rafmacalaba/datause-encoder-data huggingface dataset:
was equipped with drip irrigation systems.

To capture transfers of groundwater, which typically occur between adjacent plots so as to minimize conveyance losses,<sup>23</sup> we departed from the usual household-based sampling strategy. Instead, each respondent (borewell owner) was also asked to report on all the plots adjacent to the one containing the reference borewell, including characteristics of the landowner, details on how the plot was irrigated during the _rabi_ , if not left fallow, and on the transfer arrangement if one occurred. The number of adjacent plots varies from 1 to 7, with a mode of 3. Not only does this _adjacency_ approach provide information about transfers that did happen but also about those that could have happened but did not.

# **3.2 Recharge and uncertainty**

As in much of India, farmers in AP rely almost exclusively on groundwater during the _rabi_ (winter or dry) season, when rainfall is minimal and surface irrigation typically unavailable. Indeed, recent years have seen an explosion of borewell investment as the costs of drilling and of submersible electric pumpsets have fallen, raising concern about groundwater overexploitation (e.g., World Bank, 2005). Nonetheless, the time-series of depth to watertable across AP in the last decade and a half is dominated by _intra_ -annual variability (see Appendix Figure B.1). This is explained by the limited storage capacity of the shallow hard rock aquifers that characterize the region. Most of the recharge from monsoon rains occurring over the summer months is depleted through groundwater extraction in the ensuing _rabi_ season. In contrast to the deep alluvial aquifers of Northwest India, there are no deep groundwater reserves to mine (see Fishman et al., 2011).

This annual cycle of aquifer replenishment and draw-down throughout AP is central to our analysis of groundwater markets. Although farmers can observe monsoon rainfall along with their own borewell’s discharge prior to _rabi_ planting, they cannot perfectly forecast groundwater availability over the entire season. To measure the degree of uncertainty, as part of the borewell owner’s survey we fielded a well-flow expectations module, which was structured as follows: First, we asked owners to assess the probability distribution of flow on a typical day at the _start_ of (any) _rabi_ season, the metric for discharge being fullness of the outlet pipe (i.e., full,<sup><u>3</u></sup> 4<sup>full,</sup><sup><u>1</u></sup> 2<sup>full,</sup><sup><u>1</u></sup> 4<sup>full,empty).24Next,usingthesameformat,</sup>

> 23Most irrigation water is transfered through unlined field channels with high seepage rates. While our survey also picked up a number of transfers to non-adjacent plots using PVC pipe, usually these cases involved sharing of groundwater between well co-owners or between multiple plots of the same owner.

> 24To appreciate how discharge can be fractional for an extended period, the metaphor for the aquifer to keep in mind is that of a sponge rather than of a bathtub.

14
3 _Table I - Some Macroeconomic_<sup>_Indicators of Crisis_</sup>

||Po|land|Huns|ary|Yugo|slavia|
|---|---|---|---|---|---|---|
||I|II|I|II|I|II|
|1.GDP per capita|+7.1|-0.6|+5.9|+1.9|+5.1|+0.9|
|2.Consumption p.c.|+6.5|-0.2|+3.7|+2.6|+4.5|+0.8|
|3.Real wages|+5.6|-3.7|+2.9|+0.1|+2.1|-2.2|
|4-CA/GDP|-5.0|-3.5|-4.8|-2.5|-1.7|+0.2|
|5.Investment/GDP|33.6|25.3|31.6|25.6|32.1|24.1|

_______________________ Notes: For GDP, personal consumption and real wages average yearly per capita growth<sup>rate for the period Gross fixed</sup> investment and current account<sup>surplus (CA) as percentage of GDP</sup> (period average). Real wages In<sup>socialized sector of the economy.</sup> Period I = 1970 to 1978 (except<sup>for Yugoslavia 1970-79).</sup> Period II = 1979 to 1987 (except<sup>for Yugoslavia 1980-87).</sup> Sources: For all data except<sup>current account</sup><sup>_Statistical_</sup> _Yearbooks_ of the three countries.<sup>For the current account:</sup> _International Financial Statistics_<sup>(various Issues),</sup><sup>_Bilten_</sup> _Narodne Banke Jugoslavije_ (various<sup>issues), and</sup><sup>_Poland: Recent_</sup> _Economic Developments,_ International Monetary Fund, August 1987.

