rafmacalaba/datause-encoder-data huggingface dataset:
reported in Tables 3, 4 and 5. The top row in Table 7 reproduces the estimates from the base case (top panel, Table 2). As already noted, these base line estimates suggest strong spatial convergence within urban areas and in the country as a whole while no substantial change in rural areas.

The estimates in Table 3 can be used to compute the contribution of infrastructure and geo-climatic variables to spatial disparity. The estimate for urban areas in 2000 is for example computed as a di¤erence between the baseline estimate in panel A and estimate in panel H in Table 3. This estimate of infrastructure and geography’s contribution can be taken as the total (upper bound) contribution including its direct and indirect e¤ects on productivity and sorting. We compute the contribution of sorting by taking the di¤erence between estimates in panel A (baseline) and panel G in Table 4. We also compute the lower bound estimates for the contribution of infrastructure and sorting to spatial disparity. We de...ne the lower bound for infrastructure’s contribution as equal to baseline estimate (� in Table 7) minus portion that remains unexplained by observed attributes (�) and the portion that is explained by sorting alone (�2): This estimate sets the contribution of sorting at its highest level, and nets it out from the joint contribution of sorting and infrastructure to spatial disparity. This is a lower bound estimate because a large part of sorting itself is outcome of di¤erential infrastructure provision. We use the same methodology to estimate a lower bound estimate for the e¤ect of sorting on the magnitude of spatial disparity indices. Because the patterns of spatial convergence are di¤erent between rural and urban areas, we discuss them separately.

In urban areas, the decline in neighborhood correlations between 2000 and 2010 is mirrored by the decline in infrastructure’s contribution to neighborhood similarity. For instance, spatial disparity within urban areas fell by 0.086, nearly a 22.5 percent decline from its 2000 level. The contribution of infrastructure and geo-climatic variables declined by 0.10 from 0.285 in 2000 to 0.181 in 2010. Even the lower bound estimates imply a considerable decline in neighborhood 25
# **1. Introduction**

The economic impact of trade reforms has been one of the most studied topics in economics in the last decades (e.g. Pavcnik (2002); Amiti and Konings (2007); Caliendo and Parro (2015); De Loecker et al. (2016)). The empirical literature has mainly focused on the changes in import tariffs across the 1980s and 1990s, as tariffs were the main trade policy instrument for most of the 20th century.

Modern trade policy, however, has become more complex. Greater product sophistication and increasing public concern around health and safety have resulted in the proliferation of non-tariff measures (NTMs).<sup>1</sup> By 2017, NTMs were applied to goods covering around 60 percent of import values in developing countries and over 80 percent in high-income countries (UNCTAD and World Bank, 2018). In the same year, data from the _Global Trade Alert_ suggest that import tariffs accounted for just 20 percent of all trade policy measures adopted globally.

Despite their extensive use, little systematic evidence exists on the economic impact of NTMs. The few existing studies on the topic use aggregate measures of NTMs, which lump highly heterogeneous regulations together (Kee et al., 2009; Webb et al., 2020), or focus on one-off liberalization of a specific NTM, usually quotas (Khandelwal et al., 2013; Harrigan and Barrows, 2009), which however represent only around 2 percent of all applied NTMs (UNCTAD and World Bank, 2018). An important reason behind the paucity of evidence is the difficulty in collecting sufficiently comprehensive and detailed data on NTMs (Goldberg and Pavcnik, 2016).

To help fill this gap, we build the first available time-varying dataset on all NTMs applied to the universe of goods produced in Indonesia from 2008 to 2015. We exploit the experience of Indonesia because it fits remarkably well the general pattern of trade policy over the past three decades: import tariffs liberalization since the 1990s and a contemporaneous increase the application of NTMs (Patunru and Rahardja, 2015).

Our novel dataset improves on existing NTM data in three crucial aspects. First, instead of looking at industry aggregates or categories of goods (e.g. food _vs_ durable), we focus on ten-digit _Harmonized System_ (HS-10 digits) products. Second, we exploit the most disaggregate available classification of NTMs to assign narrowly defined measures

> 1The United Nations Conference on Trade and Development (UNCTAD) defines NTMs as _"policy measures other than ordinary customs tariffs that can potentially have an economic effect on international trade in goods, changing quantities traded, or prices or both"_ .

