegetation (tree, shrub, herbaceous cover) (_<_50%)|1540|0.221|
|Mosaic natural vegetation (_>_50%); cropland (_<_50%)|1429|0.205|
|Shrubland|731|0.105|
|Grassland|584|0.084|
|Cropland, rainfed - Herbaceous cover|151|0.022|
|Cropland, irrigated or post-fooding|148|0.021|
|Mosaic herbaceous cover (_>_50%); tree and shrub (_<_50%)|124|0.018|
|Mosaic tree and shrub (_>_50%); herbaceous cover (_<_50%)|123|0.018|
|Tree cover, broadleaved, deciduous, open (15-40%)|111|0.016|
|Bare areas|44|0.006|
|Consolidated bare areas|41|0.006|
|Tree cover, fooded, saline water|33|0.005|
|Tree cover, broadleaved, evergreen, closed to open (_>_15%)|19|0.003|
|Sparse vegetation (tree, shrub, herbaceous cover) (_<_15%)|19|0.003|
|Tree cover, broadleaved, deciduous, closed to open (_>_15%)|10|0.001|
|Sparse shrub (_<_15%)|9|0.001|
|Water bodies|8|0.001|
|Deciduous shrubland|4|0.001|
|Sparse herbaceous cover (_<_15%)|2|0|
|Shrub or herbaceous cover, fooded - fresh, saline or brakish water|1|0|
|TOTAL|6956|1|

S29
One of the major limitations of approaches using the co-movement of prices is the lack of a theoretical foundation. A small body of empirical literature rejects the time-series approach on the abovementioned grounds and uses the Enke-Samuelson-Takayama-Judge spatial price equilibrium conditions as its starting point<sup>1</sup> . A seminal contribution is Baulch (1997a), which builds on earlier work from Sexton et al. (1991) and Spiller and Wood (1986). Following the competitive spatial price equilibrium conditions, Baulch (1997a) identifies three regimes in his PBM: in regime I, at the parity bounds, the price difference between two locations is exactly j k kj equal to the transaction cost (pt −pt = tct ); in regime II, inside the parity bounds, the price j k kj difference between two locations is lower than the transaction cost (pt −pt < tct ) and in regime III, outside the parity bounds, the price difference exceeds the transaction cost (ptj − k kj _kj_ pt > tct ), where _tc_ represents the transaction cost of trading goods from _k_ to _j_ . Regimes I and II are consistent with competitive spatial price equilibrium and market integration. In the absence of adequate transaction cost data, Baulch (1997a) uses the observed transaction costs of a single cross-sectional observation and extrapolates them to all periods and all locations. These regimes are characterized by price differences less the extrapolated transaction costs. A switching regression technique is used to estimate the probabilities of the three regimes under the assumption of normally distributed errors within the parity bounds and half normally distributed errors above and below the parity bounds. The PBM is a flexible tool to diagnose market integration that overcomes the limitations of price co-movement techniques.<sup>2</sup> Extensions have focused on relaxing the assumption on the distribution of errors (Barrett and Li, 2002), complementing the analysis with trade flow data (Barrett 2001; Barrett and Li 2002), and shifts in probabilities (Park et al. 2002; Negassa and Myers 2007). In this paper, we propose a modification of Baulch’s original contribution (Baulch 1997a) and apply the proposed approach to the Malawi maize market.

5
Table A.5: Sample sizes for different rounds and instruments

|Variable|2012<br>Baseline|2013<br>Midline|2014<br>Endline|
|---|---|---|---|
|No of Schools (principal data)|416|403|390|
|No of Schools (teacher data)|406|387|360|
|No of Schools (student data)|408|417|391|
|No of Schools (inspection data)||387|388|
|No of Teachers (teacher data)|1,165|1,068|1,120|
|No of Teachers (inspection data)||1,057|1,179|
|No of Grade 2 Students|19,621|12,327|—|
|No of Grade 3 Students|18,663|22,053|21,368|
|No of Grade 4 Students|—|14,139|14,765|

