rafmacalaba/datause-encoder-data huggingface dataset:
24. The extent to which Latin America is experiencing deindustrialization can be illustrated by the rapidly declining share of manufacturing in GDP in these countries (Figure 5). Figure 6 gives an indication on the causes of this development showing that labor productivity in manufacturing has been increasing rapidly in China, but has not yet recovered to earlier peaks in Brazil and Mexico.

**Figure 5: Manufacturing exports of Latin America and the Caribbean by technology level**<sup>**8**</sup>

<!-- Start of picture text --> (a) LAC Manufacturing as a Percentage of Total Exports (b) LAC Total Exports<br>60% 0.9<br>0.8<br>50%<br>0.7<br>40% 0.6 HT<br>HT<br>0.5 MT<br>30%<br>MT<br>0.4 LT<br>LT<br>20% 0.3 PPRB<br>10% 0.2<br>0.1<br>0%<br>0<br>Trillions<br><!-- End of picture text --> Note: Technology classification from Lall (2000). HT = High tech (electronics, scientific equipment, aircraft); MT = Medium tech (automobiles, processed chemicals and metals, and machinery); LT = Low tech (textiles, basic metal manufactures); PPRB = Primary products and resource based goods.

Source: Authors’ calculation and WITS Comtrade SITC Revision 2.

<!-- Start of picture text --> Figure 6: Labor productivity in manufacturing<br>60000 (value added per employee, US$)<br>China<br>50000<br>Brazil<br>40000<br>Mexico<br>30000<br>20000<br>10000<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006<br>Source: UNIDO (2011).<br><!-- End of picture text --> 8 The evolution of the share of manufacturing exports may be affected by the substantive increase in commodity prices over the past decade.

9
and trained PO staff in how to collect information) prior to program intervention. The data set may be considered as a baseline for any evaluation of PRIME intervention. This baseline survey was conducted in December 2006 immediately following the lean season. The baseline data was analyzed for various research topics, such as vulnerability, coping, and seasonal migration (Khandker, Khalily, and Samad 2009).

The _monga_ -vulnerable, ultra-poor households were offered the PRIME program through 235 PRIME branches in 5 districts of the northwest region.<sup>11</sup> The intervention was first implemented in Lalmonirhat district in 2006 and in the other 4 districts (Rangpur, Kurigram, Gaibandha, and Nilphamari) the following year. PRIME beneficiaries meet in small groups of 25–30 each at a mutually agreed time and place. However, unlike regular microfinance, PRIME groups may save any amount of money and are qualified for a loan against any amount of savings. PRIME groups receive loans and other benefits for both year-round and specific lean-period activities, such as consumption-smoothing loans. Yearround activities include a flexible microcredit program that allows loans for production and consumption, with a repayment schedule convenient for borrowers. PRIME also offers extension services for income-generating activities, training, primary health care, and remittance services.

Between December 2008 and February 2009 a detailed survey was administered by InM that sampled target households to assess the impact of PRIME. A multi-stage cluster sampling technique was used to draw a random sample of 4,589 households from 16 upazilas, 61 unions, and 271 villages from the total area that received the intervention. Interestingly, not all selected households eligible for the PRIME intervention were

> 11 Greater Rangpur’s 5 districts include 34 upazilas (subdistricts).  In its first phase, the PRIME intervention targets 23 upazilas (covering 482,948 households), 209 unions, and 2,531 villages (InM 2009).  A second phase, beginning in early 2010, covers the remaining 11 upazilas.

17
poverty and as a policy to absorb the increasing number of job seekers in the Jordanian labor force<sup>28</sup> . As highlighted later on in the study (refer to Table 10), residents of the Northern governorates have usurped a large share of such employment, especially in the military and defense sector.

