rafmacalaba/datause-encoder-data huggingface dataset:
sufficiently small to rule out mortality selection as an important source of bias. With less than 1 percent of the population forcibly displaced, the sample selection bias potentially caused by forced migration is not as high as in many other conflict situations, but should not be ignored. More importantly, Nepal has seen growing numbers of voluntary migration over the past decade, and these migration patterns may well be correlated with conflict intensity. Since conflict intensity is measured at the district level in this paper, migration within district does not pose any estimation issues. In their case study of Chitwan, Bohra-Mishra and Massey (forthcoming) find that conflict-related violence _does not_ increase the probability of migration outside the district, but the experience in Chitwan is unlikely to be representative of the whole country. I tackle the migration issue in several ways, depending on data availability. In the marriage analysis, migration bias is likely to be particularly severe since women move to the place of residence of their groom upon marrying, which could be in a different district. Even if these movements were unrelated to conflict intensity, classical measurement error in exposure to conflict would lead to attenuation bias. Therefore, I provide estimates for the whole DHS sample as well as for the sample of women who, at the start of the conflict in 1996, already lived where they are interviewed in 2006. I also provide a robustness check in which I use the NLSS, and so can construct the conflict exposure variable on the basis of conflict-related casualties in the district of residence year by year. In the education analysis, there may also be a migration bias when education outcomes are observed at an age at which some individuals may have migrated away from the district. One of the specifications addresses this concern by comparing the change in educational outcomes for individuals aged 14 or younger, between the 2001 DHS and the 2006 DHS. At such a young age, voluntary migration is extremely unlikely to have occurred.

# **Education**

# a. <u>Specification 1: Plain difference-in-difference</u>

This specification corresponds to the most traditional case of difference-in-difference. I define two cohorts, one not exposed to conflict (“control”) and one exposed to conflict (“treated”), and compare the difference in primary attainment between these two cohorts and across districts characterised by different levels of conflict intensity.

An inspection of the data shows that, although the legal age for the start of schooling is 6 years old and there are five years of primary schooling, a sizeable proportion of children is enrolled in primary school before 6 (70.1% at 5) and until 14 (16.1%) in the 2006 DHS, with 14
**Table 9 Pooled OLS Method with Additional Controls**

|||||Time to|||
|---|---|---|---|---|---|---|
||Regulations|Finance|Entry|Export|Infrastructure|Property|
|RealGDP(PPP)|0.977|1.005|1.063|0.974|0.983|0.991|
||(0.026)***|(0.041)***|(0.033)***|(0.032)***|(0.030)***|(0.029)***|
|Log(Area)|-0.086|-0.110|-0.145|-0.087|-0.085|-0.094|
||(0.024)***|(0.045)**|(0.039)***|(0.034)**|(0.036)**|(0.033)***|
|Remoteness|-0.323|-0.422|-0.422|-0.431|-0.470|-0.482|
||(0.123)**|(0.199)**|(0.169)**|(0.147)***|(0.146)***|(0.151)***|
|Export Growtht,t-1|0.033|0.047|0.021|0.033|0.064|0.052|
||(0.055)|(0.074)|(0.059)|(0.058)|(0.056)|(0.055)|
|log(TTRI)|-0.188|-0.432|-0.404|-0.373|-0.374|-0.374|
||(0.080)**|(0.110)***|(0.109)***|(0.080)***|(0.088)***|(0.099)***|
|log(MATTRI)|-0.172|-1.103|0.194|0.495|-0.445|-0.482|
||(0.036)***|(0.300)***|(0.143)|(0.287)*|(0.146)***|(0.144)***|
|log(MATTRI)*Indicator|0.154|0.251|-0.036|-0.212|0.254|0.056|
||(0.052)***|(0.085)***|(0.013)***|(0.086)**|(0.109)**|(0.023)**|
|Indicator|0.312|-0.071|-0.001|-0.275|0.611|0.116|
||(0.086)***|(0.127)|(0.023)|(0.151)*|(0.162)***|(0.033)***|
|Size of Trade|0.118|0.079|0.093|0.105|0.099|0.105|
||(0.015)***|(0.024)***|(0.019)***|(0.017)***|(0.015)***|(0.015)***|
|FDI|0.002|0.009|0.003|0.002|0.003|0.003|
||(0.000)***|(0.008)|(0.001)***|(0.001)***|(0.000)***|(0.000)***|
|Constant|2.564|3.962|2.653|4.954|3.401|3.485|
||(1.343)*|(2.011)*|(1.746)|(1.590)***|(1.444)**|(1.600)**|
|Observations|196|167|195|195|192|189|
|R-squared|0.972|0.950|0.958|0.967|0.967|0.968|

