rafmacalaba/datause-encoder-data huggingface dataset:
References: Ahmad Ehtisham and Nicholas Stern (1991), “The theory and practice of tax reform in developing countries”. Cambridge University Press. New York.

Deaton, A and J. Muellbauer (1980), “Economics and Consumer Behavior” Cambridge University Press. New York.

Deaton, Angus (1987), “Estimation of Own- and Cross-Price Elasticities from Household Survey Data”, _Journal of Econometrics_ , 36:7-30.

> Deaton, Angus (1988), “Quality, Quantity and Spatial Variation of Price”, _American Economic Review_ , 78:418-430.

Deaton, Angus (1990), “Price Elasticities from Survey Data. Extensions and Indonesian Results”, _Journal of Econometrics_ , 44:281-309.

Deaton, A. and F. Grimard (1992), “Demand Analysis and Tax Reform in Pakistan”, _LSMS working paper_ . 0253-4517; no. 85.

Deaton, Angus (1997), “The analysis of household surveys : a microeconometric approach to development policy”, Johns Hopkins University Press. Baltimore.

Hall, Peter (1995), “Methodology and Theory for the Bootstrap”, in Robert F. Engle and Daniel L. McFadden, eds., _Handbook of Econometrics_ , 4, Amsterdam, Elsevier, 2341-81.

Ianchovichina, E., A. Nicita, and I. Soloaga "Trade Reform and Poverty: The Case of México," _World Economy_ ; 25 _,_ 945-972.

McCulloch N., A. Winters and J. Cicera (2001), “Trade Liberalization and Poverty: A Handbook”.CEPR.

21
# <u>Institutions</u>

We use two variables obtained from Freedom House to measure the influence of institutions on agricultural productivity. The measure of political rights reflects the electoral process, political pluralism and participation, and functioning of the government. The civil liberties measure includes aspects of freedom of expression and belief, associational and organizational rights, rule of law, and personal autonomy and individual rights. The hypothesis is that the physical, legal, and regulatory infrastructure and institutions support overall, including agricultural, development; however, it is possible that these effects may already be captured by the development indicator variable.

# <u>Incentives</u>

Three measures of incentives are included in the estimation. The effects of these variables would be over and above their indirect effect through resource allocation. The mechanism for the direct effect is through the choice of technique. Relative prices reflect the terms of trade between the agricultural sector and the overall economy. Price variability is a measure of the market risk faced by agricultural producers. In addition to the sector-specific risk, there is an economy-wide market risk, that of price volatility for the economy as a whole, measured by the rate of inflation. Inflation may also serve as an indicator of overall macroeconomic instability, which is thought to inhibit growth.

# <u>Physical environment</u>

Two country-specific variables are included in the analysis to represent the physical environment: potential dry matter and factor of water availability. These are expected to have a positive impact on agricultural productivity.<sup>29</sup>

# **Sample description**

The sample was determined by data availability. It consists of annual data from 30 countries for a 29-year period (1972-2000). The information conveyed by the sample is summarized in Table 4. In the first column, we report the average annual growth rates of the variables for the whole sample period. Agricultural output grew at a rate of 5.4%. All three components of agricultural capital grew faster than the other inputs. Agricultural fixed capital and treestock grew at rates of 5.8%. Livestock also grew during this period, but at a slower rate of 3.6%. Led by increasing rates of off-farm migration, agricultural labor declined at the average rate of 0.6%. Fertilizer grew on average at the rate of 1.9%. There was little change in the agricultural area. The terms of trade of agriculture

> 29 The measures are based on Buringh, van Heemst, and Staring (1975) and were used in Mundlak and Hellinghausen (1982) and Binswanger et al. (1987).

14
particular, in Teheran the food price index is smaller than the non-food price index, and ∆ is equal to 24; therefore, proposition 2 implies that switching from an SI deflation strategy to an MI strategy will _decrease_ real expenditures of all households with a food budget share _lower_ that 24%. Vice-versa, in the rural Northeast, the food price index is larger than the non-food price index, and ∆ is equal to 43.7; accordingly, switching from an SI deflation strategy to an MI strategy will _decrease_ real expenditures for all households with a food budget share _higher_ than 43.7%. The distributional effects of the two scenarios are clarified by the bottom panel of Table 2, which shows average budget shares by decile of (nominal) per capita expenditure. According to proposition 2, we should expect the MI approach to “underestimate” welfare compared to the SI approach for the top 4 deciles of the welfare distribution in urban Tehran, and for the bottom 7 deciles in rural Northeast. In Table 2, deciles where households are “penalized” by MI have been highlighted by using the bold type.

