rafmacalaba/datause-encoder-data huggingface dataset:
promises better quality of service, lower transactions costs and expanded trade across the region.

Figure 8: Internet Users per 10,000 Inhabitants in 2003 <!-- Start of picture text --> 1800<br>1553 Bangladesh<br>1600<br>Bhutan<br>1400<br>India<br>1200 1107 Maldives<br>1000 Nepal<br>800 Pakistan<br>600 534 Sri Lanka<br>400 169 South Asia average<br>200 212175 103106 East Asia average<br>18<br>34 Global Average<br>0<br><!-- End of picture text --> Source: Authors’ calculation based on International Telecommunication Union (2004) Data What is lacking in South Asia compared to East Asia, in part is derived from limited cooperative action to promote progress. For example, the Asia Pacific Economic Cooperation (APEC) has targeted achieving paperless trading among all member countries by 2010. This is being advanced by computerizing custom procedures through United Nations Rules For Electronic Data Interchange for Administration Commerce and Transport (UN/EDIFACT) and reducing the number of documents required for sea, air and land transport. Under this initiative, each member country incorporated strategies to achieve paperless trading in its Individual Action Plans in APEC. For instance, Vietnam established a development plan to employ information technology for customs administration during the period of 2001-2005, and “has been marking a new progress in the process of modernization, simplification and harmonization of customs procedures” (APEC, 2003). The overall savings from this paperless trading initiative is expected to be “between 1.5 to 15 per cent of the landed cost of an imported item.” (Australian Department of Foreign Affairs and Trade and Chinese Ministry of Foreign Trade and Economic Cooperation, 2001). With strong regional initiatives like those in East Asia, South Asia could accelerate development of information technology infrastructure and concurrently lower transactions costs. Other examples of regional cooperation, such as those supported by the World Bank in the Trade and Transport Facilitation in Southeast Europe (TTFSE) project on customs and border reform could also be considered as one model of reform for South Asia.<sup>24</sup>

> 24 The Trade and Transport Facilitation in Southeast Europe Program initiated in 2000 by the national governments in Southeast Europe, the World Bank, and the United States in collaboration with the European Union. The project is aimed at reducing transport costs, eliminating corruption, and providing European Union-compatible customs standards, and some progress have been made.  For details, see <u>http://www.seerecon.org/ttfse/.</u>

18
# **FIGURE 2 The Relationship Between Inequality and the 90-10 Ratio, 1987-88**

<!-- Start of picture text --> 1 2 3 4 5<br>Village 90-10 Ratio in 1987-88<br>Village Gini, 1987-88 Village Mean Log Deviation, 1987-88 Fitted values Fitted values<br>.4<br>.3<br>.2<br>Village Inequality in 1987-88<br>.1<br>0<br><!-- End of picture text --> _Notes:_

- 1) Each scatter plot is a graph of village inequality (for the Mean Log Deviation, or the Gini Coefficient) versus the village 90-10 ratio in 1987-88. The relationship represented in these plots underlies the “reduced form” or “first stage” for the correction of potential measurement error of inequality, using the 90-10 ratio as an instrument.

- 2) For the Mean Log Deviation, the _t_ -value of the coefficient on the 90-10 ratio is 24.68 ( _F_ = 609.26), while similar _t_ -value for the Gini cofficient regression on the 90-10 ratio is 20.38 ( _F_ = 415.43). As an aside, the _t_ -value of a regression of the Mean Log Deviation on the Gini Coefficient is 46.70 ( _F_ = 2180.66).
**<u>Table 3. Probit results of village participation in ICDS</u>**

