rafmacalaba/datause-encoder-data huggingface dataset:
technically inefficient by OX-/OX and scale inefficient by OX'/OX, giving a total level of inefficiency, relative to the CRTS frontier, of OX-/OX.

# _Results and Interpretation_

The objective of this section is to measure total efficiency and to separate the scale effects resulting from farms being an inappropriate size from the technical efficiency of farmers. The top row of results in Table 4 shows the mean efficiency levels for the three homelands, for the 1991 cropping year, decomposed into total, technical and scale efficiencies.

**Table** 4: Total, Technical and Scale Efficiency Levels - 1991 (Frontier=1.00)

||KaN|e||Lebow|a||Venda|||
|---|---|---|---|---|---|---|---|---|---|
|Item|Total|Tech|Scale|Total|Tech|Scale|Total|Tech|Scale|
|Mean Eff. by Region -|0.358|0.703|0.487|0.427|0.794|0.547|0.476|0.671|.698|
|#Efficient|6|35|6|4|31|5|6|12|7|
|%Efficient|7.52|43.8|7.52|6.3|48.4|7.8|20|40|23.3|
|MeanEff.Poaled<br>Sam ple|0.250<br>_<br>_<br>_|0637<br>_<br>_<br>_|0.394<br>_<br>_<br>_|0.372<br>_<br>_<br>_|0.828<br>_<br>_<br>_|0.485<br>_<br>_<br>_|0.069<br>_<br>_<br>_<br>_|0.740<br> <br>_<br>_<br>_<br>_|0.113<br>_<br>_<br>_|
|# Efficient|3|35|3|1|38|1|0|1|0|
|% Efficient|3.75|43.75|3.75|156|5938|156|0|367|0|

The results are in agreement with the previous analysis of the survey data. These efficiency levels of the farms are calculated with respect to the best-practice farms within each region, which means that inter-regional efficiencies are not compared. For KaNgwane, the mean level of total efficiency, relative to the best practice farms is 35.8%, which is lower than for Lebowa (42.7%) or Venda (47.6%). As the most advanced and commercialized region, KaNgwane has greater variation in input levels than the two other regions, as Table 3 suggested. This is particularly true of farm size and the small farms in KaNgwane are scale inefficient, relative to the larger units, giving the lowest average scale efficiency of 48.7%. Conversely, Venda, which has the least variation in farm size, has a mean scale efficiency of 69.8%, which more than compensates for the low level of technical efficiency (47.6%) in the total efficiency calculations.

The next two rows show the number and percentage of farms that are on the efficiency frontier. For all three regions, at least 40% of the farms are technically efficient, but only a little over 7% are large enough to be scale efficient in KaNgwane and Lebowa, whereas 23.3% are scale efficient in Venda. Farrns with a wide range of characteristics determine the frontiers. For KaNgwane, there are six efficient farms, which range in size from one to twenty hectares. Yields range from 0.19 to 4.6 tons per hectare, labor per 20
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for the poor. The decomposition indicates also a decline of households’ engagement in business activities, particularly among the poorest groups.

The improvements of households’ endowments were coupled with an increase of the returns to those endowments, but only for the poorest quintile group. Except for the first two deciles, returns appear to have declined over time. But this decline masks divergent trends across the different attributes. As observed from the table in Figure 10, the gains from household businesses, essentially nonfarm activity, increased quite significantly between 2007 and 2011/12 particularly for the three bottom deciles. Returns to land seem also to have improved over time, though less significantly for the poor. The returns to community infrastructure also improved, indicating a higher positive influence of access to local markets and roads on needy households’ living standards.

Large household size and number of children seem to be a continuing constraint on household well‐ being, although their negative impact appears to have diminished somewhat, as is apparent from the positive change in the returns to demographic structure.

However, the observed improvements in the returns to some household attributes have been offset by a significant decline of the returns to assets followed by a decline of returns to education, inducing a loss of returns for the moderate poor and better‐off households.