* Pooled OLS results. Robust standard errors clustered by country are in parentheses. All regressions control for year fixed effects.*** p<0.01, ** p<0.05, * p<0.1.

19
characteristics causing indebtedness include low income (Anderloni and Vandone 2008), income instability (Webley and Nyhus 2001), low wealth (Del-Río and Young 2005; Disney, Bridges and Gathergood 2008) and low returns on the investment that the loan is used for (Gonzalez 2008; Hulme 2007).<sup>5</sup>

# <u>3. Long panel survey data and its characteristics</u>

Microcredit has become an important instrument in Bangladesh and elsewhere to assist the poor. But as microcredit becomes more widespread and borrowing rises rapidly, policymakers are keen to know whether its benefits can be sustained in the long run. One long-term indicator of the welfare of MFI borrowers is their degree of over-indebtedness and how over-indebtedness relates to the increasing competition and supply of microcredit from a host of lenders. The current concerns about microcredit in Bangladesh actually may come from both supply and demand sides. On the supply side, there has been unprecedented growth (over $2 billion annual disbursement and 30 million individual accounts), increased competition among NGOs, high interest rates despite competition and product innovations. On the demand side, rising competition among the borrowers for the limited markets, unskilled entrepreneurship, and more frequent weather-related disasters are making micro-investments less profitable and more risky. However, a study of over-indebtedness requires a longitudinal perspective with borrowing data from various sources including MFIs, as well as information on household assets and other characteristics and selected community characteristics.

Fortunately, our panel surveys have these data for households over a period of 20 years. The first survey in 1991/92 was carried out jointly by the World Bank and Bangladesh Institute of Development Studies (BIDS) to study the role of microfinance in poverty reduction for the

> 5 In what follows, we tried to implement an empirical model that attempts to identify some of the drivers of the causes of indebtedness from both demand and supply sides, notwithstanding the limitations of the survey data used here.

9
for other productive activities. Longer waiting times in public health facilities will increase the cost of taking children there. This cost will increase as the household’s opportunity cost of time increases. Households very close to the poverty line in Peru have an extremely high opportunity cost of time. Poor households in Peru are characterized not by unemployment but by underemployment<sup>54</sup> . The poor usually have multiple low paying jobs that, in conjunction, entail long hours. Taking a child to a public health facility with long waiting times could mean that parents will not be able to earn enough income to feed the rest of their family. Households below the regional household wealth median are considered to be “poor” given that during that period the average per capita income for those households was the equivalent of $719<sup>55</sup> . This hypothesis is not confirmed in the data since even though only children form urban households who are not considered “poor” are benefiting from increases in public spending, the differential impact across subsamples is not statistically significant.

> 54 According to the 2005–2010 Permanent Employment Survey, during 2006 only 8% of the labor force in Metropolitan Lima was unemployed. However of the remaining 92%, 52% was underemployed.

> 55 There is no information about income in the Young Lives Study. To create an income measure I estimated an OLS regression using information from the ENAHO surveys. I regressed per capita income on age, education level, insurance ownership, type of toilet facility, household size, household wealth score, regional public expenditure level, a time dummy, and regional dummies. Using the estimated coefficients, I estimated predicted per capita income for the sample in the Young Lives Study. Then I calculated the mean value of this predicted income for households with wealth scores below the regional household wealth median and for households above the median annual predicted per capita income of $1,311.