|||_No sta_|_te dummie_|_s_||_State_|_dummies_||
|---|---|---|---|---|---|---|---|---|
||_1_|_992_|_1_|_998_|_1_|_992_|_1_|_998_|
||coef.|std. er|r_._<br>coef.|std. err.|<br>coef.|std. err|.<br>coef.|<br>std. err_._|
|**Demographic characteristics**|**of the vil**|**lage**|||||||
|village population (log)|0.179***|_0.035_|0.147***|_0.030_|0.153***|_0.042_|0.187***|_0.035_|
|share of children (age<16)|0.804|_0.916_|-1.969**|_0.869_|1.522|_0.997_|-0.991|_0.974_|
|share of girls (age<5)|0.370|_1.562_|1.225|_0.934_|1.132|_1.653_|1.004|_1.023_|
|share of women (age 16+)|2.688*|_1.388_|-2.092*|_1.152_|3.544**|_1.528_|-1.387|_1.256_|
|share of elderly (age 60+)|-2.144*|_1.210_|0.332|_0.560_|-1.185|_1.392_|0.179|_0.633_|
|% of mothers – primary<br>education|0.133|_0.200_|0.078|_0.163_|-0.075|_0.234_|0.145|_0.187_|
|% of mothers - secondary+<br>education|0.365*|_0.195_|0.177|_0.140_|0.257|_0.226_|0.250|_0.163_|
|**Wealth and infrastructure of**|**the villa**|**ge**|||||||
|Average household wealth<br>(assets) index|-0.001|_0.003_|0.005*|_0.003_|-0.009**|_0.004_|-0.007**|_0.003_|
|Distance to the Tehsil/district<br>headquarters|0.002|_0.002_|0.003***|_0.001_|0.001|_0.002_|0.002|_0.001_|
|<br>Distance to the nearest railway<br>station|<br>0.002***|_0.001_|0.003***|_0.001_|0.001|_0.001_|0.002*|_0.001_|
|Village is connected to all-<br>weather road|-0.019|_0.075_|||-0.101|_0.081_|||
|Distance to all-weather road|||-0.005|_0.003_|||-0.004|_0.003_|
|Village is electrified|0.604***|_0.100_|0.489***|_0.084_|0.427***|_0.112_|0.263***|_0.095_|
|No drainage|-0.182**|_0.072_|0.055|_0.063_|-0.095|_0.081_|0.003|_0.071_|
|Primary Health Center (PHC)|0.216*|_0.131_|0.050|_0.099_|0.169|_0.142_|0.087|_0.110_|
|in the village|||||||||
|<br>Natural calamity last 2 years|-0.055|_0.071_|||-0.074|_0.077_|||
|**Community activities and de**<br>**programs in the village**|**velopmen**|**t**|||||||
|Cooperative society|0.228***|_0.078_|0.155*|_0.081_|0.242***|_0.085_|0.127|_0.089_|
|Fair price shop|0.201***|_0.077_|0.238***|_0.065_|0.163*|_0.091_|0.270***|_0.073_|
|Mahila Mandal|0.278***|_0.075_|0.337***|_0.073_|0.297***|_0.084_|0.307***|_0.083_|
|IRDP|-0.041|_0.077_|0.211***|_0.067_|-0.094|_0.084_|0.190**|_0.075_|
|NREP|-0.115|_0.086_|-0.078|_0.104_|-0.145|_0.096_|-0.089|_0.111_|
|TRYSEM|0.129|_0.092_|0.060|_0.088_|0.108|_0.094_|-0.018|_0.095_|
|EGS|0.099|_0.092_|-0.023|_0.120_|0.305***|_0.108_|-0.040|_0.128_|
|DWACRA|||0.287***|_0.087_|||0.343***|_0.098_|
|IAY|||0.195***|_0.066_|||0.358***|_0.077_|
|SGNY|||0.089|_0.114_|||-0.089|_0.139_|
|Constant|-3.147***|_0.779_|-0.860|_0.626_|-3.732***|_0.873_|-1.756**|_0.725_|
|R2 (pseudo)|0.134||0.183||0.148||0.251||
|Observations|1613||2137||1565||2137||

_Note_ : Standard errors in parentheses; the coefficients on state dummies are not reported * significant at 10%; ** significant at 5%; *** significant at 1% _Source_ : NFHS I and II.

