rafmacalaba/datause-encoder-data huggingface dataset:
||**Table 19: La**|**rge firm sam**|**ple**|||
|---|---|---|---|---|---|
|Dependent variable: Employment<br>Growth(logdifference, %, annual)|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|Informal Competition (Country level|1.667|1.312|3.490|3.079|2.500|
|<br>cell average)|(4.327)|(4.313)|(4.286)|(4.384)|(4.521)|
|Initial Employment (logs)|-1.110*|-0.964*|-1.336***|-1.497***|-1.518***|
||(0.579)|(0.503)|(0.495)|(0.456)|(0.493)|
|GDP per capita (lagged, logs)||-1.654**|-1.722**|-1.942**|-1.798**|
|||(0.675)|(0.725)|(0.772)|(0.764)|
|Age of Firm (logs)|||-0.886|-0.853|-0.866|
||||(0.630)|(0.632)|(0.639)|
|Manager Experience (logs)|||<br>-0.158|<br>-0.270|<br>-0.174|
||||(0.617)|(0.574)|(0.570)|
|Exports (proportion of sales)|||4.307***|4.182***|4.320***|
||||(1.457)|(1.344)|(1.407)|
|Female Top Manger Y:1 N:0|||-0.544|-0.645|-0.619|
||||(0.949)|(1.050)|(1.026)|
|Multi-establishment firm Y:1 N:0|||<br>2.093***|<br>2.040***|<br>2.106***|
||||(0.780)|(0.708)|(0.733)|
|Crime losses Y:1 N:0||||2.592**|2.647**|
|||||(1.104)|(1.106)|
|Courts obstacle Y:1 N:0||||-2.090***|-1.995***|
|||||(0.761)|(0.753)|
|Power outages (hours)||||0.000|0.001|
|||||(0.004)|(0.005)|
|Finance obstacle (0-4 scale)||||-0.962***|-0.969***|
|||||(0.284)|(0.275)|
|Overdraft Y:1 N:0||||<br>1.832***|<br>1.878***|
|||||(0.581)|(0.530)|
|Inflation (CPI, annual %)|||||<br>0.130***|
||||||(0.048)|
|Primary Enrollment (Gross rate)|||||0.038|
||||||(0.050)|
|Industry fixed effects|No|Yes|Yes|Yes|Yes|
|Year fixed effects|No|Yes|Yes|Yes|Yes|
|Constant|6.647*|13.719|16.710**|21.724**|15.691|
||(3.997)|(8.505)|(8.326)|(8.276)|(10.593)|
|Number of observations|5,326|5,326|5,326|5,326|5,326|
|R-squared|0.004|0.034|0.053|0.072|0.075|
|Huber-White robust standard errors a<br>*** (1%), ** (5%), * (10%).|nd clustered o|n the country|in brackets. Si|gnificance is d|enoted by|

72
<!-- Start of picture text --> c. Households w/loans from gov't banks d. Average Market (Daily) Access by Region<br>45 90<br>40 80<br>35 70<br>30 60<br>25 1993 50 1993<br>20 1998 40 1998<br>15 30<br>10 20<br>5 10<br>0 0<br>e. Average Health Clinic Access by f. Primary & Secondary School Access for<br>Region 1993, by Region<br>120 120<br>100 100<br>80 80<br>1993<br>60 60<br>1998 40<br>40<br>20<br>20<br>0<br>0<br>Primary Lower Secondary<br>NU RRNCCSCCCH SE MDTOTAL NU RR NCCSCC CH SE MDTOTAL<br>NU RRNCCSCCCH SE MD NU RR NCC SCC CH SE MDTOTAL<br>TOTAL<br>Percent Percent<br>Percent Percent<br><!-- End of picture text --> This extensive service coverage also has little regional differences, despite the fact that the income levels of the regions are quite different. Even in the least developed regions, the minimal level of service is available. This is especially true of basic education, roads, health clinics, and markets. In cases where the basic infrastructure was limited in 1993, such as household electricity connections and formal credit, there is significant growth in coverage between 1993 and 1998. In cases where there are regional differences, most of these differences may not be as significant as they seem. For instance, the Mekong Delta region has low road access however, it turns out that waterways are widespread in this region, which enables cheaper transportation.

The share of communes that lacked all three services; roads, daily markets, and electricity, constituted only 4.6 percent of the communes. 46.6 percent of communes had all three services. If the communes with waterways are added to the number of 9
# **Figure 8 Share of Households in Largest Lineage Group (1995) and Characteristics of the Most Recent Election**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Share of Households in the Largest Patrilineal Clan (1995)<br>Number of Candiates Public Speech<br>.8<br>1.4<br>.6<br>1.2<br>.4<br>1<br>.8 .2<br>in the Most Recent Election in the Most Recent Election<br>Candidates Made Public Speech<br>Number of Candidates (per 100 households)<br>.6 0<br><!-- End of picture text --> Source: 2004 Rural Household Social Network, Labor Allocation and Land Use Survey, RCRE, Ministry of Agriculture.