**Table 2. Implications of Proposition 2**

||**Teheran metro urban**|**Northeast rural**|
|---|---|---|
|Food Paasche (I<sup>f</sup>)|1.06|0.96|
|Non-food Paasche (I<sup>nf</sup>)|2.17|0.41|
|∆|24.0|43.7|
|Proposition 2 implies:|xMI<br>h< xSI<br>hwhenwh< ∆|xMI<br>h< xSI<br>hwhenwh> ∆|
|**Per capita expenditure deciles**|**Average food bud**|**get shares, **wh **(%)**|
|1|40.8|**53.0**|
|2|33.0|**49.5**|
|3|28.9|**47.5**|
|4|27.8|**44.6**|
|5|25.3|**44.3**|
|6|23.3|**42.1**|
|7|**20.8**|**42.9**|
|8|**20.2**|40.9|
|9|**18.1**|42.7|
|10|**16.1**|41.6|

h h Note: Expenditure deciles where xMI < xSI are in bold type.

_Source_ : Authors calculation based on HEIS 2019 29
percentage points in Argentina. Regarding labor force participation, Freire et al. (2015) again find gaps that favor Natives in some countries and non-Natives in others for urban areas. These differences go from 8 percentage points in favor of natives in Ecuador to 9 percentage points in favor of non-natives in Colombia. The Native disadvantage of 1.4 percentage points that we find in Argentina regardless of location falls in the middle of this range. For urban areas in Argentina, Natives have an advantage of 0.4 percentage points in labor force participation. Freire et al. (2015) find a disadvantage for Natives in access to computers that goes from 3 percentage points for El Salvador to 27 percentage points for Brazil and Panama. These gaps are greater than the 2.8 percentage points disadvantage that we document for Argentina for the use of computers.<sup>6</sup>

# **3.2 The role of location in explaining differences in outcomes**

To assess whether the differences in outcomes are due to differences in the geographical distribution of the Native and non-Native population, we control for location. Column (5) in Table 2 shows the estimated difference after adding location controls (department and rural/urban fixed effects). Adding location controls reduces the differences between Natives and non-Natives for most outcomes (on average by 35%), but it does not eliminate the differences. Thus, the differences in outcomes between Natives and non-Natives are not all due to Natives and non-Natives living in different parts of the country or in rural versus urban areas. Even within small geographical units, we find that, on average, Natives tend to have worse economic outcomes than non-Natives.<sup>7</sup> Of course, the fact that the documented disadvantage of Natives survives adding location fixed effects does not mean that the disadvantage is constant across locations. Table 4 shows the differences in outcomes between Natives and non-Natives in rural and urban areas. While for a majority of outcomes the disadvantage is significantly greater in rural areas, this is not the case for all outcomes. The difference in disadvantage between rural and urban areas is not for formal and unem- statistically significant employment ployment. In addition, Natives in rural areas are more likely to own the house and land in which they live than non-Natives, contrary to what is observed in urban areas.

> 6 This comparison is limited by the differences across censuses of different countries on the questions on the use of or access to computers.

> 7 Table A2 in the Appendix shows that this is also the case for the US.