Table A.6: Teacher characteristics

|Variable|N|(1)<br>Male<br>Mean/SE|N|(2)<br>Female<br>Mean/SE|N|(3)<br>Total<br>Mean/SE|T-test<br>Diference<br>(1)-(2)|
|---|---|---|---|---|---|---|---|
|Class size|377|43.363<br>(0.900)|755|52.375<br>(0.558)|1132|49.374<br>(0.494)|-9.011***|
|Permanent|386|0.902<br>(0.015)|784|0.955<br>(0.007)|1170|0.938<br>(0.007)|-0.054***|
|Insufcient— recognition|385|0.249<br>(0.022)|784|0.281<br>(0.016)|1169|0.270<br>(0.013)|-0.031|
|Insufcient— salary|385|0.675<br>(0.024)|784|0.759<br>(0.015)|1169|0.731<br>(0.013)|-0.084***|
|Monthly salary (USD)|340|125.268<br>(1.254)|743|115.562<br>(0.598)|1083|118.610<br>(0.585)|9.706***|
|Choose teaching|381|0.635<br>(0.025)|775|0.732<br>(0.016)|1156|0.700<br>(0.013)|-0.096***|

_Notes_ : The value displayed for t-tests are the differences in the means between male and female teachers, using baseline data. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

7
cohabiting individuals because others, by definition, have only non marital sex if they are sexually active. Married men in cities are more likely to have extra marital relationships, while the urban status does not influence the dependent variable for married women. The probability of having sexual intercourse outside marriage increases for females who have been in successive marriages, suggesting difficulty committing to one partner for women married more then once. This could be a potential source of endogeneity; individuals that have extra-marital sexual intercourse could have a higher probability of divorce and therefore a greater likelihood of being in more then one marriage. Secondary education is negatively correlated with the likelihood of having extra-marital sex for females, but there is no evidence on the impact of education for males. Compared to Protestants, females Catholics appear more likely to have extra-marital affairs in Lesotho.

# **4.4 Abstinence during the last 12 months**

Abstinence is another strategy to prevent AIDS, in addition to fidelity and condom use. The LDHS investigates if the interviewed did not have sexual intercourse in the last year.

# [Insert table 7]

In table 7, we consider the probability of not having had sex in the last 12 months as the dependent variable. As expected, the probability of not having had sex decreases for currently married individuals. Abstinence is also negatively associated with being formerly married, both for males and females. Being a widow increases the likelihood of not having had sex in the last 12 months, while having been in more than one marriage decreases abstinence for women. Women with some primary education are more likely to have abstained during the last year. The likelihood of not having had sex decreases with wealth. Finally, being a Catholic man or a woman with no religion tends to decrease the probability of abstinence, compared with being Protestant.

15
|Four Digit ISIC<br>Description|rsic<br>Code||||||
|---|---|---|---|---|---|---|
|PHOTOGRAPHIC AND OPTICAL<br>GOODS<br><br>|||||||
|WATCHESANDCLOCKS<br>|rr|es|es|e|s||
|<br><br>JEWELERY AND RELATED<br>ARTICLES<br><br>|||<br>|<br>|||
|MUSICAL,INSTRUMENTS<br>||soon|wo||soo||ta||ao||se|az||
|<br><br>SPORTING AND ATHLETIC<br>GOODS|||||||
|MANUFACTURING INDUSTRIES,<br>N.E,C.|||||408|7|

-

° 57
# **II.2 Linking generations and coresidence**

The data collection is organized at the household level, so it is possible to link only those individuals who live in the same household at the time of the interview. The data set includes a variable that by means of 62 different values details the relationship between the individual and the head of the household. Based on this variable, I classify individuals into five different generations where the head corresponds to generation zero (see Table 1), and based on the generation number I use individuals who live with at least one member of the immediately previous generation, where these old generation members are considered as “pseudo-parents”.<sup>5</sup> Table 1 provides the details of the assignment.

