rafmacalaba/datause-encoder-data huggingface dataset:
Entry and Exit in the Export Market: The analytical model reviewed in Section II implies that this combination of sunk costs and uncertainty should induce persistence in producers' exporting stawus. That is, those who have already incurred the sunk start-up costs should be relatively likely to export in the current period. Some preliminary evidence on this prediction is provided by transition rates into and out of the export market, which are summarized in Table 2. Each row describes a transition from the exporting status in column I to the status in column 2. The entries in the table are the proportion of plants in each of the period t categories that choose each of the two possible categories in year _t+1.A3_ The top panel applies to the 19 major manufacturing industries, and the - bottom panel applies to the 650 plants in the four major exporting industries food, textiles, paper, and chemicals - that will be used to estimate the econometric model in the next section.

The top row of each panel indicates that, of the plants that did not export in year t, more han 95 percent of them did not export in year t _+1._ For the plants initially in the export market, the proportion of manufacturing plants that remain in the market from one year to the next varies from 83 percent to 91 percent over tine, and the proportion of plants in our four-industry subsample that remains in the market varies from 85 percent to 95 percent. Clearly, there is substantial persistence in the plant-level pattems of export market participation. Nonetheless, only 36 percent of the plants in the subsample that exported at some time remained exporters for the whole sample period, and among plants that _did_ change exporting status, 60 percent did so more than once.<sup>14</sup> Persistence in exporting status might be caused by sunk costs, as the hysteresis models suggest. Alternatively, it might be caused by underlying plant heterogeneity: persistent cross-plant

**'3** The data correspond to the group of plants dtat were io operation in each year 1981-1989. There are 2369 plants; in this group and they represem approximately 40 percent of the number of piano in operadon in any year. On average, 18.1 percent of these pla participated in the export market in any single year and they accounted fbr 62.3 percent of the total number of exporters and 61.6 percent of the value of manufactur **e** d xports.

'' The appendix summarizes the patems of multiple switches into and out of the export market and the ability of the empirical model to explain these patrns.

'11
27 of all OECD countries (71%) instead of the actual 16%, it would have grown 0.6 percentage points faster per year. These results suggest that the exogenous component of both bank and stock market development have an economically large impact on economic growth. Note, however, using quarterly data and vector autoregressive techniques, Arestis, Demetriades, and Luintel (2000) find that the economic effect of stock market liquidity on growth is positive and significant, but smaller economically than that found in Levine and Zervos (1998), Rousseau and Wachtel (2000), and Beck and Levine (2002). While differences in data frequency, country coverage, sample period and econometric technique may account for the differences, future works needs to clarify the economic impact of stock market development on economic growth.

# **2.5. Microeconomic studies of finance and growth**

Industry-level and firm-level data have also been brought to bear on the question of whether financial development promotes economic growth. By circumventing weaknesses with cross-country and panel studies, the microeconomic research seeks to resolve causality issues and to document in greater detail the mechanisms, if any, through which finance influences economic growth.
policy recommendations regarding greenhouse gas emissions control. The typical output of an IAM is the future trajectories of key economic variables including GDP per capita with and without climate change, as well as income paths under different policy scenarios.<sup>6</sup> The earliest estimates of the impact of climate change on poverty that we are aware of are those by Anderson (2006). Anderson estimates the impacts of climate change on poverty for Sub-Saharan Africa and South Asia based on PAGE 2002 (Policy Analysis Greenhouse Effect).<sup>7</sup> The PAGE model provides estimates of output and growth in the future without and with climate change. Under the A2 climate change scenario in which global mean temperature increases by 3.9°C by 2100, the cost of climate change in India and South East Asia, and in Africa and the Middle East is predicted by PAGE 2002 to be around a 2.5 and 1.9 percent loss in GDP, respectively, compared with what could have been achieved in a world without climate change. Anderson converts these output and growth projections into poverty impacts, by using regional poverty-output elasticity estimates, population forecasts, and the two assumptions that (i) average household income grows at 0.8 times the rate of GDP per capita<sup>8</sup> and (ii) the distribution of income remains constant. Based on these projections, the author reports that by 2100 climate change could cause up to an additional 12 million people living on less than $2 a day in South Asia and an additional 24 million people in Sub-Saharan Africa.<sup>9</sup> While the poverty predictions are based on highly aggregative and simplified model that does not take into account adaptation, the illustrative results suggest that climate change will negatively affect poverty. As the _Stern Review_ (Stern 2007) rightly notes, these poverty impacts are likely to be smaller if aggregate growth in these countries and regions proceeds faster than what is assumed by the IPCC A2 SRES scenario (which assumes, among other things, a high population by 2010 (15 billion) and a world GPD growing at 2 percent per year). In fact, recent GDP and population growth trends suggest that the A2 scenario’s view has been pessimistic, and hence Anderson’s poverty impacts might overestimate the actual impact.