21
Table 26: IV regressions of ERP Production variables on ICT use

||Processes|Products|
|---|---|---|
|∆Share of labor that uses computer|0.0109***|0.00939**|
||(0.00396)|(0.00384)|
|F- frst stage|14.150|14.150|
|Sargan p-value|0.8987|0.3312|
|_N_|641|641|

Robust standard errors in parentheses. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

Table 27: IV regressions of Internet use scores on ICT use

||Internet use|Information|Customers and Suppliers|Payments and fnance|Marketing|Human Resources|
|---|---|---|---|---|---|---|
|∆Share of labor that uses internet|0.0243***|0.0106***|0.0481***|0.00580*|0.0135**|0.0451***|
||(0.00448)|(0.00263)|(0.00915)|(0.00323)|(0.00616)|(0.00857)|
|F- frst stage|18.178|18.178|18.178|18.178|18.178|18.178|
|Sargan p-value|0.9991|0.6907|0.8827|0.4302|0.8875|0.6023|
|Observations|641|641|641|641|641|641|

Standard errors in parentheses. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.
<!-- Start of picture text --> Log Male and Female Working-age Population (15-64)<br>&<br>c<br>Lo<br>ou<br>><br>S,<br>aeoo a PL<br>D<br>touot e oat<br>i5<br>—<br>SuSy<br>or<br>@<br>=<br>moO<br>o=<br>4<br>ro)<br>8 10 12 14 16 18 20<br>Log Female Working-age Population<br>Notes: 45 degree digaonal shown. Country label shown if WAPop_M/WAPop_F>1.25 or <0.8<br>Working age population by sex usually from WDI (SP.POP.1564.FE.IN and SP.POP.1564.MA.IN)<br><!-- End of picture text --> <!-- Start of picture text --> Adjusted Basic GEGI for Unequal Male-Female Population Adjusted Full GEGI for Unequal Male-Female Population<br>fo}<br>© &<br>a327) -rt ‘<br>= oO @NPL e<br>[O) e @SAU @SAU<br>B e NP) e 3So<br>=Zs<br>=> 2<br>g @OMN g @OMN<br>g Sipperre & @KW®BHR@ARE<br>@QAT @QAT<br>°°<br>020 40 60 80 0 20 40 60 80<br>GEGI basic Full GEGI<br>Notes: 45 degree digaonal shown. Country label shown if WAPop_M/WAPop_F>4/3 or <3/4 Notes: 45 degree digaonal shown. Country label shown if WAPop_M/WAPop_F>1.25 or <0.8.<br>Adjusted basic GEGI takes account of differences in male-female populations Adjusted fullc GEGI takes account of differences in male-female populations<br><!-- End of picture text -->
standardized in this area).

**The pure** tax yield rises along,with inflation. However, if V gradually declines **as P** riseq (an empirical trulsm), the seignorage yield declines. Governments at times are said to pusb up aggregate demand, hoping to finance the corresponding fiscal deficit via seignorage and the inflation tax. As we will see below, the assertion in prac%ice seems a bit far-fetched. A final means of evading capacity liaitations is through imports -- the economy can satisfy increase **d** emand while **meeting** -cal supply restrictions through this device. Two problems **ar;** One, technical, is that essentially non-traded goods such as infr4structure services and the construction component of investment are not easy to bring in to alleviate capacity limits. With high demand, prices for such comsodities are bound to rise. Second, foreign exchange has to be available from reserves or capital inflows to finance extra purchases abroad. Such plenitude of dollars is rare in the Third World.

2. The Fiscal Deficit Against this macroeconomic backdrop, we can ask a key question for stabilization programs: How does a government get into fiscal difficulty -- conventionally defined as a large and/or increasing public sector deficit -- in the first place? Three views predominate in the literature. They can be labelled the political deficit, the structur-I deficit, and the inflation tax approaches respectively.