**Table 1:** Relationship to household head and identification of different generations

|**Relationship to the head**|**Generation**|**Relationship to the head**|**Generation**|
|---|---|---|---|
|Grandparent|-2|Sibling of sibling-in-law|0|
|Great grandparent|-2|Ex-spouse|0|
|Parent/parent-in-law|-1|Child|1|
|Parent|-1|Biological child|1|
|Stepparent|-1|Adopted child|1|
|Parent-in-law|-1|Stepchild|1|
|Aunt/uncle|-1|Child-in-law|1|
|Head|0|Spouse/partner of child|1|
|Spouse/partner|0|Unmarried partner of child|1|
|Spouse|0|Nephew/niece|1|
|Unmarried partner|0|Foster child|1|
|Same-sex spouse/partner|0|Tutored/foster child|1|
|Sibling/sibling-in-law|0|Tutored child|1|
|Sibling|0|Grandchild|2|
|Stepsibling|0|Grandchild or great grandchild|2|
|Sibling-in-law|0|Great grandchild|2|
|Cousin|0|Great-great grandchild|2|

Notes: Categories not classified are: Other relative, not elsewhere classified; other relative with different family name; non-relative; friend; housemate/roommate; visitor; godparent; godchild; domestic employee; relative of employee; spouse of servant; child of servant; other relative of servant; roomer/boarder/lodger/foster child; boarder; boarder or guest; lodger; employee, boarder or guest; other specified non-relative; agregado; temporary resident, guest; group quarters; group quarters, non-inmates; institutional inmates; non-relative, n.e.c.; other relative or non-relative; unknown.

Figure 1 shows the unweighted average rate of coresidence by age in the sample pooling all

> 5A similar approach is followed by Alesina et al. (2021) with Census data from Africa.

7
22 chemical firms referred to earlier.' Focusing on the profitability differential, p.-p,, (a) automatically eliminates any bias in favor of exports resulting from inflationary gains on inventories as these would apply equally to domestic sales, and (b) similarly, while both metallurgy and chemicals were beneficiaries of implicit CMEA subsidies on energy and raw materials, this would not necessarily create a bias in favor of exports over domestic sales.

**Table S. Tzends in Export Profitability and Volumes.**

||1989:11F|<br>1989:1V|1 1990:1|**1990:11**|**1990:111**|**11990:V**|**1991:1**|
|---|---|---|---|---|---|---|---|
|||Export|Profitability|-Domestic<br>Sale|s Profitability (|%**pts)**||
|Metallurgy|6.8|11.9|-9.5|.14.6|-10.3<br>_|<br>**.8.0**|_<br>23.9|
|Cbemical|31.2|34.7|13.4|5.0|3.2|0.2|-37.6|
||||Ratio of Ex|port to Dome|stic Sales(%)|||
|Metallurgy|15.9|19.6|17.0|26.2|29.8|22.8|13.6|
|Chemical|26 .8|**35.7**|48.8|76.8|64.8|**67 .5**|**_45.5_**|

The numbers in Table **_5_** are remarkable and confirm the suspicions generated by the real exchange rate graph: (a) export sales were more profitable in late 1989 than at any other point over the sample; (b) the profit difference with domestic sales fell steadily (chemicals) and erratically (metallurgy), with an abrupt drop in 1991:1, suggesting that while domestic prices could be adjusted to compensate for the dollarization of CMEA inputs, export prices were constrained by the nominal exchange rate anchor; (c) export sales as a ratio of domestic sales took off in 1990, partly because domestic sales were falling, but were distinctly weakening by late 1990.

The coincidence of rising export volumes with declining profitability is clear from Table **_5._** Regression results based on the 22 firms in this sub-sample (10 metallurgy, 12 chemicals) over the 7 quarters 1989:M-1991:1, are reported in Box 3.

**Box 3: Export Boom.** 1989:111 - 1991:1

**The regression was based on the following specification:**

**X/D** = **fp.-p,, DD),**

**where the left hand side variable is the ratio of export o domestic saes and the explanatory variables *rc the profit differential and an indicator of economy-wide sector domestic demand (DD, or domestic sales dchlated by the sectoral PPI). The foUowing results were obtained (t-statistics** in **pareatbesi***

**XID** = **0.1873 (p.-pJ** - **_0.00052_ DD (1.18) (-2.01)**

**Rbar sqd. =0.52, S.E. of regression-0.61, Autocorr. of error0.34.**

**9Firm- reported the export P&L account sparately, which enabled the calculation of export profitability. However, variable cost were not sepately identified. Since export cost were given sepamtely, the rtio of export to domesti sales by volume was approximated by the ratio of the repective cosu, eliminating the need for firm-level price deflatorc.**