**Figure 10: Returns Effect and Endowments Effects over Time, HBS 2007–2011**

<!-- Start of picture text --> .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Quantiles<br>Confidence interval / endowment effect Confidence interval /returns effect<br>Endowment effect Returns effect<br>Extreme poor Poor Middle class Richest<br>Total 0.147*** 0.058*** 0.019* ‐0.076***<br>Endowments 0.075** 0.178*** 0.125*** 0.043<br>Demographic Structure ‐0.019*** ‐0.026*** ‐0.022*** ‐0.013***<br>.3<br>.2<br>.1<br>0<br>-.1<br>Difference in log real per capita total expenditures -.2<br><!-- End of picture text --> 42
**Appendix Table 3: Children in monetary poor households in 2022 at the $2.15 (extreme poverty) line in 2017 PPP by region**

|**Region**|**Extreme poor**<br>**(millions)**|**Headcount poverty**<br>**rate(%)**|**Share of extreme**<br>**poor(%)**|**Share of population**<br>**(%)**|
|---|---|---|---|---|
|**East Asia and Pacific**|11.72|2.83|3.51|19.67|
|**East Asia and Pacific without**<br>**China**|11.52|5.68|4.78|15.20|
|**South Asia**|62.03|9.66|18.60|30.50|
|**South Asia without India**|9.80|5.19|4.07|14.13|
|**Sub-Saharan Africa**|237.03|40.18|71.06|28.02|
|**Sub-Saharan Africa without**<br>**Nigeria**|196.89|40.68|81.70|36.27|
|**Latin America and Caribbean**|9.66|5.91|2.90|7.76|
|**Europe and Central Asia**|.82|.47|.24|8.17|
|**Middle East and North Africa**|12.30|9.95|3.69|5.87|
|**Total**|333.54|15.84|100.00|100.00|

**Appendix Table 4: Children in monetary poor households at the $2.15 (extreme poverty) line in 2017 PPP in 2013, 2017 and 2022 by region (%)**

|**Region**|**2013**|**2017**|**2020**|**2022**|
|---|---|---|---|---|
|**East Asia and Pacific**|7.2|3.6|3.2|2.83|
|**East Asia and Pacific without**<br>**China**|11.4|6.6|6.4|5.68|
|**South Asia**|22.3|15.0|13.8|9.66|
|**South Asia without India**|12.9|7.7|6.7|5.19|
|**Sub-Saharan Africa**|45.2|42.5|41.0|40.18|
|**Sub-Saharan Africa without**<br>**Nigeria**|46.5|43.4|41.5|40.68|
|**Latin America and Caribbean**|7.7|7.6|6.7|5.91|
|**Europe and Central Asia**|0.6|0.7|0.6|.47|
|**Middle East and North Africa**|3.4|8.8|9.9|9.95|
|**Total**|20.7|17.7|17.2|15.84|

**Appendix Table 5: Children in monetary poor households in 2022 at the $2.15 (extreme poverty) line in 2017 PPP – trends in shares by regions between 2013 and 2022 (%)**

|**Region**|**2013**|**2017**|**2020**|**2022**|
|---|---|---|---|---|
|**East Asia and Pacific**|7.2|4.1|3.7|3.5|
|**East Asia and Pacific without China**|8.9|5.5|5.3|4.8|
|**South Asia**|33.8|26.2|24.7|18.6|
|**South Asia without India**|9.2|5.8|5.1|4.1|
|**Sub-Saharan Africa**|54.8|63.1|65.0|71.1|
|**Sub-Saharan Africa without Nigeria**|75.1|78.9|79.7|81.7|
|**Latin America and Caribbean**|3.0|3.4|3.0|2.9|
|**Europe and Central Asia**|0.3|0.3|0.3|0.2|
|**Middle East and North Africa**|0.9|2.9|3.3|3.7|
|**Total**|100.0|100.0|100.0|100.0|

12
term. An amendment to the Constitution passed by the Congress in 2004 and approved by the Constitutional Court in 2005 allows a head of state to run for a consecutive term in office. In 1991, Colombia adopted a two-round system, where the runoff is avoided if in the first round the winner receives more than 50 percent of the valid votes.