# _III.3. Patterns of Inequality_

The inequality trend has mirrored largely the welfare and poverty patterns observed in the years 2000s. Indeed inequality, as measured by the Gini coefficient, has dropped across the Kingdom from 0.345 in 2002 to 0.329 in 2010. However, the decline was not homogeneous and some regional differences can be depicted. Calculating the Gini coefficient across various regions of Jordan, Table 3 below suggests that the decrease in inequality in rural areas was stronger than in urban ones. Consequently, these regional results signal a strong negative correlation in Jordan between household per capita consumption and inequality indicators. Indeed, governorates that registered the largest growth in household welfare, such as the Northern regions, saw the strongest decline in inequality during the period 2002-2010. This once again supports the claim of a pro-poor economic growth in the past decade. That being said, only three governorates saw an increase in inequality among which is the capital Amman<sup>29</sup> . Despite the mild increase in inequality in Amman, this potentially has a strong effect on inequality perceptions in Jordan. This is especially the case when one considers that residents of the capital are usually more vocal due to better access to policy makers and to means of expressing their discontent in a louder fashion. This pushes the analysis to provide a quick snapshot of the winners and losers from the welfare growth process in Jordan. This attempt helps in better understanding popular complaints over the decline in the overall wellbeing of the population despite the positive patterns depicted for household consumption, poverty and most importantly inequality. Winners and losers are discussed next in the analysis.

> 28 Jordan has a fertility rate of 3.8 (in 2009 and 3.7 in 2002) that is higher than the 2.4 (in 2009 and 2.5 in 2002) estimated for middle income countries and even higher than rate registered for the lower middle income countries of 3 (in 2009 and 3.3 in 2002). Source: World Development Indicators 2011. 29 The other two being Ajlun and Karak according to the calculations in Table 3.

11
profits from NA in proportion to dividends.<sup>23</sup> We present results in Figure 7, which documents the share of income flowing to the top 1% and 0.1%, using our baseline and alternative methodologies. For the top 1% share, the gap is approximately 3 - 4 p.p. across the period - in recent years our baseline measure is approximately 28 - 30% vs. 25 - 26% when we do not use the corporate ownership data. However, the gap is much larger when we consider the top 0.1% share. Our baseline estimates suggest the top 0.1% earners receive between 18 - 20% of total income in recent years, whereas those figures are in the range 10 - 12% using the alternative methodology. These results suggest that using realized dividend income to adjust for undistributed corporate profits might be an incomplete exercise, and detailed data on corporate ownership might provide a important lens for measurement of top income inequality.

# **7.2 Persistence at the top**

Our key results show a substantial persistence of the share of total income flowing to the highest earners individuals across time. A distinct but equally important question relates to the identity of those individuals at the top of the income distribution (Acciari _et al._ , 2022, Auten _et al._ , 2013, H ́erault _et al._ , 2022). In one scenario, even if the share of income flowing to the top is stable, the identity of those highest earners changes substantially as top firms are challenged by incumbents; as the demand for skills change in the job markets; and as innovators gain access to new income generating activities. Alternatively, the same individuals occupy the top of the income distribution over time – either because the majority of their income are returns to wealth that persist over time, because their successful firms are unchallenged in the market, or because opportunities for other to achieve high incomes are hampered by other structural factors.

Our panel data of individual taxpayers allow us not only to measure income levels and concentration at the top, but also to evaluate _dynamics_ at the top – given that we observe an individual at the top in a given period, how likely it is they will persist at the top in future years? Evaluating the stability of individuals at the top of the distribution is also an indirect to assess the of the administrative data - way quality while a high degree of economic mobility might lead to large changes in the identity of top earners over time, on a shorter horizon we would expect to observe some degree of stability.

Our main exercise, presented in Figure 8, is to compute the probability that indi-

> 23To be precise, we first merge survey and tax microdata without including undistributed profits in the income definition of tax records. When expanding the joint distribution from microdata to match national accounts, we follow the same procedure as in our baseline measures, matching total distributed capital income in the microdata to the sum of household property income and corporate profits in the national accounts.

17
**Table 10: Marginal determinants of living in the same residence including inheritance, schooling and parental characteristics**