> 6 For a detailed description of IAMs in the context of climate change control see Kelly and Kolstad (1999). 7  PAGE is an IAM used extensively by the Stern Review (Stern 2007).

> 8  It is a common practice to multiply the growth rate in GDP by 0.8 so as to get an approximation of the growth rate in consumption. This adjustment factor, however, is not explicitly documented in any published paper that we are aware of.

> 9 The Stern Review reports Anderson’s results based on the 95th percentile of the climate change damage distribution. Under these higher damages by 2100 climate change could cause up to an additional 46 million poor people in South Asia and an additional 98 million people in Sub-Saharan Africa.

8
# **B. An Empirical Model**

To render equations (1) and (2) empirically useful, we must surmount several obstacles. Our first problem is to measure the performance vector, _ai._ Somehow we must measure unit production costs (cj,) and product attractiveness (ajt) concepts using plantlevel data on revenues, intermediate input costs, market shares, labor costs, and crude capital stock proxies.

To render unit production costs observable we use a novel normalization. Specifically, we define one unit of the/li plant's product to be whatever that plant can produce with a dollar's worth of intermediate inputs. Since products are differentiated this does not imply that a plant using a relatively large amount of intermediate inputs is producing a relatively valuable output. However, if firms were to differ in the efficiency with which they convert intermediate goods into final output, this assumption would have the undesirable implication that efficiency gains reduce output when the physical volume of final production doesn't change.<sup>6</sup> Thus our normalization is accompanied by two strong assumptions: firms exhibit constant returns homothetic technologies, and these technologies differ across producers only because of differences in _primary_ factor efficiency. That is, some firms use labor and capital more effectively than others, and this is the _only_ reason marginal cost schedules differ across firms.

With these assumptions we can calculate unit (variable) production costs and output prices at thej]h plant in year t as _c =_ - and p,, = L , where Cj, is total _Jt I I_

> 6 Variation in factor prices across plants will also undermine our basis for inference. We controlled for regional variation in factor prices using regional dummies and never found significant effects, so we dropped them for the results reported herein.
-4-

search is usually very weak.9<sup>1</sup> Our analyses of employment survey data for Mexico, confirm that, in practice, the distinction between these two categories is very fuzzy for certain groups of individuals, implying that the official definition of unemployment tends to underestimate the true number of people who are in fact jobless.21 In particular, we find a large fraction of men who appear to be idle--*.-iese individuals are out of work, able to work, not studying and not takiri6<sup>care of the household. If these idle male workers</sup> are counted among the unemployed, the aggregate unemployment rate increases from 2.8 percent to 5.5 percent in l991.Y/ Table 1 reports the distribution of unemployment by age and education categories. Using the standard definition of unemployment, we find that for men as much as 60 percent of total unemployment is accounted for by individuals below the age of 25. The comparable fraction for females is even higher, at about 77 percent. Young workers are thus a very large fraction of the unemployed. As regards education, 53 percent of total male unemployment and 62 percent of total female unemployment corresponds to individuals with 7 to 12 years of schooling. Individuals with completed secondary education (9 years of schooling) account for 20 percent of total male unemployment and about 19 percent of total female unemployment. Those with high school education (10-12 years of schooling) account for an additional 20 percent of male unemployment and a stunning 35 percent of female unemployment. These figures suggest that unemployment is concentrated among those with a certain level of education and not, as could be expected, among the least educated. However, this conclusion has to be modified when the alternative definition of unemployment is used. Taking into account those individuals who report being idle increases the fraction of males with less than secondary education in total unemployment.