2
_Note: Energy includes electricity, natural gas, town gas, liquified energy sources, liquid fuels, coal, other solid fuels, and heat energy_ _Figure 7: Energy spending share (over expenditure) and energy poverty incidence rates, by definition (2021)_ _Figure 8: Energy spending share (over income) and energy poverty incidence rates and, by definition (2021)_ <!-- Start of picture text --> 70 62.3 60.0 51.9<br>60 50.0<br>50<br>40.0<br>40<br>30.0<br>30<br>20 13.5 9.9 9.5 7.9 20.0 12.7 12.7 9.5 7.9<br>10 10.0<br>0 0.0<br>2M M/2 M/2 (equ.) 2M M/2 M/2 (equ.)<br>(expenditure-based) 10 percent measure (income-based) 10 percent measure<br>Energy spending share Energy spending share<br><!-- End of picture text --> _Notes: The energy spending share is the average expenditure on energy divided by the average household income (on the right) and by the average household expenditure (on the left). We consider the following measures of energy poverty: (1) A household is considered energy poor as soon as the share of energy expenditure (compared to equivalised disposable income) is above twice the national median of energy spending shares (2M); (2) a household is energy poor as soon as absolute energy expenditure is below half the median of absolute energy expenditure (M/2); (3) a household is energy poor as soon as equivalized energy expenditure is below half the median of absolute energy expenditure (M/2 (equ.)); (4) A household is considered energy poor as soon as the share of energy expenditure is above 10 percent. Source: Own estimates based on HBS (2021)._

**An additional measure of energy poverty that is increasingly used, the low-income-high-cost measure (LIHC), is below the 2M and (equivalized) M/2 measures in Bulgaria.** This measure is close to the newly announced official measure but uses observed energy expenditures from the household budget survey rather than modeled energy expenditures. The energy poverty rate resulting from the low-income-high-cost measure is 7.1 percent (Figure 9), below the 2M and M/2 measures. However, it is important to note that the at-risk of poverty rate using income reported in the HBS 2021 results in slightly higher rates than the official measures from the EU-SILC survey (25 percent versus 22.1 percent).<sup>36</sup> The difference in the estimates reflects differences in the income distribution generated by the household budget survey compared to the EU-SILC.

> 36 We create the LIHC measure of energy poverty by utilizing the official poverty line from the European Union Statistics on Income and Living Conditions (EU-SILC) rather than the poverty line derived from the Household Budget Survey (HBS). The purpose is to align our measure more closely with the official definition, which relies on the poverty line from the SILC as a benchmark. Using the poverty line from the HBS could potentially result in a higher incidence of energy poverty. This is because the HBS poverty line is higher than the one estimated using SILC, leading to higher income poverty rates.

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# **2. Data and empirical approach**

The data on 124 microfinance institutions (MFIs) in 49 developing countries were collected by the Microfinance Information Exchange (or the MIX), a not-for-profit private organization that aims to promote information exchange in the microfinance industry.<sup>6</sup> The database contains one observation per institution from 1999 to 2002. Seventy percent of the observations are from 2002. These data, collected for publication in the _MicroBanking Bulletin_ (MBB), have been adjusted to help ensure comparability across institutions. The adjustments, which are summarized in Appendix A, include an inflation adjustment, a reclassification of some long-term liabilities as equity, an adjustment for the cost of subsidized funding, an adjustment for current-year cash donations to cover operating expenses, an in-kind subsidy adjustment for donated goods and services, loan loss reserve and provisioning adjustments, some adjustments for writeoffs, and the reversal of any interest income accrued on non-performing loans.

The institutions have been selected based in large part on the quality and extent of their data. The data set is thus not representative of all microfinance institutions. They do, however, collectively serve a large fraction of microfinance customers worldwide. A sense of the skewed size distribution of microfinance is given by Honohan’s (2004) analysis of data provided by the Microcredit Summit organization, a data set whose top end largely overlaps the data here. Honohan (2004, p. 3) finds that “the largest 30

> 6 Information on the MIX is available at their website www.themix.org.  Data for institutions is available via the MIX MARKET, a global, web-based, microfinance information platform accessible via the MIX’s website. The MFI data include outreach and impact data, financial data, audited financial statements, and general information. The MIX MARKET, previously known as “The Virtual Microfinance Market,” began as a project of the United Nations Conference on Trade and Development (UNCTAD). The project was supported by the government of Luxembourg under the guidance of a sector-wide microfinance Advisory Board.  In 2001, the Consultative Group to Assist the Poor (CGAP), a consortium of donors that promotes microfinance worldwide, expanded the scope of the MIX MARKET in collaboration with UNCTAD.  The MIX currently receives support from CGAP, the Citigroup Foundation, the Open Society Institute, the Rockdale Foundation, and other private foundations.