||(1)<br>All|(2)<br>Rural|(3)<br>Urban|(4)<br>Men|(5)<br>Women|(6)<br>Rural men|(7)<br>Rural<br>women|(8)<br>Urban men|(9)<br>Urban<br>women|
|---|---|---|---|---|---|---|---|---|---|
|Father in farming|-0.0765***|-0.0607*|-0.0367|-0.0884**|-0.0523|-0.0427**|-0.0411|-0.0346|-0.0508|
||(0.0259)|(0.0319)|(0.0533)|(0.0357)|(0.0351)|(0.0208)|(0.0475)|(0.0776)|(0.0695)|
|Mother in farm.|-0.0296|-0.0187|-0.0266|0.000695|-0.0466|0.0190|-0.0587|-0.0442|-0.0130|
||(0.0297)|(0.0347)|(0.0700)|(0.0396)|(0.0384)|(0.0190)|(0.0488)|(0.101)|(0.0924)|
|Father in non-farm|0.0747**|0.116***|0.0700*|0.0545|0.0761*|0.0547***|0.200***|0.0681|0.0597|
||(0.0299)|(0.0423)|(0.0418)|(0.0380)|(0.0398)|(0.0164)|(0.0710)|(0.0652)|(0.0530)|
|Mother in non-farm|0.0987***|0.148***|0.0887**|0.0999**|0.0605|0.0487***|0.149*|0.139*|0.0735|
||(0.0308)|(0.0433)|(0.0417)|(0.0409)|(0.0392)|(0.0179)|(0.0820)|(0.0725)|(0.0514)|
|Father‘s schooling|-0.0258|0.0924|-0.0823**|-0.0599|-0.0248|0.0157|0.0634|-0.131**|-0.0511|
||(0.0370)|(0.0662)|(0.0406)|(0.0519)|(0.0461)|(0.0479)|(0.119)|(0.0624)|(0.0505)|
|Mother‘s schooling|0.0472|0.138**|-0.0385|0.0462|-0.00292|0.0425*|0.211*|-0.0343|-0.0865|
||(0.0483)|(0.0658)|(0.0566)|(0.0720)|(0.0629)|(0.0226)|(0.123)|(0.122)|(0.0644)|
|Own schooling|-0.0482*|-0.0512|-0.0732**|-0.0636*|-0.0222|-0.0783*|0.00202|-0.0916|-0.0908**|
||(0.0277)|(0.0509)|(0.0354)|(0.0378)|(0.0366)|(0.0456)|(0.0692)|(0.0590)|(0.0455)|
|Inherited land|-0.0380|0.00648|-0.102**|0.00195|-0.0901**|0.0161|0.0239|-0.0337|-0.142**|
||(0.0290)|(0.0392)|(0.0444)|(0.0389)|(0.0402)|(0.0236)|(0.0631)|(0.0728)|(0.0573)|
|Inherited house|0.0310|-0.00573|0.0246|0.0475|0.00455|0.0418*|-0.0932|0.0149|0.0202|
||(0.0251)|(0.0366)|(0.0365)|(0.0326)|(0.0343)|(0.0220)|(0.0584)|(0.0587)|(0.0460)|
|Inherited other|0.0167|0.0297|0.00577|0.0221|-0.0217|0.0259|0.0150|0.0143|-0.00543|
||(0.0261)|(0.0340)|(0.0442)|(0.0343)|(0.0357)|(0.0211)|(0.0536)|(0.0695)|(0.0571)|
|Observations<br>|3,190|1,652|1,538|1,382|1,761|702|912|663|849|
|Pseudo R<sup>2</sup>|0.173|0.287|0.330|0.326|0.109|0.360|0.202|0.415|0.314|

Notes: Robust standard errors in parentheses, clustered at the household level; *** p<0.01, ** p<0.05, * p<0.1. Marginal effects are reported. The regressions include controls listed in Table 5.

36
**TABLE 1:** Distribution of Loan Purposes **by** Type **of** Lender (Percent)

||||Pur p|o s e||||
|---|---|---|---|---|---|---|---|
||Sample<br>Size<br>|Prod-<br>uction|Farm<br>Equip-<br>ment|Constr-<br>uction|Consump-<br>tion|Social<br>(Wedding<br>Funeral,<br>etc.)|Other|
|S.U.lr<br>_ce_<br>|(Number|||||||
|of L oans|of Loans)|||||||
|_rO'rp.Sl._|209|92.3|4.3|1.9|0|1.0|0.5|
|l C mal|44|9.1|4.6|20.5|15.9|27.3|2 2.7|