Lines 4 and 5 Illustrate<sup>the adjustment pattern of the</sup> 1980's. Substantial current account<sup>deficits that the countries</sup> were able to run in the previous<sup>decade were no longer feasible as</sup> voluntary lending dried up. This<sup>compelled Hungary and Pola.nd to</sup> reduce the deficit to about<sup>half of Its previous amount (in terms</sup> of GDP),5 while In the case of<sup>Yugoslavia, the current account</sup> moved Into surplus. The squeeze<sup>on domestic demand thus</sup> originated from two directions:<sup>real output practically ceased to</sup> grow while resource transfer<sup>from abroad either decreased or</sup> became negative.

The last line In Table<sup>1 shows that all three countries</sup> attempted to protect personal<sup>consumption by severe cuts In fixed</sup> Investment. The share of gross<sup>fixed Investment In GDP decreased</sup> by 8 percentage points In<sup>Poland and Yugoslavia and by 6 points in</sup> Hungary. The decline In current<sup>consumption</sup> was thus arrested at some Indeterminate cost In terms of future output<sup>and consumption.</sup> In addition, cut In Investments<sup>probably contributed to the</sup> widening of the technological gap between these countries and<sup>the</sup> West (e.g. Brada (1989)).
## **IV.B INEGI’s 2015 Intercensus**

A second part of the analysis that will be detailed in the next section uses the 2015 intercensal survey, which is collected by INEGI.<sup>12</sup> This survey is gathered at the midpoint between censuses and contains information on individual’s income, education, dwelling’s characteristics, durable asset ownership, and consumption vulnerability measures (among other things). The survey includes information from individuals in more than 6 million households from all Mexican municipalities. The access to this data with restricted geographical identifiers was obtained through INEGI’s microdata lab.<sup>13</sup> Our analysis focuses on individuals aged 0 to 21 in 19972000 as those individuals had the highest potential exposure to PROSPERA. In total, we use 1,875,039 individuals in all the localities (urban and rural) from 24 Mexican states in our analysis.<sup>14</sup> Importantly, the use of this ulterior data source allows to look directly into measures of food consumption insecurity and resilience, while also allowing for a sharper definition of treatment status as we will explain later. At the same time with this much larger sample, we are also able to validate the results obtained from the previous data sources, in a fully representative sample of the Mexican population.<sup>15</sup>

# **V Empirical Strategy**

## **V.A Analysis Using the PROSPERA ENCEL Panel**

**Long-Term Effects.** For identification of the long-term effects of PROSPERA, we first rely on the 1997-2000 RCT experiment, which gives the cleanest source of variation available. However, since the control localities became treated about two years after the start of Progresa (i.e. by the end of 1999), this strategy should be interpreted as an _early_ versus _late treatment_ or as a treatment intensity deriving from the extra months of transfers the original treated received. _Table 5_ convincingly shows that original households in early treatment localities receive 8 _._ 8% larger transfers, during 6 _._ 6 additional two-month installments. However, these additional resources are not evenly distributed during the 20 year period between the start of the program and 2017, rather they are concentrated in 1998-99.

> 12Available at https://goo.gl/5EMD7e

> 13For confidentiality reasons, INEGI does not disclose the geographical identifiers for localities with fewer than 15,000 inhabitants. The researchers filled up a request to gain access to this information which is critical for the

> 14The selection of Mexican states for the analysis was based on the fact that a discontinuous jump was visually identified in the selection of localities. The states left out include Aguascalientes, Baja California, Colima, Chihuahua, Mexico City, Morelos, Nayarit, and Yucatan.

> 15Even though the exclusion of Mexico City might strike as harmful for the representativeness of the sample, it is important to remember that the urban extension of the program did not occur until 2004. At that time little variation of inclusion was left geographically, which is our main identification strategy here.

15
the perception among a sample of married people in Malawi that bringing up a discussion of condoms is akin to bringing an intruder into the marriage because it implies that one partner is having extramarital sex and it interferes with the marriage as something created by God for the purpose of enjoyment of sex and procreation. In a study of condom use in cohabitating and marital partnerships in KwaZulu Natal, South Africa, Maharaj and Cleland (2004), also find both men and women have strong negative attitudes towards condom use in marriage because it implies infidelity and a lack of trust.

In a study investigating the reasons for non-condom use in eight Sub-Saharan African countries, the most commonly cited reason for not using a condom at last sex with a spouse or regular partner was that they trusted their partner (Agha et al. 2002). This belief can be misguided and may have deadly consequences in contexts with high HIV prevalence rates and low testing rates, as in much of Sub-Saharan Africa. Another study of HIV transmission risk behavior of HIV positive adults in Uganda, Bunnell and others (2008) reported that almost half of those HIV positive adults who did not use condoms during the last sexual encounter gave as reason that they trusted their partner was not infected. Almost all of these unprotected sex acts were with spouses or regular partners (84 percent with cohabitating partners and 13 percent with steady partners). However, 87 percent of these HIV positive adults did not know that, in fact, they themselves were infected and only 9 percent actually knew their partner’s status. De Walque (2007) also demonstrates that there is serious risk of HIV infection, even within cohabitating, committed relationships. Based on DHS data for Burkina Faso, Cameroon, Ghana, Kenya, and Tanzania, at least two-thirds of HIV positive couples were discordant 10
section was added to record the salient issues and inefficiencies experienced by the profession and related sector.