_2/_ For a discussion of the importance of considering these "idle" workers as unemployed, see Kim B. Clark and Lawrence H. Summers, "Labor Market Dynamics and Unemployment: A Reconsideration", Brookings Papers on Economic Activity, 1979. Also Lawrence H. Summers, "Why is the Unemployment Rate so Very High near Full Employment", Brookings Papers on Economic Activity, 1986.

./ See A. Revenga and M. Riboud, "Unemployment in Mexico: An Analysis of its Characteristics and Determinants", World Bank mimeo, 1992.

i/ We do not define a comparable group of idle female workers because family responsibilities tend t. make their labor market behavior patterns much more complex, with frequent periods out of the labor force.
28 governance: a one standard deviation increase in the party age variable is associated with approximately one-fourth of a standard deviation improvement in bureaucratic quality and corruption ratings.

**Table 4:  Citizen organization, competitive elections and public policy (** Table reports only election and organization coefficients **) (** _p_ -values in parentheses, standard errors clustered by country **)**

||Gross<br>Secondary<br>School<br>Enrollment|Gross<br>Primary<br>School<br>Enrollment|Private<br>Investment/<br>GDP|Bureaucratic<br>Quality<br>(ICRG)|Corruption<br>(ICRG)|
|---|---|---|---|---|---|
|Competitive elections, 0-1|1.13|4.0|-.41|.29|0.17|
||(.55)|(.15)|(0.62)|(0.103)|(0.17)|
|Average of programmatic<br>d ibl ll|6.22|6.03|1.51|.64|1.01|
|ummy varaes over a<br>parties|(.01)|(.08)|(0.12)|(0.00)|(0.00)|
|Competitive elections, 0-1|.94|4.65|-1.05|.13|.05|
||(.67)|(.13)|(.20)|(.34)|(.69)|
|Largest gov‟t. and<br>|6.74|6.3|1.54|.55|.65|
|opposition parties<br>programmatic|(.00)|(0.01)|(.05)|(.00)|(0.00)|
|Competitive elections, 0-1|2.76|5.72|.21|.35|.26|
||(.21)|(.05)|(.81)|(.01)|(.00)|
|Age of government party –<br>leader years in office|.03<br>(.19)|.02<br>(0.44)|-.002<br>(.91)|.01<br>(.002)|.01<br>(0.002)|
|AGEOF PARTY: ONLY|COUNTRY-|YEARS WIT|HOUTCOM|PETITIVE EL|ECTIONS|
|Ae of overnment art –||||||
|g g py<br>leader years in office|.13<br>(.08)|.28<br>(0.004)|.06<br>(.04)|.02<br>(.001)|.02<br>(0.00)|
|AGEOF PARTY: ONL<br>A f|YCOUNTR|Y-YEARS W|ITHCOMPE|TITIVE ELE|CTIONS|
|ge o government party –<br>|.02|.02|-.01|.003|.003|
|leader years in office|(.40)|(0.46)|(.43)|(.15)|(0.10)|

Note: Results of ordinary least squares regressions of public policy and investment variables, using the corresponding specifications in Table 1, adding the indicated variable to capture citizen organization. Only the competitive elections and citizen organization coefficients are reported. Number of observations and R<sup>2</sup> statistics similar to Table 1.
**Figure 2: Income inequality and support for reducing inequality**