TABLE 2: Extent of Formal Credit Constraint

|tite Crs<br>(Nulber|SamDle<br>s.ze<br>of Households)|% Constrained|
|---|---|---|
|.'.s<br>;frS|145|41.3|
|or' - tCr,D~n'es|42|28.3|
||187|37.4|
Define the price of product i in country c and year t as Pc,i,t<sup>LCU.TheICPprovidesthese</sup> prices for two years, 2011 and 2017. Formally, we estimate the regression where αi,t is a product-year fixed effect. Import market concentration HHIc,s(i),t is calculated at the level of the ICP basic heading s, one of 88 product groupings that include multiple products i. An example is the basic heading for rice, which includes 9 varieties, including parboiled long-grain rice, basmati rice, and brown rice—family pack. While at least one variety of rice is observed per country, not all varieties are observed in every country. Calculating the HHI at the basic heading level ensures the market definition is consistent and comparable across countries.<sup>10</sup> Estimates of Equation 3 are reported in Table 4. In Column (1), the coefficient β4 = 0.07 (0.02) indicates a 1 percent increase in HHI (i.e., an increase of 42 units, since the mean value of HHI is 4,211 in Table 1, Panel A, Column 1) is associated with 0.07 percent higher prices. In Column (2), country-year controls are included and the coefficient falls to β4 = 0.04 (0.02), though it remains positive and statistically significant at the 5 percent level. The controls apparently explain some variation in prices correlated with HHI, but not all of it.

The value added of these regressions, beyond the magnitude of the coefficient, is that they indicate that markups associated with higher concentration appear to outweigh any lower marginal costs of importing, at least on average. This is consistent with a model of a competitive world price and importers who are price-takers but have market power in local distribution. Importing firms in India, for example, have been shown to conform to this model (De Loecker, Goldberg, Khandelwal and Pavcnik, 2016). The association between import concentration and higher prices—a symptom of the resource curse—suggests

> 10When experimenting with specifications we found the log of HHI to fit better than the level of HHI, consistent with the non-linear associations of HHI with prices, conditional on marginal cost, in the Cournot model.

19
provided by the lottery to assess causal impacts. Nevertheless, we condition on age, gender, and island of birth to improve power and control for any effect of this slight difference in age.

The second half of Table 1 then shows characteristics that can change with migration. As shown in Gibson et al. (2015), winning the ballot results in substantially higher incomes for the principal applicants – an average of NZ$410 per week for the ballot winners compared to NZ$126 per week for the ballot losers.<sup>4</sup> Many of the migrants also undertake some tertiary schooling after arrival in New Zealand, resulting in an additional year of education on average for the ballot winners. However, note that the ballot losers still average 12 years of schooling. As a result, education levels are high enough that there should not be a problem for participants to understand the experimental games (and indeed similar games have been used in populations with average schooling of half this level (Chuang and Schechter, 2015)). Both the ballot winners and losers have an average household size of around five persons, where household here refers to individuals living in the same dwelling.<sup>5</sup> We see also from Table 1 that the average migrant has spent just over nine years in New Zealand.

# **2.3 Estimation Strategy**

We estimate two parameters throughout. The first is the _intention‐to‐treat_ (ITT) effect, of being offered the opportunity to migrate through the Pacific Access Category irrespective of whether the individual does migrate. We estimate the ITT through the following equation for a particular outcome for individual or household _i:_ where _BallotWin_ takes value one if the principal applicant was chosen in the 2002 to 2005 Pacific Access Category ballots and zero otherwise, and _X_ is a vector of controls for the pre‐determined characteristics (age, gender, and island of birth), for survey type (long or short), and for each of the different PAC ballot years that the individual entered. The median and modal applicant in our sample entered only one of the ballot years between 2002 and 2005. However, since those who entered multiple times had a higher

> 4 Over the survey period the Pa’anga exchange rate averaged 0.65 NZD, and had a range of just three NZ cents. To allow comparison with results from other countries, the NZD‐USD exchange rate averaged 1.33 over this period. The income gain is similar in PPP terms (see Gibson et al, 2015).

> 5 This reflects both the establishment and growth of independent households in New Zealand among the ballot winners and a trend towards living in nuclear family in Tonga as households age. That fact that household size is the same suggests that migration does not impact decisions around childbearing and household organization.