10
variables are related to transitional growth. The changes of working-age population share, labor force participation rate, and population growth rate affect the growth via the demographic changes in labor market, hence we call this set of variables as ”demography parameters.” The change of investment rate affects growth via the capital accumulation process and we call this an ”investment parameter.” From this perspective, we can use the LTGM in order to evaluate the roles of different kinds of growth sources as follows. First, we simulate Korea’s GDP per capita from the neoclassical growth model in Section 2 by calibrating the six parameters varying over time as in the data, and consider this as the benchmark simulation. We label this version of simulation as ”Simul.” Second, we simulate by fixing all six policy parameters by their time-invariant long-run averages, i.e., by the 1960-2014 period annual average growth rates of productivity, human capital, population, and by the 1960-2014 period average values of investment rate, working-age population rate, and labor force participation rate. We label this version of simulation as ”Average,” which will capture the long-run growth effects in the sense that this simulation does not allow the time-varying patterns of the growth parameters. For this ”Average” simulation, the six parameters are set by _gAL_ = 1 _._ 9%, _gh_ = 1 _._ 5%, _gN_ = 1 _._ 3%, _SW,_ 0 = 0 _._ 65, _SE,_ 0 = 0 _._ 61, and _γ_ 0 = 0 _._ 32. Figure 10 compares these two sets of simulations with the actual data. The full simulation, ”Simul”, captures the growth path of the actual real GDP per capita very well. The gap between the actual data and the ”Simul” is due to the differences in the capital accumulation between the PWT 9.0 data (”rkna” variable) and the simulated capital stock which is constructed as in the law of motion equation (2) of the model. The ”rkna” in the PWT 9.0 data is constructed by differentiating the capital goods into four kinds of assets: structures (including residential and non-residential), machinery (including computers, communication equipment and other machinery), transport equipment and other assets (including software, other intellectual property products, and cultivated assets), with applying different depreciation rates and relative prices, while we apply the common average depreciation rate and no relative price changes in our simulation.<sup>13</sup> Thus, the gap between the ”Actual” and the ”Simul” represents the compositional changes of heterogeneous types of capital assets

> 13See Feenstra, Inklaar, Timmer (2015) and User Guide of PWT 9.0 for more detailed discussion about the capital construction of the PWT 9.0 data.

26
The chameleon-like ability to shift identities helps people adapt to the diverse roles they play in life. But a consequence is that when an identity of an individual that is associated with a stigmatized gender, class, caste, race, ethnicity, or profession is activated, he may fail to recognize and seize opportunities that would benefit him. He may engage in individually or socially harmful actions, or become the target of others’ prejudice and exclusionary behaviors. Social identities influence how an individual views a situation and also how others regard and treat him; the concept of social identity helps anchor the reciprocal influences between self and society that can generate self-reinforcing and vicious feedback cycles. In the terminology of economics, social identities can trap people in negative equilibria.

Individual identity acts are micro-phenomena. In the examples provided below, a banker over-reports his earnings; a judge misjudges the validity of a co-ethnic’s legal claim in the wake of conflict; and a student scales back effort when reminded of a marginal social group she belongs to. When such acts occur by enough people or over a long enough period of time or repeatedly over an educational career, the cumulative effect may be a macro-phenomenon: a dishonest business culture, persistent ethnic tensions, and poor educational outcomes for students from particular social groups.

# _Professional identities and organizational cultures_

A compelling example of the influence of identity on behavior comes from the financial industry. Drawing on concerns that a banking culture that tolerates dishonest behavior is to blame for recent scandals, researchers hypothesized that reminding bank employees of their professional identity would cause them to act more dishonestly than they otherwise would (Cohn and others 2014). Some 128 employees from a large international bank were recruited to perform a coin-tossing task with real earnings. Subjects were randomly assigned to either a treatment condition that made their professional identity salient, or a control condition that did not. Before beginning the coin-tossing task, subjects completed a short online survey. Those in the treatment group answered questions that “primed” or “cued” their professional identity (such as “What is your function at this bank?”), while those in the control group answered questions unrelated to their profession. Then everyone was asked to toss a coin 10 times and report the results online. Subjects were told in advance whether heads or tails was the “winning” result for 12
Further analysis shows that this failure to properly frame the questions about work and inactivity primarily affected women in 1992/93. Nine percent of females in a household reporting any agricultural activity at all provided the inactivity response “domestic duties”, but only 0.3 percent of men provided this response (Table 4). 72 percent of females of working age in these households who provided this response (and hence would be coded as inactive), reported a secondary economic activity. Nearly half of the males reporting domestic duties did the same thing. Unfortunately, most analysts (statistical offices and researchers) tend to only look at the response for the “primary” or “main” economic activity when creating aggregate employment indicators such as LFPR or distribution of employment by sector. Even for unit record data analysis, many analysts do not look at the secondary activity at all. This means that all the respondents who were actually economically active, but considered such economic activities as secondary to other non-economic activities they perform (e.g. going to school or caring for members of the household), would be misclassified as inactive and out of the labor force. If they are not counted, the LFPR would be understated by at least 8 percentage points in 1992/93.