7
**Table 1. The main characteristics of data sources - Tax Administrative Data vs. EU-SILC.**

||**Tax Administratve data**|**EU – SILC**|
|---|---|---|
|**Partcipaton**|Compulsory<br>(includes<br>all<br>employees who paid tax based<br>on their declared labor income)|Voluntary|
|**Coverage**|Cover the full labor income<br>distributon|Underreportng at the top is<br>usually a problem in the survey<br>data|
|**Tax evasion**|Data is impacted by tax evasion,<br>as the informal economy is not<br>covered|Partcipants report their total<br>income earned in the formal and<br>informal economy|
|**Data quality**|The employers report income to<br>the tax ofce|Income is self-reported; recall<br>errors and rounding may be a<br>problem|
|**Income defniton**<sup>**4**</sup>|Gross salary, 24 monthly fles|Gross labor income, annual|
|**Data size**|6.9 – 7.4 million taxpayers<br>monthly|16,630 individuals; 5,758 with<br>positve labor income|
|**Source**|Ministry of Public Finance|Harmonized data from Eurostat<br>based on data collected by the<br>Natonal Statstcs ofce|

_Source: Own elaboration_

**To compare the coverage of both data sources, we compare the aggregate value of labor income in national accounts and administrative tax data, and EU-SILC.** One option to check for potential biases

> 4 While in the EU-SILC, the income was initially collected in local currencies in a data set shared with researchers, it is recorded in euros. The income in tax data is recorded in Romanian leu (RON). We converted tax income into euros using the exchange rate provided in the database.

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# **3. Methodology and Data**

Traditionally, and popularly, the terms productivity and yield have often been used interchangeably, but yield is only a partial productivity measure for land. The problem with using yield is that without accounting for inputs other than land, increased yields may represent no increase in productivity _per se_ if the increased output is entirely due to increased use of non-land inputs (individually or in combination). The main interest from an economic perspective is how effectively all inputs (or factors of production) are being used to produce the observed output.

# _3.1 Methodological Approach_

The empirical analysis uses the Stochastic Production Frontier (SPF) approach to estimate a household level production function. The SPF allows direct and simultaneous estimation of two distinct components of overall productivity at the farm level: the productivity of the production process itself (evaluated at the best-practice frontier) and the technical inefficiency in production (evaluated as the productivity of individual households/farms relative to that frontier). An ordinary production function (not applying SFA) would conflate the two, and without explicitly accounting for the inefficiency component, is likely to give biased and inconsistent parameter estimates of the production function (similar to the omitted variable bias) (see, Wang and Schmidt, 2002; Kumbhakar and Lovell, 2000).

The simultaneous parametric approach also allows a deeper analysis of the farm-size productivity relationship, as each component may potentially reveal different relationships. For example, those producers defining the best-practice production frontier may exhibit lower/higher levels of output associated with the scale of farming (as indicated by the decreasing/increasing returns to scale or scale elasticity).<sup>9</sup> But there may also be a declining/rising trend in inefficiency with farm size –

> 9 Economies of scale and economies of size are often used inter-changeably in practice, but while the two are closely related, in production theory they are distinct concepts (Chambers, 1988, Sheng et al 2014).  The two are equivalent if and only if the production technique is homothetic (that is, input proportions do not vary with output). Returns to scale show how output changes if all inputs are changed by the same proportion.  Returns to size show how, in addition to the returns to scale effect, output changes due to input substitution. If all inputs change proportionally with size, the returns to size are equivalent to returns to scale. For Bangladesh, given the small scale of farming (with an average of the largest farm size quartile only 1.62 Ha.), it is reasonable to assume input proportions do not vary much by scale. A preliminary look at the data confirms this intuition: using data on rice plots (the predominant crop in Bangladesh),

8
Policy ReseaRch WoRking PaPeR 4813

# **Abstract**

The authors use data from three waves of the India National Family Health Survey to explore the relationship between the month of birth and the health outcomes of young children in India. They find that children born during the monsoon months have lower anthropometric scores compared with children born during the fall and winter months. The authors propose and test four hypotheses that could explain such a correlation. The results emphasize the importance of seasonal variations in affecting environmental conditions at the time of birth and determining the health outcomes of young children in India. Policy interventions that affect these conditions could effectively impact the health and achievement of these children, in a manner similar to nutrition and micronutrient supplementation programs.

This paper—a product of the Poverty Team, Development Research Group—is part of a larger effort in the department to study the effect of environmental conditions on children's health outcomes. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mlokshin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
farmers are forced to adopt a low-return and low-risk technology because their limited financial capacity would not allow them to absorb larger exogenous production shocks. They are thus confined in the vicious circle of poverty. External risk financing instruments, like insurance or contingent debt, can help these farmers to adopt higher return but riskier technologies as long as they generate enough operating profit to cover the hedging costs. This is illustrated on Figure 2a, where access to these risk financing instruments allows farmers to move from point A to point C, via point B. The move from A to B can result, for instance, from an increase in the use of risk-increasing inputs such as fertilizers.<sup>7</sup> In this example, the farmer transfers the additional risk due to the adoption of technology B to an external investor such as an insurance company, for instance, which in turn enables him to reach point C. A fraction of the additional expected profit realized from this move is used to pay for the hedging costs. The difference between the additional expected return and the hedging costs involved is the value added of the external financing arrangements.