**Figure 3: Extent of income redistribution through public transfers and support for assisting the poor as the first government priority**

<!-- Start of picture text --> RUS<br>HUN<br>LVA<br>DEU<br>ROM LTU SWE GBR SRB<br>MDA BGR CZE UKRPOL FRA<br>KSV GBR ROM BLR ITA SVN<br>ITA POL EST SVK<br>FRA LTU HRV<br>DEU EST<br>SRB BGR<br>UKR HRV MDA RUS<br>SVK HUN CZE SWE LVA ARM<br>ALB TUR MKD<br>SVN GEO<br>BIH<br>KSV<br>3 4 5 6 7 0 .1 .2 .3 .4<br>Income should be more equal Assisting the poor should be the priority nb 1 of the Gvt<br>Source: LITS II, WDI Source: LITS II, WDI<br>40 .25<br>35 .2<br>Gini 30 .15<br>Red_Gini<br>25 .1<br>.05<br>20<br><!-- End of picture text -->

# **_Support for specific groups_**

Generally speaking, there is a high degree of agreement that the disabled and the elderly deserve support from government: overall, 75 percent of respondents believe that the disabled deserve government support, and 71 percent that the elderly deserve government support. Support for redistribution for other groups is weaker: 64 percent agree that families with children should be supported, 53 percent for the working poor, 44 percent for the unemployed and 43 percent for war veterans (table 2).

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# **1. Introduction**

Lending in low-income countries is notoriously difficult. Clients typically lack adequate collateral and lenders often have limited information about the profitability of their customers. Information asymmetries coupled with costly enforcement of repayment severely limits the profitability of lenders. The problem is particularly acute in agriculture because the nature of production precludes the use of many of the mechanisms used in microfinance. For example, lenders cannot schedule frequent repayments because cash flows are only received after harvest, several months after the loan is taken. In addition, all farmers need cash at the same time, so allowing some farmers to borrow only after others have repaid their loans is problematic because some farmers would end up receiving credit when they do not need it. Even if all clients were allowed to borrow at the same time, joint liability may be ineffective if most production shocks are covariate. Finally, and perhaps most importantly, lenders may lack the ability to deny access to future loans to defaulting clients in the absence of a national system that allows individuals to be uniquely identified.

When this happens, loan defaulters can often avoid sanction by simply applying for new loans under different identities. Lenders respond by limiting the supply of credit, due to the inability to sanction unreliable borrowers and, conversely, to reward reliable borrowers with expanded credit. As a result, many smallholder farmers are severely constrained by the inability to finance crucial inputs such as fertilizer and improved seeds, particularly for export crops.<sup>1</sup> In this paper we implement a randomized field experiment to estimate the impact of biometric identification (fingerprinting) in a context—rural Malawi— characterized by a lack of a unique identification system and limited access to credit.

According to the 2006 Doing Business Report, Malawi ranked 109 out of 129 countries in terms of private credit to GDP, a frequently-used measure of financial development. Malawi also gets the lowest marks in the “depth of credit information index” which proxies for the amount and quality of information about borrowers available to lenders. Using more micro data,

> 1 The following quote from 1973 by Robert McNamara when he was the World Bank president exemplifies this view: “The miracle of the Green Revolution may have arrived, but for the most part, the poor farmer has not been able to participate in it. He simply cannot afford to pay for the irrigation, the pesticide, the fertilizer... For the small holder operating with virtually no capital, access to capital is crucial.”
conditional on the technology diffusion, climate indicators, and the other controls including location and time fixed effects, I argue that lightning intensity influence employment outcomes only through its effect on electricity outages. _μijct_ and _εijct_ represent respectively, the error terms for the first and second-stage equations. Standard errors are clustered at community level (primary sampling unit).

The main parameter of interest, _β_ measures the causal effect of electricity outages on the outcome variable. Therefore conditional on the instrument validity, _β_<sup>ˆ</sup> _IV_ recovers the local average treatment effect (LATE) of electricity outages on the outcome variable(s).