**Table 4: Reports of non-domestic secondary activities among individuals whose main activities are domestic activity, 1992/93**

|**Description**|**Agric**|**ulture househ**|**old**|
|---|---|---|---|
||**Male**|**Female**|**Total**|
|**_Inactive - Primarily activity is domestic work_**|0.7|9.2|5.1|
|**_Share of above who report non domestic secondary activity_**||||
|No|51.7|27.5|28.9|
|_Yes_|_48.3_|_72.5_|_71.1_|
|Total|100|100|100|

_Source: Author calculation based on the IHS 1992/93._ Even if screening is done well, the inclusion of inactivity options in employment status is problematic. This is evident in the 2005/6 data, where screening was included in the 7-day economic activity question but not 12 month employment one. The 12 month question was similar to those used in 1992/3, where only employment status was asked, with the inclusion of inactivity responses. As seen in Table 3, once again a sizable share of those reporting an inactivity response in the primary employment status had an economic activity as their secondary response. Not shown in the tables is that of this 10 percent of respondents found to be economically active by using secondary activity, _80 percent actually reported a primary economic activity in 7 day question, where inactivity responses were not included in the activity question_ . Again, those mismeasured mainly reported their activity type as “contributing family workers” in the 7 day recall. In addition to females working at home, the economic activity of students also tends to be undermeasured. Of the 20% of the respondents aged 14 -65 who reported being inactive full time students as their primary status in the 12 months recall period, 65% of these reported a undertaking secondary _economic_ activity in the same recall period (see Table 5).

15
11

# IV. **DATA, DECSRIPTIVE STATISTICS AND TARGETING**

The 2004 VHLSS covers just over 9000 households and just over 40000 individuals. Interviews were conducted between May and November 2004. The survey is the fourth in a series of general purpose household surveys conducted over the last 15 years in Vietnam, the first two being conducted jointly by Vietnam’s Government Statistical Office and the World Bank in 1993/94 and 1997/98. A major strength of the 2004 VHLSS, like the earlier surveys, is its comprehensive approach to the measurement of household consumption: particular care is taken to capture the value not just of what people buy of what they produce themselves or obtain through nonfinancial transactions; the consumption measure also includes the value of housing.

**Table 1** reports descriptive statistics for the outcomes studied for both the full sample and those eligible for HCFP. The first few relate to out-of-pocket payments for health care over a 12-month period. Total per capita out-of-pocket spending at VND 275 is equivalent to 6% of per capita household income (cf. **Table 2** ). As many as 32% of individuals live in households recording catastrophic out-of-pocket payments, defined here as spending that is in excess of 10% of nonfood consumption. Nearly one quarter of household out-of-pocket payments are for non-prescribed medicines, with the rest being split equally between outpatient and inpatient expenses. Vietnamese record, on average, one outpatient consultation a year, but only 30% recorded one or more outpatient visits in the 12 months prior to the survey. By contrast, 7% recorded an inpatient spell. Commune health stations and district hospitals are the most frequented type of government facility, but private providers are used extensively—13% of the sample visited at least one during the previous year. Data from the 2001/02 Vietnam National Health Survey suggest that these utilization estimates are probably underestimates. Below, it is assumed that underreporting is invariant with respect to selection into HCFP.

**Table 2** reports descriptive statistics for insurance coverage, including coverage by HCFP, as well as for the determinants of coverage. According to the VHLSS, 9% of the sample had been given a free health certificate, and a further 5% had been provided
# **Table 4 continued: first stage results**