However, if hedging costs are greater than the additional expected return generated by the adoption of a high-return and high-risk technology, then market-based instruments are not economical and thus are not purchased by farmers, as depicted in Figure 2b. The optimal risk financing solutions are illustrated in Box 3.

**Figure 2a. Risk financing instruments as crop business profitability enhancing solutions**

<!-- Start of picture text --> Expected<br>return Efficient frontier with risk<br>financing instruments<br>Efficient frontier without<br>B<br>risk financing instruments<br>Hedging costs<br>C<br>Value added of<br>risk financing<br>instruments A<br>Return variability<br>Source: Authors.<br><!-- End of picture text -->

> 7 The marginal productivity of risk-increasing inputs increases (resp. decreases) as the states of nature (i.e., the level of rainfall) are more (resp. less) favorable. Such inputs contribute to increasing the output variability.

9
Another implication of the long interval between seroconversion and AIDS is that the level of the epidemic can be seen as a stock/ fl ow issue. The prevalence rate measures the stock of seropositive individuals which is affected both by the incidence–the fl ow of individuals who seroconverted–and the mortality rates.<sup>3</sup> This paper analyzes both HIV prevalence and HIV incidence. The GPC is the fi rst study to report a signi fi cant decline in adult HIV incidence in rural Africa (Mbulaiteye and others 2002a; Whitworth and others 2002), indicating that the decline in HIV prevalence observed in Uganda since 1994—1995 is not merely the consequence of the mortality of the people previously infected but can be attributed to behavioral changes.

It is also important to note that, in light of the fact that the median interval between infection and death is around 10 years, AIDS mortality among young adults is not widespread and therefore HIV prevalence at young ages is only marginally affected by the mortality rate. This alleviates the concern raised by the "survivor bias", the bias, present at later ages, induced by the fact the individuals most at risk already died and are not observed.

The fi rst AIDS case in Uganda was diagnosed in 1982. The HIV virus was probably introduced late in the 1970s or early in the 1980s (Republic of Uganda 2000; 1995). The Ugandan government was the fi rst in Africa to recognize the extent of the epidemic (Morgan and Whitworth 2001). As early as 1986, the government set up a national AIDS Control Programme which has been in charge of disseminating the information about the HIV/AIDS epidemic. From a peak of 14 percent in the early 1990s, the prevalence rate in the adult population has now been brought down to an estimated 5 percent (UNAIDS 2003). Several studies have attributed this decline to a change in behavior associated with strong prevention campaigns (Kilian and others 1999; Republic of Uganda 2000; Whitworth and others 2002).

Uganda has a population of 25 million of which 15 percent is urban. In 2002, Uganda had a GDP per capita of US $ 1,354 US in purchasing power parity (World Bank 2003).

> 3 In the absence of migration, the relation between the prevalence of HIV, the incidence and the mortality can be summarized as such: ◦ H = (1 − H)λH − HλD ◦

> H is the change in the prevalence rate at each period, typically one year. H is the HIV prevalence rate, λH is the incidence rate, i.e. the rate at which HIV negative individuals become HIV positive and λD is the mortality rate from AIDS for HIV positive individuals.

6
for 209 districts across 9 Indian states for the years 1991-2 and 2000-1 in constant 1992-3 prices.<sup>18</sup> This NDP data is not publicly available and to my knowledge no similar sample of Indian district GDP or NDP has been analyzed in the literature. The coverage of the sample of districts is broad, comprising approximately 60% of GDP in 1991-92 and includes both northern and southern states. The selection of districts into the sample was determined by availability given that only some states collect this data at district level. The data and sources are described more fully in the Appendix.

# **3.2 Choice of Specification**

This study relies primarily on Barro-type growth regressions which include the log of initial income as the main control variable. As in Barro (2001), the basic regression equation comes from the following generic equilibrium growth equation which can be derived from a number of growth models: log ( _yi_ , _T_<sup>/</sup> _yi_ ,0 ) is the log difference in income per capita over the observation period (growth), _yi_<sup>*</sup> (<sup>_x_</sup> _i_ ) is the steady state income per capita, possibly conditional on district level variables _Xi,_ and _g_ is the exogenous growth rate of technology. The rate of growth is assumed proportional to the distance from the * steady state log ( _yi_ ( _xi_ ))− log ( _yi_ 0, ) which implies that initial income should figure negatively into the regression. The estimation equation can be written as:

> 18 The states in the sample are: Andhra Pradesh, Karnataka, Kerala, Maharashtra, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, and West Bengal. Districts are the level of government just below the state level. The median district in the sample is 5200 km<sup>2</sup> which is a little over three times the size of the median U.S. county. The median population in 1991 is 2.3M people per district. NDP data is used here because GDP was not available at district level. NDP is defined as GDP corrected for capital depreciation. All depreciation estimates were carried out by state offices where the data was collected.

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