Elections in Colombia -including the presidential election- are organized by the National Registry Office (RN–its acronym for _Registradur ́ıa Nacional_ in Spanish), the institution responsible for the civil registry and identification of people. The RN is in charge of maintaining an updated official voter registry before every election. The right to vote is granted to all Colombians aged 18 years and older. Before 1988, any citizen eligible to vote and interested in voting had to register in an electoral office. Currently, any citizen who requests and obtains a national identity card (known as _c ́edula_ ) from RN after turning 18 is automatically enrolled in the official electoral registry (Art. 49 of _Ley Estatutaria_ ).<sup>4</sup> Before 2003 these automatic registrations were assigned to the largest polling station in each municipality. Since 2003 people are automatically registered to vote near the office where they obtained their valid identification card.<sup>5</sup> In practice, however, this is not widely known. Dates of registration to vote are determined by the RN and usually span a two-week period in the months before elections. An individual can register in a new polling station each time registration opens, but the person can vote only in the last place where she was validly registered. For presidential elections, registered voters can cast their vote for a candidate in an election day in roughly 10,000 polling stations scattered in 1,119 municipalities across the country.<sup>6</sup> In the last decade, the political landscape has been dominated by the parties that implemented and expanded the FA program. Even though it was originally conceived and designed in 2001 under the government of the Colombian Conservative Party, FA became the flagship

> 4 `http://www.registraduria.gov.co/Reforma-politica-y-Elecciones-de.html`

> 5 `http://www.colombia.com/especiales/elecciones_2011/pedagogia-electoral/`

> 6 `http://www.registraduria.gov.co/Informacion/inscrip_ced_2011_preguntas.htm` , `http://www.colombia.com/especiales/elecciones_2011/pedagogia-electoral/`

6
activities with increasing returns at the plant level are pulled _disproportionately_ towards locations with good market access.

The analytic framework in this section highlights the importance of economic geography in influencing location and agglomeration at the firm level. Insights from NEG and regional science models suggest that own and inter-related industry concentrations, availability of reliable infrastructure to reduce transport costs and enhance market access, regional amenities and economic diversity are important for reducing costs, thereby influencing location and agglomeration of industry. In Section 1.2, we describe the economic geography variables that are used in this analysis. The econometric specification to evaluate the importance of these variables is described in Section 1.3. The empirical strategy is to estimate a cost function to see how costs (thereby profits) are affected by the economic geography of the region where the firm is located. If specific factors related to the local economic geography have cost reducing impacts, then firms are likely to choose regions with disproportionately higher levels of these factors.

# **1.2** Economic **Geoeraghv Variables**

_Own industry concentration:_ The co-location of firms in the same industry (localization economies) generates externalities that enhance productivity of all firms in that industry. These benefits include sharing of sector specific inputs, skilled labor, and knowledge, intra-industry linkages, and opportunities for efficient subcontracting. Firms that share specialized inputs and production technologies are more likely to cooperate in a variety of ways. In many industries, it is common for competitors in the market to launch joint projects for new product and process development. Further, a dis7
Table 2. Continued.