_Land Tenure Insecurity and Migration, Page 45_
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41
# **1. Introduction**

Many consumers in financial markets find out about the characteristics of products exclusively from providers, even when their staff has better information about the cost of the product and the incentives between customers and staff are misaligned. It is thus important to ask whether firms are successful at providing information and ultimately, whether consumers are well-informed.

Two crucial pieces of evidence, however, should give us pause. First, financial markets are characterized by substantial price dispersion (see, for example, Hortaçsu and Syverson, 2004, Stango and Zinman, 2016, Giné and Mazer, 2017 and Zinman, 2015 for a review).<sup>1</sup> While the variation in prices may be driven by riskiness or the transaction costs involved in servicing different customers, Stango and Zinman (2016) and Giné and Mazer (2017) find that the same individuals in the same months are offered credit cards and credit or savings products, respectively, with substantially different interest rates. Second, there is little comparison shopping, even when customers face substantial price dispersion. Woodward and Hall (2012), for example, show that mortgage borrowers overpay at least USD 1,000 by shopping from too few brokers.

In rational search models, consumers expand their choice set up to the point where the benefit of doing so is equal to its cost. But even when choices are provided at no additional cost, comparison frictions may still cause consumers to place more weight on non-financial factors like brand loyalty or non-professional advice from friends and family to the detriment of the cost of the product (Bertrand et al., 2010). In addition, the decision of which product to contract may involve unfamiliar concepts especially to individuals with limited financial capabilities (Lusardi and Mitchell, 2011, 2014) and as a result, financial consumers may not necessarily choose the most cost-effective product or the one most suitable to their needs (see for example, Gross and Souleles, 2002; Choi et

> 1 Table 1 in Giné and Mazer (2017) reports summary statistics for estimated costs and yields of credit and savings products, respectively, offered by a sample of financial institutions in Mexico and Peru. The total annual cost of credit including usage fees ranges from 22.1 percent in Peru to over 225 percent in Mexico. The total annual yield of a transaction account also including usage fees ranges from -14.5 percent in Mexico to 0.8 percent in Peru.

2
-62-

---------------------------------------------------------------------

__------__---------

**TABLE** 4.5

**Value** Added (Former National Accounts Concept) in Brazil and Mexico in 1975 in nationma currencies **and** at the official **exchange** rate

||Mex|ico|Br|azil|Mexicoj|
|---|---|---|---|---|---|
||(million<br>pesos)|(million<br>cruzeiros)|(million<br>pesos)|(million<br>cruzeiros|Brazil<br>)<br>(%)|
|Total Manufacturing|157<sup>,</sup>487<sup>.7ab</sup>|<sup>102,431.0a</sup>|<sup>b 395,155.</sup>|<sup>5 257,011.7</sup>|<sup>39.85</sup>|
|Grain Mill Products|1,680.1|<br>1,092.8|2,437.1|<br>1,585.1|68.94|
|Sugar & Sugar Products|2,255.4|1,466.9|5,207.6|<br>3,387.0|43.30|
|Malt and Malt Beverages|4,285.8a|<br>2, 7 8 7.5a|2,670.6|<br>1,737.0|160.48|
|Tobacco and Tobacco Products|1,177.Oa|<br>765<sup>.</sup>5<sup>a</sup>|4,592.1|<br>2,986.8|25.63|
|Textiles|6,623.0|4,307.6|13,059.5|<br>8,494.0|50.71|
|Footwear<br>and Leather<br>Products|2,472.2|1,607.9|6,045.8|<br>3,932.2|40.89|
|Pulp and Paper|4,470.6|2,907.7|5,851.6|<br>3,805.9|76.40|
|Soap and Detergents|1,847.8|<br>1,201.8|1,891.8|<br>1,230.4|97.67|
|Paints|1,098.5|714.5|3,731.5|<br>2,427.0|29.43|
|Agricultural<br>Fertilizers|1,022.9|<br>665.3ab|<br>4,622.1|3,006.2|22.13|
|Petroleum<br>Refining<br>and Product|s4,854.2ab|<br>3<sup>,</sup>157<sup>.2</sup>|21,884.2|14,233.6|22.18|
|Tires and Inner Tubes|2,258.7|1,469.1|4,310.6|<br>2,803.7|52.39|
|Cement|2,469.2|1,606.0|4,387.5|<br>2,853.6|56.27|
|Bricks|718.8|467.5|5,322.5|<br>3,461.8|13.50|
|Iron and Steel|10,444.9|<br>6,793.4|22,784.5|14,819.2|45.84|
|Radio and TV Receivers|1,738.2|<br>1,130.6|5,048.5|<br>3,283.6|34.43|
|Motor Vehicles|10,112.9<br>--------------------------|<br>6,577.5<br>------------------------------|19,023.|8 12,373.2|53-15|
|Total in our sample|59,530.3|<br>38,718.9|132,871.|3 86,420.3|44.80|
|as%of Total Manufacturing||||||
|Output|37|.80|3|3.63||

- a) indirect taxes and subsidies are deducted (see table 2.3). b) includes 3,831.7 million pesos (excl. indirect taxes and subsidies) for petroleum refining which are not shown in the census Resumen General but taken from Sistema de Cuentas Nacionales de Mexico, 1981.