26
||Goods Industries, Construction,<br>Transport, Market Services, Non<br>Market Services and ten discrete<br>powerproducing technologies.|||
|---|---|---|---|
|**IMACLIM**|12 production sectors: 5 energy<br>sectors (coal, oil, gas, electricity,<br>petroleum products), 3 transport<br>sectors (air, water, terrestrial),<br>construction, agriculture, energy-<br>intensive industry and manufacturing<br>& services (composite good).<br>+ 2 sectors with auto-production by<br>households: motorized private<br>mobility,non-motorized transport.|2001-2100 in<br>yearly steps|12 regions: USA, Canada, Europe, OECD Pacific,<br>FSU, China, India, Brazil, MENA, SSA, Rest of Asia,<br>Rest of LAC|
|**TEA**|16 sectors grouped in 5 macro<br>sectors: Agriculture (represented by<br>4 goods / sectors); Industry<br>(represented by 6 goods / sectors);<br>Energy (represented by 5 goods /<br>sectors); Transportation<br>(represented by 3 sectors); and<br>Services.|2010--2100<br>period. The base<br>year is 2010 and<br>the time step is<br>five years|16 Regions (including Africa, Australia and New<br>Zealand, Brazil, Central America, Canada, Caspian<br>region, China, EU and rest of Europe, India, Japan,<br>the Republic of Korea, Middle East, Asia, the Russian<br>Federation, South Africa, South America, United<br>States)|
|**GRACE**|All economic activities in the world,<br>as expressed by national accounts<br>data|Base year (2014).<br>The model can be<br>solved yearly until<br>a future year, such<br>as 2050 and 2100<br>by updating<br>external variables<br>every year.|15 Regions|
|**IFs**|A multisector general equilibrium<br>economic model (which uses<br>inventories as buffer stocks and to<br>provide price signals so that the<br>model chases equilibrium over<br>time); it provides labor, investment,<br>and consumption information to<br>partial equilibrium energy and<br>agriculture models.|base year of IFs is<br>2015 and the<br>model runs<br>recursively in<br>annual time steps<br>through horizons<br>up to 2100.|186 countries|
|**PHOENIX**|27 production and investment<br>sectors|2000-2150|GTAP countries|
|**POLES**|Industry, services, agriculture|Base year: 2015,<br>time steps: Yearly,<br>horizon: 2015-<br>2100|66 regions (including detailed EU28, all OECD<br>countries and main non-OECD countries)|
|**WorldScan2**|16 sectors|2000-2050|16 regions|

13
where _s;,_ = _Li_ is the ratio of labor employed in sector i to total labor employed in the economy. Taking the differential of (5) we obtain: Figure 5 shows the relative contributions of sectoral labor productivity growth and labor reallocation between sectors to economy-wide labor productivity growth. It can be seen that labor productivity growth within sectors has been the dominant contributor. The impact of labor reallocation which was negative till 1987-88 has been positive since then. Given that Malaysia has accumulated capital quite rapidly relative to labor, the increase in sectoral labor productivity and its large contribution should not be surprising.

# **IV.** EXPLANING TFP GROWTH

In theory, a large number of policies and factors can contribute to output, capital and TFP growth. There is an extensive cross-country empirical literature that has identified macroeconomic, trade and financial sector policies as being significant factors in explaining output growth [Fischer (1993), King and Levine (1993), Summers and De Long (1993)]. Given the cross-country nature of these studies over relatively long time intervals they tend to capture long-run relationships between the chosen variables. In contrast with the earlier literature we employ time-series data for an individual country rather than cross-country data. In what follows, we would not interpret our results as capturing long-run relationships, rather what we capture are shorter run dynamics in the specific Malaysian setting. We discuss below some of the key policies that influenced TFP growth, at least in the short-run. The longer run relationship between these variables may or may not be similar.