The questionnaire for importers was divided into three parts: (1) the personal histories and characteristics of individuals and firms; (2) import and sourcing strategies, as well as clearance practices and relationships with brokers; and (3) marketing strategies with customers, day-to-day management issues, and relationships with public administrative agencies.

The questionnaire for retailers and small vendors focused on their relationships with importers, their market and business relationships and practices, and the downstream marketing aspects of their activities, including administrative and regulatory issues.

The sections that follow summarize and analyze the data and information collected during the interviews. We specifically demonstrate the importance of broker practices in clearance processes, the heterogeneity of importers’ and brokers’ business models, and the implications of importers’ and brokers’ relationships with customs officials.

# **4. Quantitative Assessment of Informal Trade for Secondhand Cars and Clothing**

In terms of methodology, informality raises an important issue. All import operations, whether from the perspective of an importer or broker, are collected in the IT system, but differentiating them from formal operations is more challenging. The IT customs database houses all information on informal traders, especially in Cameroon where a tax identification number is required for an import operation and a formal broker must intermediate to officially authorize imports for trade.

The following sections describe the structure of importers’ and brokers’ markets related to personal cars and secondhand clothing. Both goods are among the main revenue sources of Douala port (and therefore the entire country) and are imported by both formal and informal traders.

9
Table 5: Panel IV Regressions

||(1)<br>OLS|(2)<br>IV|(3)<br>OLS|(4)<br>IV|
|---|---|---|---|---|
|FDI|0.110**|0.303**|-0.005|-0.036|
||(0.050)|(0.145)|(0.021)|(0.140)|
|_GDPt−_1|-0.891***|-0.929***|-7.563***|-7.523***|
||(0.250)|(0.087)|(1.256)|(0.363)|
|Pr. Cr.|1.490|1.406***|0.491|0.463|
||(1.103)|(0.297)|(0.818)|(0.352|
|School|2.741***|2.591***|-0.779|-0.690|
||(0.553)|(0.254)|(1.479)|(0.685)|
|Inf.|-0.044|0.002|0.058|0.061|
||(0.112)|(0.079)|(0.055)|(0.037)|
|Trade|-0.578|-0.973***|0.154|0.119|
||(0.418)|(0.329)|(0.359)|(0.253)|
|Govt. Cons.|0.094|0.122|0.502|0.533**|
||(0.441)|(0.196)|(0.283)|(0.225)|
|B. M. P.|-1.697|0.882|1.326|1.154|
||(9.934)|(5.481)|(3.402)|(2.136)|
|N. Obs.|709|709|705|705|
|Year FE|Yes|Yes|Yes|Yes|
|Country FE|No|No|Yes|Yes|
|CD F-Test||19.14||4.37|
|Underid test||18.87||4.41|
|P value||0.00||0.04|

**Notes:** The dependent variable is average growth rate of GDP per capita over a 10-year spell (the first spell covers 1995-2005, restricted to this start due to instrument availability). All explanatory variables are measured at the beginning of the growth spells. The instruments for FDI are built using bilateral FDI from all countries to emerging and developing countries (see Table A.2). All other variables are as in Table A.3, included in these results at their value in the initial year of the 10-year growth spell. The bottom rows show the Cragg-Donald F-statistic for the first stage results, the underidentification test, and its associated p-values. Robust standard errors clustered at the country and year level are in parentheses. Significance levels are denoted as: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