Blaming political forces for fiscal expansion is an old tradition in economics, shared by conservatives such as Schumpeter (1954) and radicals like O'Connor (1973) at different places and 8
# **ABSTRACT**

This working paper explores the use of cross-sectional analysis in order to measure the impacts of climate change on agriculture. The impact literature, using experiments on crops in laboratory settings combined with simulation models, suggests that agriculture will be strongly affected by climate change. The extent of these effects varies by country and region. Therefore, local experiments are needed for policy purposes, which becomes expensive and difficult to implement for most developing countries. The cross-sectional technique, as an alternative approach, examines farm performance across a broad range of climates. By seeing how farm performance changes with climate, one can estimate long-run impacts. The advantage of this approach is that it fully captures adaptation as each farmer adapts to the climate they have lived in. The technique measures the full net cost of climate change, including the costs as well as the benefits of adaptation. However, the technique is not concern-free. The four chapters in this working paper examine important potential concerns of the cross-sectional method and how they could be addressed, especially in developing countries. Data availability is a major concern in developing countries. The first chapter looks at whether estimating impacts using individual farm data can substitute using agricultural census data at the district level that is more difficult to obtain in developing countries. The study, conducted in Sri Lanka, finds that the individual farm data from surveys are ideal for cross-sectional analysis. Another anticipated problem with applying the cross-sectional approach to developing countries is the absence of weather stations, or discontinued weather data sets. Further, weather stations tend to be concentrated in urban settings. Measures of climate across the landscape, especially where farms are located, are difficult to acquire. The second chapter compares the use of satellite data with ground weather stations. Analyzing these two sources of information, the study reveals that satellite data can explain more of the observed variation in farm performance than ground station data. Because satellite data is readily available for the entire planet, the availability of climate data will not be a constraint. An ever continued debate is whether farm performance depends on just climate normals—the average weather over a long period of time—or on climate variance (variations away from the climate normal). Chapter 3 reveals that climate normals and climate variance are highly correlated. By adding climate variance, the studies can begin to measure the importance of weather extremes as well as normals. A host of studies have revealed that climate affects agricultural performance. Since agriculture is a primary source of income in rural areas, it follows that climate might explain variations in rural income. This is tested in the analysis in Chapter 4 and shown to be the case. The analysis reveals that local people in rural areas could be heavily impacted by climate change even in circumstances when the aggregate agricultural sector in the country does fine.
26

**Table 3:** Capital and Output Wedges, by Industry

||**2000**|**2005**|**2010**|**2000**|**2005**|**2010**|
|---|---|---|---|---|---|---|
|**Industries**|**Capi**|**tal wedge (**|**SD)**|**Outp**|**ut wedge**|**(SD)**|
|F&B|1.8047|1.7409|1.7122|1.0335|0.9270|0.9885|
|Textile|2.0199|1.9072|1.7702|0.8802|0.7103|0.6719|
|Wood|1.8587|1.7357|1.6652|0.7642|0.6344|0.6117|
|Petchem|1.5496|1.5263|1.4786|0.7837|0.8311|0.8421|
|Metal|1.9438|1.7983|1.7493|0.9733|0.8169|0.7952|
|M&E&C|1.8374|1.7878|1.8456|0.8266|0.7981|0.8076|
|Transport|1.6753|1.7523|1.7061|0.8350|0.7967|0.7682|
|**Manufacturing**|1.6637|1.6427|1.6220|0.5713|0.5681|0.6002|

Source: Authors’ calculations.

Note: F&B includes the food and beverage industries. Textile includes the textile and apparel industries. Wood includes the wood, paper products, and furniture industries. Petchem includes refined petroleum, basic chemicals, and other allied chemical products industries. Metal includes non-metallic mineral, basic metal, and fabricated metal industries. M&E&C includes machinery, and equipment, electronics, and electrical industries. Transport industry includes motor vehicles, parts and accessories, ships and boats, railway locomotives, air and space machinery, and military fighting vehicles. SD = standard deviation.