Three structural features of Malaysia stand out. First, its openness. The ratio of exports and imports to GDP for Malaysia at 190% in 1997, is high compared to Korea (77%) and Thailand (93%), Mexico (33%) and Chile (49%). Second, capital inflows. FDI inflows into Malaysia averaged nearly 5% of GDP during 1994-97, while it was less than 2% for Korea and Thailand. Third, Malaysia's financial sector loan leverage and loan growth is amongst the highest in the world. Financial sector loan leverage, defined as the ratio of banking sector loans to the private sector to nominal GDP was nearly 162% at end-1997. This is high in comparison to financial sector loan leverage in Korea (74%), Thailand (145%), Mexico (25%), Chile (60%) and UK (125%). Growth in this ratio averaged 20% during 1992-96. This is comparable to the loan growth in other East Asian countries such as Korea (17%), Thailand (23%), and the Latin American countries such as Chile (23%) during the same period. Mexico's loan growth is low at 8% because of the crisis in 1994-95. How did the banking sector and trade policies, and institutions influence Malaysia's growth process? What were the channels through which these 7
# **1 Introduction**

Research on the measurement, drivers, and consequences of economic inequalities has blossomed since the early 2000s. Despite an active ongoing debate on precise levels and trends, there is an increasing consensus that income inequality has substantially increased in most high-income countries since the 1980s, often accompanied by a decline of labor share in total income (Auten & Splinter, 2023, Piketty, 2001, 2003, Piketty & Saez, 2003, Piketty & Zucman, 2014, Smith _et al._ , 2022).

For a long time, Latin America was seen as an exception to that global trend of rising income inequality. After the ”lost decade” of the 1980s and the structural adjustment in the 1990s (Birdsall _et al._ , 2011), economies in the region were buoyed by the commodity boom of the 2000s and, starting from very high levels, registered large decreases in measured income inequality in the following decade (Alvaredo _et al._ , 2023, Azevedo _et al._ , 2013, Gasparini & Lustig, 2011). The fundamental reasons behind this trend are still up for debate, but most countries in the region faced strong wage increases for workers at the bottom of the distribution coupled with the introduction of robust cash transfer systems (Lustig _et al._ , 2013).

Nonetheless, the consensus on falling Latin American income inequality in the last two decades has been challenged recently. Most of the measurement of inequality in the period relied on household surveys, which are known to underestimate top incomes and fail to capture sources of income that are relevant for high-income earners such as dividends. A new set of studies has complemented household survey data with tax registries to provide a more detailed view of top earners in the region. In doing so, they have documented that the share of income flowing to top earners has often been stable – if not increasing – in many countries in the region since the 2000s (Flores _et al._ , 2020, Medeiros _et al._ , 2015). These results are often not contradictory with the observed compression in wage distribution documented previously – instead, they highlight the importance of correctly assessing other sources of income that are often not captured in surveys, particularly income from capital. Accounting for the full national income when measuring inequality often leads to much higher top income shares, leading De Rosa _et al._ (2022) to suggest that ”either the region is more unequal than previously thought or it is not as rich”.

In this paper, we contribute to this debate by creating Distributional National Accounts (DINA) for Honduras, a lower-middle income country in Central America. We household data with administrative tax records and macroeco- complement survey nomic aggregates to provide measures of top income shares in the country for the 2003-2019 period. Our key findings are that the shares of pre-tax income flowing to top earners in Honduras are among the highest in the world, and they have been stable over the last two decades. The highest 1% of earners in 2019 received approximately 1
pension system deficit projected at 4.5 percent of GDP. The new pension law is projected to cut pension expenditures to 6.9 percent of GDP by 2025, a fall of 3.5 percent of GDP, with a fall of similar magnitude in the pension system deficit (table 5). The new pension law still leaves the public pension system in deficit over the long term, but the deficit is reduced to relatively low levels.