24
|-1.0<br>-0.5<br>0.0<br>0.5<br>1.0<br>1.5<br>-5<br>-4<br>-3<br>-2<br>-1<br>0<br>1<br>2<br>3<br>4<br>5<br>(PME)<br>Carlo<br>s titled<br>laried,<br>49|
|---|
|**ended**<br>1994<br>1995<br>1996<br>1997<br>1998<br>1999<br>2000<br>2001<br>2002<br>S.E. to F<br><br>1994<br>1995<br>1996<br>1997<br>1998<br>1999<br>2000<br>2001<br>2002<br>I to F<br>m the Monthly Labor Survey<br>s are based on 10.000 Monte<br>except in the case of the panel<br>mployment rate, I=Informal Sa|
|**tr**<br>1993<br>1993<br>o<br>on<br>5(<br>e|
|**mployment categories**<br>**5b. De-**<br>1984<br>1985<br>1986<br>1987<br>1988<br>1989<br>1990<br>1991<br>1992<br><br>F to S.E.<br>1984<br>1985<br>1986<br>1987<br>1988<br>1989<br>1990<br>1991<br>1992<br>F to I<br>period using monthly data fr<br>ned in Annex 1. Computati<br>ith smoothing parameter 10 <br>t of the Labor Force, U=Un|
|**e**<br>1983<br>1983<br>ch<br>utli<br>er w<br>Ou|
|**een**<br>-0.4<br>-0.3<br>-0.2<br>-0.1<br>0.0<br>0.1<br>0.2<br>0.3<br>0.4<br>-0.8<br>-0.6<br>-0.4<br>-0.2<br>0.0<br>0.2<br>0.4<br>0.6<br>0.8<br>or ea<br>6) o<br>P filt<br>LF=|
|**w**<br>f<br>8<br>H<br>O|
|**Figure 5. Bilateral flows bet**<br>**5a. In Levels**<br><br>1987<br>1988<br>1989<br>1990<br>1991<br>1992<br>1993<br>1994<br>1995<br>1996<br>1997<br>1998<br>1999<br>2000<br>2001<br>2002<br>1.5<br>2.0<br>2.5<br>3.0<br>3.5<br>4.0<br>4.5<br>F to S.E.<br>S.E. to F<br><br>1987<br>1988<br>1989<br>1990<br>1991<br>1992<br>1993<br>1994<br>1995<br>1996<br>1997<br>1998<br>1999<br>2000<br>2001<br>2002<br>11<br>13<br>15<br>17<br>19<br>21<br>23<br>25<br>27<br>29<br>F to I<br>I to F<br>rates inferred from the continuous time transition matrix<br>Dec 2002 following the procedure by Geweke et al. (19<br>series have been averaged per quarter, de-trended using a<br>nd smoothed using a 3 quarter moving average smoother.<br>f-employed, F=Formal Sector.|
|1986<br>1986<br>ns<br>to<br>e<br>a<br>l|
|1984<br>1985<br>1984<br>1985<br>nsitio<br>1983<br>ns. Th<br>vels”)<br>mal Se|
|1983<br>1983<br>Tra<br>Jan<br>atio<br>Le<br>for|
|0.6<br>0.8<br>1.0<br>1.2<br>1.4<br>1.6<br>1.8<br>2.6<br>3.1<br>3.6<br>4.1<br>4.6<br>5.1<br>5.6<br>6.1<br>6.6<br>7.1<br>Note:<br>from<br>replic<br>as “In<br>SE=In|
variation in these variables for those in non-agricultural sectors. In light of these theoretical and practical arguments we used OLS rather than Heckman correction procedures.

# **_Results:_**

## **Wage/profit equation for agriculture unskilled**

|Number of obs =|11093822|
|---|---|
|F( 11, 3209)<br>=|419.53|
|Prob > F<br>=|0.000|
|R-squared<br>=|0.3021|
|Root MSE<br>=|0.83187|

||_R_|_obust Std._<br>|||||
|---|---|---|---|---|---|---|
||_Coef._|_Err._|_t_|_P>|t|_|_[95% Con_|_f. Interval]_|
|edu|0.084|0.003|27.270|0.000|0.078|0.090|
|exp|0.047|0.002|29.900|0.000|0.044|0.050|
|exp2|-0.001|0.000|-28.780|0.000|-0.001|-0.001|
|gend|-0.858|0.018|-46.640|0.000|-0.894|-0.822|
|preta|-0.138|0.027|-5.050|0.000|-0.191|-0.084|
|parda|-0.171|0.016|-10.490|0.000|-0.203|-0.139|
|nnonrem|0.043|0.009|4.790|0.000|0.025|0.060|
|drn|-0.129|0.049|-2.620|0.009|-0.225|-0.032|
|drne|-0.339|0.025|-13.590|0.000|-0.388|-0.290|
|drs|0.150|0.032|4.720|0.000|0.088|0.213|
|drcw|0.225|0.036|6.210|0.000|0.154|0.297|
|_cons|4.079|0.040|102.470|0.000|4.001|4.157|

Source: Authors’ calculations. Note: edu: years of schooling exp: age minus schooling exp2: experience squared gend: female 1 preta: black parda: mixed black nnonrem: number of non-remunerated household members d*: regional dummies with r for rural, n north, ne northeast, s south, cw center-west.