# **5.4 Hypothetical Productivity Gains from Better Allocation of Resources and Implications on Malaysia’s Growth Prospects**

Section 5.1 presented the estimates of hypothetical gains obtained by moving to the level of allocative efficiency in the United States in 1997. These hypothetical productivity gains of 13% to 36% estimated for the manufacturing sector could potentially translate into higher TFP growth, and ultimately higher gross domestic product (GDP) growth for the country. To assess the impact of the efficiency gains on GDP growth, we simulate three possible growth paths (under moderate reforms, more purposeful reforms, and aggressive reforms), based on the magnitude of the realization of productivity gains (table 4).
that covers consultation fees and standard treatment for the most common illnesses among children under five at two local clinics in Sikoro.

The two program components were cross-randomized at the compound level and provided to eligible households starting in winter 2012-13. The main follow-up survey at the end of 2013 collected a detailed nine-week panel of complete health diaries to analyze demand for acute health care (Sautmann et al. [2020]). Here, we use data from a second follow-up visit in 2014 that focuses particularly on diarrhea-related behaviors and outcomes: preventive measures and knowledge indicators, incidence of diarrhea symptoms and acute care from an abridged 1-week health calendar, and measures of malnutrition. We are interested in the interplay of preventive and acute care and their individual and joint effects on health outcomes.

Diarrhea is one of the most important causes of death for children in the region; moreover, it provides a good case study because diarrhea outcomes are likely to respond to the full range of behaviors that the two interventions aim to change: preventing the intake of contaminated water or food is one of the most effective ways of avoiding diarrhea in the first place, and mild diarrhea cases can be treated at home, but serious cases (e.g., dysentery) require clinical care. In other words, there is an important role for all three: prevention, home care, and formal care.<sup>4</sup> There are multiple reasons to think that CHW and subsidies (free care) interact with each other in determining health outcomes. First, CHW activities focus more on preventive care, while subsidies improve access to curative care. The two types of care could be substitutes in the health production function, that is, access to one reduces the need for the other; or complements, that is, adequate prevention and acute care combined lead to greater health improvements than the sum of the individual effects.

In addition, there is the possibility of “spillovers” on behavior: since both preventive and curative care depend on the parent or caretaker’s actions, receiving a prevention-focused

> 4By contrast, prevention is usually the only protection from viral disease, e.g. through vaccination. Other health issues are primarily addressed through curative care, such as bacterial infections.

4
-9-

reflected sevyeral factors. Among them were rising relative labor costs and concerns over slower growth in traditional labor intensive export industries; rising import barriers in developed countries against labor intensive manufactures; and the desire to develop domestic production of intermediate inputs to supply the earlier export industries.9 This sectoral growth drive was supported by a wide range of measures including import protection for infant industries, industry specific tax preferences and credit rationing. Targeted industries ir this sectoral growth drive included steel, metal products, chemicals, ship building, machinery and auto production.

Under this new policy, light industry saw its share of gross output fall between 1975 and 1980 (Table 3). Heavy industry, on the other hand, saw its share almost double between 1970 and 1975 and rise further by 1980. The share of manufacturing in production i,tc)eased further from 40.3 percent in 1970 to 51.0 percent in 1980. The HCI promotion also contribuited to an upgrading in exports, the share of HCI products in total exports increasing from 21.3 percent in 1972 to 38.3 percent by 1980.10 The share of agriculture in production continued to decline from 17 percent in 1970 to 8.3 percent in 1980.

Large investments in the targeted HCI industries, however, created several adverse effects during this period, including (allegedly) excessive real vage increases in these industries, insufficient investment in light industries and capital market distortions. The goverrnment response was to deign a Comprehensive Stabilization Program in mid-1979 that included

**9/** See Kwack (1986), pp. 76-77.

101 This data **is** _froa_ **Chol** (1988), p. 11 and Pyo (1989), Table 6.
# 6. REFERENCES

Abadie, A. (1997). Changes in Spanish Labor Income Structure during the 1980’s: A Quantile Regression Approach. _Investigaciones Económicas_ , 21(2), 253-72.

Altimir, O., and S. Piñera. (1982). Análisis de Descomposición de Las Desigualdades de Ingreso en la América Latina. _Trimestre Económico_ , 49(196), 813–60.