||||AGGREGA<br>|By Main Ca<br>TE SITC GR|tegories of E<br>OUPS<br>|xport Produc<br>|ts (percentage)<br>M|anufactures, of wh<br>|ich:<br>hi &|Ulld|
|---|---|---|---|---|---|---|---|---|---|---|
||Value|All|Agricultural||Ores &|Manu-||Other|Macnery|naocate|
|Exporting Country|<br>($ million)|<br>Foods|<br>Materials|Fuels|Metals|factures|Chemicals|Manufactures|<br>Transport|Trade|
|Sierra Leone|142.8|24.6|3.9|3.5|40.9|26.1<br>|_|26.0<br>|0.1<br>|1.0<br>|
|Somalia|81.0|90.4|6.8|0.2|1.1<br>|1.1<br>||0.5<br>|0.6<br>|0.4<br>|
|South Africa, Republic of|18,968.8|13.6|9.2|13.9|26.4|34.4|6.5|24.3<br>|3.6<br>|2.5<br>|
|Sudan|573.0|38.6|59.5|-|0.3|1.0<br>|-<br>|0.3<br>|0.7<br>|0.5<br>|
|Togo|267.9|23.0|21.5|-|44.7|9.1<br>|0.4<br>|8.0<br>|0.7<br>|1.8|
|Uganda|152.1|88.9|10.0|-|0.1<br>|1.1<br>|0.1<br>|0.3<br>|0.6<br>|-<br>|
|Tanzania, United Republic|284.9|49.2|22.4|_1.5_|14.5|11.8<br>|0.9<br>|8.4<br>|2.5<br>|0.5<br>|
|Zaire|999.3|8.7|4.5|12.7|55.2|16.6|0.1<br>|15.7<br>|0.8<br>|2.3<br>|
|Zambia|1,347.5|3.9|1.4|0.1|83.4|11.2<br>|0.1<br>|10.8<br>|0.3<br>|0.1<br>|
|Zimbabwe|1,467.6|44.1|7.3|0.7|15.9|30.9|1.7|25.6|3.6|1.1|
|MEMO ITEM|||||||||||
|All Sub-Saharan Africa|53,688.4|18.5|8.3|36.3|16.6<br>|18.8<br>|3.0<br>|13.8<br>|2.0<br>|1.5<br>|
|All Developing Countries|708,947.0|11.4|3.3|26.0|4.2|53.9|3.8|29.9|17.4|1.2|

Source: Data Compiled from United Nations COMTRADE records and<sup>UNCTAD, Handbook of International Trade and Development Statistics. 1992. In some cases the total trade values</sup> reported in this table may differ from those shown in Table 1. Where<sup>this occurs data on the direction of trade had to be taken from a different year than the above statistics on the</sup> composition of trade.

Note: In terms of the SITC (Revision 1) classification the products groups shown<sup>in this table are defmed as follows: All foods and Feeds (SITC 0+ 1 +22+4); Agricultural Raw Materials</sup> (SITC 2-22-27-28); Fuels (SITC 3); Ores, Minerals and Metals (SITC<sup>27+28+68); Manufactures (SITC 5+6+7+8-68); Chemicals (SITC 5); Other Manufactures (SITC 6-68); Machinery</sup> and Transport (SITC 7).
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# Gross-Output Specification (YKLM)

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villages; this comparative static will also be confirmed in the data.

# **3 Identification strategy**

This section first identifies the key identifying variation in quota quantity that will be exploited, and then reports the first stage.

## **3.1 Endogeneity of quota quantity**

As already noted, quota quantity is endogenous, correlated with many observable and unobservable local characteristics. My objective here is to identify a time-invariant, cross-sectional variable correlated with quota quantity and interact it with the price in order to generate an instrument for quota income. Given the widespread analysis in the literature of the systematic cross-sectional differences in quota policy implementation that correspond to variation in crop composition, variation in the propensity to cultivate rice will be the key source of variation this analysis will exploit.

To demonstrate the robustness of this relationship and increase precision in the primary two-stage least squares results, the analysis will utilize two different variables capturing the propensity to cultivate rice based on agronomic and climatic conditions. The first measure is constructed directly from climatic data (referred to thereafter as the “climatic index”); the second measure is drawn from an index generated by the Food and Agriculture Organization (referred to as the “FAO index”).