59
broaden by taking into account the simultaneous relationship among macro variables in the economy. The implication of public deffi t can then be analyzed hy examining the counterfactual simulatiorn of public: deficit from the constructed macro econometri( model. Hefore _we_ proceed to the policy simulation, we nee(d **t',** validate the estimated model by historical simulation.

# 8.1 Historical **simulation**

Historical simulations of the model were employed to determine themodel ability to replicate the real world. The predictive performance of the model can be determined by comparing the predicted paths of each variable with the actual paths in the sample period. The root mean square errors (RMSEs) from the statiP and dynamic simulations for the 1976-1980 period and for the 1981-1986 period are presented in Table 10. The solutions derived from the static and dynamic simulations for the period mentioned above are fairly satisfactory. The RMSEs from dynamic simulation vary from 2.4 to 63 percent of its means value.

Large simulation errors mainly arise from definitional equations, for example, the trade and current account deficits. The predicted paths of all endogenous variables turned out as expected. In comparing the RMSEs from both simulation periods, it seems that the model performs better in the period 1981-1986, possibly due to the absence of major shocks which were predominant durino the earlier period.

Theil inequality coefficient (U) is the RMS errors scaled down by the denominator such that its value always falls between 0 and 1 (Pindvck and Rubinfeld, 1981). A zero value of U indicates a perfect fit when the simulated value equals the actual value in every period. The Theil's inequality coefficients calculated from 1981-1986 dynamic simulation with the breakdown of their components are presented in Table 10.

# **8.2 Counterfactual simulation of the effects of public deficit**

We performed three simulation exercises to determine the effect of public deficit on macro variables. The public expenditures were hypothetically raised by 5 percent of their actual values from 1981 to 1986. Different methods of financing the increased spendina are assumed: (1) money-financed deficit, (2) taxfinanced deficit, and (3) bond-financecd deficit. The impact on endogenous variables are reported in Tables 12, 13, and 14, respectively.

When performing policv simulation of the effect of money-financed defirit, the claims on government by the central bank (Hg) was raised by the same amount as the increase in government spending. Hg became ani exogenous variable in equation (44), which was an identitv determining the amount of foreign borrowing (Bf), a residual in the government budget constraint. The increase in Hg 20
exercise more immune to the comparability problems facing cross-country studies. As is often acknowledged in this literature, international comparisons of the effect of growth on poverty and inequality are subject to a number of difficult issues of data comparability across countries, which can make it hard to detect the true relationships.

In addition to testing whether the pattern of growth has mattered to poverty reduction, we aim to assess how quantitatively important the pattern of growth has been to China’s (very high) overall rate of poverty reduction. We may not reject the PGH, but find that the effect is small. Or we might find that far larger reductions in poverty could have been possible if the same growth rate was more even across sectors and areas. We investigate this issue more deeply using the sub-national data, and also see if there is any evidence of a significant trade-off between the overall growth rate and its sectoral composition.

We shall also make a number of observations comparing China with India in terms of the relevance of the pattern of growth to poverty reduction. The fact that a similar study was already conducted at the provincial level for the case of India by Ravallion and Datt (2002) allows us to compare the results of China and India.

The following section describes the trends in poverty reduction across China ́s provinces that we find in the data. Section 3 examines the role played by the sectoral composition of growth, and section 4 extends this analysis to allow for differing parameters across provinces. Section 5 uses counterfactual analysis to quantify the importance of the pattern of growth to poverty reduction. Section 6 concludes.

# **2. Poverty trends in China at provincial level**

While the reduction of poverty in China has been dramatic during the last twenty-five years, it has also been quite uneven in both the temporal and the spatial dimensions (Ravallion and Chen, 2007). Table 1 shows the trend rates of poverty reduction, measured using the headcount index of poverty ( _H_ ), by province<sup>5</sup> during the study period.<sup>6</sup> _H_ is defined as the percentage of the relevant population living in households with income per capita below the poverty line. Here we use the higher of the two poverty lines used by Ravallion and Chen (2007). In all other respects the methods used in constructing the data set follow

> 5 Among the provinces we include also the municipalities: Beijing, Tianjin and Shanghai. The recent creation of a new municipality, Chongquing, preclude us from including it in our empirical analysis. Tibet is not included because data to construct the poverty measures are not availability.

> 6 In general it covers the 1983-2001 period for rural areas, and the 1986-2001 period for urban areas but there are some special cases as the reader can notice in Table 1. This is the longest time period with complete data that was feasible at the time of writing.

5