Barros, R. and J. Almeida Reis. (1991). Wage Inequality and the Distribution of Education: A Study of the Evolution of Regional Differences in Inequality in Metropolitan Brazil. _Journal of Development Economics_ , 36, 117–43.

- Bouillon, C., A. Legovini, and N. Lustig. (1998). “Rising Inequality in Mexico: Returns to Household Characteristics and the Chiapas Effect.” IADB.

- ---------. (1999). “Can Education Explain Income Inequality Changes in Mexico?” IADB.

- Bourguignon, F. (1979). Decomposable Income Inequality Measures. _Econometrica_ , 47(4), 901–20.

- Bourguignon, F., M. Fournier, and M. Gurgand. (1998). “Labor Incomes and Labor Supply in the Course of Taiwan’s Development, 1979–1994.” The World Bank. Processed.

- Buchinsky, M. (1994). Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression. _Econometrica_ , 62(2), 405-58.

- ---------. (1995). Quantile Regression, Box-Cox transformation model and the U.S. wage structure, 1963-1987. _Journal of Econometrics_ , 65, 109-54.

- ---------. (1998). Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research. _Journal of Human Resources_ , 33(1), 88-126.

- Burfisher, M. et al. (1993). Wage Changes in a U.S.-Mexico Free Trade Area: Migration Versus Stolper–Samuelson Effects. Working Paper 645. University of California, Berkeley, Department of Agricultural and Resources Economics. Processed.

- Cowell, F. (1980). On the Structure of Additive Inequality Measures. _Review of Economic Studies_ , 47, 521–31.

- Cragg, M., and M. Epelbaum. (1996). Why Has the Wage Dispersion Grown in Mexico? Is It the Incidence of Reforms or the Growing Demand for Skills? _Journal of Development Economics_ , 51(47), 99–116.

- de Ferranti, D., G. Perry, I. Gill, J. Guasch, W. F. Maloney, C. Sánchez-Páramo and N. Schady. (2003). _Closing the Gap in Education and Technology_ . Washington: The World Bank.

- de Ferranti, D., G. Perry, M. Walton and F. Ferreira. (2004). _Inequality in Latin America: Breaking with History?_ Washington: The World Bank.

- de la Torre, Rodolfo. (1997). Desigualdad, pobreza y polarización social en México. Processed.

32
<!-- Start of picture text --> Figure 2.2 : Rural Population Share of Total Population in Haiti, 1982 and 2003<br>100.0<br>90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0 Rural pop. share 1982<br>10.0 Rural pop. share 2003<br>0.0<br>Source: WDI, World Bank 2004.<br>Artibonite CenterGrand-Anse North NortheastNorthwest West SouthSouthwest Haiti<br>Percent<br><!-- End of picture text --> What is driving the population growth pattern Haiti is experiencing? There are various reasons for the demographically changing pattern and many relate to economic opportunities. It is clear, for example, that living conditions in rural Northeast are inferior to West Haiti. Rural areas in the West region are close to Port-au-Prince and the rural population therefore has easy access to goods and services that are produced, imported, or provided by the capital. Moreover, rural farmers in the West have easier access to a large market for their produce than do other regions such as the Northeast, as roads and other infrastructure is limited in the poor regions (see Section 7).

Table 2.3: Degree of Urbanization (percent), 1982-2003

|||Urban||R|ural|
|---|---|---|---|---|---|
|Region|1982|2003|Growth|1982|2003|
|Artibonite|15.6|26.0|66.7|84.4|74.0|
|Center|10.7|16.1|50.5|89.3|83.9|
|Grand-Anse|10.6|14.9|40.6|89.4|85.1|
|North|21.1|38.2|81.0|78.9|61.8|
|Northeast|18.2|37.5|106.0|81.8|62.5|
|Northwest|11.3|23.0|103.5|88.7|77.0|
|West|49.1|66.9|36.3|50.9|33.1|
|South|11.7|15.7|34.2|88.3|84.3|
|Southeast|7.2|14.6|102.8|92.8|85.4|
|Haiti|24.5|40.4|64.9|75.5|59.6|

_Source:_ IHSI 2003.

7