First, in China, as in other countries, the suitability of a region for rice cultivation is partially determined by temperature and precipitation. “Total temperature” is defined as the total accumulated temperature over a year for days with temperatures above 10 degrees Celsius; the agronomic literature suggests the total temperature must exceed 2000 degrees to cultivate rice in China (Shao et al., 2001). Similarly, “seasonal precipitation” is defined as mean precipitation observed between May and October, the key cultivation months for rice (Tang et al., 2010). For both variables, the mean total temperature or 11
In Pakistan today, distortions by direct marketing and taxing arrangements continue for some crops. Complicated regulations regarding the export of cotton are in place. Anyone can export cotton, but it is subject to a (daily-adjusted) Minimum Export Price (MEP), below which no exports can take place. In addition, a benchmark MEP is set, and a variable export duty is levied on the gap between the MEP and the benchmark price. The variable duty is imposed when international cotton prices rise (as they have recently) to keep cotton in Pakistan for the domestic textile industry. For wheat, the government maintains the farm price below the trade price through subsidized imports.

Economy-wide policies also have an important effect on agricultural incentives. Tariffs and quantitative restrictions that protect other sectors affect the equilibrium real exchange rate. In addition, protection to industry adversely affects the price of traded agricultural goods vis-avis other traded goods - an effect that operates in addition to the real exchange rate effect. The real exchange rate can be compared to the equilibrium real exchange rate in the absence of trade regime distortions<sup>2</sup> , and by this measure, the estimated influence of trade policy on the exchange rate in the past has been large (Table 1). However, this is a partial approach that only takes account of one of the many irLfluences on the real exchange rate (see Box 2).

Table 1: Annual Average Overvaluation Due to Trade Policy Distortions

||Overvaluation (%)|
|---|---|
|1972-75|25|
|1976-79|2g|
|1980-83|25|
|1984-87|21|

Source: Hamid, Nabi, and Nasim, Table 3.2 We assess the effect of policy on agricultural incentives by comparing domestic prices to parity prices (world prices, adjusted for transport costs to domestic locations). Indicators of protection and revenue transfers are constructed in the usual fashion. The nominal rate of protection is the percentage by which the producer price differs from the parity price. The transfer into or out of agriculture is the difference between the value-added in agriculture at actual prices and value-added at parity prices, adjusted for non-price transfers such as subsidies, investment, and taxation.

> **2** Dorosh and Valdes (1990) compare different methods of estimating the impact on the exchange rate of trade policy. Another approach to estimating the discrimination against agriculture is adopted by Purseli and Gulati (1993) for the Indian case. This involves a direct comparison of levels of protection in agriculture and industry. We use this approach later in this section.

3
6 $1.00/person/day), ed is the share of the population over age 25 that has completed secondary school, pop is population density (people per square mile), crd is country credit rating and REG is a set of 5 dummy variables for various regions of the developing world. The regional dummy variables (East Asia omitted) control for fixed effects by geographic area of the world.

Equation (2) can be estimated in two different ways: (1) the dependent variable can be measured using per capita remittance data from 2003; and (2) the dependent variable can be measured using pooled data, that is, per capita remittance data from both years, 1998 and 2003. Since the first method of estimating equation (2) has only a limited number of observations (n is between 44 and 55), the second method is useful for checking the robustness of results.

The results of equation (2) are shown in Table 3. Columns (1) to (3) of the table show results using the 2003 remittance data for different combinations of economic variables, and columns (4) to (6) show results using the pooled remittance data for both 1998 and 2003. Since all of the variables are expressed in log terms, the results can be interpreted as elasticities.

Four results in Table 3 are noteworthy. The first result concerns the distance variable. In all versions except one of the model the coefficient for distance is negatively and significantly related to the receipt of per capita official international remittances. On average, a 10 percent increase in distance to a remittance-sending region will reduce the level of per capita international remittances received by a developing country by between 6.4 and 6.8 percent.

This result, which parallels those of other studies concerning the relationship between distance and international migration,<sup>11</sup> seems to accord with reality. Developing countries which are located close to major remittance-sending regions – like Latin American countries located close to the United States, or South Asian countries located close to the Arab Gulf -- are also those countries which receive the largest amount of official international remittances. All other things being constant, countries which are located close to major remittance-sending regions receive more remittances because the costs of migration for their citizens are lower.