**Table 5: Projected expenditures and balance of the public pension system, percent of GDP; 2011-2025**

||2011|2012|2013|2014|2015|2020|2025|
|---|---|---|---|---|---|---|---|
|Baseline||||||||
|Expenditures|7.9|7.9|8.0|8.1|8.1|9.1|10.5|
|Balance|-1.7|-1.7|-1.8|-1.8|-1.9|-3.2|-4.5|
|New law||||||||
|Expenditures|7.5|7.2|7.2|7.1|7.1|6.9|6.9|
|Balance|-1.5|-1.2|-1.2|-1.2|-1.3|-1.4|-1.0|

_Projections do not include special pension regimes_ _Baseline refers to projections based on the pension legislation prior to 2010_ _Source: World Bank PROST projections_ The rationale for the introduction of new, defined contribution pillars is to reduce the burden on the defined benefit state pension system, as well as to stimulate capital market development. However, the transition to a multi pillar pension system involves budgetary costs, because a share of the contributions of the working population has to be diverted from the state pension system to finance the second pillar. Because they are designed to be fully funded, the second pillar pension systems raise fiscal costs in the short to medium term, but reduce costs in the long term. The government froze a planned increase in contribution rates to the second pillar in 2010 21
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- 16 -

used t'ae consumer price index (for both the country and the United States) to construct real exchange rate series. As well, we have used the rate of change of the CPI to lengthen the GDP deflator series for Uganda. We tested this procedure by comparing the results using the GDP deflator and the CPI deflator for some other countries where both were available and found the differences to be small.

The source of commodity output data for the individual countries is the FAO Production Yearbook. By country, each commodity is classified as "staple" (s), "export" (x)<sup>4</sup> or "metal or mineral" (mm). Because multiple data sources are used, there are some differences in commodity definitions (see Annex 1) which could not be avoided. The GNP series were obtained from the World Bank's National Accounts database and are valued at market prices in constant local currency (1980 values).

We assume that each time series (xt) (in logarithms) has an autoregressive structure, where the et are white noise with variance O<sup>2</sup> . In each case a regression was estimated, and the residuals were captured to become the data in the

> 4/ An "export" is defined as a crop for which the average export value over the period in question is greater than or equal to 2 percent of total agricultural export value; a Ostaple" is simply a crop which is produced in excess of 100,000 metric tons per year but does not meet the export criterion.
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consumption is contingent on the expected value of assets, and therefore depends on the probability of a shock realization. Specifically, the incentives for renouncing consumption today are reduced because households are uncertain about the possibility of profiting in the future from their sacrifices today.

**4. Implications for the twin goals: Linking the framework to poverty reduction and**

# **shared prosperity**

There are several advantages to selecting a simple framework. In addition to its relevance for unpacking the components which influence the income generation capacity of households, the framework allows us to analyze how this capacity differs across socioeconomic and demographic groups (e.g. bottom 40 percent and top 60 percent, or poor and non-poor, indigenous and nonindigenous, urban and rural).

As mentioned earlier, a shared characteristic of the World Bank’s twin goals is that they both focus on the welfare of those at the bottom of the income distribution. The first goal _to end extreme poverty at the global level by 2030_ is about moving the vast majority of those individuals currently living below the $1.25/day line, out of extreme poverty. The second goal _to promote shared prosperity in every country_ is related to a sustainable increase in the well-being of the poorer segments of society, defined as the lowest 40 percent in the income distribution.

# _The poverty headcount ratio_

To identify those who are poor, the World Bank uses the family of poverty measures defined by Foster et al. (1984), which satisfy both the monotonicity and transfer axioms proposed by Sen (1976), and the decomposability property. In general, the Foster-Greer-Thorbecke (FGT) Index (also known as the �� measure) has the property of subgroup decomposability and can represent several commonly 18
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43