# **4.2 Baseline Results**

This section presents the baseline results on the effect of outages on employment using the Afrobarometer dataset from several African countries.

## **4.2.1 First-Stage IV Regression**

Table 1 presents the results of the first-stage regressions which estimate the relationship between the lightning intensity and our measures of outages: whether a community experiences outages (columns 1-2) and the share of households in a community (columns 3-4) experiencing outages.

The results show a positive association between lightning intensity and outages. In column 2 for instance, I find that a percent increase in lightning intensity is associated with a 12 percentage point (pp) increase in the probability of a community experiencing outages. Similarly in column 4, a percent increase in lightning intensity is associated with a 9 pp increase in the share of households experiencing outages. The strength of the instrument is relatively high as the first-stage F-statistic (Fstat) exceeds the conventional benchmark of 10 (Stock and Yogo, 2005) in all specifications.

To complement the results in Table 1, Figure 1 presents a binscatter plot relationship be20
# FIGURE 4. EXCESS FEMALE DEATHS BY MEASLES<sup>VACCINATION</sup> STATUS AMONG 9-60 MONTH CHILDREN: MATLAB INTERVENTION AREA, 1982-85

<!-- Start of picture text --> 100 r<br>89.3 UNVACCINATED VACCINATED<br>D 80<br>e<br>a<br>t<br>h 60<br>p 416.7<br>e<br>r 40 -<br>1 I30.1<br>0<br>o It<br>0 20<br>0<br>FEMALE MALE<br><!-- End of picture text --> CUMULATIVE MORTALITY RATE 42 MONTHS AFTER VACCINATION
# **1. Introduction**

Otto von Bismarck famously remarked that people who like sausages and laws should not see how either of them is made. Economists may wish to say the same about profit data from microenterprises in developing countries. Self-employment and household enterprises are major sources of employment in developing countries, with Vijverberg and Mead (2000) reporting that about one-half of the households sampled in the World Bank’s Living Standards Measurement Surveys operate one or more non-farm enterprises. Self-employment is particularly important among the poor: as an example, Banerjee and Duflo (2007) note that 69 percent of the urban poor in Peru operate a nonagricultural business. Accurate measurement of profits from microenterprises is therefore critical for studying poverty and inequality, measuring the returns to education, evaluating the success of microfinance interventions, and many other important questions of interest. However, a myriad of potential problems plague the measurement of profits. The majority of microenterprises in developing countries do not keep financial records, making data collection generally reliant on recall. Money and goods are fungible between the business and the household. Inputs may be purchased in one period and sold in another, and production can be highly seasonal. And as with other income, individuals may be sensitive about revealing how much they earn, and concerned about the information being used for tax purposes.

In this paper we go inside the sausage factory of profit reporting and conduct experiments to measure the importance of these various problems, and to draw recommendations on how to collect profit data. We use data from two panel surveys of microenterprises conducted in Sri Lanka in 2005 and 2006. In the baseline surveys firm owners were asked directly for their profits in the last month, and also to report revenue and expenses. The correlation between reported profits and reported revenue minus expenses is only 0.2-0.3, and 30 percent of firms have negative revenue minus expenses. Vijverberg (1991) and Daniels (2001) report similar correlations in other countries. They conclude that net revenue, the sum of money from the business used by the household and money left after meeting business expenses and using some money in the household, may be the best single measure, but note that they have no rigid standard to compare this to.

2
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 17: Individual Share of Formally Employed Months Away from Initial Region - Tradable Sector Worker Sample - 1990-2009 <!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>-0.1<br>-0.2<br><!-- End of picture text --> Each point reflects an individual regression coefficient where the dependent variable is the share of formally employed months in the year listed on the x-axis that the individual spent working in a region other than their initial region, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with various controls (described in the text). Positive estimates imply a lower share of formally employed months away from the initial region for workers initially employed in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

56