7
Policy Research Working Paper 9041

# **Abstract**

This mixed methods study investigates why fewer than one in five impact evaluations integrates a value-for-money analysis of the development intervention being evaluated. This study distills four main insights from combined analysis of 33 semi-structured and unstructured interviews, surveys of 497 policy makers and 16 journal editors, and portfolio analyses of World Bank and worldwide impact evaluations. The study finds that low levels of training in cost data collection and analysis methods, together with a lack of standardization of the value-for-money assumptions (e.g., time horizons, discount rates, and economic or financial cost accounting) limit value-for-money integration into impact evaluations. Further eroding researchers’ incentives, demand for cost evidence from the journals that publish impact evaluations is mixed. Ill-defined standards of rigor undermine editors’ capacity to evaluate the quality of value-for-money analysis when it is integrated with impact evaluation evidence. Institutional funders of impact evaluations do not consistently demand that cost analysis be integrated into their funded evaluations. This study finds no evidence in support of the myth that policymakers do not demand cost evidence. Rather, it finds that researchers have few ways of knowing what kind of analysis policymakers need and when they need it. Improving the stock of impact evaluators who are cross trained in value-for-money methods, establishing standards in what constitutes rigor in costing, resolving methodological issues, and improving linkages between policymakers and researchers would lead to greater integration of value-for-money methods in impact evaluations.

This paper is a product of the Independent Evaluation Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jtanner@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
# **Table 4 - Effect of treatment on GBV: IPV in the past 12 months**

# <u>ITT (OLS)</u>

||Exposure to<br>Controlling<br>Behavior|Exposure to<br>Emotional Violence|Exposure to<br>Physical Violence|Exposure to Sexual<br>Violence|
|---|---|---|---|---|
|RMG|0.057|-0.000|0.020|0.033|
||(0.036)|(0.035)|(0.033)|(0.030)|
|Control Group Mean|0.517|0.346|0.328|0.210|
|N (Obs.)|949|926|964|924|
|Adj R-sq|0.006|-0.003|0.038|0.039|
|p-val|0.111|0.991|0.551|0.285|

# <u>ToT (2SLS)</u>

||Exposure to<br>Controlling<br>Behavior|Exposure to<br>Emotional Violence|Exposure to<br>Physical Violence|Exposure to Sexual<br>Violence|
|---|---|---|---|---|
|RMG|0.057|-0.000|0.020|0.033|
||(0.036)|(0.034)|(0.033)|(0.030)|
|Control Group Mean|0.521|0.343|0.324|0.211|
|N (Obs.)|949|926|964|924|
|Adj R-sq|0.006|-0.003|0.038|0.039|
|p-val|0.109|0.991|0.549|0.282|

Robust standard errors in parentheses. We control for the following baseline characteristics: age, a binary indicator for married, an indicator for ever attending school, indicators for ethnicity being Lao-Tai, Mon-Khmer, SinoTibetan, Hmong-Iumien or other, household size and monthly household income. Data on gender-based violence was not collected at baseline so we cannot control for exposure before the RMG program. All outcomes are binary outcomes where the outcome equals 1 if she has experienced this type of violence in the past 12 months. * p < 0.1, ** p < .05, *** p < 0.01.

22
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18 factor productivity to depend upon the maturity structure of debt and also on the overall degree of leverage. One potential reason for the inclusion of leverage is that financial pressure may force the firm and its managers to be more efficient.'<sup>0</sup> However, it is possible that with more leveraging, controlling shareholders may have a smaller incentive to strive for efficiency, since they reap a smaller fraction of the rewards. We estimate different specifications of the equations in terms of its dynamic structure. All equations are estimated by GMM after taking first differences (see Arellano and Bond (1992).1" The first difference transformation removes the firm specific and time invariant components of the error term. Removal of the firm specific component of the error term is important in order to avoid the coefficient of maturity simply capturing the fact that better firms may simply receive more long term debt. Lagged values (two or more periods) of the regressors and of the dependent variable are used as instruments to account for potential endogeneity of the regressors, either because the variables are decided jointly with production or because there are measurement errors. All equations contain also year dummnies. In the table we also report the test of over-identifying restrictions (denoted here as the Sargan test), distributed as chi-squared, and tests for first and second order serial correlation (ml and m2 respectively), distributed as a standardized normal.

The results suggest that when beginning of period maturity together with beginning of period leverage is added to the static version of the production function, there is no statistically significant effect on productivity. When maturity and leverage are entered as end of period variables, the effect of maturity is positive and almost

> **'° See also** Nickell **and Nicolitsas (1995) for an analysis using UK panel data.**

> **"** The DPD **program** by **Arellano and Bond (1988)** is **used for estimation.**