Note: Figures are converted at the exchange rate of 1.5375 pesos to the cruzeiro.

Source: Figures for Brazil from Censo Industrial, and for Mexico from Resumen General (except figures mentioned under footnotes a) and b)).
Page 3

# **B. PAST LEGACIES: TH1E STARTING** CONDITIONS

Until the early-1980s, the socialist banking system that prev'iled in most eastern European countries consisted of a mono-bank that performed the roles of central bank and commercial bank, and a savings bank that provided services to households. The mono-bank was responsible for issuing currency and granting short-term credit to enterprises. In addition, the specialized banks provided long-term finance for investment. Unlike banks in western countries which granted credit based on credit risk analysis, bar ks in former socialist countries granted credit based on central plan decisions. Banks, therefore, allocated credit passively and performed the role of government ageneies.

# 1. Abandoning the Basic Socialist Banking Syste

The five countries began tne reform towards a market economy in 1989. Each country differs from the others in their timing and methods of reform (see table 1). The ',reakup of the mono-bank, the establishment of a two-tier banking system and the abolishment of the central plan targets signaled that reform was underway.

Table 1. Banking Systems Starting Conditions

|Date of political opening.|1989|April 1989|November 1989|Novemnber<br>1989|<br>December<br>1989|
|---|---|---|---|---|---|
|Date of breakup of the mono-bank and<br>atartofthe two-tier banking syatem.|January 1987|January 1989|January 1990|January 1990|December<br>1990|
|Number of atate-owned commercial<br>banks. 1/|4|9|2|59|4|
|Number of private or foreign owned<br>comrnercial bank.1/|2<br>_|_5_|0|0|2|
|Number of sp~cialized banks (excluding<br>foreign exchange**banks).**1/|10|I|I|8|2|
|Numnber of**banks**specialized in foreign<br>exchange**trnsactions. 1/**|**1**|3|2|**1**|**1**|
|Number of Savings**Banks.**1/|**1**|**1**|2|**1**|**1**|
|Date of**last**revision of last legislation.**1/**|**January**1987|**January**1989|January 1990|May 1990|**April**1991|

Source: World Bank (1989,1990 and 1991b) and Thome (1992).

**1/ Estimated** at the date of the breakup of **the** mono-bank.
It would be possible to further increase the accuracy of targeting by combining a few of the above variables into a composite targeting indicator. Preliminary work on developing such an indicator using stepwise regressions shows that four variables (the number of children under 15, roof type, floor type, and the ownership of a color television), together with the choice of an appropriate poverty cut-off point, allows up to 94% of poor and non-poor households to be correctly identified in urban areas. In rural areas, developing a composite targeting indicator is more difficult, though the addition of two more variables (ethnicity and ownership of a black and white television) allows up to 75% of households to be correctly classified as poor or non-poor.<sup>20</sup>

# 6. **Summary and Conclusions**

Vietnam's current anti-poverty programs rely heavily on the geographic targeting of poor households. Yet, as in many developing countries, the relatively small number of households that are sampled in its national household surveys do not allow poverty statistics below the regional level to be estimated accurately. Meanwhile, questions have been raised about the comparability and reliability of the more disaggregated province, district and commune poverty statistics that are collected through Vietnam's administrative reporting system. This paper shows how the data collected by the 1998 Vietnam Living Standards Survey may be combined with that of the 1999 Population and Housing Census to bridge this gap and allow disaggregated maps of poverty to be constructed. The procedure to construct these maps involves two steps. First, the VLSS is used to explore the factors associated with poverty at the household level, and develop linear regression models for predicting per capita expenditures at the rural/urban and strata levels. Second, these regression models are applied to household data from the 3% enumeration sample of the Census to derive and map provincial level estimates of the percentage of people living in households whose per capita expenditures fall below the GSO-WB poverty line (the poverty headcount).

The national poverty map resulting from this two step procedure shows that poverty is concentrated in Vietnam's Northern Uplands, in particular in the six provinces that border China and Laos. Fourteen other provinces, most of which are located in the Northern Uplands, Central 20 Further details are available from the authors on request.