||Dep|endent varia|ble:|
|---|---|---|---|
|First stage results:|ln(Transportation<br>cost)|<br>Dummy:<br>Conflict<br>near<br>households<br>and<br>markets|<br>ln(Transportation<br>cost)*Dummy:<br>Conflict near<br>households and<br>markets high|
|ln(natural historicalpath)|0.952***|0.171***|0.929***|
||(24.820)|(4.170)|(6.720)|
|Fractionalization near market|-2.768***|-0.958***|2.987***|
||(-9.65)|(-1.9)|(2.440)|
|Square of fractionalization near market|2.319***|-0.863**|-5.067***|
||(8.220)|(-1.75)|(-3.82)|
|Fractionalization near households|3.223***|2.611***|-0.364|
||(13.330)|(6.030)|(-0.37)|
|Square of fractionalization near households|-2.667***|-0.353|1.32|
||(-11.07)|(-0.83)|(1.240)|
|ln(Distance to east border from household in km)|0.365***|0.027|0.791***|
||(8.600)|(0.770)|(5.850)|
|ln(Distance to east border from market in km)|-0.387***|-0.091***|-0.695***|
||(-7.79)|(-2.29)|(-4.43)|
|ln(natural historicalpath)*Fractionalization near market|0.676***|0.273**|-0.638*|
||(8.110)|(1.820)|(-1.73)|
|ln(natural historicalpath)*Square of fractionalization near market|-0.619***|0.212|1.230***|
||(-7.84)|(1.460)|(3.090)|
|ln(natural historicalpath)*Fractionalization near households|-0.842***|-0.826***|-0.316|
||(-11.4)|(-6.68)|(-1.14)|
|ln(natural historicalpath)*Square of fractionalization near households|0.722***|0.216**|0.129|
||(9.950)|(1.820)|(0.450)|
|ln(natural historicalpath)*ln(Distance to east border from household)|-0.129***|-0.007|-0.250***|
||(-8.92)|(-0.52)|(-5.22)|
|ln(natural historicalpath)*ln(Distance to east border from market)|0.104***|-0.007|0.092***|
||(7.360)|(-0.6)|(1.920)|
|Angrist Pischke test of weak identification|5575.07|70.77|148.56|
||P= 0.0000|P= 0.0000|P= 0.0000|

36
Table 12: Cost-Benet analysis of a direct SLM training intervention

|Yield and Labor<br>Benets|1|22,262|11,131<br>74<br>505<br>19<br>19<br>7<br>131|76|te of 38 Metacais per 1 USD.|USD) and assumes one extension agent services ve communities.|ication in Table 7.|weeks worked in Table 9.|
|---|---|---|---|---|---|---|---|---|
|Labor Benets|1|22,262|11,131<br>74<br>505<br>0<br>19<br>7<br>131|57|ssuming exchange ra|xtension agent (211|the winsorized spec|T on the number of|
|Yield Benets|1|22,262|11,131<br>74<br>505<br>19<br>19<br>0<br>0|-55|erms of 2012 USD, a|onthly salary of the e|timates of the ITT in|estimates of the IT|
||r of Beneciaries per community|Costs per Beneciary<br>st of trainings|cost of training<br> training cost per farmer<br> cost of extension agent per farmer<br> Benets per Beneciary<br> maize revenue<br> agricultural wage rate<br> of weeks in labor savings<br> labor earnings|erage Benets per Beneciary<br>t benets per CF|F=Contact farmer. Figures presented in t|ost per extension agent is based on the m|enets in maize revenue obtained from es|enets in endline labor savings taken from|
||Numbe<br>CFs|Average<br>Total co|Annual <br>Annual <br>Annual <br>Average<br>Annual <br>Weekly <br>Number<br>Annual|Net Av<br>Total ne|Notes: C|Annual c|Annual b|Annual b|

43
# **Figure 6. Fiscal balances in small states and other EMDEs**

_Fiscal balances in small states deteriorated markedly, on average, at the onset of the pandemic and, despite narrowing since 2020, average deficits in 2023 were larger than those in other EMDEs. Moreover, since the pandemic fiscal balances have been weaker than pre-pandemic averages in most small states. However, fiscal balances have varied significantly among small states, with deficits in EAP and LAC smaller than elsewhere; commodity-exporting small states having larger deficits; and micro states, reliant on external grants, having primary surpluses._

## **A. Overall fiscal balance**

## **B. Primary fiscal balance**

<!-- Start of picture text --> Percent of GDP Small states<br>5 EMDEs excl. small states<br>0<br>-5<br>-10<br>2011 2019 2020 2023<br>C. Share of EMDEs with weaker primary<br>balances in 2020-23 than before the pandemic<br><!-- End of picture text --> <!-- Start of picture text --> Percent of GDP<br>Small states<br>5 EMDEs excl. small states<br>0<br>-5<br>-10<br>2011 2019 2020 2023<br><!-- End of picture text -->

## **D. Average primary balance in small states, by region, 2011-23**

<!-- Start of picture text --> Percent<br>80<br>75<br>70<br>65<br>60<br>55<br>50<br>Small states EMDEs excl. small states<br><!-- End of picture text --> <!-- Start of picture text --> Percent of GDP<br>0<br>-1<br>-2<br>-3<br>EAP small LAC small Other small<br>states states states<br><!-- End of picture text --> 34