35
# **2.3.5. Methods**

Ground‐based SR and FP yields were derived by dividing the reported or measured mass of maize production by the area corresponding to the GPS‐based plot area, or 64 m<sup>2</sup> , in the case of the 8x8m crop cut sub‐plot. Satellite‐based yields were derived in two ways, following BL17.

First, “calibrated” remote sensing yields ( **RS_cal** ) were from a regression model of FP yields on MERIS Terrestrial Chlorophyll Index (MTCI) values on May 30 and June 19, 2016, using only pure stand maize plots that were at least 0.1 ha in size. The calibration focused on the pure stand plots since ground‐based objective yield estimates were not available for non‐maize crops on intercropped plots. The restriction in terms of plot area was driven by smaller plots having bigger problems with geolocation accuracies and mixed pixels in Sentinel‐2. Since FP yields are expensive to obtain and cannot be considered as part of large‐scale operations, an alternative version of the calibrated remote sensing yield was obtained ( **RS_cal_cc** ), which used CC, rather than FP, yields to calibrate the model.

The second satellite‐based approach was to estimate “uncalibrated” yields ( **RS_scym** ) by using the scalable crop yield mapper approach (Lobell et al., 2015). In this approach, a crop model and local daily weather data were used to simulate crop growth and yield for various realistic combinations of on‐farm management, such as sow date, seeding density, and fertilizer rate. The simulated values of total canopy nitrogen on the dates with available images were then translated into MTCI using published relationships (Schlemmer et al., 2013). As in the calibrated approach, the yields are then regressed on MTCI, except in the case of SCYM the regression uses simulated yield and MTCI rather than actual values. In this way, SCYM avoids reliance on any ground data for calibration, which is why it is referred to as an “uncalibrated” approach.

Both types of satellite‐based yield estimates were tested in two complementary ways. First, the yields were compared directly with the ground‐based estimates across both pure stand and intercropped plots. However, given that ground‐based estimates are subject to (different types of) measurement error and neglect a potentially substantial amount of production from non‐ maize crops, the direct comparisons between the two yield measures is not a straightforward test of the satellite‐based yields. That is, some of the discrepancy will also be due to errors in the ground‐based estimates, or discrepancies in the types of outputs that are measured. As a second form of evaluation, we performed regressions of yield on different production factors for both ground‐based and satellite‐based yields and compared the resulting coefficients. Specifically, we regressed yields on key plot characteristics, including log of plot area, log of distance to household (km), presence of cover crops, log of seed planted (kg), use of inorganic fertilizer, log of household labor days and hired labor days, number of hired laborers, soil quality index (SQI), 12
|Kenya: Adaptation to Climate Change in Arid and Semi-Arid Lands(KJoseph Oryokot|
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caused by labor market frictions. This allows us to assess the responses to trade shock in the presence of costly labor adjustment in a wide array of countries.

We use the United Nations Industrial Development Organization (UNIDO) database, which provides information on labor allocations and wages in manufactures, to estimate a map of the labor mobility costs for 22 developed countries and 25 developing countries. We estimate large costs of labor mobility. On average, the labor mobility costs in developing countries are equivalent to 4.93 times the annual wage. In developed countries, the mobility costs are 2.41—much lower, as expected. The highest costs are estimated in South Asia (5.45), Latin America (5.34), Eastern Europe and Central Asia (4.96), Middle East and North Africa (4.40), Sub-Saharan Africa (4.26), and East Asia and Pacific (3.03). Labor mobility costs are negatively correlated with per capita GDP and positively correlated with poverty rates. They are also inversely correlated with tertiary educational attainments and schooling quality, but are uncorrelated with primary and secondary education enrolment. Finally, mobility costs positively correlate with other frictions, distortions and constraints in the economy.

To illustrate how our estimates of labor mobility costs can be used for policy analysis, we run simulations of the labor market responses to trade liberalization. For each developing country, we separately explore the impacts of a hypothetical decrease in the prices of Food and Beverages and Textiles (due to tariff cuts, for instance). The magnitude of the labor mobility costs matters for the responses of these economies to such a trade shock. Typically, countries only reach close to the steady state after 6 years and the higher the mobility costs are, the longer this transition takes. This imperfect adjustment is costly. We estimate measures of trade adjustment costs and these estimates vary widely across countries. On average, the costs of adjustment to a trade shock in the food sector can be twice as high as the actual gains from trade, while the costs of adjustment to a trade shock in the textiles sector can be equivalent to about 60 percent of the gains from trade.

The rest of the paper is organized as follows. In section 2, we introduce the structural model of labor mobility costs and, in section 3, we discuss the estimation algorithm. The mapping of the estimates of the labor mobility costs is in section 4. Section 5 presents the simulations of the impacts of trade shocks. Finally, section 6 concludes.

3